Method, equipment, device and medium for communication

Abstract

The embodiment of the invention relates to a communication method, equipment, a device and a medium. In accordance with an example embodiment of the present disclosure, a first device determines a first frequency from a first set of frequencies and a second frequency from a second set of frequencies. The first device also generates a complex exponential sequence based on the first frequency and the second frequency. The complex exponential sequence includes a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency. The first device also sends the complex exponential sequence to the second device in response to the first device being in an active state. In this way, more accurate, rapid and extensible user activity detection can be constructed through the leader sequence design disclosed by the invention.

Description

Technical Field

[0001] The exemplary embodiments of this disclosure relate to the field of communication technology, and more particularly to methods, apparatuses, devices, and computer-readable media for scalable synchronous user activity detection of complex exponential sequences. Background Technology

[0002] With the booming development of key vertical sectors such as the Industrial Internet, intelligent transportation systems, and smart cities, the future society will be digitalized and data-driven. Machine-Type Communication (MTC), encompassing both large-scale and emergency performance characteristics, and near-instantaneous wireless connectivity, are key enabling technologies driving this digitalization. 5G New Radio (NR) is inherently designed to support both aspects of MTC to facilitate society's digital transformation, particularly improving overall efficiency across various vertical sectors. However, it is clear that some of the more demanding requirements of MTC cannot be fully supported by 5G networks. Furthermore, further societal development will place new and more stringent demands on wireless connectivity, especially on MTC.

[0003] With the rapid development of modern communication technologies, the optimization and upgrading of network infrastructure has become a crucial factor driving the development of the information society. Especially in the research and development of sixth-generation (6G) mobile communication technology, improving system capacity, spectrum efficiency, and user experience are key objectives. Large-scale IoT access requires efficient user activity detection and service request granting processes to ensure system reliability and scalability. In this context, scalable synchronous user activity detection (UAD) technology is particularly important. Furthermore, 6G technology emphasizes low power consumption and wide coverage to meet the needs of various application scenarios. Therefore, researching and optimizing these cutting-edge technologies is of great significance for promoting the development of future communication networks. Summary of the Invention

[0004] In a first aspect of this disclosure, a first device for a first means of communication is provided. The first means includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first means to at least: determine a first frequency from a first set of frequencies and a second frequency from a second set of frequencies; generate a complex exponential sequence based on the first and second frequencies, wherein the complex exponential sequence includes a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, the first frequency being different from the second frequency; and transmit the complex exponential sequence to a second means in response to the first means being in an active state.

[0005] In a second aspect of this disclosure, a second means for communication is provided. The second means includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second means to at least: receive, on a shared physical resource block, a superposition of one or more complex exponential sequences from one or more first devices in an active state of a first means set, wherein each complex exponential sequence comprises a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency; determine an active subset from the first means set based on the superposition of the one or more complex exponential sequences, wherein the active subset consists of one or more first devices in an active state; and determine, based on the active subset, an estimate of an equivalent transmission symbol corresponding to the active subset.

[0006] In a third aspect of this disclosure, a communication system is provided. The communication system includes: a first device according to a first aspect of this disclosure, or a second device according to a second aspect of this disclosure.

[0007] In a fourth aspect of this disclosure, a communication method is provided. The communication method includes: a first device determining a first frequency from a first set of frequencies and a second frequency from a second set of frequencies; the first device generating a complex exponential sequence based on the first and second frequencies, wherein the complex exponential sequence includes a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, the first frequency being different from the second frequency; and the first device transmitting the complex exponential sequence to a second device in response to the first device being in an active state.

[0008] In a fifth aspect of this disclosure, a communication method is provided. The communication method includes: receiving, by a second device, a superposition of one or more complex exponential sequences from one or more first devices in an active state of a first device set, on a shared physical resource block, wherein the complex exponential sequences comprise a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency; determining, by the second device, an active subset from the first device set based on the superposition of the one or more complex exponential sequences, wherein the active subset comprises one or more first devices in an active state; and determining, by the second device, an estimate of an equivalent transmission symbol corresponding to the active subset.

[0009] In a sixth aspect of this disclosure, an apparatus for communication is provided. The apparatus includes: means for determining a first frequency from a first set of frequencies and a second frequency from a second set of frequencies; means for generating a complex exponential sequence based on the first and second frequencies, wherein the complex exponential sequence includes a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, the first frequency being different from the second frequency; and means for transmitting the complex exponential sequence to a second device in response to a first device being in an active state.

[0010] In a seventh aspect of this disclosure, an apparatus for communication is provided. The apparatus includes: means for receiving, on a shared physical resource block, a superposition of one or more complex exponential sequences from one or more first devices in an active state of a first device set, wherein each complex exponential sequence comprises a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency; means for determining an active subset from the first device set based on the superposition of the one or more complex exponential sequences, wherein the active subset comprises one or more first devices in an active state; and means for determining an estimate of an equivalent transmission symbol corresponding to the active subset based on the active subset.

[0011] In an eighth aspect of this disclosure, a computer-readable medium is provided. The computer-readable medium stores instructions that, when executed by at least one processing unit, configure the at least one processing unit to perform the method according to the fourth or fifth aspect.

[0012] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] Exemplary embodiments of this disclosure are presented by way of example, and their advantages are explained in more detail below with reference to the accompanying drawings, wherein

[0014] Figure 1 A schematic diagram of an environment in which the example embodiments described in this disclosure may be implemented is shown;

[0015] Figure 2 A signaling diagram of a random access procedure comprising four steps is shown;

[0016] Figure 3 A schematic diagram of synchronous user activity detection for large-scale access is shown, according to some example embodiments of the present disclosure;

[0017] Figure 4A schematic diagram of a resource mapping for sequential transmission according to some example embodiments of the present disclosure is shown;

[0018] Figure 5 Signaling diagrams for communication according to some example embodiments of the present disclosure are shown;

[0019] Figure 6 A signaling diagram of a service request granting process via synchronous user activity detection according to some example embodiments of the present disclosure is shown;

[0020] Figure 7 A schematic graph showing a performance comparison of synchronous user activity detection according to some example embodiments of the present disclosure is provided.

[0021] Figure 8 A flowchart of a method for communication according to some example embodiments of the present disclosure is shown;

[0022] Figure 9 A flowchart of a method for communication according to some example embodiments of the present disclosure is shown;

[0023] Figure 10 A simplified block diagram of a device suitable for implementing example embodiments of the present disclosure is shown; and

[0024] Figure 11 A schematic diagram of a computer-readable medium according to some example embodiments of the present disclosure is shown.

[0025] In all the accompanying drawings, the same or similar reference numerals denote the same or similar elements. Detailed Implementation

[0026] The principles and spirit of this disclosure will now be described with reference to several exemplary embodiments illustrated in the accompanying drawings. It should be understood that these specific exemplary embodiments are described only to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way.

[0027] As used herein, the term "comprising" and similar expressions should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "an embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0028] As used herein, the term "determine" encompasses a wide variety of actions. For example, "determine" can include operations, calculations, processing, derivation, investigation, searching (e.g., searching in a table, database, or other data structure), ascertainment, etc. Furthermore, "determine" can include receiving (e.g., receiving information), accessing (e.g., accessing data in memory), etc. Additionally, "determine" can include parsing, selection, choosing, building, etc. Furthermore, "determine" can include processing steps such as detection, estimation, filtering, prediction, identification, decision-making (e.g., statistical decision-making), equation solving, optimization problem solving, etc.

[0029] In this document, unless explicitly stated otherwise, performing a step in response to A does not mean that the step is performed immediately after A, but may include one or more intermediate steps.

[0030] As used herein, the term “circuit device” / “circuit” means one or more of the following: (a) a hardware circuit implementation only (such as an implementation of analog and / or digital circuits only); and (b) a combination of hardware circuits and software, such as (if applicable): (i) a combination of analog and / or digital hardware circuits with software / firmware; and (ii) any part of a hardware processor with software (including digital signal processors, software, and memory that work together to enable devices such as optical communication devices or other computing devices to perform various functions); and (c) a hardware circuit and / or processor, such as a microprocessor or a part of a microprocessor, which requires software (e.g., firmware) for operation, but may be without software when it is not required for operation.

