WO2024113524A1

WO2024113524A1 - Nafd urllc system spectral efficiency determination method and module

Info

Publication number: WO2024113524A1
Application number: PCT/CN2023/079495
Authority: WO
Inventors: 夏心江; 王东明; 李笑寒; 孙文菲; 卜颖澜; 尤肖虎
Original assignee: 网络通信与安全紫金山实验室
Priority date: 2022-11-28
Filing date: 2023-03-03
Publication date: 2024-06-06

Abstract

An NAFD uRLLC system spectral efficiency determination method and a module, which are applied to the technical field of wireless transmission. The method comprises: according to an inter-user interference channel of uplink and downlink users, channels from the uplink user to R-APs, channels from the downlink user to T-APs, the transmission power of the uplink user and system noise, determining uplink and downlink spectral efficiencies on the basis of ensuring the maximum decoding error probabilities of the uplink and downlink users; and, on the basis of power consumption constraints and quality-of-service constraints of the uplink and downlink users, performing joint optimization on uplink and downlink transceivers with the objective of maximizing the uplink and downlink spectral efficiencies, so as to determine a target spectral efficiency, thus effectively improving the system performance.

Description

Spectral efficiency determination method and components of NAFD uRLLC system

This application claims the priority of the Chinese patent application filed with the China Patent Office on November 28, 2022, with application number 202211508485.6 and invention name “Spectral efficiency determination method and components for NAFD uRLLC system”, the entire contents of which are incorporated by reference in this application.

Technical Field

The present application relates to the field of wireless communication transmission technology, and in particular to a method, device, electronic device and readable storage medium for determining the spectrum efficiency of a cellular-free massive MIMO uRLLC system based on NAFD.

Background technique

Technologies that can achieve ultra-high reliability and low latency in 5G (5th Generation) include cloud radio access networks, cache networks, and core networks. Flexible duplex allows frequency division multiplexing on paired spectrum and time division multiplexing on unpaired spectrum, making it a potential technology for enhancing system spectrum efficiency at low latency. Compared with traditional half-duplex communication systems, CCFD (Co-frequency Co-time Full Duplex), which can perform uplink and downlink transmissions in the same time slot, can achieve twice the spectrum efficiency. However, in actual situations, CLI (Cross-Link Interference) between uplink and downlink antennas on the base station side and between uplink and downlink users will limit the spectrum efficiency gain effect. NAFD (Network-Assisted Full Duplex) is a duplex mode that truly realizes flexible control based on CCFD, spatial multiplexing and other flexible duplex modes. Base stations in NAFD can work in HD (Half duplex Communication), CCFD, hybrid duplex or other duplex modes according to actual needs.

The cell-free massive MIMO (Multiple-Input and Multiple-Output) system combines a MIMO network with a distributed antenna system, where each Aps (Access Point) covers a wide area to coherently serve a large number of users on the same video resource and is connected to the central processing unit (CPU) through a backhaul link. NAFD combined with cell-free massive MIMO is expected to overcome inter-cell interference and provide uniform QoS (Quality of Service) without handoff to cell-edge users, thereby enabling the uRLLC (Ultra-reliable and Low Latency Communications) transmission scheme in the system.

At present, the performance of the existing NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system cannot meet user needs, and the relevant technologies are all aimed at improving the receiver of uRLLC in non-cellular massive MIMO combined with CCFD, including uplink and downlink precoding, reliability and delay balance, etc. It is understandable that CCFD and NAFD are not the same, and the relevant methods of the receiver of uRLLC in non-cellular massive MIMO combined with CCFD cannot be fully applied to the joint transceiver of uRLLC in non-cellular massive MIMO based on NAFD.

In view of this, how to effectively improve the performance of the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system to meet users' high performance requirements for the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system is a technical problem that technical personnel in the relevant field need to solve.

Summary of the invention

The present application provides a method, device, electronic device and readable storage medium for determining the spectrum efficiency of a NAFD-based non-cellular massive MIMO uRLLC system, which effectively improves the performance of the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system to meet users' high performance requirements for the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system.

To solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

An embodiment of the present invention provides a method for determining the spectrum efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, including:

Based on the inter-user interference channels of uplink and downlink users, the channels from uplink users to R-APs, the channels from downlink users to T-APs, the transmission power of uplink users, and system noise, the uplink spectrum efficiency is determined on the basis of 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 uplink and downlink transceivers are jointly optimized to determine the target spectrum efficiency by maximizing the uplink and downlink weighted and spectrum efficiency.

