Devices and methods for searching for and detecting cells

Abstract

Communication circuit arrangement (206) comprising the following: a cell search circuit (306) configured to do the following: (1110) comparing a detected synchronization sequence of a detected potential cell with a previously determined reference synchronization sequence to generate a demodulated synchronization sequence encompassing a multitude of samples; Determine (1120) a phase variance between the multitude of samples of the demodulated synchronization sequence; and Comparing (1130) the phase variance with a detection threshold to classify the detected potential cell as a real cell or a false cell.

Description

Cross-reference to related registrations

[0001] This application claims priority over US patent application number 15 / 084,521, which was filed on March 30, 2016, and is incorporated herein in its entirety by reference. field of technology

[0002] Various embodiments generally relate to devices and methods for searching for and detecting cells. General state of the art

[0003] Mobile cell search procedures can rely on detecting synchronization sequences in downlink signals to identify and initiate synchronization with neighboring cells. In an LTE (LTE = Long Term Evolution) environment, as described by the 3 rdAs specified in the Generation Partnership Project (3GPP), a mobile device may need to detect and identify Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS) sequences in downlink signals received from nearby cells. A mobile device may then potentially obtain cell parameters such as a Physical Cell ID (PCI), the length of a Cyclic Prefix (CP, Extended or Normal Cyclic Prefix), a duplex mode (Time Duplex, TDD, or Frequency Duplex, FDD), and time synchronization based on the identification of such synchronization sequences. Mobile devices can then subsequently use these cell parameters in necessary mobility procedures, such as handing off measurement data to a network, network selection, cell selection, new cell selection, and handover.

[0004] DE 10 2014 104 461 A1 discloses a method and a related mobile device for reduced-complexity cell search. Brief description of the drawings

[0005] In the drawings, the same reference numerals in all different views generally refer to the same parts. The drawings are not necessarily to scale; rather, the focus is generally on illustrating the principles of the invention. The following description details various embodiments of the invention with reference to the following drawings, in which: Fig. 1 shows a mobile communications network; Fig. 2 shows an internal configuration of a mobile device; Fig. Figure 3 shows an internal configuration of a baseband modem of a mobile device; Fig. 4 shows a method for performing cell classifications; Fig. 5 shows a timeline diagram to illustrate a first synchronization sequence timeline; Fig. 6 shows a timeline diagram to illustrate a second synchronization sequence timeline; Fig. 7 shows a table that defines the synchronization sequence parameters; Fig. Figure 8 shows an example of demodulation of noisy synchronization sequences at a complex level; Fig. 9 shows a block diagram illustrating a sequential cell classification; Fig. 10 shows a block diagram illustrating a hybrid sequential and joint cell classification; Fig. 11 shows a method for detecting cells; and Fig. Figure 12 shows a method for performing cell detections. Description

[0006] The following detailed description refers to the accompanying drawings, which show specific details and embodiments for the practical implementation of the invention.

[0007] The word "exemplary" is used herein to mean "serving as an example, case study, or illustration." All embodiments or configurations described herein as "exemplary" are not necessarily to be interpreted as being preferable or advantageous over other embodiments or configurations.

[0008] The words "some" and "several" in the description and claims expressly refer to a number greater than one. Accordingly, any phrases in which the aforementioned words clearly serve to refer to a number of objects (e.g., "several [objects]" or "a multitude of [objects]") expressly refer to more than one of the objects mentioned. The terms "group (of)," "set (of)," "compilation (of)," "series (of)," "sequence (of)," "grouping (of)," etc., and the like in the description and claims, when used, each refer to a number equal to or greater than one, i.e., one or more elements. The terms "proper subset," "reduced subset," and "smaller subset" refer to a subset of a set that is not equal to the set itself, i.e., a subset of a set that contains fewer elements than the set itself.

[0009] It should be noted that any vector and matrix notations used herein are exemplary and serve only illustrative purposes. Accordingly, it is understood that the approaches discussed in detail in this disclosure are not limited to implementations using only vectors and / or matrices, but that the processes and computations associated with them can be performed equivalently using sets, sequences, groups, etc., of data, observations, information, signals, samples, symbols, elements, etc. Furthermore, it should be noted that references to a "vector" can refer to a vector of any size or orientation and may include, for example, a 1x1 vector (e.g., a scalar), a 1xM vector (e.g., a row vector), and an Mx1 vector (e.g., a column vector).It should also be noted that references to a “matrix” can be references to a matrix of any size or orientation and may include, for example, a 1x1 matrix (e.g., a scalar), a 1xM matrix (e.g., a row vector), and an Mx1 matrix (e.g., a column vector).

[0010] A "circuit," as used herein, is to be understood as any logic-implementing element, which may contain specialized hardware or a processor on which software runs. A circuit may therefore be an analog circuit, a digital circuit, a mixed-signal circuit, a logic circuit, a processor, a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an integrated circuit, an application-specific integrated circuit (ASIC), etc., or any combination thereof. Any other implementation of the respective functions, which are described in more detail below, may also be considered a "circuit."It is understood that any two (or more) of the circuits described in detail herein may also be implemented as a single circuit with essentially equivalent functionality, and conversely, any single circuit described in detail herein may also be implemented as two (or more) separate circuits with essentially equivalent functionality. Furthermore, references to a "circuit" may refer to two or more circuits that together form a single circuit.

[0011] As used herein, a "memory component" can be understood as a non-transient, computer-readable medium for storing retrievable data or information. Therefore, when a "memory component" is referred to herein, it can be assumed that this refers to a volatile or non-volatile memory component such as, but not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Flash memory, semiconductor memory, magnetic tape, hard disk drive, optical drive, etc., or any combination thereof. Furthermore, it should be noted that the term "memory component" here also includes registers, shift registers, processor registers, data buffers, etc.It should be noted that a single component referred to as a "memory module" or "memory" can consist of several different types of memory and can therefore refer to a composite component comprising one or more types of memory. It is understood that each individual memory component can be subdivided into several equivalent memory components, and vice versa. Although the memory module may be depicted as separate from one or more other components (as in the drawings), it is understood that the memory module can also be integrated into another component, for example, on a common integrated chip.

[0012] The term "base station," used in reference to a mobile communications network access point, can mean macrobase station, microbase station, NodeB, Evolved NodeB (eNB), Home eNodeB, Remote Radio Head (RRH), transmission point, etc. As used here, a "cell" in the telecommunications context can be considered a sector served by a base station. Accordingly, a cell can be a set of antennas located in the same geographical location, corresponding to a specific sectorization of a base station. A base station can therefore serve one or more cells (or one or more sectors), with each cell being characterized by its own communication channel. Furthermore, the term "cell" can also refer to a macrocell, a microcell, a femtocell, a picocell, etc.

[0013] For the purposes of this disclosure, radio communication technologies can be classified as short-range, metropolitan area network, or cellular wide area network radio communication technologies. Short-range radio communication technologies include Bluetooth, WLAN (e.g., according to an IEEE 802.11 standard), and other similar radio communication technologies. Metropolitan area network radio communication technologies include Worldwide Interoperability for Microwave Access (WiMAX) (e.g., according to an IEEE 802.16 radio communication standard such as WiMAX fixed or WiMAX mobile), and other similar radio communication technologies. Cellular Wide Area Network radio communication technologies include GSM, UMTS, LTE, LTE-Advanced (LTE-A), CDMA, WCDMA, General Packet Radio Service (GPRS), Enhanced Data Rates for GSM Evolution (EDGE), High Speed ​​Packet Access (HSPA), HSPA Plus (HSPA+) and other similar radio communication technologies.Cellular-wide-area network radio communication technologies may be generally referred to herein as "cellular" communication technologies. It is understood that the exemplary scenarios described in detail herein serve only as illustrations and are accordingly applicable in a similar manner to various other mobile communication technologies, existing or yet to be formulated, especially if features of these mobile communication technologies resemble those revealed in connection with the following examples.

[0014] The term "network," as used herein, for example, in reference to a communications network such as a mobile communications network, includes both an access area of ​​a network (e.g., a Radio Access Network (RAN) area) and a core area of ​​a network (e.g., a core network area). When used herein in relation to a mobile device, the terms "inactive radio mode" and "inactive radio status" refer to a radio control state in which the mobile device is not assigned at least one dedicated communication channel of a mobile communications network. The terms "radio link mode" and "radio link status," when used in relation to a mobile device, refer to a radio control state in which the mobile device is assigned at least one dedicated uplink communication channel of a mobile communications network.

[0015] Unless explicitly stated otherwise, the term "send" includes both direct and indirect transmission. Similarly, the term "receive" includes both direct and indirect reception, unless explicitly stated otherwise. The term "communicate" includes either only transmission, only reception, or both; that is, one-way or two-way communication, either only in the incoming direction, only in the outgoing direction, or in both directions.

[0016] Cell search can be a crucial first step in various cellular mobility procedures, such as transferring measurement data to a network, selecting networks, selecting cells, selecting new cells, or performing a handover. For cell search in an LTE environment, a mobile device can receive downlink signals on one or more frequency bands and process these signals to identify the presence of synchronization sequences, such as PSS and SSS sequences, transmitted by nearby cells. Mobile devices can then identify and establish synchronization with detectable cells by recognizing these PSS and SSS sequences.

[0017] Since not every cell may be able to synchronize its transmissions with other cells, a mobile device may not know in advance where synchronization sequences transmitted by neighboring cells will be placed in time. Consequently, a mobile device may need to capture a downlink signal data block that is at least as long as the synchronization sequence transmission period, e.g., 5 ms (a single half-frame) for PSS and SSS sequences in an LTE environment, and then process this 5 ms downlink data block to detect the presence of synchronization sequences. Afterward, the mobile device can identify neighboring cells based on the detected synchronization sequences.

[0018] Specifically, a mobile device can calculate the time-domain cross-correlation between the data captured during a search (search data window) and each of a predefined set of possible PSS sequences to identify the potential temporal positions of PSS sequences within the captured data. Based on these identified potential temporal PSS positions for the symbol clock, the mobile device can then convert the captured data into the frequency domain and subsequently perform a frequency-domain cross-correlation with each of a predefined set of possible SSS sequences to identify a transmitted SSS sequence within the captured data. By identifying a match between the captured data and a PSS-SSS pair from the predefined sets of possible PSS and SSS sequences, a mobile device can obtain the identifier of a potential cell.A mobile device can identify multiple such matches at different times in the data collected during the search using different PSS-SSS pairs and accordingly obtain a list of potential cells.

[0019] However, random channel effects and specific properties of the synchronization sequences can lead to errors in a received list of potential cells. For example, a mobile device might falsely detect a match in the data collected during a search, even though it does not correspond to a real cell, thus potentially detecting a "phantom cell" (also referred to here as a "false cell"), or it might fail to detect a real cell in the data collected during a search. Accordingly, a mobile device might receive a list of potential cells that incorrectly contains phantom cells (which should not have been detected in the data collected during the search) and / or contains no real cells (which should have been detected in the data collected during the search).

[0020] To correct such detection errors, a mobile device can perform cell classification to evaluate each potential cell, identifying and discarding phantom cells. With successful cell classification, phantom cells are thus accurately identified without falsely labeling genuine cells as such. In these cell classification procedures, the data collected during a search can be further processed (namely, after the initial evaluation stage to identify matching PSS-SSS pairs) to create an initial list of potential cells and subsequently filter out phantom cells to generate a final list of genuine cells.

[0021] As previously mentioned, a mobile device can receive a downlink data block, for example, a 5 ms block of data collected during a search, and process this downlink data to detect nearby cells. Depending on the mobile device's location, the data collected during a search can include detectable data from various neighboring cells. Fig. Figure 1 shows an example scenario in which a mobile device 102 is located near base stations 104, 106 and 108. Each of the base stations 104-108 can be sectored (e.g., with sectored antenna systems) and can accordingly be composed of several "sectors" or "cells," such as cells 104a, 104b, and 104c for base station 104, cells 106a, 106b, and 106c for base station 106, and cells 108a, 108b, and 108c for base station 108. The wireless channels 114a-114c, 116a-116c, and 118a-118c can represent the discrete wireless channels between all the respective cells 104a-104c, 106a-106c, and 108a-108c.

[0022] Fig. Figure 2 shows an internal configuration of the 102 mobile device. As in Fig. As shown in Figure 2, the mobile device 102 can include an antenna system 202, an RF transceiver 204, a baseband modem 206, and an application processor 208. The mobile device 102 can also include one or more additional components, which are shown in Figure 2. Fig. 2 not shown separately, such as additional hardware, software, or firmware elements, which include processors / microprocessors, controllers / microcontrollers, memory modules, other special or general-purpose hardware / processors / circuits, etc., to support various additional operations. The mobile device 102 may also contain various input / output devices for users (display(s), keypad(s), touchscreen(s), speakers, external button(s), camera(s), microphone(s), etc.), one or more peripheral devices, a memory module, a network connection, one or more external device interfaces, one or more subscriber identity modules (SIM), etc.