[0031] The definition of "circuit device" / "circuit" applies to all uses of this term in this application (including any claim). As another example, the term "circuit device" / "circuit" as used herein also covers only hardware circuitry or a processor (or processors), or a portion of hardware circuitry or a processor, or an implementation thereof with accompanying software or firmware. For example, if applicable to a particular claim element, the term "circuit device" also covers baseband integrated circuits or processor integrated circuits or similar integrated circuits in an OLT or other computing device.

[0032] As used herein, the term "communication network" refers to a network that conforms to any suitable communication standard, such as New Radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), Narrowband Internet of Things (NB-IoT), Low Power Wide Area Coverage (LPWA), and so on. Furthermore, communication between terminal devices and network devices in the communication network can be performed according to any suitable generation of communication protocol, including but not limited to, first-generation (1G), second-generation (2G), 2.5G, 2.75G, third-generation (3G), fourth-generation (4G), 4.5G, fifth-generation (5G) communication protocols and / or any other currently known or to be developed in the future. The exemplary embodiments of this disclosure can be applied to a variety of communication systems, including but not limited to terrestrial communication systems, non-terrestrial communication systems, or combinations thereof. Given the rapid development in the field of communications, there will certainly be future types of communication technologies and systems that can be used to implement this disclosure. This should not be construed as limiting the scope of this disclosure to only the aforementioned systems.

[0033] As used herein, the term "network device" refers to a node in a communications network through which terminal devices access the network and receive services. Depending on the terminology and technology applied, a network device can refer to a base station (BS) or access point (AP), such as a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), an NR NB (also known as a gNB), a Remote Radio Unit (RRU), a Radio Head (RH), a Remote Radio Head (RRH), a relay, an Integrated Access and Backhaul (IAB) node, low-power nodes such as femtoseconds and picoseconds, etc. In some example embodiments, the Radio Access Network (RAN) split architecture includes a Central Unit (CU) and a Distribution Unit (DU). An IAB node includes a Mobile Terminal (IAB-MT) portion and a DU portion, wherein the IAB-MT portion is analogous to a User Equipment (UE) to the parent node, and the DU portion is analogous to a base station to the next-hop IAB node.

[0034] As used herein, the term "terminal device" refers to any terminal device capable of wireless communication. By way of example and not limitation, a terminal device may also be referred to as a communication device, user equipment (UE), user station (SS), portable user station, mobile station (MS), or access terminal (AT). Terminal devices may include, but are not limited to, mobile phones, cellular phones, smartphones, Voice over IP (VoIP) phones, wireless local loop phones, tablet computers, wearable terminal devices, personal digital assistants (PDAs), portable computers, desktop computers, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback devices, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop embedded devices (LEEs), laptop-mounted devices (LMEs), Universal Serial Bus (USB) dongles, smart devices, wireless customer premises equipment (CPEs), Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in industrial and / or automated processing chain environments), consumer electronics devices, devices operating on commercial and / or industrial wireless networks, etc. Terminal devices may correspond to the mobile terminal (MT) portion of an IAB node (e.g., a relay node). In the following description, the terms "terminal device," "communication device," "terminal," "user equipment," and "UE" are used interchangeably.

[0035] As used herein, the terms “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” “downlink resource,” or “resource basic unit (RE)” may refer to any resource that performs communication (e.g., communication between a terminal device and a network device). A resource may be, for example, a resource in the time domain, a resource in the frequency domain, a resource in the spatial domain, a resource in the code domain, or any other resource capable of communication, etc. In the following, unless explicitly stated otherwise, resources in the frequency and time domains will be used as examples of transmission resources to describe certain exemplary embodiments of this disclosure. It should be understood that the exemplary embodiments of this disclosure are equally applicable to other resources in other domains.

[0036] As used herein, the term "user activity detection" refers to the process in a communication system of identifying and determining whether a user equipment (UE) is in an active state through specific algorithms and technologies. This process typically involves monitoring and analyzing signals emitted by the UE to accurately distinguish between active and inactive users. The activity state of a UE includes, but is not limited to, that is triggered and determined by the UE itself based on its own service needs.

[0037] As used herein, the term "complex exponential sequence" refers to a sequence generated by a complex exponential function that has specific amplitude, frequency, and phase characteristics. This sequence is used in signal processing and communication systems due to its favorable mathematical properties, particularly in frequency domain analysis and signal modulation.

[0038] As used herein, the terms “Sequence,” “Reference Sequence,” “Preamble,” “Preamble Sequence,” “Signal,” “Pilot Signal,” and “Reference Signal” are interchangeable. The terms “User Activity” and “User Engagement” are interchangeable. Example Environment

[0039] Figure 1 A schematic diagram of an example communication environment 100 in which exemplary embodiments described in this disclosure may be implemented is shown. Communication environment 100 may be part of a communication network. Communication environment 100 includes a second device 120 and a plurality of first devices 110, such as first devices 110-1, 110-2, ..., 110-N (N is any natural number). For ease of discussion, first devices 110-1, 110-2, ..., 110-N may be collectively referred to or individually as first devices 110. It should be understood that communication environment 100 is merely exemplary and is not intended to limit the scope of this disclosure in any way. First devices 110 and second devices 120 may communicate with each other, for example, via a radio link.

[0040] In some example embodiments, the first device 110 may be a network device, such as an access network device or a base station, such as a gNB. In some example embodiments, the second device 120 may be a terminal device, such as a UE. The first device 110 may serve the second device 120.

[0041] It should be understood that the communication environment 100 is described for illustrative purposes only and does not imply any limitation on the scope of this disclosure. For example, the exemplary embodiments of this disclosure can also be applied to systems different from communication environment 100. The number of elements or entities shown is merely illustrative and not limiting. Moreover, elements or entities can communicate with each other using any communication technology currently known and developed in the future.

[0042] It should be understood that Figure 1The number of devices and their connections shown are merely illustrative and not limiting. Communication environment 100 may include any suitable number of devices configured to implement exemplary embodiments of this disclosure. Although not shown, it should be understood that one or more other devices may be deployed in communication environment 100. Although exemplified by a network device, the first device 110 may be another device besides a network device. Although exemplified by a terminal device, the second device 120 may be another device besides a terminal device.

[0043] In the following description, for illustrative purposes, some exemplary embodiments are illustrated with the first device 110 as a user equipment and the second device 120 as a network device. However, in some exemplary embodiments, the operations described in connection with the user equipment can also be implemented at the network device or other devices. Similarly, the operations described in connection with the network device can also be implemented at the user equipment or other devices.

[0044] In some example embodiments, if the first device 110 is a network device and the second device 120 is a user equipment, the link from the second device 120 to the first device 110 is referred to as an uplink (UL), and the link from the first device 110 to the second device 120 is referred to as a downlink (DL). In the UL, the second device 120 is a transmitting (TX) device, and the first device 110 is a receiving (RX) device. In the DL, the first device 110 is a transmitting (TX) device, and the second device 120 is a receiving (RX) device.

[0045] Communication in communication environment 100 can be implemented according to any appropriate communication protocol(s). Examples of communication protocols include, but are not limited to, cellular communication protocols such as first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G), and sixth generation (6G), wireless local area network communication protocols such as IEEE 802.11, and / or any other protocols currently known or to be developed in the future.

[0046] Furthermore, communication may utilize any suitable wireless communication technology, including but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM), Spread Spectrum Orthogonal Frequency Division Multiplexing Based on Discrete Fourier Transform (DFT-s-OFDM), and / or any other technology currently known or to be developed in the future.

[0047] In 5G, MTC is divided into Ultra-Reliable Low-Latency Communication (URLLC), also known as Critical Machine-Type Communication (cMTC), which is used in automated control environments with low data load and low data rate, and Massive Machine-Type Communication (mMTC), which is used for large-scale / dense deployments and sporadic business models. Due to emerging industrial use cases and service verticalization, these two types of communication will evolve into several specialized subclasses, requiring multi-dimensional optimization and scalable design. Therefore, 6G will need to serve highly diverse applications, from holographic imaging and interconnected 360-degree extended reality (augmented / virtual / mixed reality) with extremely high data rate requirements to massive access for various types of IoT devices. One of the proposed MTC service categories in 6G is considered to be scalable cMTC, which refers to high reliability and low latency while supporting massive connectivity, such as emergency medical monitoring and factory automation.

