Calculate the EVM of the transmitter

Abstract

Apparatuses, methods, and systems for computing an EVM of a transmitter are disclosed. One apparatus includes a processor and a receiver that receives a multi-layer MIMO signal from a transmitter via a propagation channel. The processor measures the received multi-layer MIMO signal using an unbiased linear MMSE MIMO equalizer and computes an EVM of the transmitter, where the EVM of each transmission layer is computed as 100 times the square root of a mean square error of a layer estimate at an output of the unbiased linear MMSE MIMO equalizer.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 063,179, filed August 5, 2020, entitled “TRANSMITTER EVM DEFINITION FOR MULTI-LAYER TRANSMISSION,” which is incorporated herein by reference. This application also claims priority to U.S. Provisional Patent Application No. 63 / 063,163, filed August 5, 2020, entitled “TRANSMITTER EVM DEFINITION FOR AN ANTENNAPORT,” which is incorporated herein by reference.

[0003] The subject matter disclosed herein generally relates to wireless communication, and more specifically to the configuration of transmitter error vector magnitude (“EVM”) definitions for multilayer transmission. Background Technology

[0004] In wireless communication devices, the phase and amplitude distortion created by power amplifiers directly impacts communication quality. In the latest communication system protocols, the most important measurement for analyzing power amplifier performance is the Error Vector Magnitude (“EVM”). This is a measurement of modulation accuracy, or the degree to which the power amplifier is transmitting information, represented by changes in the phase and amplitude of the RF signal. EVM measurements provide in-depth insights into the communication link and are a key metric for transmitter performance.

[0005] However, due to leakage between antennas within the UE, it appears impossible to independently measure the EVM of the antenna connectors, even when the antenna precoder is an identity matrix. If the EVM is measured without addressing the leakage between the two antennas, the EVM requirement cannot be met. Summary of the Invention

[0006] A program for calculating the EVM of a transmitter is disclosed. The program can be implemented by an apparatus, system, method, or computer program product.

[0007] One method for calculating the EVM of a transmitter includes generating a multilayer transmission signal for multiple-input multiple-output (“MIMO”) and transmitting the generated multilayer transmission via a propagation channel using a transmitter. The method includes measuring the transmitted multilayer transmission signal using an unbiased linear minimum mean square error (“MMSE”) MIMO receiver and calculating the error vector magnitude (“EVM”) of the transmitter, wherein the EVM for each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the unbiased linear MMSE MIMO receiver.

[0008] Another method for calculating the EVM of a transmitter involves receiving a multi-layer MIMO signal from the transmitter via a propagation channel and measuring the received multi-layer MIMO signal using an unbiased linear MMSE MIMO equalizer. A second method involves calculating the EVM of the transmitter, where the EVM for each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the unbiased linear MMSE MIMO equalizer. Attached Figure Description

[0009] A more detailed description of the embodiments briefly described above will be rendered with reference to the specific embodiments illustrated in the accompanying drawings. It is to be understood that these drawings depict only some embodiments and are therefore not intended to be limiting of the scope; the embodiments will be described and explained with additional specificity and detail using the drawings, wherein:

[0010] Figure 1 This is a schematic block diagram illustrating one embodiment of a wireless communication system for calculating the EVM of a transmitter;

[0011] Figure 2 This is a block diagram illustrating one embodiment of the communication arrangement for calculating the EVM of a transmitter;

[0012] Figure 3 This is a block diagram illustrating one embodiment of a transmitter used for two-layer MIMO transmission;

[0013] Figure 4 This is a block diagram illustrating one embodiment of a MIMO receiver used for EVM measurements;

[0014] Figure 5 This is a block diagram illustrating one embodiment of a user equipment device that can be used to determine a transmitter EVM for multilayer transmission;

[0015] Figure 6 This is a block diagram illustrating one embodiment of a network device that can be used to determine the transmitter EVM for multilayer transmission;

[0016] Figure 7This is a block diagram illustrating an embodiment of a first method for determining a transmitter EVM for multi-layer transmission; and

[0017] Figure 8 This is a block diagram illustrating an embodiment of a second method for determining the transmitter EVM for multilayer transmission. Detailed Implementation

[0018] As those skilled in the art will understand, various aspects of the embodiments can be implemented as a system, apparatus, method, or program product. Therefore, embodiments can take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software and hardware aspects.

[0019] For example, the disclosed embodiments can be implemented as hardware circuits including custom-designed very large-scale integration (“VLSI”) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. The disclosed embodiments can also be implemented in programmable hardware devices such as field-programmable gate arrays, programmable array logic, programmable logic devices, etc. As another example, the disclosed embodiments may include one or more physical or logical blocks of executable code, which may be organized, for example, as objects, programs, or functions.

[0020] Furthermore, embodiments may take the form of a program product implemented in one or more computer-readable storage devices that store machine-readable code, computer-readable code, and / or program code, hereinafter referred to as code. The storage device may be tangible, non-transient, and / or non-transitive. The storage device may not implement signals. In one embodiment, the storage device uses only signals to access the code.

[0021] Any combination of one or more computer-readable media can be used. The computer-readable medium can be a computer-readable storage medium. The computer-readable storage medium can be a storage device for storing code. For example, the storage device can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof.

[0022] More specific examples of storage devices (a non-exhaustive list) will include the following: electrical connections having one or more wires, portable computer floppy disks, hard disks, random access memory (“RAM”), read-only memory (“ROM”), erasable programmable read-only memory (“EPROM” or flash memory), portable compact disc read-only memory (“CD-ROM”), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium can be any tangible medium that can contain or store programs used by a system, apparatus, or device to execute instructions.

[0023] The code used to perform the operations of the embodiments can be any number of lines and can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Python, Ruby, Java, Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" programming language, and / or machine languages ​​such as assembly language. The code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer via any type of network including a local area network ("LAN"), a wireless LAN ("WLAN"), or a wide area network ("WAN"), or (e.g., via the Internet using an Internet Service Provider ("ISP")) the connection can be made to an external computer.

[0024] Furthermore, the features, structures, or characteristics of the described embodiments can be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selection, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a comprehensive understanding of the embodiments. However, those skilled in the art will recognize that embodiments can be practiced without one or more specific details or using other methods, components, materials, etc. In other instances, well-known structures, materials, or operations have not been shown or described in detail to avoid obscuring various aspects of the embodiments.

[0025] Throughout this specification, references to "an embodiment," "embodiment," or similar language indicate that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment. Therefore, the phrases "in an embodiment," "in an embodiment," and similar language appearing throughout this specification may, but not necessarily all, refer to the same embodiment, but rather to "one or more, but not all, embodiments," unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise. An enumerated list of items does not imply that any or all items are mutually exclusive, unless expressly specified otherwise. The terms "a," "an," and "the" also refer to "one or more," unless expressly specified otherwise.

[0026] As used herein, a list containing the conjunction “and / or” includes any single item in the list or a combination of items in the list. For example, a list of A, B, and / or C includes only A, only B, only C, a combination of A and B, a combination of B and C, a combination of A and C, or a combination of A, B, and C. As used herein, a list using the term “one or more of…” includes any single item in the list or a combination of items in the list. For example, one or more of A, B, and C includes only A, only B, only C, a combination of A and B, a combination of B and C, a combination of A and C, or a combination of A, B, and C. As used herein, a list using the term “one of…” includes only one of any single item in the list. For example, “one of A, B, and C” includes only A, only B, or only C, and excludes combinations of A, B, and C. As used herein, “selected from the group consisting of A, B, and C” includes one and only one of A, B, or C, and excludes combinations of A, B, and C. As used in this article, “selecting members of a group consisting of A, B, and C and their combinations” includes only A, only B, only C, combinations of A and B, combinations of B and C, combinations of A and C, or combinations of A, B, and C.

[0027] Various aspects of the embodiments are described below with reference to schematic flowcharts and / or schematic block diagrams of methods, apparatus, systems, and program products according to the embodiments. It will be understood that each block of the schematic flowcharts and / or schematic block diagrams, and combinations of blocks in the schematic flowcharts and / or schematic block diagrams, can be implemented by code. This code can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that instructions executable via the processor of the computer or other programmable data processing apparatus create components for implementing the functions / actions specified in the flowcharts and / or block diagrams.

[0028] The code may also be stored in a storage device that can instruct a computer, other programmable data processing apparatus, or other device to operate in a particular manner, such that the instructions stored in the storage device produce an article of art, which includes instructions that implement the functions / actions specified in the flowcharts and / or block diagrams.

[0029] The code may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be executed on the computer, other programmable apparatus or other device to produce a computer-implemented process, such that the code executing on the computer or other programmable apparatus provides a process for implementing the functions / actions specified in the flowchart and / or block diagram.

[0030] The flowcharts and / or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, systems, methods, and program products according to various embodiments. In this regard, each block in the flowcharts and / or block diagrams may represent a module, segment, or code portion, which includes one or more executable instructions for implementing one or more specified logical functions.

