Method and component for determining spectral efficiency of nafd urllc system

Abstract

The application discloses a kind of NAFD uRLLC system's spectral efficiency determination method and component, it is applied to wireless transmission technical field.Therein, method includes according to the inter-user interference channel of uplink and downlink user, the channel of uplink user to R-AP, the channel of downlink user to T-AP, the transmission power of uplink user and system noise, on the basis of guaranteeing the maximum decoding error probability of uplink and downlink user, determines uplink and downlink spectral efficiency;Based on the power consumption constraint and quality of service constraint of uplink and downlink user, by maximizing uplink and downlink spectral efficiency as target, jointly optimizing uplink and downlink transceiver, so target spectral efficiency can be determined, effectively improve system performance.

Description

Technical Field

[0001] This application relates to the field of wireless communication transmission technology, and in particular to a method, apparatus, electronic device, and readable storage medium for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD. Background Technology

[0002] Technologies enabling ultra-high reliability and low latency in 5G (5th Generation) include cloud radio access networks, caching networks, and core networks. Flexible duplexing allows for frequency division multiplexing on paired spectrum and time division multiplexing on unpaired spectrum, making it a potential technology to enhance system spectral efficiency under low latency. Compared to traditional half-duplex communication systems, CCFD (Co-frequency Co-time Full Duplex), which allows uplink and downlink transmission in the same time slot, can achieve twice the spectral efficiency. However, in practice, CLI (Cross-Link Interference) between uplink and downlink antennas on the base station side and between uplink and downlink users limits this spectral efficiency gain. NAFD (Network-Assisted Full Duplex) is a truly flexible duplex mode that summarizes CCFD, spatial multiplexing, and other flexible duplexing technologies. In NAFD, the base station can operate in HD (Half-duplex Communication), CCFD, hybrid duplex, or other duplex modes as needed.

[0003] Cellular-free massive MIMO (Multiple-Input and Multiple-Output) systems combine MIMO networks with distributed antenna systems. Each Access Point (APS) extensively covers an area, coherently serving a large number of users on the same video resource, and is connected to a central processing unit (CPU) via a backhaul link. The combination of NAFD and cellular-free massive MIMO promises to overcome inter-cell interference and provide handover-free, uniform QoS for cell-edge users, thereby enabling uRLLC (Ultra-reliable and Low-Latency Communications) transmission schemes within this system.

[0004] Currently, the performance of existing NAFD-based ultra-reliable low-latency (URLLC) systems for cellular massive MIMO cannot meet user requirements. Related technologies primarily focus on improving receivers for uRLLC in cellular massive MIMO combined with CCFD, including uplink / downlink precoding, reliability and delay equalization. It's understandable that CCFD and NAFD are not the same; therefore, methods used in CCFD-based uRLLC receivers cannot be fully applied to NAFD-based uRLLC transceivers.

[0005] Therefore, how to effectively improve the performance of NAFD-based cellular massive MIMO ultra-reliable low-latency systems to meet users' high-performance requirements for NAFD-based cellular massive MIMO ultra-reliable low-latency systems is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0006] This application provides a method, apparatus, electronic device, and readable storage medium for determining the spectral efficiency of a NAFD-based noncellular massive MIMO uRLLC system, which effectively improves the performance of the NAFD-based noncellular massive MIMO ultra-reliable low latency system to meet users' high performance requirements for the NAFD-based noncellular massive MIMO ultra-reliable low latency system.

[0007] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions:

[0008] One embodiment of the present invention provides a method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, comprising:

[0009] Based on the inter-user interference channels of uplink and downlink users, the channel from uplink users to R-APs, the channel from downlink users to T-APs, the transmission power of uplink users, and system noise, the uplink and downlink spectral efficiency is determined while ensuring the maximum decoding error probability of uplink and downlink users.

[0010] Based on the power consumption constraints and service quality constraints of uplink and downlink users, the target spectrum efficiency is determined by jointly optimizing the uplink and downlink transceivers with the goal of maximizing the weighted sum spectrum efficiency of uplink and downlink.

[0011] Another embodiment of the present invention provides a spectral efficiency determination device for a cellular-free massive MIMO uRLLC system based on NAFD, comprising:

[0012] The spectrum efficiency determination module is used to determine the uplink and downlink spectrum efficiency based on the inter-user interference channels of uplink and downlink users, the channel from uplink users to R-APs, the channel from downlink users to T-APs, the transmission power of uplink users, and system noise, while ensuring the maximum decoding error probability of uplink and downlink users; each user operates in half-duplex mode.

[0013] The spectrum efficiency optimization module is used to jointly optimize the uplink and downlink transceivers based on the power consumption constraints and service quality constraints of uplink and downlink users, with the goal of maximizing the weighted spectrum efficiency of uplink and downlink, to determine the target spectrum efficiency.

[0014] This invention also provides an electronic device, including a processor, which executes a computer program stored in a memory to implement the steps of the method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD as described in any of the preceding claims.

[0015] Finally, this embodiment of the invention also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD as described in any of the preceding claims.

[0016] The advantage of the technical solution provided in this application is that, under the constraints of power consumption and QoS at uplink and downlink users, the uplink and downlink transceivers in the NAFD-based non-cellular massive MIMO ultra-reliable low latency system are optimized to maximize the system weighted and spectral efficiency, effectively improving the performance of the NAFD-based non-cellular massive MIMO ultra-reliable low latency system, so as to meet the high performance requirements of users for the NAFD-based non-cellular massive MIMO ultra-reliable low latency system.