[0048] Scale and flexibility will continue to be important metrics for 6G performance. 6G is expected to support connection densities of up to 10 million devices per square kilometer, which is a significant requirement not only for next-generation MTC but also for next-generation low-power wide-area coverage (LPWA) in 6G deployment.

[0049] Unlike human-centric communications, machine-centric communications (such as scalable cMTC) typically have two distinct characteristics. First, the entire system needs to support massive connectivity, with the number of devices connected to cellular base stations potentially ranging from 10,000 to 10 million. Macro-level base stations tend to provide unified, large-scale access for various types of IoT devices, offering a low-cost solution for supporting high-reliability, low-power, and low-latency large-scale connectivity. From a business perspective, telecom providers tend to adopt a unified solution. Second, the business model is sporadic; at any given moment, only a small fraction of potential devices are active. Typically, machine-type devices connect to the network asynchronously and sporadically to send small data payloads. This sporadic nature stems from the inherent burstiness of event-driven industrial internet communications or sensing environments. Most devices make random requests independently, so there is little periodicity that can be tracked and utilized. Therefore, the network cannot predict in advance when and which device will send data packets.

[0050] In 6G MTC, numerous devices submit sporadic and unpredictable service requests to the uplink scheduler to request uplink resources. A service request is a flag indicating that a device needs UL resources to transmit service request information, i.e., details of the required service, such as UL scheduling, small data transmission, RRC (Radio Resource Control) connection re-establishment, mobility management, beam failure recovery, location services, and Automatic Repeat Request (ARQ). There are two ways to issue this flag in Long Term Evolution (LTE) and New Radio (NR). The first approach is that, for devices configured with dedicated request resources, service requests are typically transmitted on the Physical Uplink Control Channel (PUCCH) using pre-configured and periodically recurring PUCCH resources dedicated to that device. Each device can be allocated dedicated PUCCH service request resources, with a period ranging from one Orthogonal Frequency Division Multiplexing (OFDM) symbol per second (to support services with extremely high latency requirements) to once every 80 milliseconds (to reduce overhead). However, as connection density increases to 10 million devices per square kilometer, this resource allocation becomes unsustainable, leading to unacceptable spectrum waste. Maintaining a dedicated PUCCH for a large number of devices with low traffic intensity is unwise. Another approach is for devices without configured service request resources to rely on a random access mechanism to request resources. This can be used to create a contention-based resource request mechanism. However, as a variant of the classic ALOHA protocol, the Physical Random Access Channel (PRACH) mechanism imposes a limit on the number of active devices allowed to access the network.

[0051] For the reasons mentioned above, neither of these methods is feasible for scalable emergency machine-type communication (cMTC). Instead, User Activity Detection (UAD) becomes a more efficient and effective way to handle unpredictable and random service requests. In reality, only a small subset of devices are active to make service requests within a given transmission slot or frame. UAD helps the base station identify the active subset from all devices and then establish a successful connection between the devices and the base station. Once the base station knows which devices have actual data transmission needs at the start of a transmission cycle, it can immediately send scheduling authorization messages to the small number of active users so that they can further provide more detailed service request information to the base station. For large-scale access, a desirable UAD would be accurate, fast, and scalable, implemented with minimal measurement resource costs. While the subset of active users is random and unknown, they may, on the other hand, have already established a connection-like state through the initial access process. In addition to the dedicated uplink control channel used for service requests, users may have already received necessary system information, registered user identifiers, synchronized uplink timings, and acquired partial knowledge of their respective uplink channels, providing the conditions for accurate synchronization of user activity detection. This disclosure considers the sequence design for synchronization of user activity detection under such a connection-like state.

[0052] In Long Term Evolution (LTE) and New Radio (NR), devices without configured service request resources rely on random access mechanisms to request resources. They employ a four-step or two-step random access procedure identical to the Physical Random Access Channel (PRACH) signaling design. For example, Figure 2A signaling diagram of a random access procedure 200 comprising four steps is shown. First, base station 202 transmits random access configuration information as part of system information block 1 on the synchronization signal block / broadcast channel to idle user equipment 201. Subsequently, idle user equipment 201 performs downlink timing synchronization. In step 1, as a typical configuration, idle user equipment 201 randomly selects a sequence from 64 Zadoff-Chu (ZC) sequences of length 643 and transmits the sequence via 643 subcarriers shared by all random access user equipments. That is, idle user equipment 201 transmits a preamble sequence to base station 202 on the physical random access channel, and then base station 202 detects the preamble sequence and estimates timing advance. In step 2, base station 202 transmits a random access response to idle user equipment 201 on the physical downlink control channel and downlink shared channel, and then idle user equipment 201 adjusts its timing advance according to the timing information. In step 3, the idle user equipment 201 sends message 3 to the base station 202 on the physical uplink shared channel. Next, in step 4, the base station 202 sends message 4 (collision resolution message) to the idle user equipment 201 on the downlink shared channel. Subsequently, the idle user equipment 201 checks for a match between message 3 and message 4, and then either confirms the automatic repeat request (HRE) or retransmits the preamble sequence to the base station 202 as in step 1.

[0053] This design suffers from several drawbacks that prevent it from becoming a practical solution for large-scale access. First, there is low performance due to insufficient physical random access channel preamble sequences. A coherent time-frequency block can only support a limited number of orthogonal preamble sequences relative to a large number of machine-type devices. The random access of numerous devices by independently selecting a sequence from the same small set inevitably leads to severe collisions and intolerable access delays. Furthermore, the repetitive cycle of transmission, collisions, and retransmission results in endless signaling exchanges between devices and the base station, which is costly compared to the small packet transmission of machine-type devices. Another drawback is the random allocation of preamble sequences, which not only causes sequence collisions leading to performance degradation (in step 1) but also requires an additional but necessary step (step 3) to report explicit user identification information to the base station (BS).

[0054] These problems can be addressed by a scalable user activity detection scheme, where different users are assigned different single-frequency complex exponential sequences. To accurately identify users associated with closely spaced and indistinguishable frequency components within a sequence of a certain length at the receiver (base station), special transmission strategies are required. These strategies fully and partially compensate for the uplink channel phase at the transmitter (user equipment), respectively, and address the frequency resolution limitation. Therefore, the activity information of the user equipment can be represented as a non-negative vector, easily resolved from mixed observations based on a set of short-length sequences. However, phase compensation requires prior acquisition of complete or partial channel phase information, which is difficult for participating user equipment in certain initial states. In fact, the frequency resolution limit is equivalent to the reciprocal of the sequence length; finite-length sequences always exhibit frequency resolution issues. Scalable user activity detection schemes face the dilemma of choosing between acquiring channel state information or infinitely extending the sequence length. As a solution, this disclosure proposes a multi-frequency complex exponential sequence for scalable synchronous user activity detection, where the frequency resolution limitation can be eliminated with finite-length sequences without requiring channel state information.

[0055] In the example embodiments of this disclosure, cellular networks for mass access are considered. Figure 3 This diagram illustrates a synchronous user activity detection method for large-scale access according to some example embodiments of this disclosure. It assumes N potential user equipments (UEs) in a cell, each with a single transmit antenna, and their traffic is sporadic. These UEs are labeled n∈S={1,…,N}. It is assumed that these N UEs have already completed the initial access procedure via a random access process, establishing a connection-like state except for a dedicated uplink control channel used for service requests. The UEs have received the necessary system information, registered user identifiers, synchronized uplink timing, and prior knowledge of their respective uplink channels. It is assumed that the timing alignment information remains valid during the user activity detection process and will not become outdated for static and low-mobility UEs. Therefore, UEs can appropriately adjust the timing advance of their uplink transmissions and achieve synchronized uplink transmissions relative to the base station's receiving window.