[0023] According to a brief overview of the operating mode of the mobile device 102, the mobile device 102 can be configured to receive and / or transmit radio signals according to one or more wireless access protocols or via one or more radio access technologies (RATs), including, for example, LTE, WLAN, WiFi, UMTS, GSM, Bluetooth, CDMA, WCDMA, etc., or combinations thereof. The RAT capabilities of the mobile device 102 are enabled by one or more subscriber identification modules (SIMs) contained within the mobile device 102 (in Fig. (2 not shown separately) can be determined. It should be noted that separate components can be provided for each compatible radio signal of any type, such as a dedicated LTE antenna, an LTE RF transceiver and a dedicated LTE baseband modem for receiving and transmitting via LTE, a dedicated UMTS antenna, a UMTS RF transceiver and a UMTS baseband modem, a dedicated WiFi antenna, a WiFi RF transceiver and a WiFi baseband modem for receiving and transmitting via WiFi, etc., where in this case the antenna 202, the RF transceiver 204 and the baseband modem 206 can be an antenna system, an RF transceiver system or a baseband modem system, respectively, each composed of the individual dedicated components. Alternatively, 102 different protocols for wireless access can be used for one or more components of the mobile device, e.g.by using several different wireless access protocols for the antenna 202, e.g., by using a common RF transceiver 204 for which several wireless access protocols are used, e.g., by using several different wireless access protocols for a common baseband modem 206, etc. According to an exemplary aspect of the disclosure, the RF transceiver 204 and / or the baseband modem 206 can be operated according to several mobile communication access protocols (i.e., in several operating modes) and can consequently be configured to support one or more protocols for access via LTE, UMTS, and / or GSM.

[0024] Furthermore, according to the brief overview of the operation of the mobile device 102, the RF transceiver 204 can receive high-frequency radio signals via the antenna 202, which may be implemented, for example, as a single antenna or as an antenna array consisting of several antennas. The RF transceiver 204 may contain various receiving circuit elements, which may include an analog circuit configured to process externally received signals, such as a mixer circuit for converting externally received RF signals into baseband and / or intermediate frequencies. The RF transceiver 204 may also contain an amplification circuit for amplifying externally received signals, such as power amplifiers (PAs) and / or low-noise amplifiers (LNAs). However, it should be noted that such components may also be implemented separately from the RF transceiver 204.The RF transceiver 204 may additionally contain various transmit circuit elements configured to transmit internally received signals, such as baseband and / or intermediate frequency signals provided by the baseband modem 206. These elements may include a mixer circuit for modulating internally received signals onto one or more high-frequency carrier waves and / or an amplification circuit for amplifying internally received signals before transmission. The RF transceiver 204 can provide these signals to the antenna 202 for wireless transmission. Therefore, whenever the reception and / or transmission of radio signals by the mobile device 102 is referred to below, this refers to an interaction between the antenna 202, the RF transceiver 204, and the baseband modem 206, as described in detail above. Fig. If 2 is not shown separately, the RF transceiver 204 can also be connected to the application processor 208.

[0025] In Fig. Figure 3 shows an internal configuration of the 206 baseband modem. As in Fig. As shown in Figure 3, the baseband modem 206 can be composed of a physical layer subsystem (PHY, Layer 1) 300 and a protocol stack subsystem (Layers 2 and 3) 310. Even if this is shown in Fig. Unless otherwise shown in Figure 3, the baseband modem 206 may also contain various additional baseband processing circuits, such as analog-to-digital converters (ADCs) and / or digital-to-analog converters (DACs), modulation and demodulation circuits, coding and decoding circuits, audio codec circuits, circuits used for digital signal processing, etc.

[0026] The baseband modem 206 can be responsible for the mobile communication functions of the mobile terminal 102 and can be configured to operate together with the RF transceiver 204 and the antenna system 202 for transmitting and receiving mobile communication signals according to various mobile communication protocols. The baseband modem 206 can be responsible for various baseband signal processing operations for both uplink and downlink signal data. Accordingly, the baseband modem 206 can receive and buffer downlink and uplink signals in the baseband and then provide the buffered downlink signals to various internal components of the baseband modem 206 for their respective processing operations.

[0027] The PHY subsystem 300 can be configured to perform the control and processing of physical layer mobile communication functions such as error detection, forward error correction encoding / decoding, channel encoding and interleaving, physical channel modulation / demodulation, physical channel mapping, radio direction finding and scanning, frequency and time synchronization, antenna diversity processing, power control and weighting, code rate adjustments, retransmission processing, etc. As described in Fig. As shown in Figure 3, the PHY subsystem 300 can contain a signal buffer 304, which can be a storage component configured to temporarily store digital signal versions, such as those received via the RF transceiver 204 (downlink) or the protocol stack subsystem 310 (uplink). A search engine 306, a measurement engine 308, and additional signal processing components of the PHY subsystem 300 can be configured to access the signal buffer 304 and process the digital baseband versions in accordance with the respective signal processing functions of the components. The signal buffer 304 is shown in Fig. 3 is represented for simplicity as a single component, whereas alternatively, each component or group of components may have a dedicated buffer for temporarily storing digital signal versions for processing. Even if this is in Fig. Unless otherwise shown in Figure 3, the functionality of the PHY subsystem 300 mentioned above can be implemented in the form of hardware and / or software components (program code executed in a processor) controlled by a PHY controller 300. A person skilled in the art will recognize how the algorithmic, control, and I / O logic for such signal processing operations can be implemented either as hardware or software logic with essentially equivalent functionality. The PHY subsystem 300 can additionally include a non-transient, computer-readable medium for storing program code, which can be accessed by the PHY controller 302, the search engine circuit 306, the measurement engine circuit 308, and other processors of the PHY subsystem 300.

[0028] The PHY controller 302 can be implemented as a processor configured to run physical layer control software and to control the various components of the PHY subsystem 300 according to the instructions of the control logic defined therein, thus equipping the mobile device 102 with the necessary physical layer functionality. As described in more detail below, the PHY controller 302 can be configured to control the search engine 306 and the measurement engine 308 for performing cell search and measurement procedures.

[0029] The baseband modem 206 can additionally include the protocol stack subsystem 310, which can be responsible for the Layer 2 / Layer 3 functionality of the protocol stack. In an LTE environment, the protocol stack subsystem 310 may be responsible for Medium Access Control (MAC), Radio Link Control (RLC), Packet Data Convergence Protocol (PDCP), Radio Resource Control (RRC), Non-Access Stratum (NAS), and Internet Protocol (IP) element processes. The protocol stack subsystem 310 can be implemented as a processor configured to run protocol stack software and control mobile communication operations of the mobile device 102, depending on the instructions of the control logic defined therein.Protocol Stack Subsystem 310 can interact with PHY Subsystem 300, for example via an interface to PHY Controller 302, to request physical layer services as specified by the protocol stack control logic, including physical layer configurations and radio measurements. Protocol Stack Subsystem 310 can also send downlink transport channel data (MAC data) to PHY Subsystem 300 for subsequent physical layer processing and transmission by PHY Subsystem 300 (via RF Transceiver 204 and Antenna System 202). Conversely, the PHY subsystem 300 can receive data from the physical uplink channel (via the RF transceiver 204 and the antenna system 202) and perform subsequent physical layer processing on the received data from the physical uplink channel before the data from the physical uplink channel is provided as uplink transport channel data (MAC data) for the protocol stack subsystem 300.When the sending and receiving of signals by the mobile terminal 102 is discussed below, this refers to an interaction between the antenna system 202, the RF transceiver 204 and the baseband modem 206 (the PHY subsystem 300 and the protocol stack subsystem 310), as described in detail in that context.

[0030] Additionally, an interface can be located between the baseband modem 206 and the application processor 208, which is implemented as a central processing unit (CPU) and is used to run various applications and / or programs of the mobile device 102, e.g., applications that are stored in a memory element of the mobile device 102 (in Fig. The application processor 208 can be configured to run an operating system (OS) of the mobile device 102 and use the interface to the baseband modem 206 to send and receive user data such as voice, video, application data, basic internet / web access data, etc. The application processor 208 can also be configured to control one or more other components of the mobile device 102, such as user input / output devices (display(s), keypad(s), touchscreen(s), speakers, external button(s), camera(s), microphone(s), etc.), peripheral devices, a memory chip, a network connection, external device interfaces, etc. The extensive functionality of the baseband modem 206, although shown in Fig. 2, which is shown separately, may be implemented partially or entirely on the application processor 208, for example by executing the functionality of the baseband modem 206 as software that is executed by the processor core of the application processor 108 (e.g., in the relevant physical layer subsystem 300 and the relevant protocol stack subsystem 310). This is considered to provide equivalent functionality, and the disclosure is therefore not limited to either architecture.

[0031] The mobile device 102 can send and receive data through protocol stack and physical layer operations across different cells in the network, such as cells 104a-104c, 106a-106c, and 108a-108c, depending on the instructions of the physical layer subsystem 300 and the protocol stack subsystem 310. As previously noted, the mobile device 102 can perform a cell search to detect neighboring cells in the network and potentially interact with them further.

[0032] In the exemplary scenario of Fig. 1. Transmission can occur through each of the cells 104a-104c, 106a-106c, and 108a-108c on a given first frequency level, i.e., on the same carrier frequency. Additionally, base stations 104-108 can each have one or more cells whose transmission occurs on one or more additional frequency levels (e.g., transmission occurs through cells 104d-104f for base station 104, cells 106d-106f for base station 106, and cells 108d-108f for base station 108). Fig. (1 not shown) possibly on a second frequency level), and accordingly, the following description, which refers to only one frequency level, is applicable analogously to one or more additional frequency levels. In a cell search procedure, the objective may be for the mobile device 102 to identify all detectable cells on one or more target frequency levels, which may include the aforementioned first frequency level of cells 104a-104c, 106a-106c, and 108a-108c.

[0033] The mobile device 102 can therefore initiate a cell search at a given time, which can be determined, for example, based on the mobility environment of the mobile device 102 as detected by an RRC element of the protocol stack subsystem 310. The RRC element might, for instance, determine, based on a combination of network commands and previous radio measurements, that radio measurements need to be performed as part of new cell selection and handover procedures. Alternatively, the mobile device 102 might be implementing a power-on procedure (if the device is powered off or in standby) or need to re-establish its connections after an out-of-coverage (OOC) scenario and therefore might need to perform a network selection (e.g., PLMN selection) and / or a cell selection.

[0034] The RRC element can therefore request a cell search from the PHY controller 302, which can then trigger a cell search at the search engine circuit 306. The search engine circuit 306 can be implemented as a hardware and / or software system and configured to receive and process digital signals provided to the baseband modem 206 by the RF transceiver 204 for performing cell searches and detections. The physical layer subsystem 300 can receive digitized downlink signals (soft data) and store them in the buffer 304, which the search engine circuit 306 can then evaluate to detect cells. Subsequently, the search engine circuit 306 can report detected cells to the PHY controller 302, whereupon the PHY controller 302 can trigger measurements on the measurement engine circuit 308 and / or report them to the RRC element of the protocol stack subsystem 310.The search engine circuit 306 can therefore be configured to receive downlink signal data captured during a search (which is stored in buffer 304) and to process the data captured during a search that was received in order to detect PSS and SSS sequences in neighboring cells.

[0035] As described in more detail later, the search engine circuit 306 can be configured to obtain a list of potential cells based on an initial PSS / SSS detection. This may involve identifying potential cells based on PSS / SSS pairs that yield sufficient cross-correlation values ​​with local copies of the predefined PSS and SSS sequences. The search engine circuit 306 can then evaluate the potential cells in the list to classify and discard any phantom cells. These phantom cells can be categorized as either "sporadic" or "deterministic." Sporadic phantom cells can be caused by random channel effects such as noise and other disturbances and can vary from cell search to cell search.A sporadic phantom cell might be falsely detected, for example, if a random noise segment erroneously results in a match in a given PSS-SSS pair. Deterministic phantom cells can be caused by specific properties of the SSS sequences used in LTE networks and can each represent a fixed relationship between an identifier of a real cell (defined by the PSS and SSS indices) and the identifier of a corresponding deterministic phantom cell. Each real cell can cause up to 21 deterministic phantom cells.

[0036] The search engine circuit 306 can evaluate a detected SSS symbol vector of each potential cell to filter out both sporadic and deterministic phantom cells from the initial list of potential cells. As described in more detail below, the search engine circuit 306 can demodulate the detected SSS symbol vector and then evaluate phase shifts between the elements of the demodulated SSS symbol vector. Subsequently, the search engine circuit 306 can classify potential cells as phantom cells if the elements of the corresponding demodulated SSS symbol vector exhibit a significantly high phase shift variance, and classify potential cells with a low phase shift variance between the elements of the corresponding SSS symbol vector as genuine cells.

[0037] Fig. Figure 4 shows a method 400 by which the search engine circuit 306 can filter out phantom cells using the phase-shift variance identification mentioned above. The corresponding functionality of the method 400 can be implemented in the search engine circuit 306 either as hardware logic, e.g., as an integrated circuit or FPGA, or as software logic, e.g., as program code that defines arithmetic, control, and I / O instructions, which are stored in a non-transient, computer-readable storage medium and executed on a processor.

[0038] The search engine circuit 306 can initially obtain a preliminary list of potential cells from cell 404 via PSS / SSS detection. Thus, during a search, the search engine circuit 306 can capture a 5 ms block of downlink signal data and process the captured data to identify data originating from neighboring cells (e.g., one or more of cells 104a-104c, 106a-106c, and 108a-108c) that are detectable as PSS and SSS sequences contained within the captured data. For example, the search engine circuit 306 can obtain the captured downlink signal data after high-frequency demodulation and digitization (e.g., at the RF transceiver 204) and store the digital versions of the captured data in the signal buffer 304.