[0017] Furthermore, embodiments of the present invention also provide corresponding implementation devices, electronic devices, and readable storage media for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, further making the method more practical. The devices, electronic devices, and readable storage media have corresponding advantages.

[0018] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, provided for an embodiment of the present invention;

[0021] Figure 2 A schematic diagram illustrating an exemplary application scenario provided by an embodiment of the present invention;

[0022] Figure 3 This is a performance comparison diagram of different methods in the verification embodiments provided by the present invention;

[0023] Figure 4 A schematic diagram comparing the spectral efficiency of different methods in the first illustrative verification embodiment provided by the present invention;

[0024] Figure 5 This is a schematic diagram illustrating the relationship between spectral efficiency and T-AP power constraint provided in an embodiment of the present invention.

[0025] Figure 6 A schematic diagram comparing the spectral efficiency of different methods in a second illustrative verification embodiment provided for the present invention;

[0026] Figure 7 A schematic diagram comparing the spectral efficiency of different methods in the third illustrative verification embodiment provided for the present invention;

[0027] Figure 8 A schematic diagram comparing the spectral efficiency of different methods in the fourth illustrative verification embodiment provided for the present invention;

[0028] Figure 9 A structural diagram of a specific embodiment of the spectral efficiency determination device for a cellular-free massive MIMO uRLLC system based on NAFD provided in this invention;

[0029] Figure 10 This is a structural diagram of a specific embodiment of the electronic device provided in this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The terms "first," "second," "third," "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may include steps or units not listed. Various non-limiting embodiments of this application are described in detail below.

[0032] First see Figure 1 , Figure 1 This is a flowchart illustrating a method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, as provided in an embodiment of the present invention. The NAFD-based cellular-free massive MIMO uRLLC system to which this embodiment applies is, for example... Figure 2 As shown, a NAFD-based cellular massive MIMO uRLLC system comprises multiple access points (APs) connected to a central processing unit (CPU) via a backhaul link. Each AP can freely choose to operate in CCFD, HD, or other flexible duplex modes. For ease of description, the receiving AP can be referred to as the R-AP, and the transmitting AP as the T-AP. Users on the uplink and downlink operate in half-duplex mode. A NAFD-based cellular massive MIMO uRLLC system may include... L T-APS, Z One R-APS, K One downlink user, J Each uplink user, and each T-AP and R-AP may include [number] uplink users. M Each user has a single antenna. The T-AP index set can be represented as follows: The index set of R-AP can be represented as The index set of downlink users can be represented as The index set of uplink users can be represented as The CPU processes downlink signals and transmits them to T-APs, which then forward the received downlink signals to downlink users. Simultaneously, R-APs receive signals from uplink users and forward them to the CPU for further processing. The T-APs and downlink users...k downlink channel It can be represented as , Indicates the first L The T-AP and the first k The transpose of the downlink channel of the i-th downlink user, the th j The uplink user to the first k Inter-user interference (IUI) channel between downlink users , No. j Uplink channel between uplink users and R-APs It can be represented as , Indicates the first j The uplink user to the first Z The uplink channel transpose of the R-AP, T-APs and the first z One R-AP inter-interference (IAI) channel can be These can be modeled as follows:

[0033] ,

[0034] ,

[0035] ,

[0036] ,

[0037] in, , , .

[0038] In the formula, Indicates T-APs up to the k Large-scale fading of downlink users Indicates T-APs up to the k Small-scale fading for each downlink user Indicates the first j The uplink user to the first k Large-scale fading of downlink users Indicates the first j The uplink user to the first k Small-scale fading for each downlink user Indicates the first j Large-scale fading between uplink users and R-APs Indicates the first j Small-scale fading between uplink users and R-APs Indicates T-APs up to the zLarge-scale fading of R-AP Indicates T-APs up to the z Small-scale fading of R-AP. Indicates the first L The T-AP to the first k Large-scale fading of downlink users Indicates the first j The uplink user to the first Z Large-scale fading of R-AP Indicates the first L The T-AP to the first z Large-scale fading of R-AP This represents an identity matrix of dimension M, and in addition to that, Indicates the first l The T-AP to the first k Channel vectors for each downlink user Indicates the first j The uplink user to the first z Channel vectors between R-APs. Assume all small-scale fadings are independent and identically distributed, and follow a certain order. .besides, , As the first parameter, and ; It is the second parameter, and , It is the third parameter, and ,in express and Path loss between , Both are simplified representations of large-scale fading.

[0039] The following describes the optimization design method for the uplink and downlink receiver precoding vectors and uplink user transmit power of the NAFD-based cellular massive MIMO uRLLC system provided in this embodiment under the constraints of uplink and downlink QoS and power consumption constraints. This method can maximize the weighted sum spectral efficiency of the NAFD-based cellular massive MIMO uRLLC system and achieve the target spectral efficiency of uplink and downlink users. The process may include the following:

[0040] S101: Based on the inter-user interference channels of uplink and downlink users, the channel from uplink users to R-APs, the channel from downlink users to T-APs, the transmission power of uplink users, and system noise, determine the uplink and downlink spectral efficiency while ensuring the maximum decoding error probability of uplink and downlink users.

[0041] S102: Based on the power consumption constraints and service quality constraints of uplink and downlink users, the uplink and downlink transceivers are jointly optimized to determine the target spectrum efficiency by maximizing the weighted sum of uplink and downlink spectrum efficiency.