[0056] Unlike the sequence collisions in the Physical Random Access Channel (PRACH) scheme, user activity detection pre-assigns a unique preamble sequence to each user equipment (UE), which is used throughout the entire user activity detection process. The assigned associated preamble sequence is the user's identifier sequence and is uniquely associated with the UE ID. In each UE activity detection time slot, the base station determines which UEs are active by detecting the presence of the preamble sequence. In a given time slot, only a small subset of potential devices (denoted as the active subset) are active. It becomes active and submits a service request for uplink data transmission. For example... Figure 3 As shown, active user devices (exist Figure 3 (S1 and S3) send a unique sequence of length M, denoted as vector. However, due to the large number of potential devices and the limited size of the coherent time-frequency block in the wireless fading channel, only a small number of orthogonal preamble sequences can be supported, and it is impossible for all users' identification sequences to be mutually orthogonal.

[0057] To enable user activity detection, the network is configured with a common physical resource block (PRB), on which all N user equipments can send their respective associated identifier sequences. Figure 4 A schematic diagram of a resource mapping for sequence transmission according to some example embodiments of the present disclosure is shown. A shared PRB consists of M resource basic units (REs) in a coherent block, each resource basic unit carrying a sequence s. n A symbol in . The channel coefficient from user n to the base station is . This indicates that it is assumed here that the base station is configured with a single receiving antenna. Therefore, the base station can receive synchronously superimposed signals, and its baseband form can be written as an M-dimensional vector, i.e. Where vector This represents the combined effect of additive noise and inter-cell interference. For user activity detection, the base station aims to identify the unknown active subset S based solely on the observation vector y. A .

[0058] Non-orthogonal preamble sequence sets make user activity detection more challenging, as they superimpose at the base station and cause severe multi-user interference, for example, when simple matched filtering or correlation-based processing is applied at the base station. As mentioned above, conventional schemes suffer from poor performance due to the lack of preamble sequences in the physical random access channel, and sequence collisions are caused by the random allocation of preamble sequences. To overcome these shortcomings, in the example embodiments of this disclosure, a multi-frequency complex exponential sequence that meets the requirements for synchronous user activity detection is proposed, and a fast user activity detection method is developed for this sequence. This technical solution provides a method for generating and transmitting a sequence from a first device to a second device, specifically involving a preamble sequence set {s}. n} n∈S The design and related detection of user activity can be transformed into a sparse signal recovery problem, since only a small portion of user devices are active in each time slot.

[0059] Some example embodiments of this disclosure relate to user activity detection schemes that include the entire process from sending a preamble sequence to receiving a data source, wherein the preamble sequence is designed to build accurate, fast, and scalable user activity detection with minimal measurement resource cost. In this disclosure, {s} n} n∈S A well-designed sequence facilitates the entire process and improves detection efficiency and performance.

[0060] The sequence design in the exemplary embodiments of this disclosure has the following advantages. First, it offers high capacity, allowing for fixed preamble sequence allocation, where each user can be pre-assigned a dedicated sequence. Unlike the random allocation of preamble sequences, this fixed allocation avoids sequence conflicts. For the distributed randomness of service requests, any prior sequence allocation coordination is futile. Furthermore, this unique association between the sequence and the user avoids the additional cost of reporting user identity information. Second, the sequence design in the exemplary embodiments of this disclosure is scalable. The required sequence length, corresponding to the cost of degrees of freedom in time-frequency resources, must be commensurate with the (average) number of actual active users and independent of the total number of user devices. The shorter the sequence length, the less time-frequency resources are consumed. It is well known that to guarantee mutual orthogonality among N user devices, the sequence length must at least satisfy M ≥ N. Conversely, for a sequence set {s} n} n∈S The expected length is comparable to the average number of active users, i.e. M≈E{|S A |} Where |S A | represents set S A The potential of E{·} is represented by the mathematical expectation. The sequence design in the exemplary embodiments of this disclosure also offers the advantage of agility, allowing for accurate and rapid user activity detection at the base station without any precoding based on channel state information at the device (transmitter) end. Finally, the sequence design in the exemplary embodiments of this disclosure exhibits constant mode characteristics, which is beneficial for transmission using low-cost radio frequency equipment.

[0061] For the first device, example embodiments of this disclosure propose generating multi-frequency complex exponential sequences for accurate, fast, and scalable user activity detection. In this way, regardless of the total number of devices, the required sequence length can be reduced to four times the actual number of active user devices. The designed non-orthogonal sequence set allows each device to be assigned a dedicated sequence, thus avoiding preamble sequence conflicts. This approach has the following advantages. First, it minimizes the sequence length required for detection: although the sequence length is less than the total number of potential users, perfect synchronous user activity detection can be achieved as long as the actual number of active users is less than one-quarter of the sequence length. Second, it has scalability for large-scale access. Since the measurement resources required for detection depend only on the number of actual active users, regardless of the total number of user devices, the solution is scalable. Third, the scheme facilitates sparsity detection because the multi-frequency design simplifies the frequency resolution problem of complex exponential sequences, thus ensuring that the sparse Bayesian learning method reaches global optimum. Finally, the fixed assignment between the preamble sequence and the user establishes a unique association, which avoids preamble sequence conflicts caused by random assignment and saves the additional process of reporting user identity information.

[0062] For the second device, the multi-frequency complex exponential sequence design of the example embodiments of this disclosure enables the detection method to quickly and accurately obtain user activity information from noise and mixed observations. A sparse Bayesian learning method is used to infer non-negative hyperparameter vectors instead of directly estimating unknown equivalent transmission symbols corresponding to all users. The support domain of the hyperparameter vectors corresponds exactly to the non-zero equivalent transmission symbols. Therefore, using the knowledge of the hyperparameter vectors, the non-zero equivalent transmission symbols corresponding to the active subset can be easily determined.

[0063] The principles and exemplary embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings.

[0064] Figure 5 Signaling diagrams for communication 500 according to some example embodiments of this disclosure are shown. For discussion purposes, reference will be made to... Figure 1 This describes the signaling diagram used for Communication 500. It is for illustrative purposes only. Figure 5 As shown, communication 500 can be implemented at the first device 110-n and the second device 120, where the value of n ranges from 1 to N. That is, the first device 110-n can be... Figure 1 Any of the first devices shown, namely, first device 110-1, first device 110-2, ..., first device 110-N. It should be understood that, although... Figure 5 Only a single first device 110-n is shown in the diagram, but multiple first devices 110 may be involved in the signaling diagram 500.

[0065] like Figure 5 As shown, the first device 110-n determines (505) a first frequency from a first frequency set and a second frequency from a second frequency set. In some example embodiments, the first frequency set and the second frequency set may be derived from the configuration of the second device 120. For the first device 110-n, the first frequency is represented as a digital frequency f. n，1 The second frequency is represented as the digital frequency f. n，2 , where 0≤f n，1 f n，2 <1,f n，1 ≠f n，2 , and f n，i ≠f k，i When n≠k and i=1,2.

[0066] In some example embodiments, a first set of devices, including one or more first devices, corresponds sequentially to a first set of frequencies and a second set of frequencies. The frequencies of the first set of frequencies and the second set of frequencies may come from the same set of frequencies or different sets of frequencies.

[0067] In some example embodiments, the first frequency set and the second frequency set come from the same frequency set, and the frequency order of one of the frequency sets is a random permutation of the frequency order of the other frequency set. That is, and From the same frequency set, and One of them is a random permutation of the other. For example, a set of the same frequencies could be... or

[0068] Alternatively, the first frequency set and the second frequency set may come from different frequency sets, where one frequency set contains frequencies arranged in ascending or descending order, and the other frequency set contains frequencies arranged randomly. In other words, and From different frequency sets, and One set is either sorted in ascending or descending order, and the other is a set sorted randomly. For example, and yes A random arrangement.

[0069] Based on a first frequency and a second frequency, the first device 110-n generates a (510-n) complex exponential sequence. The complex exponential sequence includes a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, the first frequency being different from the second frequency. Alternatively, the complex exponential sequence may include more than two subsequences.

[0070] The first device generates a (510-n) complex exponential sequence {s} n} n∈S For accurate, fast, and scalable user activity detection, the required sequence length M1+M2 only needs to be greater than 4 times the average number of active user devices, i.e., 4×E{|S A By utilizing the overall design of complex exponential sequences, the user activity detection problem can be transformed into a solvable inverse problem of linear underdetermined systems.