[0039] Next, the search engine circuit 306 can process the digital versions of the data acquired during the search and stored in buffer 304 to identify whether PSS and SSS sequences are present. According to the 3GPP specification for LTE networks, LTE cells can transmit a PSS and an SSS sequence every 5 ms according to a fixed transmission pattern, where the fixed transmission pattern can be different in frequency-duplex (FDD) mode than in time-duplex (TDD) mode. Fig. Figure 5 is a graphical representation of the downlink transmission pattern of PSS and SSS sequences in FDD operation. As shown in Fig. As shown in Figure 5, LTE cells can transmit downlink signals over a series of 10 ms long radio frames (RF), with each RF frame divided into 10 subframes, each 1 ms long. Each subframe is divided into two timeslots, each containing either 6 or 7 symbol periods, depending on the length of the cyclic prefix (CP). LTE cells can transmit a PSS sequence in the last symbol period of the first timeslot (either the 6th or 7th symbol period of the first timeslot, depending on the CP length) and an SSS sequence in the symbol period preceding the PSS sequence, and can repeat this fixed symbol pattern for all RF frames. Fig. Figure 6 is a graphical representation of the downlink transmission pattern of PSS and SSS sequences in TDD operation. As shown in Fig. As shown in Figure 6, LTE cells can transmit a PSS sequence in the 3rd symbol period of the first timeslot of the 1st and 6th subframes of each radio frame. Subsequently, LTE cells can transmit an SSS sequence in the last symbol period of the second timeslot of the 0th and 5th subframes (the 6th or 7th symbol period of the second timeslot, depending on the CP length). The time interval between the transmitted PSS sequence and the transmitted SSS sequence can therefore vary depending on both the duplex mode and the CP length. As described in more detail below, the search engine circuit 306 uses this information to determine the duplex mode and CP length of cells detected during a cell search.

[0040] Using Orthogonal Frequency Division Multiplexing (OFDM) in the downlink of LTE, each LTE cell can transmit downlink signals over a set of subcarriers, with each resource block comprising a set of 12 adjacent subcarriers (spaced 15 kHz apart). An LTE cell can transmit downlink signals (depending on the system bandwidth) over 6 to 20 resource blocks, with each discrete subcarrier of the resource blocks used capable of transmitting one symbol (per symbol period).

[0041] As defined in section 6.11 of 3GPP Technical Specification 36.211, “Physical channels and modulation”, V12.5.0 (“3GPP TS 36.211”), each cell transmits a PSS / SSS sequence pair that identifies the cell's Physical Cell ID (PCI), where the PSS sequence specifies the Physical Layer identifier (ranging from 0 to 2) and the SSS sequence specifies the Physical Layer cell identifier group (ranging from 0 to 167). The search engine circuit 306 can identify the PCI (ranging from 0 to 503) of a given cell by identifying the PSS or SSS sequence transmitted by the cell. In particular, the PSS sequence index (from the possible set of 3 predefined PSS sequences) can determine the Physical Layer identifier. NID(2) specify, while the SSS sequence index (from the possible set of 168 predefined SSS sequences) is the physical layer cell identifier group. NID(1) can specify. The PCI NID cell can then be NIDCell=3NID(1)+NID(2) specified, which consequently enables the search engine circuit 306 to obtain the PCI of a cell by identifying the specific PSS / SSS sequence pair sent by the cell.

[0042] As further specified in section 6.11 of 3GPP TS 36.211, each PSS sequence can be a sequence of length 62 generated from a frequency-domain Zadoff-Chu sequence with a square root, which, during the aforementioned PSS symbol period, is assigned to one of the 62 middle subcarriers (excluding one middle DC subcarrier) of the system bandwidth. Each SSS sequence can be a sequence of length 62 generated from a frequency-domain PN sequence (PN = pseudorandom noise), which, during the aforementioned SSS symbol period, is also assigned to one of the 62 middle subcarriers. All three possible PSS sequences and the 168 possible SSS sequences are predefined and therefore known to search engine circuit 306.

[0043] Accordingly, the search engine circuit 306 can compare locally generated or locally stored copies of the possible PSS and SSS sequences with the new search data at 404 to determine whether the new search data contains PSS and SSS sequences transmitted by detectable cells and, if so, to determine the temporal position of the PSS and SSS sequences within the new search data. By identifying the presence and temporal position of PSS and SSS sequences, the search engine circuit 306 can determine the PCI of each detectable cell and obtain a time reference for synchronizing with each detectable cell (i.e., based on the fixed temporal position of PSS and SSS sequences in a cell's intended downlink transmission sequence).

[0044] In particular, the search engine circuit 306 can calculate the cross-correlation function between each locally generated PSS sequence and the digital versions of the data acquired during a search in the time domain. Subsequently, the search engine circuit 306 can identify transmitted PSS sequences among the data acquired during a search by analyzing the peaks in the resulting cross-correlation functions. A peak of sufficient amplitude indicates the presence of a PSS sequence that matches the corresponding locally generated PSS sequence used to calculate the cross-correlation function. The search engine circuit 306 can therefore identify the peak in a specific sample of the data acquired during a search and thus obtain a time point within the data acquired during a search that marks the position of a PSS sequence in a potential cell.

[0045] The search engine circuit 306 can therefore identify one or more temporal positions in the data acquired during a search that mark the potential location of PSS sequences. Subsequently, the search engine circuit 306 can use locally generated copies of all SSS sequences to calculate the cross-correlation function between each local SSS sequence and the data acquired during the search in order to identify whether SSS sequences are present. As already mentioned in connection with Fig. 5 and Fig. As described in detail in Section 6, the temporal position of an SSS sequence and the temporal position of a PSS sequence can differ depending on the duplex mode and the CP length. Accordingly, an SSS sequence can occur at a finite number of temporal positions relative to a detected PSS sequence. Furthermore, the search engine circuit 306 can induce symbol period synchronization using the temporal position of a detected PSS sequence. Consequently, it can insert an FFT window using the detected temporal PSS sequence positions and then transform the data acquired during the search in the time domain into SSS symbols in the frequency domain. The search engine circuit 306 can also detect the PSS sequences (which occur before each SSS sequence, as described in Section 6). Fig. 5 and Fig. (as shown in Figure 6) to compensate for channel effects for SSS detection. The search engine circuit 306 can then calculate the cross-correlation functions for the SSS sequences in the frequency domain (e.g., using an element-wise "fast" correlation calculation). The search engine circuit 306 can then evaluate the resulting SSS cross-correlation functions to identify, based on the cross-correlation peaks, whether an SSS sequence is present. Depending on the position of a detected SSS sequence relative to a previously detected PSS sequence, the search engine circuit 306 can determine the duplex mode and CP length of a potential cell present in the new data acquired during the search. These are common procedures for PSS / SSS detection with cross-correlation that will be familiar to those skilled in the art.

[0046] The search engine circuit 306 can therefore identify a potential cell for each PSS-SSS pair for which a sufficient match is found at a specific temporal position of the data acquired during a search, according to the calculated cross-correlation functions, and can accordingly obtain a list of all such matches from 404. The search engine circuit 306 can additionally use multiple sets of data acquired during a search to accumulate or average PSS / SSS detection results over several half-frames for noise and interference reduction (incoherent for PSS accumulations and coherent or incoherent for SSS accumulations).

[0047] Subsequently, at 406-420, the search engine circuit 306 can evaluate each of the potential cells in the list of potential cells to classify each potential cell as a real cell or a phantom cell. As previously noted, at 404, the search engine circuit 306 can identify each potential cell by detecting a PSS-SSS pair that matches the received data acquired during the search, for example, according to cross-correlation functions. Each PSS-SSS pair can therefore correspond to one of the predefined PSS sequences and one of the predefined SSS sequences. Within the framework of procedure 400, the search engine circuit 306 can evaluate the potential cells by comparing the data acquired during the search with the identified, predefined SSS sequence to classify each potential cell as a real cell or a phantom cell.

[0048] As specified in section 6.11.2 of 3GPP TS 36.211, each SSS sequence sent through a cell is a “nested concatenation of two binary sequences of length 31”, denoted as for the even-index sequence and as d(2n + 1) for the odd-index sequence, to generate the concatenated sequence d(n), which is defined for 0 ≤ n ≤ 61. The nested sequences d(2n) and d(2n) are obtained by scrambling sequences c0(n), c1(n), z1(m0)(n) and z1(m1)(n) on shifted basic sequences z0(m0)(n) and z1(m1)(n) The following results are generated: d(2n)={s0(m0)(n)c0(n)im subframe 0s0(m0)(n)c0(n)im subframe 5 d(2n+1)={s1(m1)(n)c1(n)z1(m0)(n)im subframe 0s0(m1)(n)c1(n)z1(m1)(n)im subframe 5 where 0 ≤ n ≤ 30. As defined in section 6.11.2.1 in 3GPP TS 36.211, the scrambling sequences c0(n) and c1(n) are the sequences of ±1 depending on the PSS sequence index. NID(2), and during the scramble sequences z1(m0)(n) and z1(m1)(n) These are analogous sequences of ±1 depending on the SSS sequence index. NID(2) (about cyclic shifts m0 and m1, as in connection with Fig. 7 described in detail).

[0049] The shifted basic sequences s0(m0)(n) and s1(m1)(n), which are used to generate d(n) are defined as two different cyclic shifts of the basic sequence s̃(n) according to s0(m0)(n)=s˜((n+m0)mod 31)s1(m1)(n)=s˜((n+m1)mod 31) where s̃(n) = 1 - 2x(i), 0 ≤ i ≤ 30, defined by x(ι¯+5)=(x(ι¯+2)+x(ι¯))mod 2, 0≤ι¯≤25

[0050] The cyclic shifts m0 and m1 applied to s̃(n) are, according to the SSS sequence index, NID(1) (Physical layer cell identification group) defines the PCI, which, as already described in detail, NID cell in addition to the PSS sequence index NID(2) (Physical layer identifier within the physical layer cell identifier group). Fig. Figure 7 shows Table 6.11.2.1-1 from Section 6.11.2.1 in 3GPP TS 36.211, which shows the cyclic shifts m0 and m1 for each SSS sequence index NID(1) (Physical layer cell identification group) defined.

[0051] Accordingly, each cell can generate and send one SSS sequence d(n) during a single symbol period per half-frame (where d(n) per half-frame can alternate between two different SSS sequences according to equation (1), both of which correspond to the SSS sequence index NID(1) (Physical Layer Cell Identifier Group) of the cell), where the 62 symbols of the SSS sequence d(n) can be assigned to the 62 middle subcarriers (excluding the DC subcarrier) of the system bandwidth, as specified in Section 6.11.2.2 in 3GPP TS 36.211. As already noted, the search engine circuit 306 can be accessed via the prior temporal positioning of the PSS sequence (as in Fig. 5 and Fig. (shown in Figure 6) perform channel equalization and obtain symbol clock limits, thereby enabling the search engine circuit 306 to convert each identified SSS symbol period of the captured half-frame into the frequency domain and consequently obtain the detected SSS symbol vector r of length 62. The search engine circuit 306 can identify eligible cells by comparing each detected SSS symbol vector r with the possible set of 168 SSS sequences to identify a local, predefined SSS symbol vector d (if any) that is sufficiently similar to the detected SSS symbol vector r, e.g., based on a cross-correlation peak of sufficient amplitude.

[0052] The search engine circuit 306 can therefore obtain, at 404, a detected SSS symbol vector r (which matches an SSS symbol vector d of one of the 168 possible predefined SSS sequences) for each identified potential cell (along with a matching PSS sequence index and related processing data, cell clock information, duplex operation information, cyclic prefix information, etc.). Within the framework of procedure 400 at 406-418, the search engine circuit 306 can evaluate the detected SSS symbol vector r for each of the potential cells in order to classify each of the potential cells as either a real cell or a phantom cell.As will be described in detail later, the detected SSS symbol vector r of real cells may be distinguishable from detected SSS symbol vectors r of deterministic as well as sporadic phantom cells, thus enabling the search engine circuit 306 to apply the procedure 400 to successfully identify and discard deterministic as well as sporadic phantom cells.

[0053] As in Fig. As shown in Figure 4, the search engine circuit 306 can evaluate each potential cell by iterating through the list of potential cells obtained via PSS / SSS detection at 404. Search engine circuit 306 can retrieve the detected SSS vector r and the corresponding local SSS vector d (which triggered a match with r during SSS detection at 404) from 406. Search engine circuit 306 can then demodulate the detected SSS vector r with the local SSS vector d to obtain a demodulated SSS vector p. As defined equivalently in equations (1)-(3) above, the local SSS vector d can be a vector d = (d1,..., d N-1 ) of length 62 with N = 62, where s i ∈ {-1, +1} for 0 ≤ i ≤ N - 1. In other words, the local SSS vector d can be a pseudorandom sequence with the symbols -1 and +1, where d is derived from the shifted basic sequences s0(m0)(n) and s1(m1)(n) as per equation (1). The search engine circuit 306 can convert d, which was originally sent, into a detected SSS symbol vector r = (r0, r1, ..., r N-1 ) with N = 62 received, where r i ∈ ℂ for 0 ≤ i ≤ N - 1.

[0054] While the corresponding cell d with d i ∈ {-1, +1}, i.e., either the symbol -1 or the symbol +1, the search engine circuit 306 d can send in the form of a detected symbol vector r with r i ∈ ℂ, i.e., received with complex-valued symbols. In other words, during wireless transmission and local processing on the mobile device 102, the original symbols -1 and +1 of d can be deformed into other symbols in the complex plane, resulting in a detected SSS symbol vector r. Fig. Figure 8 shows a simple example of how originally sent SSS symbols d i∈ {-1, +1} from the real-valued (-1,0) and (+1,0) positions on the real axis into complex-valued symbols r i ∈ ℂ can be implemented. As in Fig. As shown in Figure 8, the originally transmitted / locally predefined SSS symbols of d can therefore be limited exclusively to real symbols with phase 0 or π (180), while the detected symbols of r can include both real and imaginary components and can have any phase (depending on the noise mentioned above as well as other interference).

[0055] The search engine circuit 306 can perform the demodulation process at 408 by performing the element-wise multiplication of d and r as pi=diri, 0≤i≤N−1 performs the process and consequently generates the demodulated SSS vector p.