[0042] In this embodiment, the inter-user interference channel refers to the inter-user interference channel between uplink users and downlink users; user data signals refer to the data signals of uplink users and downlink users; the channel from downlink users to T-APs refers to the channel from downlink users to each T-AP; and the uRLLC uplink and downlink spectral efficiency includes downlink spectral efficiency and uplink spectral efficiency. Those skilled in the art can calculate the spectral efficiency of any link based on the acquired data such as the inter-user interference channel of uplink and downlink users, the channel from uplink users to R-APs, the channel from downlink users to T-APs, the transmission power of uplink users, and system noise. This application does not impose any limitations on this. After determining the uplink and downlink spectral efficiency, under the common constraints of transmission power and service quality, the uplink and downlink transceivers are jointly optimized with the goal of maximizing the weighted sum of uplink and downlink spectral efficiency to obtain the system's transceivers and determine the target spectral efficiency of the current system. The transceivers mentioned in this application include transmitters at the user's location and the T-AP, and receivers at the R-AP. Specifically, the transmitter at the user's location corresponds to... The transmitter at T-AP corresponds to The receiver at the R-AP corresponds to .

[0043] In the technical solution provided by this invention, under the constraints of power consumption and QoS at uplink and downlink users, the uplink and downlink transceivers in a NAFD-based cellular massive MIMO ultra-reliable low-latency system are optimized to maximize the system weighted and spectral efficiency. This maximizes the uplink and downlink user weighted and spectral efficiency under finite code length conditions, effectively improving the performance of the NAFD-based cellular massive MIMO ultra-reliable low-latency system and meeting users' high-performance requirements for such systems.

[0044] It should be noted that there is no strict order of execution for the steps in this application. As long as they conform to a logical order, these steps can be executed simultaneously or in a certain preset order. Figure 1 This is just an illustrative example and does not mean that this is the only possible execution order.

[0045] In the above embodiments, there is no limitation on how to perform step S101. This embodiment provides an optional method for determining the uplink and downlink spectral efficiency, including the following steps:

[0046] Acquire the user-received signal at the downlink user within a single time slot;

[0047] The achievable downlink spectral efficiency under the long block regime (LBR) is determined based on the user's received signal. Based on the achievable downlink spectral efficiency, the downlink spectral efficiency is determined under a given code length and while ensuring the maximum decoding error probability of the downlink user.

[0048] Acquire the AP received signal of the R-AP, and determine the uplink baseband signal of the R-AP based on the AP received signal;

[0049] Based on the uplink baseband signal, the uplink spectral efficiency is determined while ensuring the maximum decoding error probability of the uplink user.

[0050] For ease of description and distinction, the user-received signal at the downlink user is referred to as the user-received signal, and the received signal at the R-AP is referred to as the AP-received signal. The received signals can be directly obtained. By analyzing the received signals, the corresponding signal data, interference data, noise data, and channel data are obtained, and then these data are used to calculate the maximum uplink and downlink spectral efficiency. As an optional implementation, the relationship of the obtained user-received signal can be expressed as:

[0051] ;

[0052] In the formula, For the first k User-received signals at each downlink user location D Indicates the downlink channel. For the downlink user set, For the uplink user set, U Indicates the uplink channel. For T-APs and the k Downlink channels for each downlink user For the first One downlink user, for The conjugate transpose of , when the first The downlink user and the first k When the number of downlink users is the same, it indicates that signal data has been received. The downlink user and the first k If the downlink users are different, it indicates that the received data is interference. For the first k Precoded vectors for each downlink user For the first Precoded vectors for each downlink user , For the first k Data signals from downlink users For the first Data signals from downlink users For the first k The downlink user and the first j Inter-user interference channel for uplink users , For the first j Data signals from uplink users For the first j The transmission power of each uplink user For the first k Additive white Gaussian noise (AWGN) at each downlink user receiver, with zero mean and variance. .

[0053] After acquiring the user's received signal, the downlink user's signal, uplink user's power consumption, and various channel data can be obtained. Based on this, the SINR signal calculation formula can be used to calculate the... k The signal-to-interference-plus-noise ratio (SINR) for each downlink user; the SINR calculation formula can be expressed as:

[0054] ;

[0055] After determining the signal-to-interference-plus-noise ratio, the achievable downlink spectral efficiency under long codes can be further calculated based on the Shannon rate function. The achievable downlink spectral efficiency can be expressed as:

[0056] ;

[0057] Finally, for a given code length To ensure the first k Maximum decoding error probability for each downlink user The maximum downlink spectral efficiency can be calculated using the downlink spectral efficiency calculation formula; the downlink spectral efficiency calculation formula can be expressed as:

[0058] ;

[0059] In the formula, For the first k The signal-to-interference-plus-noise ratio at each downlink user. For the first k Precoded vectors for each downlink user For the first k The variance of additive white Gaussian noise at each downlink user receiver For the first k The achievable downlink spectrum efficiency at each downlink user. For the first k Downlink spectrum efficiency for each downlink user. The reciprocal of the Gaussian Q-function. For the first k The maximum decoding error probability for each downlink user , N For a given code length, B For transmission bandwidth, T For transmission time slots, e It is the natural logarithm. The Gaussian Q-function can be expressed as: .