[0071] For example, a complex exponential sequence can be represented as a column vector. The first complex exponential subsequence is: in, The second complex exponential subsequence is: in, Among them, s n Denotes a complex exponential sequence, s n，1 Let s represent the first complex exponential subsequence. n，2 Let M1 represent the length of the first complex exponential subsequence, M2 represent the length of the second complex exponential subsequence, and f represent the length of the second complex exponential subsequence. n，1 f represents the first frequency. n，2 Indicates the second frequency, m 1，0 m represents the offset of the first complex exponential subsequence. 2，0 This represents the offset of the second complex exponential subsequence.

[0072] In some example embodiments, the first complex exponential subsequence and the second complex exponential subsequence have the same length and offset. In other example embodiments, the first complex exponential subsequence and the second complex exponential subsequence have different lengths and offsets. In some example embodiments, the length of the complex exponential sequence is greater than or equal to a predetermined threshold. This predetermined threshold is four times the number of active first devices plus one. For example, M1 represents the length of the first complex exponential subsequence, M2 represents the length of the second complex exponential subsequence, and M1 + M2 ≥ 4N. a +1, where N am represents the number of active user devices. 1，0 m represents the offset of the first complex exponential subsequence. 2，0 This represents the offset of the second complex exponential subsequence, which is the same for all user devices. Further, the complex exponential sequence s... n It has constant model properties, which makes it suitable for any element And ||s n || 2 =1.

[0073] In response to the first device 110-n being active, the first device 110 sends a complex exponential sequence (515-1) to the second device 120. In other words, the second device 120 receives a complex exponential sequence (515-2) from the first device 110-n. In some example embodiments, the complex exponential sequence is distributed on a physical resource block (PRB) according to power P. n The physical resource block is transmitted and shared by the first set of devices. To conserve spectrum resources, all N first devices share the same M1+M2 resource basic units for transmitting their allocated sequences. Therefore, the base station can only obtain the cumulative observations of the transmitted sequences from all active user equipment. It is understood that the power used to transmit the complex exponential sequence can be any suitable power. This disclosure is not limited in this respect.

[0074] No coordination between devices is required during this transmission process. Each device operates independently based on its service requests, and inactive terminals do nothing except remain silent. It is important to note that the base station possesses prior knowledge of the association patterns between sequences and users; therefore, it can identify active devices by detecting complex exponential sequences appearing in the superimposed observation vectors.

[0075] Because active users can adjust their transmission timing advance based on prior timing calibration information, achieving synchronous transmission within the cyclic prefix, block fading is a reasonable assumption. At the beginning of each transmission cycle, the base station sends a beacon, triggering uplink synchronization transmission from active user equipment. The sequences transmitted by all active user equipment arrive at the base station synchronously with respect to the receive window. Accordingly, the base station receives the signal and performs user activity detection. This signal can be described as an (M1+M2)-dimensional vector r, which can be represented as... in, This represents the equivalent transmission symbol corresponding to the first device 110-n, that is, the symbol formed by the complex exponential sequence s. n The carried complex-valued coefficients. When the first device 110-n is in an active state, x n and P nBoth are non-zero terms. Therefore, the equivalent transmission symbols corresponding to the first set of devices form a sparse vector {x}. n} n∈S Of these, only a few components are non-zero, corresponding to active user devices. In other words, non-zero components indicate that the user device is active, while zero components indicate that the user device is inactive.

[0076] After receiving a complex exponential sequence from one or more first devices 110, the second device 120 recovers N unknowns {x} from a mixture of observations of vector r received from a (M1+M2)-dimensional vector. n} n∈S That is, the equivalent transmission symbol corresponding to the first set of devices, thereby determining the active subset S. A Here, N > M1 + M2. In fact, this involves the inverse problem of an equivalent matrix-form observation model. r = Sx + n,

[0077] Where S = [s1 ... s N ], x = [x1 ... x N ] T The support region of the unknown vector x indicates the active subset S. A (Mathematically, the support domain of a vector is the set of position indices corresponding to the non-zero components of that vector.) In some example embodiments, it is assumed that the noise vector n follows an independent and identically distributed (i.i.d.) complex normal distribution with a mean of zero and a variance of σ. 2 Essentially, this is a noisy, underdetermined linear system with more unknown variables than equations, because the active subset S... A It is an unknown set that needs to be identified. Although x is a sparse vector with many zero components, its non-zero components are usually complex numbers, since there is no phase compensation at the transmitter. Therefore, it is difficult to directly derive the high-dimensional complex vector x from the low-dimensional raw received data r.

[0078] In some example embodiments of this disclosure, sparse Bayesian learning methods can be used to identify the support domain of x and estimate the non-zero component values ​​of x. For example, according to some example embodiments of this disclosure, instead of directly estimating the equivalent transmission symbol x corresponding to the first set of devices, a hyperparameter vector associated with the unknown x is first determined, which is a non-negative N-dimensional vector denoted as γ. The optimal value of γ can be determined by maximizing the marginal likelihood function, which is inversely proportional to the objective function log(σ). 2 I+SΓS H )+r H (σ 2 I+SΓS H ) -1Therefore, the optimal value of γ can be obtained by solving a nonconvex minimization problem, i.e. in Represents the set of N-dimensional nonnegative vectors; superscript Let Γ represent the conjugate transpose; Γ = diag{γ} is a diagonal matrix, with diagonal elements identical to γ. Thanks to the specially designed matrix S composed of two-frequency complex exponential sequences, the aforementioned non-convex minimization problem avoids local minima. In particular, The supporting domain and active subset S A In the absence of noise (σ) 2 →0) is the same. Therefore, the non-zero components of x can be easily solved using the optimal value of γ.

[0079] Return to Figure 5 The second device 120 receives (515-2) a superposition of one or more complex exponential sequences r from one or more active devices in the first device set. A complex exponential sequence comprises a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency. Then, based on the superposition of one or more complex exponential sequences r, the second device 120 determines (520) an active subset S from the first device set. A The estimation of active subsets is achieved through... It is determined that β is a predetermined threshold, and the active subset indicates one or more first devices 110 that are in an active state. Next, based on the active subset, the second device 120 determines (521) an estimate of the equivalent transmission symbols corresponding to the active subset, i.e.

[0080] In some example embodiments, estimation based on active subsets pass Determine the estimate of the equivalent transmission symbols corresponding to the active subset, wherein Same as Γ, except not The diagonal terms in the expression are zero.

[0081] In some example embodiments, in response to receiving a superposition of one or more complex exponential sequences, the second device 120 determines a non-negative hyperparameter vector corresponding to the first device set. The optimal value of this hyperparameter vector... Maximize a marginal likelihood function. In some example embodiments, the second device 120 is based on the values ​​of the hyperparameter vector. Determine the active subset S A The estimate, i.e.

[0082] In some example embodiments, in response to receiving a superposition of one or more complex exponential sequences, r = Sx + n, the second device 120 initializes the hyperparameter vector γ as a positive vector. For this hyperparameter vector γ, the following steps are performed iteratively until the hyperparameter vector converges, for example, initializing γ = 1: First, based on the hyperparameter vector γ, the convex component log(σ) of the marginal likelihood function is determined. 2 I+SΓS H The gradient value corresponding to the current value of the hyperparameter vector is z = diag{S}. H (σ 2 I+SΓS H ) -1 S}, z = diag{A} is a column vector determined by the diagonal terms of matrix A; subsequently, based on the current value of the hyperparameter vector γ, through The linear minimum mean square error (LMMSE) estimate of the equivalent transmission symbol x corresponding to the first device set is updated. Next, the hyperparameter vector value γ is updated based on the current value of the hyperparameter vector, the gradient value, and the LMSE estimate.

[0083] In some example embodiments, the updated value of the hyperparameter vector is equal to the product of the reciprocal of the current value of the hyperparameter vector, the reciprocal of the gradient value, and the square of the modulus of the linear minimum mean square error estimate, i.e. in Multiplication operations are performed element-wise.

[0084] In other example embodiments, by Update the hyperparameter vector γ. Alternatively, via Update the hyperparameter vector γ. In the example embodiment above, Z = diag{z}. and γ 2 Operations are performed element-wise.