[0056] The demodulated SSS vector p can provide an indication of the accuracy of the detected symbol vector r. In the case of an ideal channel, if d and r coincide, i.e., r i = d i ∈ {-1, +1} for 0 ≤ i ≤ N - 1, the demodulated vector p leads to p i = 1 for all 0 ≤ i ≤ N - 1, since every product d i r i the result will be either -1 · -1 or 1 · 1.

[0057] In the absence of carrier frequency offsets and symbol period tracking errors (as described in more detail below), it can be assumed that the demodulated SSS vector p in a non-perfect case (as in Fig. 8. Examples based on r i ∈ ℂ illustrated) to elements p iThis leads to the following, all of which lie relatively close to +1 on the complex plane (assuming that the noise is controllable), if d is a suitable match for r. However, if d is not a suitable match for r, for example, if the cell currently under consideration is a deterministic or sporadic phantom cell, the elements of p are not assumed to be the same. i not all are grouped close to +1, but may represent separate groups of positions that are grouped next to both -1 and +1.

[0058] However, due to the aforementioned effects of carrier frequency offsets and symbol period tracking errors, both constant and linearly increasing phase shifts can occur in the elements of the detected SSS symbol vector r. As already noted, the 62 SSS symbols can be assigned to the 62 middle subcarriers of the system bandwidth and accordingly separated by a fixed subcarrier spacing of, for example, 15 kHz. In the case of a carrier frequency offset between the RF unit of the transmitting cell and the demodulation frequency used by the RF transceiver 204, all elements of the detected SSS symbol vector r can exhibit a constant phase shift; that is, in addition to individual phase shifts caused by noise and symbol period tracking errors, each element can be shifted by the same phase shift, which can also be reflected in the demodulated SSS symbol vector p, to the corresponding position -1 or +1.

[0059] The search engine circuit 306 can therefore combine adjacent elements of the demodulated SSS symbol vector p at 410 to eliminate such constant phase shifts by using the corrected SSS symbol vector o as oi=pipi+1*, 0≤i≤N−2 calculated, where (.)* denotes the complex conjugate operator, whereby o = (o0, o1, ..., o N-2 ).

[0060] The search engine circuit 306 can be modified by multiplying each element p. i with the complex conjugate value of the neighboring element p i+1 counteract a constant phase shift between adjacent elements (separated by a subcarrier spacing of 15 kHz) and consequently o with the elements o iThis generates elements that do not contain this constant shift between the elements. This can also eliminate "slow noise," allowing the search engine circuit 306 to eliminate similar channel effects between adjacent elements by multiplying each element of p by its incrementally neighboring element.

[0061] The combining process at 410 can also have the additional effect of eliminating linearly increasing phase shifts between adjacent elements of p due to a symbol period synchronization error. As already described in detail, the search engine circuit 306 can convert an identified SSS symbol period into the frequency domain using an identified temporal PSS position in order to insert an FFT window for the frequency domain conversion. However, the FFT window insertion may not be perfectly aligned with the actual temporal position of the SSS sequence, which can lead to a linearly increasing phase if the symbol period is inserted outside the cyclic prefix of the SSS sequence, i.e., if the length of the cyclic prefix is ​​exceeded due to a time-dependent error in the FFT window insertion.

[0062] If such an error occurs during the FFT window insertion, the elements of p result in a linearly increasing phase shift between adjacent elements. The search engine circuit 306 can be modified by multiplying each element p. i with the complex conjugate value pi+1* of the adjacent element such linearly increasing phases into a constant, all elements o i Implement a common phase shift. Under some rare circumstances with minimal carrier frequency and clock mismatches, the search engine circuit 306 may omit the phase adjustment process, as the effects of a carrier frequency and clock mismatch may be so small that they do not significantly affect detection.

[0063] After that, the search engine circuit 306 can enter phase α. i each element of o i at 412 as αi=arctan(oi) 0≤i≤N−2 calculate the phase vector α = (α0, α1, ..., α N-2 ) to obtain.

[0064] The search engine circuit 306 can therefore retrieve the pure phase information of each element. i of the corrected SSS vector o, so that the search engine circuit 306 can ignore the effects of fluctuating signal strength in the detected SSS vector r. Consequently, the search engine circuit 306 can ignore the effects of frequency-selective attenuation and similar channel effects that might influence the amplitudes of r, which could negatively affect the classification process and, moreover, prevent the search engine circuit 306 from combining information from different detected SSS symbol vectors r without having to consider different signal levels.

[0065] After the search engine circuit 306 has received the pure phase information of o at 412, it can calculate a scalar “variance” v over the obtained angles α at 414. i as v=1N−1∑i=0N−2(αi−μ)2 calculate, taking into account the following: μ=1N−1∑i=0N−2αi

[0066] The search engine circuit 306 can eliminate constant phase shifts between the elements of o (which may have occurred, for example, as a result of an inaccurate alignment of the FFT window) by evaluating the phase variance v, since every angle α i will be offset by a constant value, consequently generating a low variance.

[0067] In the final classification step of procedure 400, the search engine circuit 306 can compare the phase variance v of the current potential cell with a phase variance threshold. If v exceeds the phase variance threshold, the search engine circuit 306 can classify the current potential cell as a phantom cell and discard it from the list of potential cells at 418. If v does not exceed the phase variance threshold, the search engine circuit 306 can retain the current potential cell in the list of potential cells and consequently classify it as a real cell.

[0068] Both deterministic and sporadic phantom cells can be associated with high values ​​for phase variance v, while real cells can be associated with considerably lower values ​​for phase variance v. This consequently allows search engine circuit 306 to successfully distinguish between real cells and phantom cells as a result of the phase variance evaluation at 416. As already described in detail, the local SSS vector d can be expressed as the nested concatenation d(n) of the nesting sequences d(2n) and d(2n + 1), as defined in equation (1) for generation, where one nesting sequence is derived from the shifted basic sequence. s0(m0) and the other nesting sequence from the shifted base sequence s1(m1) is generated. The detected SSS vector r can be expressed similarly as a nested concatenation r(n) of the corresponding nesting sequences r(2n) and r(2n + 1), i.e., the elements of the detected SSS vector r with even and odd indices, respectively.

[0069] In the case of sporadic phantom cells, which, as already described in detail, can be triggered at 404 by noise mistakenly identified as a PSS-SSS pair, neither of the two nesting sequences r(2n) and r(2n + 1) is correlated with the local SSS sequence d(n), which accordingly does not lead to phase elements α i with a high variance, i.e., the values ​​of the phase elements α i are therefore not similar. As can be seen from equation (4), the demodulation of the mismatch between r and s does not lead to p. i= 1 for 0 ≤ i ≤ N - 1. Accordingly, the phase variance v is large, which consequently enables the search engine circuit 306 to identify and discard sporadic phantom cells at 416 and 418.

[0070] As previously noted, deterministic phantom cells can be caused by special properties of SSS sequences. Specifically, deterministic phantom cells can always be caused when one SSS nesting sequence of an identified PSS-SSS pair of potential cells is correct and the other SSS nesting sequence is incorrect. In other words, the SSS sequence r(n) of the identified PSS-SSS pair may contain the correct shifted basic sequence. s0(m0) or s1(m1) for either r(2n) and r(2n + 1) (see equation (1)) and incorrect for the other. This can result from PSS-SSS detection if a local SSS sequence generated from an incorrect SSS nesting sequence and a correct SSS nesting sequence leads to a sufficient cross-correlation for initial inclusion in the list of eligible cells.

[0071] Since a single correct SSS nesting sequence may be sufficient to produce a deterministic phantom cell, each real cell can produce up to 21 phantom cells. As shown in Fig. As can be seen in Figure 7, some cyclic shifts m0 and m1 are used for several physical layer cell identifier groups. NID(1) It is used multiple times. For example, the cyclic shift m0 = 0 is used for 7 different physical layer cell identifier groups. NID(1) Used multiple times. Considering the 3 additional options for physical layer identification. NID(1) to define the PCI NID cell Such real cells can be mistaken for up to 21 different deterministic phantom cells, where each deterministic phantom cell corresponds to a PSS-SSS pair for which at least one of the SSS nesting sequences is correct.

[0072] Since only one of the SSS nesting sequences is correct for deterministic phantom cells, i.e., the elements of r are derived from the correct shifted basic sequence either with an even or an odd index, the values ​​of the demodulated SSS sequence p and the resulting phase elements α are inot similar thereafter. In particular, the elements of the corrected SSS symbol vector o may be rather random, since one of the nested sequences (elements of r with even or odd index) is correct, while the other is random, thus generating o with a high degree of randomness. Accordingly, the phases α of o do not point in the same direction, consequently resulting in a metric v for high phase variance for deterministic phantom cells. For example, considering the elements p gerade = p 2i with even index and the elements p ungerade = p 2i+1 with odd index for i=0,1,...,N2−1 of a given demodulated SSS vector p, if p gerade correlated with s and p ungerade is random (so that the currently eligible cell becomes a deterministic phantom cell), the elements of p geradeIdeally (assuming no noise has occurred), all elements are equal to 1, whereas the elements of p ungerade would be predominantly random. Accordingly, the element-wise product would result in oi=pipi+1*, 0 ≤ i ≤ N - 2 elements of o pointing in random directions, which consequently results in a high phase variance v between the elements of o. Based on this, the search engine circuit 306 can distinguish between deterministic phantom cells and real cells.

[0073] The search engine circuit 306 can therefore identify and discard both deterministic and sporadic phantom cells via phase variance evaluation at 416 and 418. An example phase variance threshold for distinguishing between real cells and phantom cells might be 15 degrees, 30 degrees, etc., and can easily be configured to several different values ​​to adjust the filter sensitivity, with lower phase variance thresholds resulting in strong filtering and higher phase variance thresholds resulting in weak filtering.

[0074] After classifying the current potential cell, the search engine circuit 306 can proceed to 420 to iteratively repeat steps 406-420 on the next potential cell (if any) in the list of potential cells. Consequently, the search engine circuit 306 can evaluate each potential cell in the list to classify each potential cell as either a real cell (retained in the list of potential cells) or a phantom cell (discarded in the list of potential cells). The search engine circuit 306 can execute steps 406-420 sequentially, one potential cell at a time, or in parallel, for example, on several potential cells simultaneously.

[0075] After evaluating all eligible cells in the list of eligible cells at 422, the search engine circuit 306 can obtain a final list of eligible cells, which includes only those cells suitable for classification as a true cell based on the phase variance metric. The search engine circuit 306 can report this final list of eligible cells to the PHY controller 302, which can then provide the final list to the measurement engine circuit 308 for further measurement. Subsequently, the PHY controller 302 can report the resulting measurements to the RRC element of the protocol stack subsystem 310. Based on the measurements reported to the RRC element, the RRC element can perform mobility operations such as handover, cell selection, new cell selection, PLMN selection, reporting measurements to a network, etc.The search engine circuit 306 can reduce the computational load of the measurement engine circuit 308 by accurately identifying and eliminating phantom cells in the list of eligible cells, thereby saving additional energy.

[0076] The search engine circuit 306 can additionally perform a further evaluation on the final list of potential cells generated by procedure 400, for example, to perform further phantom cell filtering on the final list of potential cells and thus further narrow down the list of potential cells. Alternatively, the search engine circuit 306 can employ additional filtering techniques between 404 and 406 to narrow down the initial list of potential cells obtained via PSS / SSS detection and then apply 406-420 to the narrowed list of potential cells. These are examples of "sequential classification," in which several discrete classification stages are applied successively to repeatedly filter an initial list of potential cells.Consequently, each classification level can output a further narrowed list of eligible cells and together generate a final list of eligible cells at the output of the last classification level.

[0077] Fig. Figure 9 shows a simple example of such a sequential classification, which may include three separate classification stage circuits 910, 920, and 930. Each of the classification stage circuits 910-930 may be implemented as standalone or integrated hardware and / or software in the search engine circuit 306 or the PHY controller 302 and may each perform its own feature extractions and filters to identify and discard phantom cells in an initial list of eligible cells. The search engine circuit 306 or the PHY controller 302 may obtain the initial list of eligible cells via PSS / SSS detection as in procedure 404 (404) and subsequently provide the initial list of eligible cells to a system 900 used for sequential classification, which is located in Fig. Provide the image shown in 9.

[0078] As in Fig. As shown in Figure 9, the initial list of potential cells can include two "strong real cells" (R1 and R3; i.e., easily classifiable as real cells), one "weak real cell" (r2; i.e., difficult to classify as a real cell), three "weak phantom cells" (g1, g2, and g4; i.e., easily classifiable as phantom cells), and two "strong phantom cells" (G3 and G5; i.e., difficult to classify as phantom cells). Each of the classification stage circuits 910-930 can receive a list of potential cells and perform feature extractions and filtering to identify phantom cells based on specific criteria and discard the identified phantom cells in a restricted list of potential cells generated as the output of the classification stage.

[0079] Each of the 910-930 classification stages can apply a feature extraction technique such as the phase variance evaluation described in detail above, frequency-offset-based strong filtering, a comparison of the energy of unprocessed synchronization signals with the energy of filtered synchronization signals, signal-to-noise ratio averaging filtering, and other such phantom cell filtering techniques. In frequency-offset-based strong filtering, the classification stage can obtain a carrier frequency offset estimate for a cell and compare the carrier frequency offset estimate with a worst-case Doppler shift and oscillator deviation to determine whether the carrier frequency offset estimate falls within the worst-case range of possible carrier frequency offsets.If the carrier frequency offset estimate lies outside the worst-case range of possible carrier frequency offsets, the classification stage can classify the cell as a phantom cell and, conversely, classify the cell as a real cell if the carrier frequency offset estimate falls within the worst-case range of possible carrier frequency offsets. The classification stage can determine the carrier frequency offset estimate for each cell using a coarse frequency estimation function (i.e., a carrier frequency offset estimate based on the phase difference of correlating received PSS sequences in the time domain) and a more accurate frequency estimation function (e.g.,a carrier frequency offset estimate based on the residual phase after channel correction of received SSS sequences using PSS sequences as pilot) is obtained, whereby the baseband carrier frequency offset estimate for each cell is derived from the coarse carrier frequency offset estimate and the more accurate carrier frequency offset estimate.