[0060] As another alternative implementation, the first z The AP received signal of an R-AP can be represented as:

[0061] ;

[0062] In the formula, For the first z The AP receives signals from one R-AP. For the first z Inter-antenna interference channels from R-AP to T-APs For the first j The uplink user to the first z Uplink channels of one R-AP For the first z Zero-mean additive white Gaussian noise at each R-AP, with variance of IAI channel Modeling as ,in , indicating the first l The T-AP to the first z Non-ideal channels between R-APs Indicates the first l The T-AP to the first z Corresponding estimated channel for non-ideal channels between R-APs Indicates the first l The T-AP to the first z The channel estimation error of the non-ideal channel between R-APs, and .in, M represents The identity matrix of M, where M is a real number. For the first z For the R-AP at the first l The residual error gain of a T-AP is used to represent the residual error gain of imperfect IAI elimination in the digital or analog domain.

[0063] After appropriate interference cancellation, the baseband representation formula can be invoked first to determine the first... z The uplink baseband signal of an R-AP; the baseband representation formula can be expressed as:

[0064] ;

[0065] in, .set up The z-th R-AP is used for demodulation of the z-th R-AP. Uplink user data signal The merged vector. Then, the noise calculation formula is called to calculate the first... j The signal-to-interference-plus-noise ratio at the CPU for each uplink user; the noise calculation formula can be expressed as:

[0066] ;

[0067] Among them, the j Uplink user interference plus noise power It can be represented as:

[0068] .

[0069] No. j The achievable uplink spectral efficiency for a given uplink user can be expressed as: Similarly, for a given channel block length... In order to ensure the first j The maximum decoding error probability for each uplink user does not exceed The maximum uplink spectral efficiency can be calculated by calling the uplink spectral efficiency calculation formula; the uplink spectral efficiency calculation formula can be expressed as:

[0070] ;

[0071] In the formula, This is the uplink baseband signal. For the first z Channel estimation error of a non-ideal channel from R-AP to T-APs; For the first j The signal-to-interference-plus-noise ratio at the CPU for each uplink user For the first jInterference plus noise power at each uplink user. The z-th R-AP is used for demodulation of the z-th R-AP. j The combined vector of decoded uplink user data signals, H Indicates conjugate transpose; For the first j Uplink spectrum efficiency for each uplink user. For the first j The achievable uplink spectrum efficiency for each uplink user No. j The maximum decoding error probability for each uplink user. The reciprocal of the Gaussian Q-function. N Given a code length.

[0072] Based on the above embodiments, maximizing the weighted sum spectral efficiency of the uRLLC system under transmission power and QoS constraints, for the transceiver Joint optimization is performed. This means that the uplink and downlink transceiver power and user transmit power can be jointly optimized by invoking the uRLLC system optimization formula, which can be expressed as:

[0073] ;

[0074] In the formula, for gather, This refers to the parameters corresponding to the transceiver. For the first k Precoded vectors for each downlink user For demodulation of the first j The combined vector of data signals from each uplink user. D Indicates the downlink channel. For the downlink user set, For the uplink user set, U Indicates the uplink channel. For the first k Spectrum efficiency weights for each downlink user For the first j Spectrum efficiency weights for each uplink user. For the first k Downlink spectrum efficiency for each downlink user. For the first j Uplink spectrum efficiency for each uplink user. For the first k The downlink user in the first l The precoding vector of each T-AP, For the first lPower consumption budget for a T-AP For the first j Power consumption (i.e., transmission power consumption) of each uplink user. For the first j Power consumption budget for each uplink user For the first k Power consumption constraints for each downlink user For the first j Power consumption constraints for each uplink user; For the first k QoS constraints for each downlink user For the first j QoS constraints for each uplink user For the first k The minimum QoS constraint for each downlink user. For the first j The minimum QoS constraint for each uplink user.

[0075] To determine the target spectral efficiency through joint optimization, this application also provides two different methods for solving the problem of weighted sum spectral efficiency of uRLLC systems, which may include the following:

[0076] As an optional implementation, this embodiment can jointly optimize the uplink and downlink transceivers by calling the concave-convex algorithm optimization formula. The concave-convex algorithm optimization formula can be expressed as:

[0077] ;

[0078] in, , , , ,

[0079] ,

[0080] , ,

[0081] ,

[0082] ,

[0083] ,

[0084] , ,

[0085] , , , , , ; ;

[0086] In the formula, , For a set, , , , , , , , , , , , , , , , All are auxiliary variables. For the first k The downlink user and the first j Inter-user interference channel for uplink users For the first k The variance of additive white Gaussian noise at each downlink user receiver For code length, For the first k Precoded vectors for each downlink user For the first j Power consumption of each uplink user for The precoded vector value at n iterations For the first One downlink user, , For intermediate parameters, For T-APs and the k Downlink channels for each downlink user for The conjugate transpose of . This represents the first preset function. For the first For each uplink user, similarly, if = j This indicates that signal data has been received. ≠ j This indicates that the received data is interference. This indicates the second preset function. for In the n The value of the next iteration. for The conjugate transpose of . For the first l The T-AP to the first k Channel vectors for each downlink user for In the n The value of the next iteration. for The conjugate transpose of . For demodulation of the first j The combined vector of data signals from each uplink user. for In the n The value of the next iteration. for In the n The value of the next iteration. For the first z The variance of additive white Gaussian noise at each R-AP point For the first z The first R-AP process j Receiver vectors for each uplink user for In the n The value of the next iteration. For intermediate parameters, For the first Power consumption of each uplink user for In the n The value of the next iteration. for The conjugate transpose of . In the first n The value of the next iteration. for In the n The value of the next iteration. For the first l The T-AP to the first k Precoded vectors for each downlink user For dimension LM The identity matrix of LM, LM=L M, L, and M are all real numbers. for In the n The value of the next iteration. For dimension ZM The identity matrix of ZM, ZM=Z M, Z, and M are all real numbers. for In the n The value of the next iteration. for In then The value of the next iteration. This represents the third preset function. e It is the natural logarithm. First preset function. Second preset function Third preset function All of these are user-defined functions, and the expressions for each user-defined function are as follows:

[0087] ,

[0088] ,

[0089] ,

[0090] in, Represents two scalar variables, Represents a variable in vector form. Represents a matrix variable; n represents the number of iterations. and They represent and At the current feasible point in the nth iteration, This indicates taking the real part.