[0031] It should also be noted that in some alternative implementations, the functions annotated in the boxes may occur in a different order than those annotated in the figures. For example, in fact, two boxes shown consecutively may be executed substantially concurrently, or these boxes may sometimes be executed in reverse order, depending on the functionality involved. Other steps or methods that are functionally, logically, or effectively equivalent to one or more boxes or portions thereof in the illustrated figures are conceivable.

[0032] While various arrow and line types may be used in flowcharts and / or block diagrams, they are not intended to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used only to indicate the logical flow of the depicted embodiment. For example, an arrow may indicate a wait or monitoring period of unspecified duration between enumeration steps in the depicted embodiment. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a hardware-based dedicated system or a combination of dedicated hardware and code that performs the specified function or action.

[0033] The description of elements in each figure may refer to elements in the process diagram. The same numbers refer to the same elements in all figures, including alternative embodiments of the same elements.

[0034] Generally, this disclosure describes systems, methods, and apparatuses for mechanisms of calculating the error vector magnitude (“EVM”) of a transmitter. In some embodiments, the methods can be performed using computer code embedded in a computer-readable medium. In some embodiments, the apparatus or system may include a computer-readable medium containing computer-readable code that, when executed by a processor, causes the apparatus or system to perform at least a portion of the solution described below.

[0035] The problem to be solved is defining the UE-transmitted EVM for multi-layer MIMO transmission. Due to leakage between antennas within the UE, it seems impossible to independently measure the EVM of the antenna connectors, even when the antenna precoder is an identity matrix. If the EVM is measured without addressing the leakage between the two antennas, the EVM requirements cannot be met.

[0036] In some embodiments, a linear zero-forcing MIMO equalizer (also referred to herein as a linear zero-forcing MIMO receiver) is used to determine the transmitter EVM of a multilayer transmission. However, for the same signal, the EVM determined by the linear zero-forcing MIMO equalizer is greater than the EVM determined by the linear MIMO MMSE equalizer. However, the linear MIMO MMSE equalizer (also referred herein as a linear MIMO MMSE receiver) is biased, and therefore the resulting EVM measurement is incorrect.

[0037] Currently, there is no protocol in 3GPP specifying how the requirements for EVM transmission should be defined for multi-layer MIMO. In some proposals, the EVM can be specified for each MIMO layer. In other proposals, the EVM can be specified for each antenna connector.

[0038] The purpose of the EVM requirement for the transmitter is to limit the noise floor at the receiver caused by transmitter noise. It is presumably that for multi-layer MIMO transmission, the goal of the EVM requirement is to limit the noise floor / error caused by transmitter noise at each MIMO layer. Therefore, the relationship between the daily antenna connector EVM at the UE antenna connector and the layer-by-layer EVM at the gNB should be investigated.

[0039] This disclosure describes the use of a linear unbiased MMSE MIMO equalizer (also referred to herein as a linear unbiased MMSE MIMO receiver, unbiased linear MMSE MIMO equalizer, or unbiased linear MMSE MIMO receiver) that reduces the EVM relative to a zero-forcing MIMO equalizer. This disclosure also shows that the resulting EVM can be achieved by the UE, independent of the propagation channel between the UE and the gNB (i.e., the 5G base station).

[0040] This document describes the relationship between the EVM at the transmitter antenna connector and the EVM at each layer of the receiver, assuming the same number of transmit and receive antennas. Based on this analysis, a solution for defining and specifying the EVM at the UE for multi-layer transmission is described. While the examples and descriptions below use a UE transmitter when describing the transmission device, in other examples, the transmission device may be a gNB or other base station; therefore, the transmitter EVM determined according to the description below may be the UE transmitter EVM, the gNB transmitter EVM, or the transmitter EVM of another transmitter.

[0041] In various embodiments, the transmission device generates multilayer transmission signals for MIMO and transmits the generated multilayer transmission signals (to the evaluation device) via a propagation channel using a transmitter. The evaluation device uses a linear unbiased MMSE MIMO equalizer to measure the transmitted multilayer transmission signals and calculates the EVM of the transmitter, where the EVM of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer.

[0042] Figure 1 A wireless communication system 100 for calculating the EVM of a transmitter is depicted according to embodiments of the present disclosure. In one embodiment, the wireless communication system 100 includes at least one remote unit 105, a radio access network (“RAN”) 120, and a mobile core network 140. The RAN 120 and the mobile core network 140 form a mobile communication network. The RAN 120 may consist of a base unit 121, wherein the remote unit 105 communicates with the base unit 121 using a wireless communication link 123. Although the specific number of remote units 105, base units 121, wireless communication links 123, RAN 120, and mobile core network 140 is... Figure 1 As depicted herein, those skilled in the art will recognize that any number of remote units 105, basic units 121, wireless communication links 123, RAN 120, and mobile core network 140 may be included in the wireless communication system 100.

[0043] In one implementation, RAN 120 conforms to the 5G system specified in the 3rd Generation Partnership Project (“3GPP”) specifications. For example, RAN 120 may be a next-generation radio access network (“NG-RAN”) implementing New Radio (“NR”) radio access technology (“RAT”) and / or Long Term Evolution (“LTE”) RAT. In another example, RAN 120 may include non-3GPP RATs (e.g., Or an IEEE 802.11 series compliant WLAN. In another embodiment, RAN 120 conforms to the LTE system specified in the 3GPP specification. However, more generally, the wireless communication system 100 may implement another open or proprietary communication network, such as Global Microwave Access Interoperability (“WiMAX”) or other networks such as the IEEE 802.16 series standards. This disclosure is not intended to be limited to any particular wireless communication system architecture or protocol implementation.

[0044] In one embodiment, remote unit 105 may include computing devices such as desktop computers, laptop computers, personal digital assistants (“PDAs”), tablet computers, smartphones, smart TVs (e.g., internet-connected TVs), smart devices (e.g., internet-connected devices), set-top boxes, game consoles, security systems (including security cameras), in-vehicle computers, network devices (e.g., routers, switches, modems), etc. In some embodiments, remote unit 105 includes wearable devices such as smartwatches, fitness trackers, optical head-mounted displays, etc. Moreover, remote unit 105 may be referred to as UE, subscriber unit, mobile device, mobile station, user, terminal, mobile terminal, fixed terminal, subscriber station, user terminal, wireless transmit / receive unit (“WTRU”), device, or other terms used in the art. In various embodiments, remote unit 105 includes a subscriber identity and / or identification module (“SIM”) and a mobile device (“ME”) that provides mobile terminal functions (e.g., radio transmission, handover, voice encoding and decoding, error detection and correction, signaling, and access to the SIM). In some embodiments, the remote unit 105 may include a terminal device (“TE”) and / or be embedded in a device or apparatus (e.g., the computing device described above).

[0045] Remote unit 105 can communicate directly with one or more basic units 121 in RAN 120 via uplink (“UL”) and downlink (“DL”) communication signals. Additionally, the UL and DL communication signals can be carried via wireless communication link 123. Here, RAN 120 is an intermediate network providing remote unit 105 with access to the mobile core network 140.

[0046] In some embodiments, remote unit 105 communicates with application server 151 via a network connection to mobile core network 140. For example, application 107 in remote unit 105 (e.g., a web browser, media client, telephone, and / or Internet Protocol Voice (“VoIP”) application) can trigger remote unit 105 to establish a Protocol Data Unit (“PDU”) session (or other data connection) with mobile core network 140 via RAN 120. Mobile core network 140 then uses the PDU session to relay services between remote unit 105 and application server 151 in packet data network 150. The PDU session represents a logical connection between remote unit 105 and user plane function (“UPF”) 141.

[0047] To establish a PDU session (or PDN connection), remote unit 105 must register with mobile core network 140 (also referred to as "attached to mobile core network" in the context of fourth-generation ("4G") systems). Note that remote unit 105 may establish one or more PDU sessions (or other data connections) with mobile core network 140. In this way, remote unit 105 may have at least one PDU session for communicating with packet data network 150. Remote unit 105 may establish additional PDU sessions for communicating with other data networks and / or other communication peers.

[0048] In the context of a 5G system (“5GS”), the term “PDU session” refers to a data connection that provides end-to-end (“E2E”) user plane (“UP”) connectivity between the remote unit 105 and the specific data network (“DN”) via UPF 141. A PDU session supports one or more Quality of Service (“QoS”) streams. In some embodiments, a one-to-one mapping may exist between QoS streams and QoS profiles, such that all packets belonging to a specific QoS stream have the same 5G QoS identifier (“5QI”).

[0049] In the context of 4G / LTE systems, such as Evolved Packet System (“EPS”), a Packet Data Network (“PDN”) connection (also known as an EPS session) provides end-to-end connectivity between the remote unit and the PDN. The PDN connectivity procedure establishes the EPS bearer, i.e., a tunnel between the remote unit 105 and the packet gateway (“PGW”, not shown) in the mobile core network 140. In some embodiments, a one-to-one mapping exists between the EPS bearer and the QoS profile, such that all packets belonging to a specific EPS bearer have the same QoS Class Identifier (“QCI”).