[0080] When comparing the energy of unprocessed synchronization signals with the energy of filtered synchronization signals, the classification stage circuit can evaluate the energy before and after a customized filtering of the PSS sequences during PSS detection, and then compare them. For example, the search engine circuit 306 might apply a customized filter to all temporal positions of data acquired during a search where a cell could be located, then apply the customized filter (e.g., as a cross-correlation calculation) and evaluate the resulting output to determine whether a PSS sequence is present. The classification stage circuit can evaluate the signal energy before and after the application of the customized filter and then differentiate between real cells and phantom cells based on the ratio of the signal energy before and after.

[0081] The classification stage can obtain a channel cross-energy vector, i.e., a coarse cross-energy vector, for each potential PSS sequence (each PSS peak value). The classification stage can then filter the potential PSS sequences using the channel cross-energy vector to obtain a filtered cross-energy vector. Finally, the classification stage can scale the coarse cross-energy vector by a threshold vector to obtain a detection cross-energy vector and compare the filtered cross-energy vector with the detection cross-energy vector.If the filtered cross-energy vector is below the detection cross-energy vector, the classification stage circuit can classify the PSS sequence in question as a phantom cell (so that the PSS sequence in question is consequently not considered further), and conversely, classify the PSS sequence in question as a real cell if the filtered cross-energy vector is above the detection cross-energy vector. Since such comparisons of the energy of unprocessed synchronization signals with the energy of filtered synchronization signals evaluate potential PSS sequences, these comparisons can be performed prior to the SSS evaluation.

[0082] In signal-to-noise ratio (SNR) averaging, the classification stage can evaluate the SNR of each eligible cell (which can be calculated at the measurement engine circuit 308 and then provided to the classification stage) and classify each eligible cell as a true cell or a phantom cell based on the SNR. This can be done, for example, by classifying eligible cells with a high SNR as true cells and eligible cells with a low SNR as phantom cells. The classification stage can calculate an average SNR value from multiple SNR measurements and then compare the average SNR metric for this classification to a SNR threshold.

[0083] Accordingly, each of the classification stage circuits 910-930 can use different feature extraction and filtering techniques to identify and remove phantom cells from the list of eligible cells. Fig. Figure 9 illustrates an exemplary sequence for such removal processes, in which the classification stage 910 removes the weak phantom cell g1, the classification stage 920 removes the weak phantom cell g4 and the weak real cell r2, and the classification stage 930 removes the weak phantom cell g2 and the strong phantom cell G5. In this exemplary scenario, the system used for sequential classifications may incorrectly classify the strong phantom cell G3 as a real cell and incorrectly reject the weak real cell r2 as a phantom cell.This can occur as a result of the sequential processes of the classification stage circuits 910-930, because it is assumed that each feature extraction and filtering stage performs strong filtering, namely by exclusively classifying each potential cell either as a phantom cell or as a real cell and consequently not providing soft data or intermediate data.

[0084] Instead of sequential strong filtering, the PHY controller 302 can also perform a "common" classification by evaluating soft results from multiple feature extraction and filtering stages in parallel. Fig. Figure 10 shows an exemplary architecture in which each of the classification stages 1010-1030 performs filtering on a received list of eligible cells to identify and discard phantom cells before outputting a list of the remaining eligible cells. The last classification stage 1030 can output the list of remaining eligible cells to a circuit 1040 used for joint classifications, which can additionally receive the feature information obtained for the remaining eligible cells and generated by each of the classification stages 1010-1030, as well as further feature information from external sources.The circuit 1040, used for joint classification, can then apply the received feature information to perform a joint classification on the list of remaining eligible cells provided by the classification stage circuit 1030. The circuit 1040, used for joint classification, can be implemented as hardware and / or software logic in the PHY controller 302 (or alternatively, separately from it) and can receive the list of remaining eligible cells and the feature information from the classification stage circuits 1010-1030, which, as already described in detail, can be implemented individually or together in the search engine circuit 306 or in the PHY controller 302. Even if in . Fig. Where three classification step circuits are shown, any number of classification step circuits can be used within the scope of protection of the present disclosure.

[0085] A system 1000 used for parallel classifications according to Fig. Classification stage 10 can be optimized in various ways, one or more of which can be implemented. In particular, classification stage circuits 1010-1030 can use filter standards that, compared to the case of sequential strong filtering, Fig. 9 are “weaker”. In other words, the classification stage circuits 1010-1030 can use less stringent thresholds and other filter criteria to prevent the discarding of genuine cells, while still ensuring that cells that appear to be phantom cells (weak phantom cells) can be identified and discarded. Because the circuit 1040, which performs joint classifications, is configured to make parallel decisions using feature information from all of the classification stage circuits 1010-1030, such weak filtering can remain effective.

[0086] Additionally and / or alternatively, the classification stages 1010-1030 can be ordered sequentially according to increasing complexity, for example, if classification stage 1010 has the lowest computational complexity and classification stage 1030 the highest. Since it can be assumed that each classification stage narrows down the list of eligible cells by discarding any phantom cells that meet the criteria, the remaining list of eligible cells that passes through the classification stages can be progressively narrowed by identifying and filtering out phantom cells. Accordingly, the later classification stages evaluate fewer eligible cells than the earlier ones.To reduce overall computational complexity, the calculation stages can be ordered so that those with the lowest complexity are processed first and those with the highest complexity last. Consequently, the most complex calculation stages evaluate smaller sets of potential cells. This classification system therefore prevents unnecessary energy consumption.

[0087] Because the common classification circuit 1040 is placed downstream of the classification stage circuits, it can only evaluate a small number of the eligible cells. While it would be possible to apply the common classification circuit 1040 to each of the eligible cells in the initial list, this might excessively increase the computational load and lead to significant performance losses. Therefore, the common classification circuit 1040 can only process the remaining eligible cells of the classification stage circuits.

[0088] The common classification circuit 1040 can then evaluate these remaining potential cells based on the feature information provided by each of the classification stage circuits 1010-1030 for the remaining cells. This feature information might include, for example, probability metrics indicating the likelihood that a cell is a real cell or a phantom cell, or other evaluation metrics obtained by each of the classification stage circuits. The common classification circuit 1040 can therefore aggregate the feature information provided by each of the classification stage circuits 1010-1030 to classify the remaining cells.Since the joint classification 1040 may be able to use feature information from different sources, the joint classification 1040 may be capable of a classification that is more accurate compared with the relatively limited decisions made by each of the classification circuits 1010-1030 individually.

[0089] The circuit 1040, used for joint classification, can be applied to all of the eligible cells in the initial list of eligible cells obtained via PSS / SSS detection; however, this can further increase the computational complexity. Therefore, a relatively weak filtering can be performed using the sequential classification stage circuits 1010-1030 to eliminate cells that appear to be phantom cells from the initial list of eligible cells. Consequently, the initial list of eligible cells is narrowed down to a shorter list of remaining eligible cells, which the circuit 1040 uses for evaluation via a joint classification.

[0090] The joint classification circuit 1040 can perform the joint classification process as a support vector machine or as another similar linear classifier. For example, each classification stage of the joint classification circuit 1040 can provide one or more feature information metrics (e.g., soft probabilities or other evaluation information) that identify each other cell. The joint classification circuit 1040 can then treat the feature information metrics for each eligible cell as a vector and subsequently evaluate the vector using a predefined model for phantom and real cells.If a given potential cell vector meets the model's criteria for phantom cells, the common classification circuit 1040 can classify the corresponding potential cell as a phantom cell, and conversely, it can classify the corresponding potential cell as a real cell if the potential cell vector meets the model's criteria for real cells.

[0091] Circuit 1040, used for joint classifications, can maintain the predefined model for cell classification during a training phase, for example, during calibration (i.e., before runtime). In such a training phase, a large number of exemplary phantom cells and real cells can be evaluated using the classification stage circuits 1010-1030 to obtain the resulting feature information for each of the exemplary cells. Based on the feature information for each of the exemplary cells, a feature vector can then be defined for each exemplary cell in a multidimensional feature space. Since most or even all phantom cells share many common phantom cell properties, such as a low signal-to-noise ratio, high frequency offset variances, high signal energy, etc.,, the resulting exemplary cell feature vectors can be analyzed to determine whether areas with phantom cells and areas with real cells can be identified within the multidimensional feature space, with the exemplary feature vectors of real cells concentrated in the area (areas) with real cells, while the exemplary feature vectors of phantom cells concentrated in the area (areas) with phantom cells.

[0092] The circuit 1040, used for joint classifications, can then be preconfigured with the defined areas containing phantom cells or real cells within the multidimensional feature space, whereby the areas containing phantom cells or real cells are determined in a calibration training phase. During runtime, the circuit 1040, used for joint classifications, can obtain the feature information of each other existing cell from the classification stage circuits 1010-1030 and generate the corresponding cell feature vector. Subsequently, the circuit 1040, used for joint classifications, can apply the previously determined areas containing phantom cells or real cells to evaluate the resulting feature vector of each other existing potential cell in order to determine whether the feature vector falls within the area containing phantom cells or real cells.fall into the category of real cells, and then classify the cell in question accordingly.

[0093] This can be done in conjunction with a Support Vector Machine, which can receive training vectors and generate a separating hyperplane for use in classification. Accordingly, during a calibration stage, a separating hyperplane can be generated using training vectors (based on example vectors for real cells and phantom cells) and then programmed into the 1040 circuit, which is used for joint classifications, for use in classifications.The circuit 1040, which serves for joint classifications, can then use the separating hyperplane to distinguish between real cells and phantom cells, for example by comparing the cell feature vector for each remaining cell from the cell classification circuits 1010-1030 and classifying each cell with a cell feature vector that falls on a side with real cells on the separating hyperplane (i.e. in the region with real cells in the multidimensional feature space) as a real cell and each cell with a cell feature vector that falls on a phantom cell side on the separating hyperplane (i.e. in the region with phantom cells in the multidimensional feature space) as a phantom cell.Several hyperplane algorithms have become established and can be used in the calibration stage to generate the separating hyperplane that is to be used by the circuit 1040 for joint classifications.

[0094] Assuming independent characteristics and a clearly defined boundary between real cells and phantom cells, the circuit 1040, used for joint classifications, can accurately classify the remaining eligible cells provided by the classification stage circuit 1030. A cost function can be used to define the boundary between real cells and phantom cells, especially if the boundary is not clearly defined. Such a cost function allows the costs of false positives (reported phantom cells) to be compared to the costs of false positives (missed real cells), and assigns costs and a probability to each case.The distinction between real cells and phantom cells can then be defined in such a way as to minimize the cost function, thereby enabling the circuit 1040, which serves for common classifications, to carry out an appropriate classification of the remaining eligible cells.

[0095] Since the joint classification circuit 1040 is potentially capable of evaluating features from various sources, it can successfully distinguish between phantom cells and real cells. Furthermore, because the classification stage circuits 1010-1030 can be applied sequentially before the joint classification circuit 1040, the circuit 1040 may only need to evaluate a true subset of the list of eligible cells using a joint classification, thus avoiding any increased computational load or performance losses when evaluating all cells in the list.

[0096] Because the computational intensity of phase variance calculation in Method 400 can be greater than in other feature extraction algorithms, such phase variance feature extraction can be performed in a later sequential stage of a system used for sequential classifications (which may precede a common classification, as in Fig. (10 shown). Accordingly, a classification step-by-step process implemented in the phase variance evaluation at 406-418 may need to evaluate fewer remaining eligible cells, thereby saving energy and avoiding excessive computational load.

[0097] Instead of being pre-configured according to the cell classification model, the circuit 1040 used for joint classifications can also apply machine learning techniques during runtime to dynamically update the cell classification model based on cell classification results, which may include an adjustment of the area(s) with real cells, the area(s) with phantom cells and / or the associated cost functions.

[0098] Both Procedure 400 and System 1000, used for parallel classifications, can contain "workaround" mechanisms that avoid a full evaluation of cells that are likely to be strong cells. As previously described in detail, Search Engine Circuit 306 can obtain the initial list of potential cells via PSS / SSS detection at 404. Noise and other random channel effects can cause Search Engine Circuit 306 to erroneously include phantom cells in the list of potential cells during the initial PSS / SSS detection. Accordingly, it is plausible to assume that potential cells exhibiting only low levels of noise effects are less likely to be phantom cells than potential cells exhibiting higher levels of noise effects.Consequently, the search engine circuit 306 can be configured to calculate the signal-to-noise ratio (or to obtain such a measurement from the measurement engine circuit 308) of a potential cell before initiating the phase variance evaluation of the potential cell at 408. The search engine circuit 306 can then compare the signal-to-noise ratio to a signal-to-noise ratio threshold. If the signal-to-noise ratio for the potential cell is above the signal-to-noise ratio threshold, the search engine circuit 306 can "bypass" the phase variance evaluation at procedure 400 and classify the potential cell as a true cell. If the signal-to-noise ratio for the cell in question is below the signal-to-noise ratio threshold, the search engine circuit 306 can proceed with the evaluation of the cell in question according to 408-418.Since it is unlikely (due to the expected minimal noise effects during the initial PSS-SSS detection at 404) that potential cells with a high signal-to-noise ratio are phantom cells, the search engine circuit 306 can assume that such potential cells are real cells, thus avoiding the computational load and performance losses associated with performing the full phase variance evaluation on these potential cells. The search engine circuit 306 can therefore be configured to calculate the signal-to-noise ratio of each potential cell (e.g., by performing a signal-to-noise ratio calculation on SSS symbols instead of conventional cell-specific reference signal (CRS) symbols) and subsequently evaluate the calculated signal-to-noise ratio before 408.