[0085] In some example embodiments, the threshold β can be set based on a balance between the false alarm probability and the missed alarm probability. In other example embodiments, to accelerate the iteration process, after each iteration, an update value of the hyperparameter vector γ and a smaller threshold value β are applied. low The active subset is roughly determined in advance, i.e. Then, in subsequent iterations, use Instead of Γ. Therefore, in the columns of the iterative process matrix S, it will change accordingly. The number of elements in the middle is continuously reduced and deleted, which significantly reduces the amount of computation.

[0086] Return to Figure 5In some example embodiments, in response to the first device 110-n being active, it monitors (522) a scheduling grant message from the second device 120 on the downlink control channel. In some example embodiments, the second device 120 sends (525-1) the scheduling grant message to one or more of the first devices 110-n included in the active subset.

[0087] In some example embodiments, in response to the first device 110-n receiving (525-2) a scheduling authorization message, it is determined (527) that the first device 110-n has been detected as active by the second device. Subsequently, based on the scheduling authorization message, the first device 110-n sends (530-1) service request information to the second device 120, that is, the second device 120 receives (530-2) the service request information from the first device 110-n.

[0088] In other example implementations, in response to the first device 110-n not receiving a scheduling authorization message, the first device 110-n determines (532) that it has not been detected as active by the second device. In the next user activity detection, the first device 110-n sends (535-1) a complex exponential sequence to the second device 120. After the second device 120 receives (535-2) the complex exponential sequence from the first device 110, it re-executes the aforementioned process on the second device side for determining whether the first device 110-n is active.

[0089] Figure 6 A signaling diagram of a service request granting procedure 600 that performs synchronous user activity detection according to some example embodiments of the present disclosure is shown. Figure 6 This includes a service request granting cycle for synchronous user activity detection, and incorporates the aforementioned complex exponential sequence sending and receiving method into the entire process.

[0090] In step 1, all user equipment (UEs), including active UE 1, active UE 2, and inactive UEs, establish a connection-like state without dedicated service request resources. In steps 2 and 3, the synchronous user activity detection sending and receiving methods of this disclosure are applied as integral building blocks in the service request / scheduling granting procedure. In step 2, active UEs initiate service requests by simultaneously sending their allocated dual-frequency complex exponential sequences. In step 3, the base station performs synchronous user activity detection using sparse Bayesian learning. Subsequently, in step 4, the base station provides a scheduling grant message for the detected UEs via a common downlink control channel. Active UEs check the downlink control channel to determine if they are detected as active UEs. In step 4, the downlink control channel is a low-rate downlink control channel that can be monitored by all UEs. The base station notifies the detected UEs in the mapping list. Each row of the list is for a detected UE, containing the identifier of the detected UE and the corresponding scheduling grant message. Based on the scheduling grant message, the base station allocates uplink resources for further transmission of specific service request information only to the detected UEs. Any UE can view this list. By checking, the active terminal can determine whether it has been successfully detected by the base station. If so, in step 5, the detected active user equipment proceeds to provide the base station with detailed service request information, such as a buffer status report. If not, the undetected active user equipment will initiate a service request in the next synchronous user activity detection time slot by retransmitting its allocated dual-frequency complex exponential sequence.

[0091] In the exemplary embodiments of this disclosure, the performance of dual-frequency and single-frequency complex exponential sequence synchronous user activity detection schemes was compared, with both dual-frequency and single-frequency complex exponential sequences having a length of 258. Table 1 lists the detailed simulation parameters. The performance of user activity detection was evaluated based on the false alarm probability and the missed alarm probability. The simulation results were averaged from more than 10,000 independent experiments, each experiment independently and uniformly generating 64 valid user devices. It should be understood that what is shown in Table 1 is merely exemplary and is not intended to limit the scope of this disclosure. Table 1

[0092] Figure 7Schematic graphs comparing the performance of synchronous user activity detection according to some example embodiments of this disclosure are shown, representing user activity detection performance at different signal-to-noise ratios (SNR). Simulation results demonstrate that the proposed dual-frequency complex exponential sequence can improve user activity detection performance by more than an order of magnitude compared to a single-frequency complex exponential sequence. This significant advantage stems from the dual-frequency design eliminating the frequency resolution limitation. In contrast, for single-frequency sequences, frequency resolution remains a major cause of performance degradation.

[0093] Figure 8 A flowchart of a method 800 for communication according to some example embodiments of the present disclosure is shown. Method 800 may be implemented, for example, in a communication environment 100, such as at a first device 110.

[0094] In block 810, the first device 110 determines a first frequency from a first set of frequencies and a second frequency from a second set of frequencies.

[0095] In block 820, the first device 110 generates a complex exponential sequence based on a first frequency and a second frequency. The complex exponential sequence includes a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, the first frequency being different from the second frequency.

[0096] In box 830, the first device 110, in response to the first device being active, sends a complex exponential sequence to the second device.

[0097] In some example embodiments, the complex exponential sequence is: The first complex exponential subsequence is: in, The second complex exponential subsequence is: in, Among them, s n Denotes a complex exponential sequence, s n，1 Let s represent the first complex exponential subsequence. n，2 Let M1 represent the length of the first complex exponential subsequence, M2 represent the length of the second complex exponential subsequence, and f represent the length of the second complex exponential subsequence. n，1 f represents the first frequency. n，2 Indicates the second frequency, m 1，0 m represents the offset of the first complex exponential subsequence. 2，0 This represents the offset of the second complex exponential subsequence.

[0098] In some example embodiments, the length of the first complex exponential subsequence and the length and offset of the second complex exponential subsequence are the same or different, and the length of the complex exponential sequence is greater than or equal to a predetermined threshold.

[0099] In some example embodiments, the predetermined threshold is four times the number of first devices in an active state plus one.

[0100] In some example embodiments, a first set of devices, including one or more first devices, corresponds sequentially to a first set of frequencies and a second set of frequencies. The frequencies of the first set of frequencies and the second set of frequencies may come from the same set of frequencies or different sets of frequencies.

[0101] In some example embodiments, in response to the first frequency set and the second frequency set coming from the same frequency set, the frequency order of one of the frequency sets in the first frequency set and the second frequency set is a random permutation of the frequency order of the other frequency set.

[0102] In some example embodiments, in response to the first frequency set and the second frequency set being from different frequency sets, one of the frequency sets contains frequencies arranged in ascending or descending order, while the other frequency set contains frequencies arranged randomly.

[0103] In some example embodiments, the complex exponential sequence is transmitted in a physical resource block (PRB) based on a certain power, wherein the physical resource block is shared by a first set of devices.

[0104] In some example embodiments, the first device 110 also enables the first device 110 to monitor scheduling authorization messages from the second device on the downlink control channel in response to the first device being active.

[0105] In some example embodiments, the first device 110 also causes the first device 110 to determine that the first device is detected as active by the second device in response to receiving a scheduling authorization message; and to send service request information to the second device based on the scheduling authorization message.

[0106] In some example embodiments, the first device 110 also causes the first device 110 to determine that it is not detected as active by the second device in response to not receiving a scheduling authorization message; and to send a complex exponential sequence to the second device in the next user activity detection.

[0107] In some example embodiments, the first device 110 includes a terminal device, and the second device includes a network device.

[0108] Figure 9A flowchart of a method 900 for communication according to some example embodiments of the present disclosure is shown. Method 900 may be implemented, for example, in a communication environment 100, or at a second device 120.

[0109] In block 910, second device 120 receives, on a shared physical resource block, a superposition of one or more complex exponential sequences from one or more active first devices in the first device set. A complex exponential sequence comprises a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency.

[0110] In box 920, the second device 120 determines an active subset from the first device set based on the superposition of one or more complex exponential sequences. The active subset consists of one or more first devices that are in an active state.

[0111] In box 930, the second device 120 estimates the equivalent transmission symbols corresponding to the active subset based on the active subset.

[0112] In some example embodiments, the second device 120, in response to receiving a superposition of one or more complex exponential sequences, determines a non-negative hyperparameter vector corresponding to the first device set, wherein the optimal value of the hyperparameter vector maximizes a marginal likelihood function; and determines an active subset based on the value of the hyperparameter vector.