[0050] Basic unit 121 may be distributed across a geographical area. In some embodiments, basic unit 121 may also be referred to as an access terminal, access point, base station, base station, node B (“NB”), evolved Node B (abbreviated as eNodeB or “eNB”, also known as Evolved Universal Terrestrial Radio Access Network (“E-UTRAN”) node B), 5G / NR node B (“gNB”), home node B, relay node, RAN node, or any other term used in the art. Basic unit 121 is typically part of an RAN—such as RAN 120—which may include one or more controllers communicatively coupled to one or more corresponding basic units 121. These and other elements of the radio access network are not illustrated but are generally well known to those skilled in the art. Basic unit 121 is connected to mobile core network 140 via RAN 120.

[0051] Basic unit 121 can serve multiple remote units 105 within its service area, such as a cell or cell sector, via wireless communication link 123. Basic unit 121 can communicate directly with one or more remote units 105 via communication signals. Typically, basic unit 121 transmits DL communication signals to serve remote units 105 in the time, frequency, and / or spatial domains. Furthermore, DL communication signals can be carried via wireless communication link 123. Wireless communication link 123 can be any suitable carrier in the licensed or unlicensed radio spectrum. Wireless communication link 123 facilitates communication between one or more remote units 105 and / or one or more basic units 121. Note that during NR operation on unlicensed spectrum (referred to as "NR-U"), basic unit 121 and remote units 105 communicate via unlicensed (i.e., shared) radio spectrum.

[0052] In one embodiment, the mobile core network 140 is a 5GC or Evolved Packet Core (“EPC”), which can be coupled to a packet data network 150, such as the Internet and other data networks like private data networks. The remote unit 105 may have a subscription or other account with the mobile core network 140. In various embodiments, each mobile core network 140 belongs to a single mobile network operator (“MNO”). This disclosure is not intended to be limited to any particular wireless communication system architecture or protocol implementation.

[0053] Mobile core network 140 includes several network functions (“NFs”). As depicted, mobile core network 140 includes at least one UPF 141. Mobile core network 140 also includes multiple control plane (“CP”) functions, including but not limited to Access and Mobility Management Functions (“AMF”) 143, Session Management Functions (“SMF”) 145, Policy Control Functions (“PCF”) 147, Unified Data Management Functions (“UDM”), and User Data Repository (“UDR”) serving RAN 120. Although the specific number and types of network functions are not detailed in the text... Figure 1 As described herein, those skilled in the art will recognize that any number and type of network functions can be included in the mobile core network 140.

[0054] One or more UPF 141s are responsible for packet routing and forwarding, packet inspection, QoS handling, and external PDU sessions for interconnecting data networks (DNs) in the 5G architecture. AMF 143 is responsible for NAS termination signaling, NAS encryption and integrity protection, registration management, connection management, mobility management, access authentication and authorization, and security context management. SMF 145 is responsible for session management (i.e., session establishment, modification, and release), remote unit (i.e., UE) IP address allocation and management, DL data notification, and service redirection configuration for appropriate service routing in UPF 141.

[0055] PCF 147 is responsible for the unified policy framework, providing policy rules for CP functions and accessing subscription information for policy decisions in the UDR. UDM is responsible for generating authentication and key agreement (“AKA”) credentials, handling user identifiers, granting access, and managing subscriptions. The UDR is a repository of subscriber information and can be used to serve multiple network functions. For example, the UDR can store subscription data, policy-related data, subscriber-related data that is permitted to be exposed to third-party applications, etc. In some embodiments, the UDM and UDR are located in the same location and are depicted as a combined entity “UDM / UDR” 149.

[0056] In various embodiments, the mobile core network 140 may also include a network repository function (“NRF”) (which provides network function (“NF”) service registration and discovery, enabling NFs to identify the appropriate services among themselves and communicate with each other via an application programming interface (“API”), a network exposure function (“NEF”) (responsible for making network data and resources easily accessible to customers and network partners), an authentication server function (“AUSF”), or other NFs defined for the 5GC. When present, the AUSF may act as an authentication server and / or authentication proxy, thereby allowing the AMF 143 to authenticate the remote unit 105. In some embodiments, the mobile core network 140 may include an authentication, authorization, and accounting (“AAA”) server.

[0057] In various embodiments, the mobile core network 140 supports different types of mobile data connections and different types of network slices, where each mobile data connection utilizes a specific network slice. Here, a "network slice" refers to a portion of the mobile core network 140 optimized for a specific service type or communication service. For example, one or more network slices may be optimized for enhanced mobile broadband ("eMBB") service. As another example, one or more network slices may be optimized for ultra-reliable low-latency communication ("URLLC") service. In other examples, network slices may be optimized for machine-type communication ("MTC") service, massive MTC ("mMTC") service, and Internet of Things ("IoT") service. In still other examples, network slices may be deployed for application-specific services, vertical services, specific use cases, etc.

[0058] Network slice instances can be identified by a single network slice selection aid information (“S-NSSAI”), while the set of network slices authorized for use by remote unit 105 is identified by network slice selection aid information (“NSSAI”). Here, “NSSAI” refers to a vector value that includes one or more S-NSSAI values. In some embodiments, various network slices may include individual instances of network functions, such as SMF 145 and UPF 141. In some embodiments, different network slices may share some common network functions, such as AMF 143. For illustration purposes, different network slices are not shown in the diagram. Figure 1 They are shown in the figure, but their support is assumed.

[0059] Although Figure 1 The components of the 5G RAN and 5G core network are described, but the described embodiment of the EVM for the computational transmitter is applicable to other types of communication networks and RATs, including IEEE 802.11 variants, Global System for Mobile Communications (“GSM”, i.e., 2G digital cellular networks), General Packet Radio Service (“GPRS”), General Mobile Telecommunications System (“UMTS”), LTE variants, CDMA 2000, Bluetooth, ZigBee, Sigfox, and others.

[0060] Furthermore, in the LTE variant of the mobile core network 140 where EPC is used, the described network functions can be replaced by appropriate EPC entities, such as the Mobility Management Entity (“MME”), Serving Gateway (“SGW”), PGW, Home Subscriber Server (“HSS”), etc. For example, AMF 143 can be mapped to the MME, SMF 145 can be mapped to the control plane portion of the PGW and / or the MME, UPF 141 can be mapped to the SGW and the user plane portion of the PGW, UDM / UDR 149 can be mapped to the HSS, etc.

[0061] In the following description, the term "gNB" is used for base station / basic unit, but can be replaced by any other radio access node, such as RAN node, ng-eNB, eNB, base station ("BS"), access point ("AP"), etc. Additionally, the term "UE" is used for mobile station / remote unit, but can be replaced by any other remote device, such as remote unit, MS, ME, etc. Furthermore, the operation is primarily described in the context of 5G NR. However, the solutions / methods described below are equally applicable to other mobile communication systems with EVMs that compute transmitters (e.g., UE transmitters or gNB transmitters).

[0062] EVM is a measure of modulation accuracy, or the degree to which the power amplifier in remote unit 105 is transmitting information, represented by changes in the phase and amplitude of the RF signal. Therefore, remote unit 105 can send transmission signal 113 (e.g., multilayer transmission) to test equipment 111. Upon receiving transmission signal 113, test equipment 111 calculates the transmitter EVM for multilayer transmission. Note that in other embodiments, remote unit 105 can transmit to base unit 121, where base unit 121 calculates the transmitter EVM for multilayer transmission.

[0063] As mentioned above, linear zero-forcing MIMO equalizers and linear MMSE MIMO equalizers are considered for defining the EVM for multilayer transmission. This disclosure evaluates these equalizers used to define the EVM, and additionally, evaluates an unbiased linear MMSE MIMO receiver (also referred to herein as an unbiased linear MMSE MIMO equalizer).

[0064] Several aspects were considered when evaluating these receivers, including whether the EVM defined using this method could be similarly implemented at the gNB receiver, regardless of the propagation channel between the UE and the gNB. If not, then using this method to define the transmitter EVM might have questionable value, as the transmitter EVM cannot be mapped to the corresponding noise floor at the receiver. However, this contribution shows that for each of the proposed receivers, the EVM is independent of the propagation channel between the UE and the gNB.

[0065] For these receivers, it is important that the mean of the data symbol estimates is unbiased, as the estimates should be unbiased so that the correct measurement used to calculate the error of the EVM is obtained. This contribution shows that the data symbol estimates for linear zero-forcing MIMO receivers and linear unbiased MMSE MIMO receivers are unbiased. However, it is also shown that the data symbol estimates for the MMSE estimator are biased, such that the mean of the estimates is not equal to the true value, and as a result, the error measurement used to calculate the EVM has a non-zero mean.

[0066] Finally, this disclosure provides expressions for the EVM of both linear zero-forcing MIMO receivers and linear unbiased MMSE MIMO receivers, and shows that, in general, the EVM will depend on both the precoding matrix and the layer. Furthermore, this is true even when the precoding matrix is ​​equal to the identity matrix.