[0091] Specifically, this embodiment uses a CCCP-based method (CCCP is a monotonically decreasing global optimization method) to solve the aforementioned problem of joint optimization design of uplink and downlink transceivers. An auxiliary variable is introduced { ≥0}、{ ≥0}、{ ≥0} and { If ≥0}, then the joint optimization problem is equivalently transformed into:

[0092]

[0093] in, Although the objective function and constraints C1, C2, C5, and C6 are approximately convex, the transformed problem remains non-convex due to the existence of downlink constraints C7 and C9 and uplink constraints C8 and C10. To transform the original problem from non-convex to convex, the following inequalities are first introduced:

[0094] ,

[0095] ,

[0096] ,

[0097] in, Represents two scalar variables, Represents a variable in vector form. Let n represent the number of iterations in the matrix. and They represent and The current feasible point in the nth iteration.

[0098] (1) Downward constraint approximation: Process downward constraints C9 and C7 in sequence. First, in C9... It can be further simplified; constraint C9 can be equivalently transformed into:

[0099] ,

[0100] Introducing auxiliary variables C11 can be further written as:

[0101] ,

[0102] ,

[0103] Inequality D1 can be used to approximate C12 and C13, which can be approximated as:

[0104] ,

[0105] ,

[0106] in, .

[0107] Next, another auxiliary variable is introduced. Constraint C7 can be rewritten as the following two equations:

[0108] ,

[0109] ,

[0110] It can be observed that constraint C17 has been rewritten as a convex constraint, but C16 is still a non-convex constraint. Therefore, with some simple changes, C16 is rewritten as follows:

[0111] ,

[0112] Using inequality D2, C18 can finally be approximated as:

[0113] ,

[0114] (2) Approximation of Upward Constraints: Upward constraints C8 and C10 are similar to those of downward constraints. First, constraint C10 is approximated by some simple operations to obtain:

[0115] ,

[0116] Furthermore, the inequality can be rewritten as:

[0117] A series of auxiliary variables are introduced to address constraint C20. The variables satisfy:

[0118] ,

[0119] ,

[0120] ,

[0121] .

[0122] All four constraints C21-C24 are non-convex. We will first deal with the simpler constraints C23 and C24. We will first perform some simple inequality transformations on constraint C23 to obtain... Next, using the approximation of inequality D3, we obtain:

[0123] ,

[0124] Similarly, applying the inequality D2 to approximate C24, we obtain:

[0125] ,

[0126] After approximating constraints C23 and C24, we continue to approximate C21 and C22. Constraint C21 is rewritten using mathematical inequality transformations as follows:

[0127] ,

[0128] It can be observed that both sides of inequality C27 are non-convex and non-concave functions. Let For functions Approximately, we can obtain:

[0129] ,

[0130] in, For the introduced auxiliary variables, and { ≥0}, for In the n The value of the next iteration. In addition, when When the inequality holds true, the equality holds. (C27) Divide both sides equally. Applying inequality D1 to C27, we can obtain:

[0131] ,

[0132] in, .

[0133] After approximating constraint C21, new auxiliary variables are introduced for constraint C22. Therefore, constraint C22 can be equivalently transformed into the following two equations:

[0134] ,

[0135] Using inequality D2 to approximate C29 and C30, we get:

[0136] ,

[0137] At this point, the convex approximation of constraint C10 has been completed. Moving on to constraint C8, C8 can be equivalently replaced by the following two inequalities:

[0138] ,

[0139] ,

[0140] in, This is a new variable introduced. Since C34 is already a convex inequality, we only need to deal with equation C33. C33 can be approximated through the following steps:

[0141] ,

[0142] ,

[0143] ,

[0144] ,

[0145] in, These are all auxiliary variables. Since constraint C38 is already convex, we need to handle C35-C37. Using inequalities D2 and D3, constraints C35-C37 can be approximated as:

[0146] ,

[0147] ,

[0148] .

[0149] To further reduce the algorithm complexity based on CCCP proposed in the above embodiments, this embodiment also presents another solution to the joint optimization problem, which can be called the hybrid ZF-MRT beamforming algorithm. This method can jointly optimize the uplink and downlink transceivers by calling the hybrid algorithm optimization relation. The hybrid algorithm optimization relation is as follows:

[0150] ,

[0151] in, , , , , ,

[0152] , ,

[0153] ,

[0154] , ,

[0155] ,

[0156] , ,

[0157] ,

[0158] ,

[0159] ;

[0160] , , , , , , , , , , , , , , , , All are auxiliary variables. , To combine factor variables, For the use of hybrid beamforming, the first lThe T-AP transmits to the first k Power of each downlink user for In the n The value of the next iteration. For the R-AP set, for In the n The value of the next iteration. for In the n The value of the next iteration. For the first j Power consumption of each uplink user This indicates the second preset function. for In the n The value of the next iteration. for In the n The value of the next iteration. This represents the first preset function. For demodulation of the first j The combined vector of data signals from each uplink user. for In the n The value of the next iteration. for The conjugate transpose of . for In the n The value of the next iteration. For the first z The first R-AP process j Receiver vectors for each uplink user For R-APs and the Uplink channels for each uplink user , These are all intermediate parameters. for In the n The value of the next iteration. No. Power consumption of each uplink user for In the n The value of the next iteration. For dimension ZM The identity matrix of ZM, and similarly, ZM = Z M, Z, and M are all real numbers. For the first z The variance of additive white Gaussian noise at each R-AP point for In then The value of the next iteration. for In the n The value of the next iteration. e It is the natural logarithm.