[0113] In some example embodiments, the second device 120, in response to receiving a superposition of one or more complex exponential sequences, initializes the hyperparameter vector as a positive vector; and iteratively performs the following steps until the hyperparameter vector converges: determining the gradient value of the convex component of the marginal likelihood function corresponding to the current value of the hyperparameter vector; updating the linear minimum mean square error (LMMSE) estimate of the equivalent transmission symbols corresponding to the first device set based on the current value of the hyperparameter vector; and updating the hyperparameter vector value based on the current value of the hyperparameter vector, the gradient value, and the LMSE estimate.

[0114] In some example implementations, the updated value of the hyperparameter vector is equal to the product of the reciprocal of the current value of the hyperparameter vector, the reciprocal of the gradient value, and the square of the modulus of the linear minimum mean square error estimate, wherein the product is performed element-wise.

[0115] In some example embodiments, the second device 120 also causes the second device 120 to send scheduling authorization messages to one or more first devices included in the active subset.

[0116] In some example embodiments, the first device 110 includes a terminal device, and the second device 120 includes a network device.

[0117] In some example embodiments, a communication apparatus (e.g., first apparatus 110) may include components for performing corresponding steps of method 800. These components may be implemented in any suitable manner. For example, components may be implemented as circuit devices or software modules.

[0118] In some example embodiments, the first device may include components for determining a first frequency from a first set of frequencies and a second frequency from a second set of frequencies; components for generating a complex exponential sequence based on the first and second frequencies, the complex exponential sequence including a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, the first frequency being different from the second frequency; and components for sending the complex exponential sequence to the second device in response to the first device being in an active state.

[0119] In some example embodiments, the complex exponential sequence is: The first complex exponential subsequence is: in, The second complex exponential subsequence is: in, Among them, s n Denotes a complex exponential sequence, s n，1 Let s represent the first complex exponential subsequence. n，2 Let M1 represent the length of the first complex exponential subsequence, M2 represent the length of the second complex exponential subsequence, and f represent the length of the second complex exponential subsequence. n，1 f represents the first frequency. n，2 Indicates the second frequency, m 1，0 m represents the offset of the first complex exponential subsequence. 2，0 This represents the offset of the second complex exponential subsequence.

[0120] In some example embodiments, the length of the first complex exponential subsequence and the length and offset of the second complex exponential subsequence are the same or different, and the length of the complex exponential sequence is greater than or equal to a predetermined threshold.

[0121] In some example embodiments, the predetermined threshold is four times the number of first devices in an active state plus one.

[0122] In some example embodiments, a first set of devices including one or more first devices corresponds sequentially to a first set of frequencies and a second set of frequencies, wherein the frequencies of the first set of frequencies and the second set of frequencies come from the same set of frequencies or different sets of frequencies.

[0123] In some example embodiments, in response to the first frequency set and the second frequency set coming from the same frequency set, the frequency order of one of the frequency sets in the first frequency set and the second frequency set is a random permutation of the frequency order of the other frequency set.

[0124] In some example embodiments, in response to the first frequency set and the second frequency set being from different frequency sets, one of the frequency sets contains frequencies arranged in ascending or descending order, while the other frequency set contains frequencies arranged randomly.

[0125] In some example embodiments, the complex exponential sequence is transmitted in a physical resource block (PRB) based on a certain power, wherein the physical resource block is shared by a first set of devices.

[0126] In some example embodiments, the first device further includes a component for monitoring scheduling authorization messages from the second device on a downlink control channel in response to the first device being in an active state.

[0127] In some example embodiments, the first device further includes: a component for determining, in response to receiving a scheduling authorization message, that the first device is detected as active by the second device; and a component for sending service request information to the second device based on the scheduling authorization message.

[0128] In some example embodiments, the first device further includes: a component for determining, in response to not receiving a scheduling authorization message, that the user is not detected as active by the second device; and a component for sending a complex exponential sequence to the second device in the next user activity detection.

[0129] In some example embodiments, the first device 110 includes a terminal device, and the second device includes a network device.

[0130] In some example embodiments, a communication device (e.g., second device 120) may include components for performing corresponding steps of method 900. These components may be implemented in any suitable manner. For example, the components may be implemented as circuit devices or software modules.

[0131] In some example embodiments, the second device may include means for receiving, on a shared physical resource block, a superposition of one or more complex exponential sequences from one or more first devices in an active state of the first device set, a complex exponential sequence comprising a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency; means for determining an active subset from the first device set based on the superposition of one or more complex exponential sequences, the active subset consisting of one or more first devices in an active state; and means for estimating equivalent transmission symbols corresponding to the active subset based on the active subset.

[0132] In some example embodiments, the second device further includes: a component for determining a non-negative hyperparameter vector corresponding to the first device set in response to receiving a superposition of one or more complex exponential sequences, wherein the optimal value of the hyperparameter vector maximizes a marginal likelihood function; and a component for determining an active subset based on the value of the hyperparameter vector.

[0133] In some example embodiments, the second device further includes: a component for initializing the hyperparameter vector as a positive vector in response to receiving a superposition of one or more complex exponential sequences; and a component for iteratively performing the following steps until the hyperparameter vector converges: a component for determining the gradient value of the convex component of the marginal likelihood function corresponding to the current value of the hyperparameter vector; a component for updating the linear minimum mean square error (LMMSE) estimate of the equivalent transmission symbols corresponding to the first device set based on the current value of the hyperparameter vector; and a component for updating the hyperparameter vector value based on the current value of the hyperparameter vector, the gradient value, and the LMSE estimate.

[0134] In some example implementations, the updated value of the hyperparameter vector is equal to the product of the reciprocal of the current value of the hyperparameter vector, the reciprocal of the gradient value, and the square of the modulus of the linear minimum mean square error estimate, wherein the product is performed element-wise.

[0135] In some example embodiments, the second means further includes a component for sending a scheduling authorization message to one or more of the first means contained in the active subset.

[0136] In some example embodiments, one or more complex exponential sequences are sent in a physical resource block (PRB), wherein the physical resource block is shared by a first set of devices.

[0137] In some example embodiments, the first device 110 includes a terminal device, and the second device 120 includes a network device.

[0138] The above combination Figures 1 to 9Several examples of the first device 110 and the second device 120 are described. In some example embodiments, the first device 110 and the second device 120 may be combined into a communication system. Any other suitable components or devices may also be included in the communication system.

[0139] Figure 10 This is a simplified block diagram of a device 1000 suitable for implementing an example embodiment of the present disclosure. The device 1000 can be used to implement a first device 110 and a second device 120 in a communication environment 100. As shown, the device 1000 includes one or more processing units 1010, one or more memories 1020 coupled to the processing units 1010, and a communication module 1040 coupled to the processing units 1010.

[0140] The communication module 1040 is used for bidirectional communication. In some example embodiments, the communication module 1040 may have at least one antenna to facilitate communication. In some example embodiments, the communication module 1040 may have one or more communication interfaces. A communication interface may represent any interface required for communication with other network elements.

[0141] Processing unit 1010 can be any type suitable for a local technology network and can include, but is not limited to, one or more of a general-purpose computer, a special-purpose computer, a microcontroller, a digital signal controller (DSP), and a controller-based multi-core controller architecture. Device 1000 can have multiple processors, such as application-specific integrated circuit chips, which are time-dependent on a clock synchronized with the main processor.

[0142] Memory 1020 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 1024, erasable programmable read-only memory (EPROM), flash memory, hard disk, optical disc (CD), digital video disc (DVD), and other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, random access memory (RAM) 1022 and other volatile memories that do not persist during power-off periods.

[0143] Computer program 1030 includes computer-executable instructions that are executed by associated processing unit 1010. Computer program 1030 may be stored in ROM 1024. Processing unit 1010 may perform any appropriate actions and processes by loading computer program 1030 into RAM 1022.

[0144] The exemplary embodiments of this disclosure can be implemented by means of computer program 1030, enabling device 1000 to perform as described in the reference. Figures 2 to 9Any process discussed in this disclosure. Exemplary embodiments of this disclosure may also be implemented by hardware or by a combination of software and hardware.

[0145] In some example embodiments, the computer program 1030 may be tangibly contained in a computer-readable medium, which may include in device 1000 (such as in memory 1020) or other storage devices accessible by device 1000. The computer program 1030 may be loaded from the computer-readable medium into RAM 1022 for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. Figure 11 An example of a computer-readable medium 1100 in the form of a CD or DVD according to some exemplary embodiments of the present disclosure is shown. A computer program 1030 is stored on the computer-readable medium 1100.