[0067] Figure 2 This is a block diagram illustrating one embodiment of a communication arrangement 200 for calculating the EVM of a transmitter. Arrangement 200 relates to a transmitter 205 and an evaluator 220 for calculating the EVM of the transmitter 205. As depicted, the transmitter includes multiple antennas. In some embodiments, the multiple transmitter antennas (“Tx antennas”) are arranged as one or more antenna ports (i.e., Tx antenna ports), each antenna port including multiple antennas, and each antenna having an antenna connector. In some embodiments, transmitter 205 is an embodiment of remote unit 105, and evaluator 220 is an embodiment of test device 111 or base unit 121. However, in other embodiments, the transmitter may be an embodiment of base unit 121, wherein evaluator 220 is an embodiment of test device 111 or another base unit 121.

[0068] Transmitter 205 generates a multilayer transmission signal for MIMO and transmits the multilayer transmission signal 210 to evaluator 220 via propagation channel 215. As described below, evaluator 220 measures the multilayer transmission signal 210 using an unbiased linear MMSE MIMO equalizer 230 and calculates the EVM of transmitter 205. Note that receiver 225 of evaluator 220 may include multiple antennas. In some embodiments, multiple receiver antennas (“Rx antennas”) are arranged as one or more antenna ports (i.e., Rx antenna ports), each antenna port including multiple antennas, and each antenna having an antenna connector. Importantly, to improve EVM accuracy, the multilayer transmission signal 210 can be received by receiver 225 using the same number of antennas used by transmitter 205. For example, Rx antenna ports may include the same number of antennas as transmitter antenna ports.

[0069] Figure 3 An example of a UE implementation 300 for a transmitter 205 used for two-layer MIMO transmission is depicted. The transmitted signals are given below.

[0070] z = GWx + n,

[0071] Where W is a rank-2 precoder, and the data vector x consists of two data symbols, such that x T = [x1 x2]. Vector n T =[n1 n2] is the transmitter noise at the two antenna connectors, and its covariance is given by ∑=E(n H n) is given. Matrix G is given by the following.

[0072]

[0073] Where g 1,1 and g 2,2 Let g represent the complex gain of the first transmitter and the second transmitter, and g 1,2 and g 2,1 This indicates any signal leakage between the first signal path and the second signal path.

[0074] Figure 4 A high-level block diagram of a MIMO receiver and evaluator (“receiver / evaluator”) 400 for EVM measurements is depicted. The receiver / evaluator 400 can be implemented by a basic unit 121 such as a gNB or another RAN node, or by a test device 111. The receiver / evaluator 400 is coupled to two antennas (antenna #1 and antenna #2) using antenna connectors for each antenna. Here, the receiver / evaluator 400 receives two layers of MIMO signals, such as signals generated and transmitted by transmitter 205.

[0075] In the depicted embodiment, the receiver / evaluator 400 includes a separate RF correction block for each antenna, such as a first RF correction block 405 for the first antenna connector and a second RF correction block 410 for the second antenna connector. In other embodiments, the receiver / evaluator 400 may use a common RF correction block for both antenna connectors because the signals are combined in the channel before being received by the receiver / evaluator 400.

[0076] Fast Fourier Transform (“FFT”) block 415 receives the output of RF correction block 405, while FFT block 420 receives the output of RF correction block 410. Both FFT blocks send their outputs to channel estimation block 425. Linear unbiased MMSE MIMO receiver 430 receives inputs from FFT block 415, FFT block 420, and channel estimation block 425. Linear unbiased MMSE MIMO receiver 430 estimates the data symbols for the MIMO transmission, as described below.

[0077] For each transport layer, the linear unbiased MMSE MIMO receiver 430 estimates the inverse tone map to the transmit layer. In the depicted embodiment, a first inverse tone map 435 is associated with a first transport layer (“Layer 1”), and a second inverse tone map 440 is associated with a second transport layer (“Layer 2”).

[0078] In the depicted embodiment, the receiver / evaluator 400 includes a first EVM block 445 for calculating the EVM of each layer of the first transport layer, and a second EVM block 450 for calculating the EVM of each layer of the second transport layer.

[0079] To demodulate a two-layer MIMO transmission, the receiver (i.e., receiver / evaluator 400) must have at least two receive antennas. Since transmitter noise also propagates through the channel, the signal received by the receiver is given by the following...

[0080] y = H(GWx + n),

[0081] Where H is the channel matrix given below.

[0082]

[0083] And hij represents the complex gain from the j-th transmitting antenna to the i-th receiving antenna.

[0084] In the discussion above, three different receiver types were considered to define the UE delivery EVM for multi-layer MIMO. Linear zero-forcing MIMO receivers and linear unbiased zero-forcing MIMO receivers have been shown to be feasible for defining the EVM because they have the following two characteristics.

[0085] These estimators are unbiased, making Therefore, the error measurement used for EVM calculation has a zero mean value, and the mean square error reflects the expected link performance.

[0086] The resulting EVM definition does not depend on the propagation channel H, and therefore the EVM can be implemented by the receiver regardless of the channel between the transmitter (i.e., the UE) and the receiver (i.e., the gNB).

[0087] Conversely, the MMSE estimator produces a biased estimate of the data sign, making... Therefore, the error measurement used for EVM calculation uses a non-zero average value, and thus the mean square error cannot be directly mapped to link performance. Furthermore, signal power is overestimated because the average value of the MMSE estimator is typically less than the true average value. For this reason, a linear MMSE EMIMO receiver is not a feasible solution for defining the EVM of a multilayer MIMO transmission.

[0088] As the primary solution for defining the transmitter EVM for multi-layer MIMO, the linear zero-forcing MIMO equalizer can be used to define and measure the transmitter EVM for multi-layer MIMO transmission.

[0089] As a second solution for defining the transmitter EVM for multi-layer MIMO, the unbiased linear MMSE MIMO equalizer is used to define and measure the transport EVM for multi-layer MIMO transmission.

[0090] Regarding zero-forcing MIMO receivers, based on the model given above, the signal observed by the receiver (i.e., the gNB and / or test equipment) is given as follows.

[0091] y = H(GWx + n)

[0092] Note that if each layer reference symbol is transmitted, the receiver can directly measure the channel HGW. If each line reference symbol is used, the receiver measures the channel HG and estimates HGW by multiplying by the precoding matrix W. Let A ZF Defined as

[0093] A ZF =(HGW) -1

[0094] Then, the zero-forcing receiver is given by the following

[0095]

[0096] in

[0097] v ZF =W -1 G -1 n

[0098] A zero-forcing receiver is unbiased because

[0099]

[0100] The noise covariance is given by the following

[0101]

[0102] in

[0103] ∑′=G -1 ∑G -H

[0104] Furthermore, ∑′ depends on the transmitter front-end impairment G, but is independent of the channel H.

[0105] The vector EVM at the output of a linear zero-forcing MIMO receiver can be defined as

[0106]

[0107] If the precoder matrix is ​​the identity matrix I, then

[0108]

[0109] Based on the above, the following observations are made regarding the linear zero-forcing MIMO receiver. Here, it is observed that the linear zero-forcing MIMO estimator is unbiased, making...

[0110]

[0111]

[0112] Therefore, the error measurement used for EVM calculation has a zero mean value, so the mean square error reflects the expected link performance.

[0113] It was also observed that, since the definition of EVM does not depend on the propagation channel H, the EVM can be implemented by the receiver regardless of the channel between the transmitter (i.e., the UE) and the receiver (i.e., the gNB), as long as the channel H is invertible.

[0114] This characteristic is important because the channel used by the test equipment to evaluate the EVM, via direct connection to the antenna connector, is an identity matrix such that H = I. If the definition of the EVM depends on the propagation matrix H, then the definition and requirements may not offer any practical benefit when setting a lower bound on channel quality due to transmitter impairments.

[0115] Further observation shows that unless the covariance matrix W H ∑'W is proportional to the identity matrix; otherwise, the EVM would depend on both the precoding matrix and the layer. However, as mentioned above, for the same signal, the EVM determined by the linear zero-forcing MIMO equalizer is greater than the EVM determined by the linear MIMO MMSE equalizer.

[0116] Regarding linear MMSE MIMO equalizers, the linear MMSE MIMO receiver is given below:

[0117]

[0118] This can be shown

[0119] A MMSE =W H G H H H (HGWW H G H H H +H∑H H )-1 .

[0120] Expanding on the above results:

[0121]

[0122] in

[0123] v MMSE =W H G H H H (HGWW H G H H H +H∑H H ) -1 Hn.

[0124] To calculate A MMSE The receiver only needs to measure and estimate two quantities, and these are the covariance H∑H of the composite channel HGW and the transmitter noise received before equalization. H As discussed above, the receiver can combine the knowledge of the precoder W with the per-layer reference symbols or the daily antenna reference symbols to estimate the channel HGW. Using the same reference symbols, the noise Hn can be estimated as...

[0125]

[0126] And thus, H∑H H It can be estimated as

[0127]

[0128] Due to the quantities HGW and H∑H H Both can be estimated at the receiver, thus enabling the implementation of a linear MMSE MIMO receiver.