[0161] Specifically, in this embodiment, two merging factor variables are introduced. and ,make ,in , . express The link gain of an orthogonal basis in null space can be expressed as:

[0162] .

[0163] make , and ,therefore It can be rewritten as:

[0164] .

[0165] When using hybrid beamforming, the first l The AP transmits to the first k The power of a single user can be expressed as:

[0166] ,

[0167] in, , and .

[0168] The new problem using the hybrid beamforming algorithm can then be written as follows:

[0169] .

[0170] in, , , , , .

[0171] in, For the first j The interference and noise power of each uplink user. To solve this new problem, a new variable can be introduced. The variable satisfies and shrink , making or .because ,so and It can be reformulated as:

[0172] .

[0173] in, .

[0174] Therefore, the original problem can be transformed into:

[0175] ;

[0176] in, . , , , , and It is an auxiliary variable, and { ≥0、 ≥0、 ≥0、 ≥0、 ≥0、 ≥0}; Since the objective function and constraints C49, C50, C51, and C52 are non-convex, the new problem requires further convex approximation. Similar to the above embodiments, the CCCP method can be used to obtain convex approximations of constraints C49, C50, C51, and C52 respectively.

[0177] (1) First, process constraints C49 and C51. Introduce auxiliary variables { ≥0, ≥0, ≥0}, the left side of constraint C49 can be transformed into:

[0178] ,

[0179] in,

[0180] ,

[0181] Will It can be approximated by the following formula:

[0182] ,

[0183] in, Therefore, constraint C49 can be approximated as:

[0184] ,

[0185] Further processing of C51 is required. After rearranging terms, constraint C51 becomes... Substituting into the scaling inequality D2, we get:

[0186] .

[0187] (2) Continue processing constraints C50 and C52. First, C50 can be rewritten as:

[0188] By introducing auxiliary variables The above formula can be approximated as:

[0189] ,

[0190] ,

[0191] ,

[0192] .

[0193] After further transformation, C55, C56, C57, and C58 can be transformed into:

[0194] ,

[0195] ,

[0196] ,

[0197] ,in, From the formula Update.

[0198] Next, constraint C52 will be processed, and new auxiliary variables will be introduced. Transform C52 into the following two inequalities:

[0199] ,

[0200] ,

[0201] Since C63 is a non-convex expression, auxiliary variables need to be introduced again. Transform it into the following inequality:

[0202] ,

[0203] ,

[0204] ,

[0205] The above three equations can be transformed again into:

[0206] ,

[0207] ,

[0208] .

[0209] To verify the effectiveness of the method provided in this embodiment, that is, to demonstrate the improvement in system performance compared to traditional CCFD and HD solutions, a series of verification experiments were also conducted in this embodiment. Figure 3 This demonstrates that the technical solution proposed in this embodiment has good convergence. Figure 3 The NAFD in this application corresponds to the technical solution for joint optimization of uplink and downlink transceivers based on the CCCP algorithm (referred to as NAFD for ease of description). Figure 3 The term "Hybrid" in this application refers to the technical solution for joint optimization of uplink and downlink transceivers based on the CCCP hybrid beamforming algorithm (referred to as "Hybrid" for ease of description). Figure 3 The horizontal axis represents the number of iterations, and the vertical axis represents the spectral efficiency. Figure 3 It can be seen that NAFD achieves the best steady-state performance, followed by Hybrid. Figure 4 When , , , At that time, spectral efficiency increases with the number of users. The changing image, in which, For the self-interference residual noise power, from Figure 4 As can be seen from this embodiment, the performance of NAFD and Hybrid is better than that of CCFD and TDD (Time Division Duplex) schemes. When the number of users is less than 4, the spectral efficiency SE increases rapidly with the number of users. This is because the system performance is better when T-AP is much larger than R-AP. However, when the number of users is greater than a certain value, the interference between users increases and the SE increases slowly. Figure 5 The relationship between SE and T-AP power constraints is shown. For NAFD and Hybrid, increasing the T-AP power constraint can provide more spectral gain. Figure 6 Given , , , In this case, the spectral efficiency obtained by NAFD and Hybrid increases with the improvement of IAI cancellation, and when At that time, the SE of the three schemes (NAFD, Hybrid, and CCFD) is not affected by IAI suppression. At that time, the spectral efficiency obtained by NAFD and Hybrid was lower than that of TDD scheme. Figure 7Given , , , , As the number of interference increases, the channel gain also increases because interference is easier to suppress. Figure 8 The relationship between SE and the number of antennas M is given. , , , Under these circumstances, the technical solutions provided in this embodiment can all achieve better spectral efficiency.

[0210] This invention also provides a corresponding apparatus for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, further enhancing the practicality of the method. The apparatus can be described from both a functional module and hardware perspective. The following describes the apparatus for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, and the apparatus described below corresponds to the spectral efficiency determination method described above.