[0099] The parallel classification system 1000 can be similarly configured to implement such workarounds to avoid the computational load and performance losses associated with evaluating excessive numbers of cells. For example, the parallel classification system 1000 can similarly obtain the signal-to-noise ratio (SNR) for eligible cells, for instance, before the classification stage 1010 (e.g., via a CRS-based SNR estimation at the measurement engine circuit 308), and then omit the evaluation at the classification stages 1010-1030 if the SNR for an eligible cell exceeds a SNR threshold.The system 1000, which is used for parallel classifications, can then evaluate such bypassed potential cells at the circuit 1040, which is used for joint classifications, or alternatively, additionally bypass the circuit 1040, which is used for joint classifications, and consequently declare such cells with a high signal-to-noise ratio to be real cells without further evaluation.

[0100] As previously noted, one of the classification stage circuits 1010-1030 can use the evaluation of the average signal-to-noise ratio as a feature extraction and filtering criterion. Accordingly, the system 1000 used for parallel classifications can bypass eligible cells with a sufficient signal-to-noise ratio that are identified in all or most further evaluations within the system 1000 used for parallel classifications by a signal-to-noise ratio average calculation classification stage circuit.Accordingly, a signal-to-noise ratio (SNR) average calculation classification stage in System 1000, used for parallel classifications, can be placed first or, alternatively, after other feature extraction classification stages, so that only eligible cells with a high SNR are bypassed, while the other respective criteria of the preceding feature extraction classification stages are also applicable. System 1000, used for parallel classifications, can use further criteria to bypass eligible cells in all or some further evaluations within System 1000, for example, based on the fact that an eligible cell generates a feature metric that exceeds a respective feature threshold of one of the classification stages 1010-1030.

[0101] According to an advantageous aspect of the present disclosure, the phase variance evaluation in method 400 can be performed in the last classification stage of the system 1000 used for parallel classifications and accordingly provide the joint classification with further eligible cells compatible with the phase variance threshold, in addition to any prior feature extraction evaluations used in any prior classification stages. The circuit 1040 used for joint classifications can then refer to the list of further eligible cells provided by the last classification stage with the feature information provided by the last classification stage (e.g.,The phase variance v of each remaining potential cell and / or other evaluation criteria from procedure 400 (such as α, o, p, etc.) are used to perform a parallel classification. Subsequently, the circuit 1040, used for joint classifications, can classify the remaining potential cells to generate the final list of potential cells, which can then be processed. This allows mobility procedures such as cell measurements (e.g., at the measurement engine circuit 308), reporting measurements to a network, handover, cell selection, new cell selection, or PLMN selections to be performed.

[0102] Fig. Figure 11 shows a method for detecting cells. As in Fig. As shown in Figure 11, the procedure includes comparing a detected synchronization sequence of a detected potential cell with a previously determined reference synchronization sequence to generate a demodulated synchronization sequence containing a plurality of samples (1110), determining a phase variance between the plurality of samples of the demodulated synchronization sequence (1120), and comparing the phase variance with a detection threshold to classify the detected potential cell as a real cell or as a false cell (1130).

[0103] Fig. Figure 12 shows a method 1200 for performing cell detections. As in Fig. As shown in Figure 12, the procedure comprises: 1200 evaluating one or more initial potential cells according to a first cell detection criterion to obtain a first cell detection metric for each of the one or more initial potential cells; 1210 selecting one or more first suitable potential cells from the one or more initial potential cells based on the first cell detection metric of each of the one or more initial potential cells; 1220 evaluating the one or more first suitable potential cells according to a second cell detection criterion to obtain a second cell detection metric for each of the one or more first suitable potential cells; 1230Selecting one or more second suitable potential cells from the one or more first suitable potential cells based on the second cell detection metric of each of the one or more first suitable potential cells (1240) and evaluating the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable potential cells according to a combined cell detection criterion to classify each of the one or more second suitable potential cells as a true cell or as a false cell (1250).

[0104] According to one or more further exemplary aspects of the disclosure, one or more of the above relating to the process can be further incorporated into process 1100 and / or process 1200. Fig.integrate the features described in 1-10. In particular, the procedure 1100 can be configured to carry out further and / or alternative processes, as described in detail in connection with the mobile terminal 102 and / or the baseband modem 206.

[0105] The terms “user terminal equipment”, “UE”, “mobile terminal equipment”, “user equipment”, etc. can refer to any wireless communication devices such as mobile phones, tablets, laptops, personal computers, wearables, multimedia playback devices, electrical or household appliances, vehicles, etc., as well as any number of other electronic devices that can communicate wirelessly.

[0106] It should be noted that the implementations of the procedures described in detail herein are exemplary and should therefore be understood as being capable of being implemented in corresponding devices. Furthermore, it should be noted that the implementations of the devices described in detail herein should be understood as being capable of being implemented as corresponding procedures. It is therefore understood that a device corresponding to a procedure described in detail herein may contain one or more components configured to perform any aspect of the related procedure.

[0107] The following examples concern further aspects of the revelation:

[0108] Example 1 is a method for detecting cells, wherein the method includes comparing a detected synchronization sequence of a detected potential cell with a previously determined reference synchronization sequence to generate a demodulated synchronization sequence containing a plurality of samples, determining a phase variance between the plurality of samples of the demodulated synchronization sequence, and comparing the phase variance with a detection threshold to classify the detected potential cell as a real cell or a false cell.

[0109] In Example 2, the subject of Example 1 may optionally further include receiving signal data and performing cell detections on the signal data to identify an initial list of potential cells, including the detected potential cell.

[0110] In Example 3, the subject of Example 2 may optionally further include generating a demodulated synchronization sequence for each detected eligible cell from the initial list of eligible cells and classifying each detected eligible cell from the initial list of eligible cells as a real cell or a false cell based on a phase variance of a multitude of samples of each demodulated synchronization sequence.

[0111] In Example 4, the subject of Example 1 may optionally further include receiving signal data and processing the signal data to identify the detected potential cell.

[0112] In Example 5, the subject of Example 4 may optionally include processing the downlink signal data to identify the detected potential cell, performing a comparison between the signal data and each of a variety of predetermined reference synchronization sequences to identify the detected potential cell.

[0113] In Example 6, the subject of Example 5 may optionally further include identifying the previously determined reference sequence from the multitude of previously determined reference synchronization sequences based on the comparison.

[0114] In Example 7, the subject of Example 5 or Example 6 can optionally include the fact that the comparison is a cross-correlation.

[0115] In Example 8, the subject of one of Examples 5 to 7 may optionally include comparing the signal data with each of the plurality of predetermined reference synchronization sequences to identify the detected potential cell, determining a cross-correlation between the signal data and the respective predetermined reference synchronization sequence for each predetermined reference synchronization sequence of the plurality of predetermined reference synchronization sequences, and identifying the detected potential cell based on the cross-correlation for each of the plurality of predetermined reference synchronization sequences.

[0116] In Example 9, the subject of one of Examples 5 to 8 may optionally include that the multitude of predetermined reference synchronization sequences contains a Primary Synchronization Signal (PSS) and / or a Secondary Synchronization Signal (SSS).

[0117] In Example 10, the subject of one of Examples 1 to 9 may optionally include comparing the detected synchronization sequence of the detected cell in question with the previously determined reference synchronization sequence to generate the demodulated synchronization sequence, determining the component-wise product of a plurality of samples of the detected synchronization sequence and a plurality of samples of the previously determined reference synchronization sequence to obtain the plurality of samples of the demodulated synchronization sequence.

[0118] In Example 11, the subject of any of Examples 1 to 10 may optionally include comparing the detected synchronization sequence of the detected cell in question with the previously determined reference synchronization sequence to generate the demodulated synchronization sequence, demodulating the detected synchronization sequence with the previously determined reference synchronization sequence to obtain an initial demodulated synchronization sequence containing a plurality of samples, and performing phase matching on the plurality of samples of the initial demodulated synchronization sequence to generate the plurality of samples of the demodulated synchronization sequence.

[0119] In Example 12, the subject of Example 11 may optionally include performing phase matching on the plurality of samples of the initial demodulated synchronization sequence to generate the plurality of samples of the demodulated synchronization sequence, and removing a constant frequency offset from the plurality of samples of the initial demodulated synchronization sequence to generate the plurality of samples of the demodulated synchronization sequence.

[0120] In Example 13, the subject of Example 11 or Example 12 may optionally include performing phase matching on the plurality of samples of the initial demodulated synchronization sequence to generate the plurality of samples of the demodulated synchronization sequence, and removing a linearly varying frequency offset from the plurality of samples of the initial demodulated synchronization sequence to generate the plurality of samples of the demodulated synchronization sequence.

[0121] In Example 14, the subject of Example 11 may optionally include demodulating the detected synchronization sequence to obtain the initial demodulated synchronization sequence containing the plurality of samples, and determining the component-wise product of the detected synchronization sequence and the previously determined reference synchronization sequence to obtain the plurality of samples of the initial demodulated synchronization sequence.

[0122] In Example 15, the subject of one of Examples 1 to 14 may optionally include that the predetermined synchronization sequence is a predetermined pseudorandom sequence.

[0123] In Example 16, the subject of one of Examples 1 to 15 may optionally include the fact that the previously determined synchronization sequence consists of two nested PN sequences (PN = Pseudorandom Noise).

[0124] In Example 17, the subject of one of Examples 1 to 16 may optionally include that the previously determined synchronization sequence is a Secondary Synchronization Sequence (SSS).

[0125] In Example 18, the subject of one of Examples 1 to 17 may optionally include determining the phase variance between the plurality of samples of the demodulated synchronization sequence, determining the angle of each of the plurality of samples of the demodulated synchronization sequence to obtain a plurality of angles, and determining a variance of the plurality of angles to obtain the phase variance.

[0126] In Example 19, the subject of one of Examples 1 to 18 may optionally include comparing the phase variance with the detection threshold to classify the detected potential cell as a real cell or a false cell, classifying the detected potential cell as a real cell if the phase variance is below the detection threshold, or classifying the detected potential cell as a false cell if the phase variance is above the detection threshold.

[0127] In Example 20, the subject of one of Examples 1 to 19 may optionally further include carrying out a radio measurement on the detected potential cell if the detected potential cell is classified as a real cell.

[0128] In Example 21, the subject of one of Examples 1 to 20 may optionally further include performing cell selection and / or new cell selection and / or handovers and / or cell measurements and / or mesh selection with the detected cell, if the detected potential cell is classified as a real cell.

[0129] Example 22 is a non-transient, computer-readable medium containing instructions which, when processed by a processor of a communication device, instruct the communication device to perform the procedure according to one of Examples 1 to 21.

[0130] Example 23 is a non-transient, computer-readable medium containing instructions which, when executed by a processor, instruct the processor to perform the procedure according to one of Examples 1 to 21.

[0131] Example 24 is a communication device configured to perform the procedure according to one of Examples 1 to 21.

[0132] Example 25 is a communication circuit configured to carry out the procedure according to one of Examples 1 to 21.

[0133] Example 26 is a method for performing cell detections, wherein the method is to evaluate one or more initial potential cells according to a first cell detection criterion to obtain a first cell detection metric for each of the one or more initial potential cells, to select one or more first suitable potential cells from the one or more initial potential cells based on the first cell detection metric of each of the one or more initial potential cells, and to evaluate the one or more first suitable potential cells according to a second cell detection criterion to obtain a second cell detection metric for each of the one or more first suitable potential cells.Selecting one or more second suitable potential cells from the one or more first suitable potential cells based on the second cell detection metric of each of the one or more first suitable potential cells, and evaluating the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable potential cells according to a combined cell detection criterion to classify each of the one or more second suitable potential cells as a true cell or a false cell, includes.

[0134] In Example 27, the subject of Example 26 may optionally include that the first cell detection criterion and the second cell detection criterion are different cell detection criteria selected from a group consisting of a detection criterion for the phase variance of demodulated synchronization sequences, a detection criterion for the signal-to-noise ratio, a detection criterion for the frequency offset, and a detection criterion for the signal energy of filtered synchronization sequences.

[0135] In Example 28, the subject of Example 26 or Example 27 may optionally include that the first cell detection metric or the second cell detection metric is a phase variance of a demodulated synchronization sequence, or a signal-to-noise ratio, or a frequency offset, or a signal energy of a filtered synchronization sequence.

[0136] In Example 29, the subject of one of Examples 26 to 28 may optionally further include performing cell detections to detect the one or more initial potential cells prior to evaluating the one or more initial potential cells according to the first cell detection criterion.

[0137] In Example 30, the subject of Example 29 may optionally include performing cell detections to detect the one or more initial potential cells, comparing a received signal with one or more predetermined reference synchronization sequences to identify the one or more initial potential cells.

[0138] In Example 31, the subject of Example 30 may optionally include that the one or more previously determined reference synchronization sequences contain one or more Primary Synchronization Sequences (PSSs) or one or more Secondary Synchronization Sequences (SSSs).

[0139] In Example 32, the subject of one of Examples 26 to 31 may optionally include further evaluation of the one or more initial eligible cells according to one or more further cell detection criteria in order to obtain one or more further cell detection metrics for each of the one or more initial eligible cells, prior to evaluating the one or more initial eligible cells according to the first cell detection criterion, wherein evaluating the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable eligible cells according to the combined cell detection criterion in order to classify each of the one or more second suitable eligible cells as a true cell or as a false cell, and evaluating the first cell detection metrics.the second cell detection metrics and one or more further cell detection metrics according to the combined cell detection criterion to classify each of the one or more second suitable eligible cells as a real cell or as a false cell.