[0129] Given vector data symbol x The expected value is given by the following

[0130]

[0131] in

[0132] Q = W H G H (GWW H G H +∑) -1 GW,

[0133] Furthermore, it is assumed that the channel matrix H is invertible. Therefore, it is evident that unless Q is the identity matrix, the estimation... It is biased. If we consider the expected value of the data symbols and treat the symbols on other layers as noise, then the above vector becomes...

[0134]

[0135] And only in Q 1，1 =Q 2，2 When = 1, the estimator is unbiased. However, since the MMSE estimator is always biased, the measurement errors of the two layers will have values ​​respectively derived from 1-Q. 1，1 and 1-Q 2，2 The given non-zero average value, and the error measurement used to calculate EVM, will be incorrect.

[0136] Assuming the channel H is invertible, the error v MMSE It can be simplified to

[0137] v MMSE =W H G H (GWW H G H +∑) -1 n.

[0138] Let P denote the error covariance given below:

[0139]

[0140] And note that the mean square error of the first and second layers is given below.

[0141]

[0142]

[0143] It is worth noting that because matrices Q and P do not depend on H, the mean square error does not depend on the channel.

[0144] Finally, if the precoding matrix is ​​an identity matrix, then matrices Q and P can be further simplified to...

[0145] Q = G H (GG H +∑) -1 G

[0146] as well as

[0147] P=G H (GG H +∑) -1 ∑(GG H +∑) -1 G.

[0148] Based on the above, it was observed that the estimator is biased relative to the linear MMSE MIMO receiver, making...

[0149]

[0150]

[0151] Therefore, the error measurements used for EVM calculation have a non-zero mean, and thus the mean squared error cannot be directly mapped to link performance. Furthermore, signal power is overestimated because the mean of the MMSE estimator is typically smaller than the true mean.

[0152] Based on this observation, linear MMSE MIMO receivers are not suitable for measuring EVM transmitters in multilayer MIMO transmissions.

[0153] Regarding unbiased linear MMSE MIMO equalizers, unbiased linear MMSE MIMO receivers can be obtained by scaling the MMSE receiver. Specifically, let...

[0154]

[0155] in

[0156]

[0157] And, as in the previous chapters,

[0158] Q = W H G H (GG H +Σ) -1 GW.

[0159] Regarding this receiver,

[0160]

[0161] as well as

[0162]

[0163] The data sign estimation is unbiased because

[0164]

[0165]

[0166] And the variance of the noise is given by the following

[0167]

[0168] As in the previous chapters,

[0169] P = W H G H (GG H +∑) -1 ∑(GG H +∑) -1 GW

[0170] Finally, the mean square error of the linear unbiased MMSE estimator is given by the following:

[0171]

[0172]

[0173] Therefore, the EVM of the first and second layers is given as follows.

[0174]

[0175]

[0176] Since matrices Q and P do not depend on the channel matrix H, the EVM definition does not depend on the propagation channel H, and therefore the EVM definition can be implemented regardless of the channel between the UE and the receiver, as long as the channel H is invertible.

[0177] Based on the above, the following observations are made relative to a linear unbiased MMSE MIMO receiver.

[0178] It was observed that the estimator is unbiased, such that

[0179]

[0180]

[0181] Therefore, the error measurement used for EVM calculation has a zero mean value, and the mean square error reflects the expected link performance.

[0182] It was also observed that, since the definition of EVM does not depend on the propagation channel H, the EVM can be implemented by the receiver as long as the channel H is invertible, regardless of the channel between the transmitter (i.e., UE) and the receiver (i.e., gNB).

[0183] Further observation reveals that unless the covariance matrix is ​​proportional to the identity matrix, the mean square error of the linear unbiased MMSE estimator is...

[0184]

[0185] Furthermore, the EVM will depend on the precoding matrix and the layers.

[0186] Based on the above observations, both linear zero-forcing MIMO receivers and linear unbiased MMSE MIMO receivers are feasible candidates for defining the transmitter EVM for multilayer MIMO transmission. Linear MMSE MIMO receivers should not be considered because the resulting data symbol estimates are always biased, and therefore, the error measurement used for EVM calculations will have a non-zero average and be incorrect.

[0187] Figure 5 User equipment device 500, which can be used to compute the EVM of a transmitter according to embodiments of this disclosure, is depicted. In various embodiments, user equipment device 500 is used to implement one or more of the solutions described above. User equipment device 500 may be an embodiment of the remote unit 105 and / or transmitter 205 described above. Furthermore, user equipment device 500 may include processor 505, memory 510, input device 515, output device 520, and transceiver 525.

[0188] In some embodiments, input device 515 and output device 520 are combined into a single device, such as a touchscreen. In some embodiments, user equipment device 500 may not include any input device 515 and / or output device 520. In various embodiments, user equipment device 500 may include one or more of the following: processor 505, memory 510, and transceiver 525, and may not include input device 515 and / or output device 520.

[0189] As depicted, transceiver 525 includes at least one transmitter 530 and at least one receiver 535. In some embodiments, transceiver 525 communicates with one or more cells (or radio coverage areas) supported by one or more basic units 121. In various embodiments, transceiver 525 may operate on unlicensed spectrum. Furthermore, transceiver 525 may include multiple UE panels supporting one or more beams. Additionally, transceiver 525 may support at least one network interface 540 and / or application interface 545. One or more application interfaces 545 may support one or more APIs. One or more network interfaces 540 may support 3GPP reference points such as Uu, N1, PC5, etc. Other network interfaces 540 may be supported, as understood by those skilled in the art.

[0190] In one embodiment, processor 505 may include any known controller capable of executing computer-readable instructions and / or performing logical operations. For example, processor 505 may be a microcontroller, microprocessor, central processing unit (“CPU”), graphics processing unit (“GPU”), auxiliary processing unit, field-programmable gate array (“FPGA”), or similar programmable controller. In some embodiments, processor 505 executes instructions stored in memory 510 to perform the methods and routines described herein. Processor 505 is communicatively coupled to memory 510, input device 515, output device 520, and transceiver 525.

[0191] In various embodiments, processor 505 controls user equipment device 500 to implement the UE behaviors described above. In some embodiments, processor 505 may include an application processor (also referred to as a "main processor") that manages application domain and operating system ("OS") functions, and a baseband processor (also referred to as a "baseband radio processor") that manages radio functions.

[0192] In various embodiments, processor 505 generates a multilayer MIMO transmission signal and controls transceiver 525 to transmit the generated multilayer transmission signal to an evaluation device via a propagation channel. As described herein, the evaluation device uses an unbiased linear MMSE MIMO equalizer to measure the transmitted multilayer transmission signal and calculates the EVM of the transmitter, wherein the EVM of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer.

[0193] In some embodiments, multi-layer MIMO transmission includes two layers of MIMO transmission. In this embodiment, the mean square error of each transmission layer is a function of the covariance of the transmitter noise. In some embodiments, the generated multi-layer MIMO transmission signal includes a reference symbol for each layer or a daily antenna reference symbol.

[0194] In one embodiment, memory 510 is a computer-readable storage medium. In some embodiments, memory 510 includes volatile computer storage media. For example, memory 510 may include RAM, including dynamic RAM (“DRAM”), synchronous dynamic RAM (“SDRAM”), and / or static RAM (“SRAM”). In some embodiments, memory 510 includes non-volatile computer storage media. For example, memory 510 may include a hard disk drive, flash memory, or any other suitable non-volatile computer storage device. In some embodiments, memory 510 includes both volatile and non-volatile computer storage media.

[0195] In some embodiments, memory 510 stores data related to the EVM of the computational transmitter. For example, memory 510 may store the various parameters described above, panel / beam configurations, resource assignments, strategies, etc. In some embodiments, memory 510 also stores program code and related data, such as an operating system or other controller algorithms operating on device 500.

[0196] In one embodiment, input device 515 may include any known computer input device, including a touchpad, button, keyboard, stylus, microphone, etc. In some embodiments, input device 515 may be integrated with output device 520, such as as a touchscreen or similar touch-sensitive display. In some embodiments, input device 515 includes a touchscreen, allowing text to be entered using a virtual keyboard displayed on the touchscreen and / or by handwriting on the touchscreen. In some embodiments, input device 515 includes two or more different devices, such as a keyboard and a touchpad.

[0197] In one embodiment, output device 520 is designed to output visual, audible, and / or tactile signals. In some embodiments, output device 520 includes an electronically controllable display or display device capable of outputting visual data to a user. For example, output device 520 may include, but is not limited to, a liquid crystal display (“LCD”), a light-emitting diode (“LED”) display, an organic LED (“OLED”) display, a projector, or a similar display device capable of outputting images, text, etc., to a user. As another non-limiting example, output device 520 may include a wearable display, such as a smartwatch, smart glasses, a head-up display, etc., that is separate from but communicatively coupled to the rest of user equipment device 500. Further, output device 520 may be a component of a smartphone, personal digital assistant, television, desktop computer, laptop computer, personal computer, vehicle dashboard, etc.