[0211] From the perspective of functional modules, see Figure 9 , Figure 9 This is a structural diagram of a spectral efficiency determination device for a cellular-free massive MIMO uRLLC system based on NAFD, provided in an embodiment of the present invention. The device may include:

[0212] The spectrum efficiency determination module 901 is used to determine the uplink and downlink spectrum efficiency based on the inter-user interference channel of uplink and downlink users, the channel from uplink users to R-APs, the channel from downlink users to T-APs, the transmission power of uplink users, and system noise, while ensuring the maximum decoding error probability of uplink and downlink users.

[0213] The spectrum efficiency optimization module 902 is used to jointly optimize the uplink and downlink transceivers based on the power consumption constraints and service quality constraints of uplink and downlink users, with the goal of maximizing the weighted spectrum efficiency of uplink and downlink, to determine the target spectrum efficiency.

[0214] The functions of each module of the spectral efficiency determination device for a cellular-free massive MIMO uRLLC system based on NAFD in this embodiment of the invention can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can be referred to the relevant descriptions in the above method embodiments, and will not be repeated here.

[0215] The spectral efficiency determination device for a cellular-free massive MIMO uRLLC system mentioned above is described from the perspective of functional modules. Furthermore, this application also provides an electronic device, which is described from the perspective of hardware. Figure 10 This is a schematic diagram of the structure of the electronic device provided in one embodiment of this application. For example... Figure 10 As shown, the electronic device includes a memory 100 for storing a computer program; and a processor 101 for executing the computer program to implement the steps of the method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD as described in any of the above embodiments.

[0216] In some embodiments, the above-described electronic device may further include a display screen 102, an input / output interface 103, a communication interface 104 (or network interface), a power supply 105, and a communication bus 106. For ease of illustration, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0217] Those skilled in the art will understand that Figure 10 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, such as sensors 107 that perform various functions.

[0218] The functions of each functional module of the electronic device described in the embodiments of the present invention can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can be referred to the relevant descriptions in the above method embodiments, which will not be repeated here.

[0219] As can be seen from the above, the embodiments of the present invention can effectively improve the performance of NAFD-based cellular massive MIMO ultra-reliable low latency systems, so as to meet users' high performance requirements for NAFD-based cellular massive MIMO ultra-reliable low latency systems.

[0220] It is understood that if the spectral efficiency determination method for a cellular-free massive MIMO uRLLC system based on NAFD in the above embodiments is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes: USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), electrically erasable programmable ROM, register, hard disk, multimedia card, card-type memory (such as SD or DX memory), magnetic memory, removable disk, CD-ROM, magnetic disk or optical disk, and other media capable of storing program code.

[0221] Based on this, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD as described in any of the above embodiments.

[0222] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0223] The foregoing has provided a detailed description of the spectral efficiency determination method, apparatus, electronic device, and readable storage medium for a cellular-free massive MIMO uRLLC system based on NAFD. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of the invention, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, characterized in that, include: Based on the inter-user interference channels of uplink and downlink users, the channel from uplink users to R-APs, the channel from downlink users to T-APs, the transmission power of uplink users, and system noise, the uplink and downlink spectral efficiency is determined while ensuring the maximum decoding error probability of uplink and downlink users. Based on the power consumption constraints and service quality constraints of uplink and downlink users, the target spectrum efficiency is determined by jointly optimizing the uplink and downlink transceivers with the goal of maximizing the weighted spectrum efficiency of uplink and downlink. Specifically, the uRLLC system optimization formula is invoked to jointly optimize the uplink and downlink transceivers. The uRLLC system optimization formula is as follows: ； In the formula, for gather, For the precoding vector of the k-th downlink user, This is the combined vector used to demodulate the data signal of the j-th uplink user. Let j be the power consumption of the j-th uplink user. For the downlink user set, For the uplink user set, The spectral efficiency weight for the k-th downlink user. Let be the spectral efficiency weight for the j-th uplink user. Let be the downlink spectrum efficiency corresponding to the k-th downlink user. Let J be the uplink spectrum efficiency for the j-th uplink user. For the precoding vector of the k-th downlink user at the l-th T-AP, For the power consumption budget of the l-th T-AP, For the power consumption budget of the j-th uplink user, For the power consumption constraint of the k-th downlink user, For the power consumption constraint of the j-th uplink user; For the QoS constraints of the k-th downlink user, For the QoS constraints of the j-th uplink user, Let be the minimum value of the QoS constraints for k downlink users. This represents the minimum QoS constraint value for the j-th uplink user.

2. The method according to claim 1, characterized in that, Determining downlink spectral efficiency includes: Acquire the user-received signal at the downlink user within a single time slot; Based on the user's received signal, the achievable downlink spectral efficiency under long code is determined, and based on the achievable downlink spectral efficiency, the downlink spectral efficiency is determined under a given code length and while ensuring the maximum decoding error probability of the downlink user.

3. The method according to claim 1, characterized in that, Determining uplink spectral efficiency includes: Acquire the AP received signal of the R-AP, and determine the uplink baseband signal of the R-AP based on the AP received signal; Based on the uplink baseband signal, the uplink spectral efficiency is determined while ensuring the maximum decoding error probability of the uplink user.