[0146] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. While various aspects of exemplary embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as in the non-limiting examples, hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0147] This disclosure also provides at least one computer program product tangibly stored on a computer-readable storage medium. In some example embodiments, the computer-readable storage medium may be non-transitory. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a real or virtual processor of a target to perform the above-referenced... Figure 8 The method described in 800 or reference Figure 9 The method described in 900. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for a program module can be executed locally or in a distributed device. In a distributed device, the program module can reside in both local and remote storage media.

[0148] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0149] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and so on. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0150] A computer-readable medium can be any tangible medium that contains or stores a program for or relating to an instruction execution system, apparatus, or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0151] Furthermore, although the operation of the methods of this disclosure is described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps. It should also be noted that the features and functions of two or more devices according to this disclosure may be embodied in one device. Conversely, the features and functions of one device described above may be further divided and embodied by multiple devices.

[0152] While this disclosure has been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the specific embodiments disclosed. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A first device for communication, comprising: At least one processor; as well as At least one memory, the at least one memory storing instructions, the instructions, when executed by the at least one processor, cause the first device to at least: Determine the first frequency from the first frequency set and the second frequency from the second frequency set; Based on the first frequency and the second frequency, a complex exponential sequence is generated, wherein the complex exponential sequence includes a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, the first frequency being different from the second frequency; and In response to the first device being active, the complex exponential sequence is sent to the second device.

2. The first apparatus according to claim 1, wherein the complex exponential sequence is: The first complex exponential subsequence is: in, The second complex exponential subsequence is: in, Among them, s n Let s represent the complex exponential sequence. n，1 Let s represent the first complex exponential subsequence. n，2 Let M1 represent the length of the first complex exponential subsequence, M2 represent the length of the second complex exponential subsequence, and f represent the length of the second complex exponential subsequence. n，1 f represents the first frequency. n，2 Indicates the second frequency, m 1，0 m represents the offset of the first complex exponential subsequence. 2，0 This represents the offset of the second complex exponential subsequence.

3. The first apparatus according to claim 2, wherein the length of the first complex exponential subsequence and the length and offset of the second complex exponential subsequence are the same or different, and the length of the complex exponential sequence is greater than or equal to a predetermined threshold.

4. The first device according to claim 3, wherein the predetermined threshold is four times the number of first devices in an active state plus one.

5. The first apparatus of claim 1, wherein a first apparatus set comprising one or more of the first apparatuses corresponds sequentially to the first frequency set and the second frequency set, wherein the frequencies of the first frequency set and the second frequency set originate from the same frequency set or different frequency sets.

6. The first apparatus of claim 5, wherein in response to the first frequency set and the second frequency set being from the same frequency set, the frequency order of one of the frequency sets is a random permutation of the frequency order of the other frequency set.

7. The first apparatus of claim 6, wherein in response to the first frequency set and the second frequency set being from different frequency sets, one of the frequency sets contains frequencies arranged in ascending or descending order, and the other frequency set contains frequencies arranged randomly.

8. The first apparatus according to claim 1, wherein the complex exponential sequence is transmitted in a physical resource block (PRB) based on a certain power, wherein the physical resource block is shared by the first apparatus set.

9. The first apparatus of claim 1, wherein the instructions, when executed by the at least one processor, further cause the first apparatus to: In response to the first device being active, scheduling authorization messages from the second device are monitored on the downlink control channel.

10. The first apparatus of claim 9, wherein the instructions, when executed by the at least one processor, further cause the first apparatus to: In response to receiving the scheduling authorization message, it is determined that the first device is detected as active by the second device; and Based on the scheduling authorization message, the service request information is sent to the second device.

11. The first apparatus of claim 9, wherein the instructions, when executed by the at least one processor, further cause the first apparatus to: In response to the absence of a scheduling authorization message, it is determined that the device is not detected as being in an active state by the second device; and The complex exponential sequence will be sent to the second device in the next user activity detection.

12. The first apparatus according to claim 1, wherein the first apparatus includes a terminal device, and the second apparatus includes a network device.

13. A second means for communication, comprising: At least one processor; as well as At least one memory, the at least one memory storing instructions, the instructions, when executed by the at least one processor, cause the second device to at least: On a shared physical resource block, a superposition of one or more complex exponential sequences from one or more first devices in an active state of a first device set is received, wherein one complex exponential sequence comprises a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency; Based on the superposition of the one or more complex exponential sequences, an active subset is determined from the first set of devices, wherein the active subset consists of the one or more first devices that are in an active state; as well as Based on the active subset, an estimate of the equivalent transmission symbols corresponding to the active subset is determined.

14. The second apparatus of claim 13, wherein determining the active subset comprises: In response to receiving the superposition of the one or more complex exponential sequences, a non-negative hyperparameter vector corresponding to the first set of devices is determined, wherein the optimal value of the hyperparameter vector maximizes a marginal likelihood function; as well as The active subset is determined based on the values ​​of the hyperparameter vector.

15. The second apparatus of claim 14, wherein the instructions, when executed by the at least one processor, further cause the second apparatus to: In response to receiving the superposition of the one or more complex exponential sequences, the hyperparameter vector is initialized to a positive vector; and Perform the following steps iteratively until the hyperparameter vector converges: Determine the gradient value of the convex component of the marginal likelihood function corresponding to the current value of the hyperparameter vector; Based on the current value of the hyperparameter vector, update the linear minimum mean square error (LMMSE) estimate of the equivalent transmission symbol corresponding to the first device set; and The hyperparameter vector value is updated based on the current value of the hyperparameter vector, the gradient value, and the linear minimum mean square error estimate.

16. The second device according to claim 15, wherein The updated value of the hyperparameter vector is equal to the product of the reciprocal of the current value of the hyperparameter vector, the reciprocal of the gradient value, and the square of the modulus of the linear minimum mean square error estimate, wherein the product is performed element-wise.

17. The second apparatus of claim 13, wherein the instructions, when executed by the at least one processor, further cause the second apparatus to: The scheduling authorization message is sent to the one or more first devices contained in the active subset.

18. The second apparatus of claim 13, wherein the first apparatus includes a terminal device and the second apparatus includes a network device.

19. A communication method, comprising: The first device determines a first frequency from a first frequency set and a second frequency from a second frequency set; A complex exponential sequence is generated by a first device based on the first frequency and the second frequency, wherein the complex exponential sequence includes a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, and the first frequency is different from the second frequency; as well as The first device, in response to being in an active state, sends the complex exponential sequence to the second device.

20. A communication method, comprising: The second device receives, on a shared physical resource block, a superposition of one or more complex exponential sequences from one or more active first devices in the first device set, wherein one complex exponential sequence comprises a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency; An active subset is determined from the first set of devices by a second device based on the superposition and sum of the one or more complex exponential sequences, wherein the active subset consists of the one or more first devices that are in an active state; as well as The second device determines an estimate of the equivalent transmission symbols corresponding to the active subset based on the active subset.

21. An apparatus for communication, comprising: Components for determining a first frequency from a first frequency set and a second frequency from a second frequency set; A component for generating a complex exponential sequence based on the first frequency and the second frequency, wherein the complex exponential sequence includes a first complex exponential subsequence associated with the first frequency and a second complex exponential subsequence associated with the second frequency, the first frequency being different from the second frequency; as well as A component for sending the complex exponential sequence to a second device in response to the first device being in an active state.

22. An apparatus for communication, comprising: A component for receiving, on a shared physical resource block, a superposition of one or more complex exponential sequences from one or more first devices in an active state of a first device set, wherein one complex exponential sequence comprises a first complex exponential subsequence associated with a first frequency and a second complex exponential subsequence associated with a second frequency, the first frequency being different from the second frequency; A component for determining an active subset from the first set of devices based on the superposition and sum of the one or more complex exponential sequences, wherein the active subset consists of the one or more first devices in an active state; as well as A component for determining an estimate of the equivalent transmission symbols corresponding to the active subset based on the active subset.

23. A computer-readable storage medium having a computer program stored thereon, the computer program including instructions that, when executed by a processor on a device, cause the device to perform the method according to claim 19 or claim 20.

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