[0198] In some embodiments, output device 520 includes one or more speakers for generating sound. For example, output device 520 may generate an audible alarm or notification (e.g., a beeping sound or a chime). In some embodiments, output device 520 includes one or more haptic devices for generating vibration, motion, or other haptic feedback. In some embodiments, all or part of output device 520 may be integrated with input device 515. For example, input device 515 and output device 520 may form a touchscreen or similar touch-sensitive display. In other embodiments, output device 520 may be located near input device 515.

[0199] Transceiver 525 communicates with one or more network functions of a mobile communication network via one or more access networks. Transceiver 525 operates under the control of processor 505 to transmit and receive messages, data, and other signals. For example, processor 505 may selectively activate transceiver 525 (or a portion thereof) at specific times to send and receive messages.

[0200] Transceiver 525 includes at least one transmitter 530 and at least one receiver 535. One or more transmitters 530 can be used to provide UL communication signals to base unit 121, such as the UL transmissions described herein. Similarly, one or more receivers 535 can be used to receive DL communication signals from base unit 121, as described herein. Although only one transmitter 530 and one receiver 535 are illustrated, user equipment device 500 can have any suitable number of transmitters 530 and receivers 535. Further, the transmitter(s) 530 and receiver(s) 535 can be of any suitable type. In one embodiment, transceiver 525 includes a first transmitter / receiver pair for communicating with a mobile communication network via licensed radio spectrum and a second transmitter / receiver pair for communicating with a mobile communication network via unlicensed radio spectrum.

[0201] In some embodiments, a first transmitter / receiver pair for communicating with a mobile communication network via licensed radio spectrum and a second transmitter / receiver pair for communicating with a mobile communication network via unlicensed radio spectrum may be combined into a single transceiver unit, such as a single chip performing functions for use with both licensed and unlicensed radio spectrum. In some embodiments, the first transmitter / receiver pair and the second transmitter / receiver pair may share one or more hardware components. For example, certain transceivers 525, transmitters 530, and receivers 535 may be implemented as physically separate components accessing shared hardware resources and / or software resources—such as, for example, network interface 540.

[0202] In various embodiments, one or more transmitters 530 and / or one or more receivers 535 may be implemented and / or integrated into a single hardware component, such as a multi-transceiver chip, a system-on-a-chip, an application-specific integrated circuit (“ASIC”), or other types of hardware components. In some embodiments, one or more transmitters 530 and / or one or more receivers 535 may be implemented and / or integrated into a multi-chip module. In some embodiments, other components, such as a network interface 540 or other hardware components / circuits, may be integrated with any number of transmitters 530 and / or receivers 535 into a single chip. In such embodiments, transmitters 530 and receivers 535 may be logically configured as transceivers 525 using one or more common control signals, or configured as modular transmitters 530 and receivers 535 implemented in the same hardware chip or multi-chip module.

[0203] Figure 6 A network device 600, which can be used to compute the EVM of a transmitter according to embodiments of the present disclosure, is depicted. In one embodiment, the network device 600 may be an implementation of an evaluation device, such as a test device 111, a basic unit 121, an evaluator 220, and / or a receiver / evaluator 400, as described above. Furthermore, the basic network device 600 may include a processor 605, a memory 610, an input device 615, an output device 620, and a transceiver 625.

[0204] In some embodiments, input device 615 and output device 620 are combined into a single device, such as a touchscreen. In some embodiments, network device 600 may not include any input device 615 and / or output device 620. In various embodiments, network device 600 may include one or more of the following: processor 605, memory 610, and transceiver 625, and may not include input device 615 and / or output device 620.

[0205] As depicted, transceiver 625 includes at least one transmitter 630 and at least one receiver 635. Here, transceiver 625 communicates with one or more remote units 105. Additionally, transceiver 625 may support at least one network interface 640 and / or application interface 645. The application interface(s) 645 may support one or more APIs. The network interface(s) 640 may support 3GPP reference points such as Uu, N1, N2, and N3. Other network interfaces 640 may be supported, as understood by those skilled in the art.

[0206] In one embodiment, processor 605 may include any known controller capable of executing computer-readable instructions and / or performing logical operations. For example, processor 605 may be a microcontroller, microprocessor, CPU, GPU, auxiliary processing unit, FPGA, or similar programmable controller. In some embodiments, processor 605 executes instructions stored in memory 610 to perform the methods and routines described herein. Processor 605 is communicatively coupled to memory 610, input device 615, output device 620, and transceiver 625.

[0207] In various embodiments, network device 600 is a RAN node (e.g., gNB) communicating with one or more UEs, as described herein. In this embodiment, processor 605 controls network device 600 to perform the RAN behaviors described above. When operating as a RAN node, processor 605 may include an application processor (also referred to as a "main processor") that manages application domain and operating system ("OS") functions and a baseband processor (also referred to as a "baseband radio processor") that manages radio functions.

[0208] In various embodiments, processor 605 generates a multilayer MIMO transmission signal and controls transmitter 630 to transmit the generated multilayer transmission signal to an evaluation device via a propagation channel. As described herein, the evaluation device uses an unbiased linear MMSE MIMO equalizer to measure the transmitted multilayer transmission signal and calculates the EVM of transmitter 630, wherein the EVM of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer.

[0209] In some embodiments, multi-layer MIMO transmission includes two layers of MIMO transmission. In this embodiment, the mean square error of each transmission layer is a function of the covariance of the transmitter noise. In some embodiments, the generated multi-layer MIMO transmission signal includes a reference symbol for each layer or a daily antenna reference symbol.

[0210] In various embodiments, receiver 635 receives multilayer MIMO signals from a transmitter (e.g., a UE transmitter or a gNB transmitter) via a propagation channel. Processor 605 uses an unbiased linear MMSE MIMO equalizer to measure the received multilayer MIMO signals and calculates the transmitter's EVM, where the EVM for each transport layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer.

[0211] In some embodiments, when the channel matrix H of the propagation channel is invertible, the EVM definition used to calculate the transmitter's EVM is independent of the propagation channel. In some embodiments, the EVM definition used to calculate the transmitter's EVM is a function of the precoding matrix used to generate the multilayer transmission signal. Note that if matrix H has full rank, or equivalently, if its determinant is not zero, or equivalently, if H... -1 It can be defined as making H*H -1 If I (the identity matrix) is equal to the matrix H, then the matrix H is invertible.

[0212] In some embodiments, multilayer transmission includes two layers of MIMO transmission, wherein the mean square error of each transmission layer is a function of the covariance of the transmitter noise. In some embodiments, the EVM of the first transmission layer is calculated as follows:

[0213]

[0214] And the EVM of the second transport layer is calculated as

[0215]

[0216] Where the value Q 1,1 Q 1,2 Q 2,1 Q 2,2 Form a matrix Q representing a linear MMSE MIMO equalizer, where the values ​​P 1,1 P 2,2 It is the value of matrix P, which represents the noise covariance matrix of a linear MMSE MIMO equalizer.

[0217] In some embodiments, the average error of the unbiased linear MMSE MIMO is zero. In some embodiments, the generated multilayer transmission signal includes a reference symbol for each layer or a daily linear reference symbol.

[0218] In some embodiments, the transmitter is a UE transmitter for transmitting uplink MIMO signals to a base station. In this embodiment, the processor 605 may further use a calculated EVM to define the base station's noise floor due to transmitter noise.

[0219] In one embodiment, memory 610 is a computer-readable storage medium. In some embodiments, memory 610 includes volatile computer storage media. For example, memory 610 may include RAM, including dynamic RAM (“DRAM”), synchronous dynamic RAM (“SDRAM”), and / or static RAM (“SRAM”). In some embodiments, memory 610 includes non-volatile computer storage media. For example, memory 610 may include a hard disk drive, flash memory, or any other suitable non-volatile computer storage device. In some embodiments, memory 610 includes both volatile and non-volatile computer storage media.

[0220] In some embodiments, memory 610 stores data related to the EVM of the computation transmitter. For example, memory 610 may store the aforementioned parameters, configurations, resource assignments, policies, etc. In some embodiments, memory 610 also stores program code and related data, such as the operating system or other controller algorithms operating on device 600.

[0221] In one embodiment, input device 615 may include any known computer input device, including a touchpad, button, keyboard, stylus, microphone, etc. In some embodiments, input device 615 may be integrated with output device 620, such as as a touchscreen or similar touch-sensitive display. In some embodiments, input device 615 includes a touchscreen, allowing text to be entered using a virtual keyboard displayed on the touchscreen and / or by handwriting on the touchscreen. In some embodiments, input device 615 includes two or more different devices, such as a keyboard and a touchpad.

[0222] In one embodiment, output device 620 is designed to output visual, audible, and / or tactile information. In some embodiments, output device 620 includes an electronically controllable display or display device capable of outputting visual data to a user. For example, output device 620 may include, but is not limited to, an LCD display, an LED display, an OLED display, a projector, or a similar display device capable of outputting images, text, etc., to a user. As another non-limiting example, output device 620 may include a wearable display, such as a smartwatch, smart glasses, a head-up display, etc., that is separate from but communicatively coupled to the rest of network device 600. Further, output device 620 may be a component of a smartphone, personal digital assistant, television, desktop computer, laptop computer, personal computer, vehicle dashboard, etc.