4. The method according to claim 2, characterized in that, The downlink spectral efficiency is: ； in, ； ； In the formula, Let be the downlink spectrum efficiency corresponding to the k-th downlink user. Let be the achievable downlink spectrum efficiency at the k-th downlink user. The signal-to-interference-plus-noise ratio at the k-th downlink user. For the downlink channels of T-APs and the k-th downlink user, for The conjugate transpose of; For the precoding vector of the k-th downlink user, Let J be the transmission power of the j-th uplink user. This is the inter-user interference channel between the k-th downlink user and the j-th uplink user. Let Variance be the variance of the additive white Gaussian noise at the receiver of the k-th downlink user. The reciprocal of the Gaussian Q-function. Let N be the maximum decoding error probability for the k-th downlink user, N be the given code length, and e be the natural logarithm. For the downlink user set, For the uplink user set, For the first One downlink user.

5. The method according to claim 3, characterized in that, The uplink spectral efficiency is: ； in, ； ； In the formula, Let J be the uplink spectrum efficiency for the j-th uplink user. Let be the achievable uplink spectrum efficiency for the j-th uplink user. For the j-th uplink user, the signal-to-interference-plus-noise ratio at the CPU is given. The maximum decoding error probability of the j-th uplink user Let J be the transmission power of the j-th uplink user. Add noise power to the interference at the j-th uplink user. Let H be the combined vector used at the z-th R-AP to demodulate the j-th uplink user data signal, and let H denote the conjugate transpose. This is the uplink channel from the j-th uplink user to the z-th R-AP.

6. The method according to claim 1, characterized in that, The concave-convex algorithm is used to optimize the relational formula, and joint optimization is performed on the uplink and downlink transceivers. The concave-convex algorithm optimization relational formula is as follows: ； in, , , , , ； ， ， ， ， ， ， ； ， ， ， ， ， ； ； In the formula, For a set, , , , All are auxiliary variables. This is the inter-user interference channel between the k-th downlink user and the j-th uplink user. Let Variance be the variance of the additive white Gaussian noise at the receiver of the k-th downlink user. For code length, For the precoding vector of the k-th downlink user, for The precoded vector value at n iterations , For the downlink channels of T-APs and the k-th downlink user, This represents the first preset function. For the first One uplink user, This indicates the second preset function. Let l be the channel vector from the l-th T-AP to the k-th downlink user. Let V be the variance of the additive white Gaussian noise at the z-th R-AP. For the z-th R-AP, process the receiver vector of the j-th uplink user. For intermediate parameters, Let be the residual error gain at the z-th R-AP with respect to the l-th T-AP. Let be the identity matrix of dimension LM*LM. It is an identity matrix of dimension ZM*ZM. This represents the third preset function, where e is the natural logarithm.

7. The method according to claim 1, characterized in that, The hybrid algorithm optimization formula is used to jointly optimize the uplink and downlink transceivers. The hybrid algorithm optimization formula is as follows: ； in, , , , , , ， ， ， ， ， ， ， ， ， ， ; , , , , , , , , , All are auxiliary variables. To combine factor variables, This represents the power transmitted by the l-th T-AP to the k-th downlink user when using hybrid beamforming. For the R-AP set, This indicates the second preset function. This represents the first preset function. This is the combined vector used to demodulate the data signal of the j-th uplink user. For the z-th R-AP, process the receiver vector of the j-th uplink user. For R-APs and the Uplink channels for each uplink user , These are all intermediate parameters. No. Power consumption of each uplink user Let be the residual error gain at the z-th R-AP with respect to the l-th T-AP. It is an identity matrix of dimension ZM*ZM. Let be the variance of the additive white Gaussian noise at the z-th R-AP, and e be the natural logarithm.

8. A device for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, characterized in that, include: The spectrum efficiency determination module is used to determine the uplink and downlink spectrum efficiency based on the inter-user interference channel of uplink and downlink users, the channel from uplink users to R-APs, the channel from downlink users to T-APs, the transmission power of uplink users, and system noise, while ensuring the maximum decoding error probability of uplink and downlink users. The spectrum efficiency optimization module is used to jointly optimize the uplink and downlink transceivers based on the power consumption constraints and service quality constraints of uplink and downlink users, with the goal of maximizing the weighted spectrum efficiency of uplink and downlink transceivers, and to determine the target spectrum efficiency. The spectrum efficiency optimization module is further used to: invoke the uRLLC system optimization formula to jointly optimize the uplink and downlink transceivers, wherein the uRLLC system optimization formula is: ； In the formula, for gather, For the precoding vector of the k-th downlink user, This is the combined vector used to demodulate the data signal of the j-th uplink user. Let j be the power consumption of the j-th uplink user. For the downlink user set, For the uplink user set, The spectral efficiency weight for the k-th downlink user. Let be the spectral efficiency weight for the j-th uplink user. Let be the downlink spectrum efficiency corresponding to the k-th downlink user. Let J be the uplink spectrum efficiency for the j-th uplink user. For the precoding vector of the k-th downlink user at the l-th T-AP, For the power consumption budget of the l-th T-AP, For the power consumption budget of the j-th uplink user, For the power consumption constraint of the k-th downlink user, For the power consumption constraint of the j-th uplink user; For the QoS constraints of the k-th downlink user, For the QoS constraints of the j-th uplink user, Let be the minimum value of the QoS constraints for k downlink users. This represents the minimum QoS constraint value for the j-th uplink user.

9. An electronic device, characterized in that, The system includes a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the steps of the method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system as described in any one of claims 1 to 8.

10. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for determining the spectral efficiency of a cellular-free massive MIMO uRLLC system as described in any one of claims 1 to 8.

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