[0140] In Example 33, the subject of one of Examples 26 to 32 may optionally include evaluating the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable potential cells according to the combined cell detection criterion to classify each of the one or more second suitable potential cells as a true cell or as a false cell; generating a classification vector for each of the one or more second suitable potential cells, wherein each classification vector contains the first detection metric and the second detection metric of the corresponding cell of the one or more second suitable potential cells; and classifying each of the one or more second suitable potential cells according to the corresponding classification vector.

[0141] In Example 34, the subject of Example 33 may optionally include classifying each of the one or more second suitable potential cells according to the corresponding classification vector and comparing the classification vector of each of the one or more second suitable potential cells with a predetermined cell model in order to determine, according to the predetermined cell model, whether each of the one or more second suitable potential cells is a real cell or a phantom cell.

[0142] In Example 35, the subject of Example 34 may optionally include comparing the classification vector of each of the one or more second suitable potential cells with the predetermined cell model to determine, according to the predetermined cell model, whether each of the one or more second suitable potential cells is a real cell or a phantom cell; and comparing the classification vector of each of the one or more second suitable potential cells with a classification hyperplane to identify whether the classification vector falls within a region of phantom cells according to the predetermined cell model or a region of real cells according to the predetermined cell model.

[0143] In Example 36, the subject of one of Examples 26 to 35 may optionally include evaluating the one or more initial potential cells according to the first cell detection criterion in order to obtain the first cell detection metric for each of the one or more initial potential cells, and evaluating a received signal in order to determine the first cell detection metric for each of the one or more initial potential cells.

[0144] In Example 37, the subject of Example 36 may optionally include selecting the one or more first suitable potential cells from the one or more initial potential cells based on the first cell detection metric of each of the one or more initial potential cells, comparing the first cell detection metric of each of the one or more initial potential cells with the first cell detection criterion, and selecting only those cells of the one or more initial potential cells whose first cell detection metrics satisfy the first cell detection criterion as the one or more first suitable potential cells.

[0145] In Example 38, the subject of Example 36 may optionally include that the first cell detection criterion is a first detection threshold and that the selection of the one or more first suitable eligible cells from the one or more initial eligible cells based on the first cell detection metric of each of the one or more initial eligible cells includes comparing the first cell detection metric of each of the one or more initial eligible cells with the first detection threshold and selecting the one or more first suitable cells based on whether the first cell detection metric of each of the one or more initial eligible cells exceeds the first detection threshold.

[0146] In Example 39, the subject of one of Examples 26 to 38 may optionally include evaluating the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable potential cells according to the combined cell detection criterion, in order to classify each of the one or more second suitable potential cells as a true cell or as a false cell, and identifying one or more true cells from the one or more second suitable potential cells.

[0147] In Example 40, the subject of Example 39 may optionally include that the one or more real cells are a real subset of the one or more second suitable potential cells.

[0148] In Example 41, the subject of Example 39 or Example 40 may optionally further include performing a radio measurement on at least one of the one or several real cells.

[0149] In Example 42, the subject of Example 39 or Example 40 may optionally further include performing cell selection and / or new cell selection and / or handovers and / or cell measurements and / or selecting networks containing at least one of the one or more real cells.

[0150] In Example 43, the subject of one of Examples 26 to 42 may optionally include that the one or more first eligible suitable cells are a proper subset of the one or more initial eligible cells.

[0151] In Example 44, the subject of Example 43 may optionally include that the one or more initial potential cells that are not included in the one or more first suitable potential cells do not meet the first cell detection criterion.

[0152] Example 45 is a non-transient, computer-readable medium containing instructions which, when processed by a processor of a communication device, instruct the communication device to perform the procedure according to one of Examples 26 to 45.

[0153] Example 46 is a non-transient, computer-readable medium containing instructions which, when executed by a processor, instruct the processor to perform the procedure according to one of Examples 26 to 45.

[0154] Example 47 is a communication device configured to carry out the procedure according to one of Examples 26 to 45.

[0155] Example 48 is a communication circuit configured to carry out the procedure according to one of Examples 26 to 45.

[0156] Example 49 is a communication circuit arrangement comprising a cell search circuit configured to compare a detected synchronization sequence of a detected potential cell with a predetermined reference synchronization sequence, to generate a demodulated synchronization sequence containing a plurality of samples, to determine a phase variance between the plurality of samples of the demodulated synchronization sequence, and to compare the phase variance with a detection threshold to classify the detected potential cell as a real cell or a false cell.

[0157] In Example 50, the object from Example 49 may optionally also contain a control circuit configured to trigger a cell search on the cell search circuit.

[0158] In Example 51, the subject of Example 50 may optionally further include a control circuit configured to perform cell selection and / or new cell selection and / or handovers and / or cell measurements and / or mesh selection with the detected cell when the detected potential cell is classified as a real cell.

[0159] In Example 52, the subject of Example 50 or Example 51 may optionally include the fact that the control circuit and the cell search circuit are implemented in a baseband modem circuit.

[0160] In Example 53, the subject of Example 49 or Example 52 may optionally further include a radio transceiver, with the communication circuit arrangement configured as a mobile communication device.

[0161] In Example 54, the subject of Example 53 may optionally include that the radio transceiver is configured to receive signal data and that the cell search circuit is further configured to perform cell detections on the signal data in order to identify an initial list of eligible cells, including the detected eligible cell.

[0162] In Example 55, the subject of Example 54 may optionally include that the cell search circuit is further configured to generate a demodulated synchronization sequence for each detected eligible cell of the initial list of eligible cells and to classify each detected eligible cell of the initial list of eligible cells as a real cell or as a false cell based on a phase variance of a plurality of samples of each demodulated synchronization sequence.

[0163] In Example 56, the subject of Example 53 may optionally include that the radio transceiver is configured to receive signal data and that the cell search circuit is configured to process the signal data in order to identify the detected potential cell.

[0164] In Example 57, the subject of Example 56 may optionally include that the cell search circuit is configured to process the signal data in order to identify the detected potential cell by performing a comparison between the signal data and each of a variety of predetermined reference synchronization sequences to identify the detected potential cell.

[0165] In Example 58, the subject of Example 57 may optionally include that the cell search circuit is further configured to identify the predetermined reference sequence from the multitude of predetermined reference synchronization sequences based on the comparison.

[0166] In Example 59, the subject of Example 57 or Example 58 may optionally include that the comparison is a cross-correlation.

[0167] In Example 60, the subject of Example 57 may optionally include the cell search circuit being configured to compare the signal data with each of the plurality of predetermined reference synchronization sequences in order to identify the detected potential cell by determining a cross-correlation between the signal data and the respective predetermined reference synchronization sequence for each of the plurality of predetermined reference synchronization sequences and identifying the detected potential cell based on the cross-correlation for each of the plurality of predetermined reference synchronization sequences.

[0168] In Example 61, the subject of one of Examples 57 to 60 may optionally include that the plurality of predetermined reference synchronization sequences contains a Primary Synchronization Signal (PSS) and / or a Secondary Synchronization Signal (SSS).

[0169] In Example 62, the subject of one of Examples 49 to 61 may optionally include that the cell search circuit for comparing the detected synchronization sequence of the detected potential cell with the previously determined reference synchronization sequence in order to generate the demodulated synchronization sequence is configured by determining the component-wise product of a plurality of samples of the detected synchronization sequence and a plurality of samples of the previously determined reference synchronization sequence to obtain the plurality of samples of the demodulated synchronization sequence.

[0170] In Example 63, the subject of one of Examples 49 to 62 may optionally include that the cell search circuit is configured to compare the detected synchronization sequence of the detected potential cell with the predetermined reference synchronization sequence in order to generate the demodulated synchronization sequence, by demodulating the detected synchronization sequence with the predetermined reference synchronization sequence to obtain an initial demodulated synchronization sequence containing a plurality of samples, and by performing phase matching on the plurality of samples of the initial demodulated synchronization sequence to generate the plurality of samples of the demodulated synchronization sequence.

[0171] In Example 64, the subject of Example 63 may optionally include that the cell search circuit is configured to perform phase matching on the multitude of samples of the initial demodulated synchronization sequence in order to generate the multitude of samples of the demodulated synchronization sequence by removing a constant frequency offset from the multitude of samples of the initial demodulated synchronization sequence in order to generate the multitude of samples of the demodulated synchronization sequence.

[0172] In Example 65, the subject of Example 63 or Example 64 may optionally include that the cell search circuit is configured to perform phase matching on the plurality of samples of the initial demodulated synchronization sequence in order to generate the plurality of samples of the demodulated synchronization sequence by removing a linearly varying frequency offset from the plurality of samples of the initial demodulated synchronization sequence in order to generate the plurality of samples of the demodulated synchronization sequence.

[0173] In Example 66, the subject of one of Examples 63 to 65 may optionally include that the cell search circuit is configured to demodulate the detected synchronization sequence in order to obtain the initial demodulated synchronization sequence containing the plurality of samples by determining the component-wise product of the detected synchronization sequence and the previously determined reference synchronization sequence to obtain the plurality of samples of the initial demodulated synchronization sequence.

[0174] In Example 67, the subject of one of Examples 49 to 66 may optionally include that the predetermined synchronization sequence is a predetermined pseudorandom sequence.

[0175] In Example 68, the subject of one of Examples 49 to 67 may optionally include that the previously determined synchronization sequence consists of two nested pseudorandom sequences.

[0176] In Example 69, the subject of one of Examples 49 to 68 may optionally include that the previously determined synchronization sequence is a Secondary Synchronization Sequence (SSS).

[0177] In Example 70, the subject of one of Examples 49 to 69 may optionally include that the cell search circuit is configured to determine the phase variance between the plurality of samples of the demodulated synchronization sequence by determining the angle of each of the plurality of samples of the demodulated synchronization sequence to obtain a plurality of angles, and determining a variance of the plurality of angles to obtain the phase variance.

[0178] In Example 71, the subject of one of Examples 49 to 70 may optionally include that the cell search circuit for comparing the phase variance with the detection threshold, in order to classify the detected potential cell as a real cell or as a false cell, includes classifying the detected potential cell as a real cell if the phase variance is below the detection threshold, or classifying the detected potential cell as a false cell if the phase variance is above the detection threshold.

[0179] In Example 72, the subject of one of Examples 49 to 72 may optionally further include a measuring circuit configured to perform a radio measurement on the detected potential cell if the detected potential cell is classified as a real cell.

[0180] Example 73 is a cell detection circuit arrangement comprising a first cell classification circuit configured to evaluate one or more initial eligible cells according to a first cell detection criterion to obtain a first cell detection metric for each of the one or more initial eligible cells, and configured to select one or more first suitable eligible cells from the one or more initial eligible cells based on the first cell detection metric of each of the one or more initial eligible cells; and a second cell classification circuit configured to evaluate the one or more first suitable eligible cells according to a second cell detection criterion to obtain a second cell detection metric for each of the one or more first suitable eligible cells.is configured and is configured to select one or more second suitable potential cells from the one or more first suitable potential cells based on the second cell detection metric of each of the one or more first suitable potential cells, and includes a circuit for common cell classifications configured to evaluate the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable potential cells in order to classify each of the one or more second suitable potential cells as a true cell or a false cell.

[0181] In Example 74, the subject of Example 73 may optionally include that the first cell detection criterion and the second cell detection criterion are different cell detection criteria selected from a group consisting of a phase variance detection criterion of demodulated synchronization sequences, a signal-to-noise ratio detection criterion, a frequency offset detection criterion, and a signal energy detection criterion of filtered synchronization sequences.

[0182] In Example 75, the subject of Example 73 or Example 74 may optionally include that the first cell detection metric or the second cell detection metric is a phase variance of a demodulated synchronization sequence, or a signal-to-noise ratio, or a frequency offset, or a signal energy of a filtered synchronization sequence.

[0183] In Example 76, the subject of one of Examples 73 to 75 may optionally further include a cell detection circuit configured to perform cell detections to detect the one or more initial eligible cells prior to the evaluation of the one or more initial eligible cells by the first cell classification circuit according to the first cell detection criterion.

[0184] In Example 77, the subject of Example 76 may optionally include that the cell detection circuit is configured to perform cell detections to detect the one or more initial potential cells by comparing a received signal with one or more predetermined reference synchronization sequences to identify the one or more initial potential cells.

[0185] In Example 78, the subject of Example 77 may optionally include that the one or more previously determined reference synchronization sequences contain one or more Primary Synchronization Sequences (PSSs) or one or more Secondary Synchronization Sequences (SSSs).

[0186] In Example 79, the subject of one of Examples 73 to 78 may optionally further include one or more additional cell classification circuits configured to evaluate the one or more initial eligible cells according to one or more additional cell detection criteria, in order to obtain one or more additional cell detection metrics for each of the one or more initial eligible cells, prior to the evaluation of the one or more initial eligible cells by the first cell classification circuit according to the first cell detection criterion, wherein the circuit used for joint cell classifications is configured to evaluate the first cell detection metrics and the second cell detection metrics of each of the one or more second eligible cells according to the combined cell detection criterion.to classify each of the one or more second suitable potential cells as a real cell or as a false cell, by evaluating the first cell detection metrics, the second cell detection metrics and the one or more further cell detection metrics according to the combined cell detection criterion, to classify each of the one or more second suitable potential cells as a real cell or as a false cell, is configured.