[0223] In some embodiments, output device 620 includes one or more speakers for generating sound. For example, output device 620 may generate an audible alarm or notification (e.g., a beeping sound or a chime). In some embodiments, output device 620 includes one or more haptic devices for generating vibration, motion, or other haptic feedback. In some embodiments, all or part of output device 620 may be integrated with input device 615. For example, input device 615 and output device 620 may form a touchscreen or similar touch-sensitive display. In other embodiments, output device 620 may be located near input device 615.

[0224] Transceiver 625 includes at least a transmitter 630 and at least one receiver 635. One or more transmitters 630 can be used to communicate with a UE, as described herein. Similarly, one or more receivers 635 can be used to communicate with network functions in a PLMN and / or RAN, as described herein. Although only one transmitter 630 and one receiver 635 are illustrated, network device 600 can have any suitable number of transmitters 630 and receivers 635. Furthermore, the transmitter(s) 630 and receiver(s) 635 can be of any suitable type.

[0225] Figure 7 One embodiment of a method 700 for calculating the EVM of a transmitter according to embodiments of the present disclosure is depicted. In various embodiments, method 700 is performed by transmission devices (such as remote unit 105, transmitter 205, and / or user equipment device 500) and evaluation devices (such as test device 111, base unit 121, receiver / evaluator 400, and / or network device 600), as described above. In some embodiments, method 700 is performed by a processor, such as a microcontroller, microprocessor, CPU, GPU, auxiliary processing unit, FPGA, etc.

[0226] Method 700 begins and (i.e., by the transmitting device) generates 705 a multilayer transmission signal for MIMO. Method 700 includes transmitting the multilayer transmission signal generated at 710 via a propagation channel using a transmitter. Method 700 includes measuring the transmitted multilayer transmission signal at 715 using an unbiased linear MMSE MIMO equalizer (i.e., by an evaluation device). Method 700 includes calculating the error vector magnitude (“EVM”) of the transmitter at 720. Here, the EVM for each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the unbiased linear MMSE MIMO equalizer. Method 700 ends.

[0227] Figure 8One embodiment of a method 800 for calculating the EVM of a transmitter according to embodiments of the present disclosure is depicted. In various embodiments, method 800 is performed by evaluation equipment, such as test equipment 111, basic unit 121, receiver / evaluator 400 and / or network device 600, as described above. In some embodiments, method 800 is performed by a processor, such as a microcontroller, microprocessor, CPU, GPU, auxiliary processing unit, FPGA, etc.

[0228] Method 800 begins and receives a multilayer MIMO signal 805 from the transmitter via a propagation channel. Method 800 includes measuring the received multilayer MIMO signal 810 using an unbiased linear MMSE MIMO equalizer. Method 800 includes calculating the EVM of the transmitter 815, where the EVM of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer. Method 800 ends.

[0229] This document discloses a first system for calculating the EVM of a transmitter according to embodiments of the present disclosure. The first system may be implemented by transmission equipment (such as remote unit 105, transmitter 205, and / or user equipment device 500) and evaluation equipment (such as test equipment 111, basic unit 121, receiver / evaluator 400, and / or network device 600), as described above. The transmission equipment generates multilayer transmission signals for MIMO and transmits the generated multilayer transmission signals (to the evaluation equipment) via a propagation channel using the transmitter. The evaluation equipment uses an unbiased linear MMSE MIMO equalizer to measure the transmitted multilayer transmission signals and calculates the EVM of the transmitter, wherein the EVM of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer.

[0230] In some embodiments, when the channel matrix H of the propagation channel is invertible, the EVM definition used to calculate the transmitter's EVM does not depend on the propagation channel. In some embodiments, the EVM definition used to calculate the transmitter's EVM is a function used to generate the precoding matrix of the multilayer transmission signal.

[0231] In some embodiments, multilayer transmission includes two layers of MIMO transmission, wherein the mean square error of each transmission layer is a function of the covariance of the transmitter noise. In some embodiments, the EVM of the first transmission layer is calculated as:

[0232]

[0233] And the EVM of the second transport layer is calculated as follows:

[0234]

[0235] Where the value Q 1,1 Q 1,2 Q 2,1 Q 2,2 Form a matrix Q representing a linear MMSE MIMO equalizer, where the values ​​P 1,1 P 2,2 It is the value of matrix P, which represents the noise covariance matrix of a linear MMSE MIMO equalizer.

[0236] In some embodiments, the average error of the unbiased linear MMSE MIMO is zero. In some embodiments, the generated multilayer transmission signal includes a reference symbol for each layer or a daily linear reference symbol.

[0237] In some embodiments, the transmitter includes a user equipment transmitter for transmitting uplink MIMO signals to the base station. In some embodiments, the evaluation device may further use a calculated EVM to define the base station's noise floor due to transmitter noise.

[0238] This document discloses a first method for calculating the EVM of a transmitter according to embodiments of the present disclosure. The first method can be performed by a system including a UE transmitter (e.g., remote unit 105, transmitter 205 and / or user equipment device 500) and a receiver (e.g., basic unit 121, test device 109, receiver / evaluator 400 and / or network device 600), as described above.

[0239] The first method includes generating a multilayer transmission signal for MIMO and transmitting the generated multilayer transmission signal via a propagation channel using a transmitter. The first method includes measuring the transmitted multilayer transmission signal using an unbiased linear MMSE MIMO equalizer and calculating the error vector magnitude (“EVM”) of the transmitter. Here, the EVM for each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the unbiased linear MMSE MIMO equalizer.

[0240] In some embodiments, when the channel matrix H of the propagation channel is invertible, the EVM definition used to calculate the transmitter's EVM is independent of the propagation channel. In some embodiments, the EVM definition used to calculate the transmitter's EVM is a function used to generate the precoding matrix of the multilayer transmission signal.

[0241] In some embodiments, multilayer transmission includes two layers of MIMO transmission, wherein the mean square error of each transmission layer is a function of the covariance of the transmitter noise. In some embodiments, the EVM of the first transmission layer is calculated as follows:

[0242]

[0243] And the EVM of the second transport layer is calculated as

[0244]

[0245] Where the value Q 1,1 Q 1,2 Q 2,1 Q 2,2 Form a matrix Q representing a linear MMSE MIMO equalizer, where the values ​​P 1,1 P 2,2 It is the value of matrix P, which represents the noise covariance matrix of a linear MMSE MIMO equalizer.

[0246] In some embodiments, the average error of the unbiased linear MMSE MIMO equalizer is zero. In some embodiments, the generated multilayer transmission signal includes a reference symbol for each layer or a daily linear reference symbol.

[0247] In some embodiments, the transmitter includes a user equipment transmitter for transmitting uplink MIMO signals to a base station. In this embodiment, the first method may further include defining the base station's noise floor due to transmitter noise using a calculated EVM.

[0248] This document discloses a first apparatus for calculating the EVM of a transmitter according to embodiments of the present disclosure. The first apparatus may be implemented by a transmission device in a mobile communication network, such as the remote unit 105, transmitter 205, and / or user equipment device 500 described above. The first apparatus includes a transceiver and a processor that generates a multi-layer MIMO transmission signal and controls the transceiver to transmit the generated multi-layer transmission signal to an evaluation device via a propagation channel. Here, the evaluation device uses an unbiased linear MMSE MIMO equalizer to measure the transmitted multi-layer transmission signal and calculates the EVM of the transmitter, wherein the EVM of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer.

[0249] In some embodiments, multi-layer MIMO transmission includes two layers of MIMO transmission. In this embodiment, the mean square error of each transmission layer is a function of the covariance of the transmitter noise. In some embodiments, the generated multi-layer MIMO transmission signal includes a reference symbol for each layer or a daily antenna reference symbol.

[0250] This document discloses a second apparatus for calculating the EVM of a transmitter according to embodiments of the present disclosure. The second apparatus may be implemented by evaluation equipment, such as test equipment 111, basic unit 121, receiver / evaluator 400 and / or network device 600, as described above.

[0251] The second device includes a receiver that receives multilayer MIMO signals from a transmitter via a propagation channel and a processor that measures the received multilayer MIMO signals using an unbiased linear MMSE MIMO equalizer. The processor calculates the EVM of the transmitter, where the EVM of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer.

[0252] In some embodiments, when the channel matrix H of the propagation channel is invertible, the EVM definition used to calculate the transmitter's EVM does not depend on the propagation channel. In some embodiments, the EVM definition used to calculate the transmitter's EVM is a function used to generate the precoding matrix of the multilayer transmission signal.

[0253] In some embodiments, multilayer transmission includes two layers of MIMO transmission, wherein the mean square error of each transmission layer is a function of the covariance of the transmitter noise. In some embodiments, the EVM of the first transmission layer is calculated as follows:

[0254]

[0255] And the EVM of the second transport layer is calculated as

[0256]

[0257] Where the value Q 1,1 Q 1,2 Q 2,1 Q 2,2 Form a matrix Q representing a linear MMSE MIMO equalizer, where the values ​​P 1,1 P 2,2 It is the value of matrix P, which represents the noise covariance matrix of a linear MMSE MIMO equalizer.

[0258] In some embodiments, the average error of the unbiased linear MMSE MIMO is zero. In some embodiments, the generated multilayer transmission signal includes a reference symbol for each layer or a daily linear reference symbol.

[0259] In some embodiments, the transmitter is a user equipment transmitter for transmitting uplink MIMO signals to a base station. In this embodiment, the processor may further use a calculated EVM to define the base station's noise floor due to transmitter noise.

[0260] This document discloses a second method for calculating the EVM of a transmitter according to embodiments of the present disclosure. The second method can be performed by an evaluation device, such as test device 111, basic unit 121, receiver / evaluator 400, and / or network device 600, as described above. The second method includes receiving a multilayer MIMO signal from the transmitter via a propagation channel and measuring the received multilayer MIMO signal using an unbiased linear MMSE MIMO equalizer. The second method includes calculating the EVM of the transmitter, wherein the EVM of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the linear unbiased MMSE MIMO equalizer.

[0261] In some embodiments, when the channel matrix H of the propagation channel is invertible, the EVM definition used to calculate the transmitter's EVM does not depend on the propagation channel. In some embodiments, the EVM definition used to calculate the transmitter's EVM is a function used to generate the precoding matrix of the multilayer transmission signal.

[0262] In some embodiments, multilayer transmission includes two layers of MIMO transmission, wherein the mean square error of each transmission layer is a function of the covariance of the transmitter noise. In some embodiments, the EVM of the first transmission layer is calculated as follows:

[0263]

[0264] And the EVM of the second transport layer is calculated as

[0265]

[0266] Where the value Q 1,1 Q 1,2 Q 2,1 Q 2,2 Form a matrix Q representing a linear MMSE MIMO equalizer, where the values ​​P 1,1 P 2,2 It is the value of matrix P, which represents the noise covariance matrix of a linear MMSE MIMO equalizer.

[0267] In some embodiments, the average error of the unbiased linear MMSE MIMO is zero. In some embodiments, the generated multilayer transmission signal includes a reference symbol for each layer or a daily linear reference symbol.

[0268] In some embodiments, the transmitter is a user equipment transmitter for transmitting uplink MIMO signals to a base station. In this embodiment, the second method may include using a calculated EVM to define the base station's noise floor due to transmitter noise.

[0269] The embodiments may be practiced in other specific forms. The described embodiments are to be considered illustrative in all respects only, and not restrictive. Therefore, the scope of the invention is indicated by the appended claims, and not by the foregoing description. All modifications falling within the equivalent meaning and scope of the claims are to be included within their scope.

Claims

1. An evaluation apparatus, comprising: Memory; as well as Processor, the processor being configured to cause the device to: Generate multilayer transmission signals for multiple-input multiple-output MIMO; The generated multilayer transmission signal for MIMO is transmitted via a propagation channel using a transmitter; The transmitted multilayer transmission signal for MIMO is measured using an unbiased linear minimum mean square error (MMSE) MIMO equalizer. as well as The error vector magnitude EVM of the transmitter is calculated based on the noise covariance matrix used for the unbiased linear MMSE MIMO equalizer and further based on multiple per-layer EVM values ​​corresponding to the multilayer transmission signals for MIMO, wherein the per-layer EVM value of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the unbiased linear MMSE MIMO equalizer.

2. The apparatus according to claim 1, wherein, When the channel matrix H of the propagation channel is invertible, the EVM definition used to calculate the multiple per-layer EVM values ​​of the transmitter does not depend on the propagation channel.

3. The apparatus according to claim 2, wherein, The EVM definition used to calculate the multiple per-layer EVM values ​​of the transmitter is a function used to generate the precoding matrix for the multilayer transmission signal used in MIMO.

4. The apparatus according to claim 1, wherein, Multilayer transmission includes two layers of MIMO transmission, where the mean square error of each transmission layer is a function of the covariance of the transmitter noise of the transmitter.

5. The apparatus according to claim 4, wherein, The EVM value for each layer of the first transport layer is calculated as follows: Among them, the EVM value of each layer of the second transport layer is calculated as follows: Where, the value Q 1,1 Q 1,2 Q 2,1 Q 2,2 Form a matrix Q representing the unbiased linear MMSE MIMO equalizer, where the value P 1,1 P 2,2 It is the value of matrix P representing the noise covariance matrix.

6. The apparatus according to claim 1, wherein, The transmitter includes a user equipment transmitter for transmitting uplink MIMO signals to a base station, wherein the processor is configured to cause the device to use a calculated EVM to define the noise floor of the base station due to transmitter noise.

7. The apparatus according to claim 1, wherein, The generated multilayer transmission signal for MIMO includes a reference symbol for each layer or a daily line reference symbol.

8. A method for calculating the error vector magnitude (EVM) of a transmitter, the method comprising: Generate multilayer transmission signals for multiple-input multiple-output MIMO; The generated multilayer transmission signal for MIMO is transmitted via a propagation channel using a transmitter; The transmitted multilayer transmission signal for MIMO is measured using an unbiased linear minimum mean square error (MMSE) MIMO equalizer. as well as The error vector magnitude EVM of the transmitter is calculated based on the noise covariance matrix used for the unbiased linear MMSE MIMO equalizer and further based on multiple per-layer EVM values ​​corresponding to the multilayer transmission signals for MIMO, wherein the per-layer EVM value of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the unbiased linear MMSE MIMO equalizer.

9. The method according to claim 8, wherein, When the channel matrix H of the propagation channel is invertible, the EVM definition used to calculate the multiple per-layer EVM values ​​of the transmitter does not depend on the propagation channel.

10. The method according to claim 9, wherein, The EVM definition used to calculate the multiple per-layer EVM values ​​of the transmitter is a function used to generate the precoding matrix for the multilayer transmission signal used in MIMO.

11. The method according to claim 8, wherein, Multilayer transmission includes two layers of MIMO transmission, where the mean square error of each transmission layer is a function of the covariance of the transmitter noise of the transmitter.

12. The method according to claim 11, wherein, The EVM value for each layer of the first transport layer is calculated as follows: Among them, the EVM value of each layer of the second transport layer is calculated as follows: Where, the value Q 1,1 Q 1,2 Q 2,1 Q 2,2 Form a matrix Q representing the unbiased linear MMSE MIMO equalizer, where the value P 1,1 P 2,2 It is the value of matrix P representing the noise covariance matrix.

13. The method according to claim 8, wherein, The transmitter includes a user equipment transmitter for transmitting uplink MIMO signals to a base station, and the method further includes defining the noise floor of the base station due to transmitter noise using a calculated EVM.

14. The method according to claim 8, wherein, The generated multilayer transmission signal for MIMO includes a reference symbol for each layer or a daily line reference symbol.

15. A system for calculating the error vector magnitude (EVM) of a transmission device, comprising: The conveying device, the conveying device: Generate multilayer transmission signals for multiple-input multiple-output MIMO; as well as The multilayer transmission signal generated for MIMO is transmitted via a propagation channel using a transmitter; and Evaluation equipment, the evaluation equipment: The transmitted multilayer transmission signal for MIMO was measured using an unbiased linear minimum mean square error (MMSE) MIMO equalizer; and The error vector magnitude EVM of the transmitter is calculated based on the noise covariance matrix used for the unbiased linear MMSE MIMO equalizer and further based on multiple per-layer EVM values ​​corresponding to the multilayer transmission signals for MIMO, wherein the per-layer EVM value of each transmission layer is calculated as 100 times the square root of the mean square error of the layer estimate at the output of the unbiased linear MMSE MIMO equalizer.

16. The system according to claim 15, wherein, When the channel matrix H of the propagation channel is invertible, the EVM definition used to calculate the multiple per-layer EVM values ​​of the transmitter does not depend on the propagation channel.

17. The system according to claim 16, wherein, The EVM definition used to calculate the multiple per-layer EVM values ​​of the transmitter is a function used to generate the precoding matrix for the multilayer transmission signal used in MIMO.

18. The system according to claim 15, wherein, Multilayer transmission includes two layers of MIMO transmission, where the mean square error of each transmission layer is a function of the covariance of the transmitter noise of the transmitter.

19. The system according to claim 18, wherein, The multiple per-layer EVM values ​​of the first transport layer are calculated as follows: Among them, multiple per-layer EVM values ​​of the second transport layer are calculated as follows: Where, the value Q 1,1 Q 1,2 Q 2,1 Q 2,2 Form a matrix Q representing the unbiased linear MMSE MIMO equalizer, where the value P 1,1 P 2,2 It is the value of matrix P representing the noise covariance matrix.

20. The system according to claim 15, wherein, The generated multilayer transmission signal for MIMO includes a reference symbol for each layer or a daily line reference symbol.

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