[0187] In Example 80, the subject of one of Examples 73 to 79 may optionally include that the circuit used for joint cell classifications evaluates the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable potential cells according to the combined cell detection criterion, in order to classify each of the one or more second suitable potential cells as a true cell or as a false cell, by generating a classification vector for each of the one or more second suitable potential cells, wherein each classification vector contains the first detection metric and the second detection metric of the corresponding cell of the one or more second suitable potential cells.and classifying each of the one or more second suitable eligible cells according to the corresponding classification vector.

[0188] In Example 81, the subject of Example 80 may optionally include that the circuit used for common cell classifications is configured to classify each of the one or more second suitable potential cells according to the relevant classification vector by comparing the classification vector of each of the one or more second suitable potential cells with a predetermined cell model in order to determine, according to the predetermined cell model, whether each of the one or more second suitable potential cells is a real cell or a phantom cell.

[0189] In Example 82, the subject of Example 81 may optionally include the circuit used for common cell classifications being configured to compare the classification vector of each of the one or more second suitable potential cells with the predetermined cell model in order to determine, according to the predetermined cell model, whether each of the one or more second suitable potential cells is a real cell or a phantom cell, by comparing the classification vector of each of the one or more second suitable potential cells with a classification hyperplane in order to identify whether the classification vector falls into a region of phantom cells according to the predetermined cell model or a region of real cells according to the predetermined cell model.

[0190] In Example 83, the subject of any of Examples 73 to 82 may optionally include that the first cell classification circuit is configured to evaluate the one or more initial eligible cells according to the first cell detection criterion in order to obtain the first cell detection metric for each of the one or more initial eligible cells by evaluating a received signal in order to determine the first cell detection metric for each of the one or more initial eligible cells.

[0191] In Example 84, the subject of Example 83 may optionally include the first cell classification circuit being configured to select the one or more first suitable eligible cells from the one or more initial eligible cells based on the first cell detection metric of each of the one or more initial eligible cells by comparing the first cell detection metric of each of the one or more initial eligible cells with the first cell detection criterion and selecting only those cells of the one or more initial eligible cells whose first cell detection metrics satisfy the first cell detection criterion as the one or more first suitable eligible cells.

[0192] In Example 85, the subject of Example 83 may optionally include that the first cell detection criterion is a first detection threshold and that the first cell classification circuit is configured to select the one or more first suitable eligible cells from the one or more initial eligible cells based on the first cell detection metric of each of the one or more initial eligible cells by comparing the first cell detection metric of each of the one or more initial eligible cells with the first detection threshold and selecting the one or more first suitable eligible cells based on whether the first cell detection metric of each of the one or more initial eligible cells exceeds the first detection threshold.

[0193] In Example 86, the subject of any of Examples 73 to 85 may optionally include that the circuit used for joint cell classifications is configured to evaluate the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable eligible cells according to the combined cell detection criterion, in order to classify each of the one or more second suitable eligible cells as a true cell or as a false cell, by identifying one or more true cells from the one or more second suitable eligible cells.

[0194] In Example 87, the subject of Example 86 may optionally include that the one or more real cells are a real subset of the one or more second suitable potential cells.

[0195] In Example 88, the subject of Example 86 or Example 87 may optionally further include a measuring circuit configured to perform a radio measurement on at least one of the one or more real cells.

[0196] In Example 89, the subject of Example 86 or Example 87 may optionally further include a control circuit configured to perform cell selection and / or new cell selection and / or handovers and / or cell measurements and / or network selection with at least one of the one or more real cells.

[0197] In Example 90, the subject of one of Examples 73 to 89 may optionally include that the one or more first eligible suitable cells are a proper subset of the one or more initial eligible cells.

[0198] In Example 91, the subject of Example 90 may optionally include that the one or more initial potential cells that are not included in the one or more first suitable potential cells do not satisfy the first cell detection criterion.

[0199] Example 92 is a communication device that includes the cell detection circuit arrangement according to one of Examples 73 to 91.

[0200] In Example 93, the object from Example 92 can optionally also contain a radio transceiver.

[0201] All acronyms defined in the above description also apply in all claims contained herein.

[0202] Although the invention was primarily demonstrated and described with reference to specific embodiments, it is understood by those skilled in the art that various modifications can be made to these embodiments with regard to their form and details without departing from the concept and scope of protection of the invention as defined by the appended claims. The scope of protection of the invention is therefore defined by the appended claims, and all modifications that are consistent with the meaning of the claims and fall within their scope of equivalence are thus to be considered as included therein.

Claims

[1] Communication circuit arrangement (206) comprising the following: a cell search circuit (306) configured to do the following: (1110) comparing a detected synchronization sequence of a detected potential cell with a previously determined reference synchronization sequence to generate a demodulated synchronization sequence encompassing a multitude of samples; Determine (1120) a phase variance between the multitude of samples of the demodulated synchronization sequence; and Comparing (1130) the phase variance with a detection threshold to classify the detected potential cell as a real cell or a false cell. [2] Communication circuit arrangement (206) according to claim 1, further comprising a control circuit configured to perform cell selection and / or new cell selection and / or handovers and / or cell measurements and / or network selection with the detected potential cell when the detected potential cell is classified as a real cell. [3] Communication circuit arrangement (206) according to claim 1, further comprising a radio transceiver (204), wherein the communication circuit arrangement (206) is configured as a mobile communication device. [4] Communication circuit arrangement (206) according to claim 3, wherein the radio transceiver (204) is configured to receive signal data, and wherein the cell search circuit (306) is further configured to perform cell detections on the signal data in order to identify an initial list of eligible cells, including the detected eligible cell. [5] Communication circuit arrangement (206) according to claim 4, wherein the cell search circuit (306) is further configured for the following: Generating a demodulated synchronization sequence for each detected potential cell from the initial list of potential cells; and Classifying each detected potential cell from the initial list of potential cells as a real cell or a false cell based on a phase variance of a multitude of samples of each demodulated synchronization sequence. [6] Communication circuit arrangement (206) according to claim 3, wherein the radio transceiver (204) is configured to receive signal data, and wherein the cell search circuit (306) is configured to process the signal data in order to identify the detected potential cell. [7] Communication circuit arrangement (206) according to claim 6, wherein the cell search circuit (306) is configured to process the signal data in order to identify the detected potential cell by: Performing a comparison between the signal data and each of a multitude of predefined reference synchronization sequences to identify the detected potential cell. [8] Communication circuit arrangement (206) according to claim 7, wherein the cell search circuit (306) is further configured for the following: Identifying the predetermined reference synchronization sequence from the multitude of predetermined reference synchronization sequences based on comparison. [9] Communication circuit arrangement (206) according to claim 7 or claim 8, wherein the plurality of predetermined reference synchronization sequences comprises a Primary Synchronization Signal, PSS, and / or a Secondary Synchronization Signal, SSS. [10] Communication circuit arrangement (206) according to any one of claims 1 to 8, wherein the cell search circuit (306) is configured to compare the detected synchronization sequence of the detected potential cell with the previously determined reference synchronization sequence in order to generate the demodulated synchronization sequence by: Demodulating the detected synchronization sequence with the previously determined reference synchronization sequence to obtain an initial demodulated synchronization sequence encompassing a multitude of samples; and Performing phase matching on the multitude of samples of the initial demodulated synchronization sequence to generate the multitude of samples of the demodulated synchronization sequence. [11] Communication circuit arrangement (206) according to claim 10, wherein the cell search circuit (306) is configured to demodulate the detected synchronization sequence in order to obtain the initial demodulated synchronization sequence comprising the plurality of samples by: Determining a component-wise product of the detected synchronization sequence and the previously determined reference synchronization sequence to obtain the multitude of samples of the initial demodulated synchronization sequence. [12] Communication circuit arrangement (206) according to any one of claims 1 to 8, wherein the previously determined reference synchronization sequence is a Secondary Synchronization Sequence, SSS. [13] Communication circuit arrangement (206) according to any one of claims 1 to 8, wherein the cell search circuit (306) is configured to compare the phase variance with the detection threshold in order to classify the detected potential cell as a real cell or as a false cell, comprises: Classify the detected potential cell as a true cell if the phase variance is below the detection threshold; or Classify the detected potential cell as a false cell if the phase variance is above the detection threshold. [14] Method for detecting cells, the method comprising: (1110) comparing a detected synchronization sequence of a detected potential cell with a previously determined reference synchronization sequence to generate a demodulated synchronization sequence encompassing a multitude of samples; Determine (1120) a phase variance between the multitude of samples of the demodulated synchronization sequence; and Comparing (1130) the phase variance with a detection threshold to classify the detected potential cell as a real cell or a false cell. [15] Method according to claim 14, wherein comparing the detected synchronization sequence of the detected cell in question with the previously determined reference synchronization sequence to generate the demodulated synchronization sequence comprises: Demodulating the detected synchronization sequence with the previously determined reference synchronization sequence to obtain an initial demodulated synchronization sequence encompassing a multitude of samples; and Performing phase matching on the multitude of samples of the initial demodulated synchronization sequence to generate the multitude of samples of the demodulated synchronization sequence. [16] Cell detection circuit arrangement comprising the following: a first cell classification circuit configured for the following: Evaluating (1210) one or more initial eligible cells according to a first cell detection criterion to obtain a first cell detection metric for each of the one or more initial eligible cells, and configured to select (1220) one or more first suitable eligible cells from the one or more initial eligible cells based on the first cell detection metric of each of the one or more initial eligible cells; a second cell classification circuit configured to evaluate (1230) the one or more first suitable potential cells according to a second cell detection criterion to obtain a second cell detection metric for each of the one or more first suitable potential cells, and configured to select (1240) one or more second suitable potential cells from the one or more first suitable potential cells based on the second cell detection metric of each of the one or more first suitable potential cells; and a circuit used for common cell classifications, configured to evaluate (1250) the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable eligible cells, in order to classify each of the one or more second suitable eligible cells as a real cell or as a false cell. [17] Cell detection circuit arrangement according to claim 16, wherein the first cell detection criterion and the second cell detection criterion are different cell detection criteria selected from a group consisting of a detection criterion for the phase variance of demodulated synchronization sequences, a detection criterion for the signal-to-noise ratio, a detection criterion for the frequency offset and a detection criterion for the signal energy of filtered synchronization sequences. [18] Cell detection circuit arrangement according to claim 16 or claim 17, further comprising a cell detection circuit configured to perform cell detections to detect the one or more initial eligible cells prior to evaluating the one or more initial eligible cells by the first cell classification circuit according to the first cell detection criterion. [19] Cell detection circuit arrangement according to claim 16 or claim 17, further comprising: one or more additional cell classification circuits configured to evaluate the one or more initial eligible cells according to one or more additional cell detection criteria to obtain one or more additional cell detection metrics for each of the one or more initial eligible cells prior to the evaluation of the one or more initial eligible cells by the first cell classification circuit according to the first cell detection criterion, and wherein the circuit used for joint cell classifications for evaluating the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable eligible cells is configured according to the combined cell detection criterion to classify each of the one or more second suitable eligible cells as a true cell or as a false cell by the following: Evaluating the first cell detection metrics, the second cell detection metrics, and one or more further cell detection metrics according to the combined cell detection criterion to classify each of the one or more second suitable potential cells as a real cell or as a false cell. [20] Cell detection circuit arrangement according to claim 16 or claim 17, wherein the circuit used for joint cell classifications is configured to evaluate the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable eligible cells according to the combined cell detection criterion in order to classify each of the one or more second suitable eligible cells as a real cell or as a false cell by the following: Generating a classification vector for each of the one or more second suitable eligible cells, wherein each classification vector comprises the first detection metric and the second detection metric of the corresponding cell of the one or more second suitable eligible cells; and Classify each of the one or more second suitable potential cells according to the corresponding classification vector. [21] Cell detection circuit arrangement according to claim 20, wherein the circuit used for common cell classifications is configured to classify each of the one or more second suitable eligible cells according to the corresponding classification vector by the following: Comparing the classification vector of each of the one or more second suitable potential cells with a predetermined cell model to determine, according to the predetermined cell model, whether each of the one or more second suitable potential cells is a real cell or a phantom cell. [22] Cell detection circuit arrangement according to claim 21, wherein the circuit used for joint cell classifications is configured to compare the classification vector of each of the one or more second suitable potential cells with the predetermined cell model in order to determine, according to the predetermined cell model, whether each of the one or more second suitable potential cells is a real cell or a phantom cell: Comparing the classification vector of each of the one or more second suitable potential cells with a classification hyperplane to identify whether the classification vector falls into a region of phantom cells according to the previously determined cell model or a region of real cells according to the previously determined cell model. [23] Cell detection circuit arrangement according to claim 16 or claim 17, wherein the circuit used for joint cell classifications is configured to evaluate the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable eligible cells according to the combined cell detection criterion in order to classify each of the one or more second suitable eligible cells as a real cell or as a false cell by the following: Identifying one or more genuine cells from the one or more other suitable potential cells. [24] Cell detection circuit arrangement according to claim 23, further comprising a control circuit configured to perform cell selection and / or new cell selection and / or handovers and / or cell measurements and / or network selection with at least one of the one or more real cells. [25] Method for performing cell detections, the method comprising: Evaluate (1210) one or more initial potential cells according to a first cell detection criterion to obtain a first cell detection metric for each of the one or more initial potential cells; Select (1220) one or more first suitable potential cells from the one or more initial potential cells based on the first cell detection metric of each of the one or more initial potential cells; Evaluate (1230) the one or more first suitable potential cells according to a second cell detection criterion to obtain a second cell detection metric for each of the one or more first suitable potential cells; Selecting (1240) one or more second suitable potential cells from the one or more first suitable potential cells based on the second cell detection metric of each of the one or more first suitable potential cells; and Evaluate (1250) the first cell detection metrics and the second cell detection metrics of each of the one or more second suitable potential cells according to a combined cell detection criterion to classify each of the one or more second suitable potential cells as a true cell or as a false cell.

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