Another aspect of the present invention provides a spectral efficiency determination device for a cellular-free massive MIMO uRLLC system based on NAFD, including:

The spectrum efficiency determination module is used to determine 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, and the uplink users. The transmission power and system noise are taken into consideration to determine the uplink and downlink spectrum efficiency on the basis of ensuring the maximum decoding error probability of uplink and downlink users; each user works in half-duplex mode;

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 the uplink and downlink users, and determine the target spectrum efficiency by maximizing the uplink and downlink weighted and spectrum efficiency.

An embodiment of the present invention also provides an electronic device, comprising a processor, wherein the processor is used to implement the steps of the method for determining the spectrum efficiency of a NAFD-based non-cellular massive MIMO uRLLC system as described in any of the preceding items when executing a computer program stored in a memory.

Finally, an embodiment of the present invention further provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining the spectrum efficiency of a NAFD-based non-cellular massive MIMO uRLLC system as described in any of the preceding items are implemented.

The advantage of the technical solution provided in the present application is that, under the power consumption constraints of uplink and downlink users and the QoS constraints of uplink and downlink users, the uplink and downlink transceivers in the NAFD-based cellless massive MIMO ultra-high reliability and low latency system are optimized to maximize the system weighting and spectrum efficiency, and the performance of the NAFD-based cellless massive MIMO ultra-high reliability and low latency system is effectively improved to meet the high performance requirements of users for the NAFD-based cellless massive MIMO ultra-high reliability and low latency system.

In addition, the embodiments of the present invention also provide corresponding implementation devices, electronic devices and readable storage media for the spectrum efficiency determination method of the NAFD-based non-cellular massive MIMO uRLLC system, which further makes the method more practical, and the devices, electronic devices and readable storage media have corresponding advantages.

It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and it is not difficult for ordinary technicians in this field to understand them. In other words, other drawings can be obtained based on these drawings without any creative work.

FIG1 is a flow chart of a method for determining the spectrum efficiency of a cellular-free massive MIMO uRLLC system based on NAFD provided by an embodiment of the present invention;

FIG2 is a schematic diagram of a framework of an exemplary application scenario provided by an embodiment of the present invention;

FIG3 is a schematic diagram showing performance comparison of different methods in a verification embodiment provided by an embodiment of the present invention;

FIG4 is a schematic diagram showing a comparison of spectrum efficiencies corresponding to different methods in a first exemplary verification embodiment provided by an embodiment of the present invention;

FIG5 is a schematic diagram of the relationship between spectrum efficiency and T-AP power constraint provided by an embodiment of the present invention;

FIG6 is a schematic diagram showing a comparison of spectrum efficiencies corresponding to different methods in a second exemplary verification embodiment provided by an embodiment of the present invention;

FIG7 is a schematic diagram showing a comparison of spectrum efficiencies corresponding to different methods in a third exemplary verification embodiment provided by an embodiment of the present invention;

FIG8 is a schematic diagram of comparing spectrum efficiencies corresponding to different methods in a fourth exemplary verification embodiment provided by an embodiment of the present invention;

FIG9 is a structural diagram of a specific implementation of a device for determining spectrum efficiency of a cellular-free massive MIMO uRLLC system based on NAFD provided in an embodiment of the present invention;

FIG. 10 is a structural diagram of a specific implementation of an electronic device provided in an embodiment of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

The terms "first", "second", "third", "fourth", etc. in the specification and claims of this application and the above drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "include" and "have" and any variations of the two are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may include steps or units that are not listed. The following describes in detail various non-limiting embodiments of the present application.

First, refer to Figure 1, which is a flow chart of a method for determining the spectrum efficiency of a NAFD-based cellular-free massive MIMO uRLLC system provided in an embodiment of the present invention. The NAFD-based cellular-free massive MIMO uRLLC system applicable to this embodiment is shown in Figure 2. The NAFD-based cellular-free massive MIMO uRLLC system includes multiple APs, which are connected to the central processing unit (CPU) through a backhaul link. Each AP can freely choose to work in CCFD, HD or other flexible duplex modes. For the convenience of description, the receiving AP can be called R-AP, and the sending AP can be called T-AP. Each user in the uplink and downlink works in half-duplex mode. The NAFD-based cellular-free massive MIMO uRLLC system may include L T-APSs, Z R-APSs, K downlink users, and J uplink users. Each T-AP and R-AP may include M antennas, and each user has a single antenna. The index set of T-APs can be expressed as The index set of R-AP can be expressed as The index set of downlink users can be expressed as The index set of uplink users can be expressed as The CPU processes the downlink signal and transmits it to the T-APs, which then transmit the received downlink signal to the downlink user. At the same time, the R-APs receive the signal from the uplink user and forward it to the CPU for further processing. The downlink channel h _D,k between the T-APs and the downlink user k can be expressed as represents the transpose of the downlink channel between the Lth T-AP and the kth downlink user, the inter-user interference (IUI) channel h _IUI,j,k between the jth uplink user to the kth downlink user, and the uplink channel h _U,j between the jth uplink user and R-APs can be expressed as represents the transposition of the uplink channel from the jth uplink user to the zth R-AP, and the interference (IAI) between T-APs and the zth R-AP The channel can be This can be modeled as:

in,

where Λ _D,k represents the large-scale fading from T-APs to the k-th downlink user, g _D,k represents the small-scale fading from T-APs to the k-th downlink user, β _IAI,j,k represents the large-scale fading from the j-th uplink user to the k-th downlink user, g _IAI,j,k represents the small-scale fading from the j-th uplink user to the k-th downlink user, Λ _U,j represents the large-scale fading from the j-th uplink user to R-APs, g _U,j represents the small-scale fading from the j-th uplink user to R-APs, Λ _IAI,z represents the large-scale fading from T-APs to the z-th R-AP, and G _IAI,z represents the small-scale fading from T-APs to the z-th R-AP. β _D,L,k represents the large-scale fading from the Lth T-AP to the kth downlink user, β _U,j,Z represents the large-scale fading from the jth uplink user to the Zth R-AP, β _IAI,L,z represents the large-scale fading from the Lth T-AP to the zth R-AP, and I _M represents the identity matrix of dimension M. In addition, represents the channel vector from the lth T-AP to the kth downlink user, represents the channel vector from the jth uplink user to the zth R-AP. Assume that all small-scale fading are independent and identically distributed and obey besides, Ω is the first parameter, and Ω∈{IAI,U,D}; is the second parameter, and is the third parameter, and in express and The path loss between These are simplified representations of large-scale fading.

The following discusses the optimization design method of the uRLLC uplink and downlink receiver precoding vector and uplink user transmission power under the QoS constraints and power consumption constraints of the uplink and downlink of the NAFD-based non-cellular massive MIMO uRLLC system provided by this embodiment, which can maximize the weighted sum spectrum efficiency of the NAFD-based non-cellular massive MIMO uRLLC system and obtain the target spectrum efficiency of the uplink and downlink users. The implementation process may include the following contents:

S101: Determine the uplink and downlink spectrum efficiencies based on the maximum decoding error probability of the uplink and downlink users, according to the inter-user interference channels of the uplink and downlink users, the channels from the uplink users to the R-APs, the channels from the downlink users to the T-APs, the transmission power of the uplink users, and the system noise.

S102: Based on the power consumption constraints and service quality constraints of the uplink and downlink users, the uplink and downlink transceivers are jointly optimized with the goal of maximizing the uplink and downlink weighted and spectrum efficiencies to determine the target spectrum efficiency.

In this embodiment, the inter-user interference channel refers to the inter-user interference channel between the uplink user and the downlink user, the user data signal refers to the data signal of the uplink user and the data signal of the downlink user, the channel from the downlink user to T-APs refers to the channel from the downlink user to each T-AP, and the uRLLC uplink and downlink spectrum efficiency includes the downlink spectrum efficiency and the uplink spectrum efficiency. Those skilled in the art can calculate the inter-user interference channel of the uplink and downlink users, the channel from the uplink user to R-APs, the channel from the downlink user to T-APs, the transmission power of the uplink user, and the system noise according to the calculation method of the spectrum efficiency of any link, and this application does not make any limitation on this. After determining the uplink and downlink spectrum efficiency, under the common constraints of transmission power and service quality, with the goal of maximizing the uplink and downlink weighted and spectrum efficiency, the uplink and downlink transceivers are jointly optimized to obtain the transceiver of the system, and the target spectrum efficiency of the current system is determined. The transceiver mentioned in the present application includes transmitters at the user and the T-AP, and a receiver at the R-AP. Specifically, the transmitter at the user corresponds to p _U,j , the transmitter at the T-AP corresponds to w _D,k ; and the receiver at the R-AP corresponds to u _U,j .

In the technical solution provided in the embodiment of the present invention, under the power consumption constraints of uplink and downlink users and the QoS constraints of uplink and downlink users, the uplink and downlink transceivers in the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system are optimized to maximize the system weighting and spectrum efficiency, and the uplink and downlink user weighting and spectrum efficiency can be maximized under the condition of limited code length, effectively improving the performance of the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system, so as to meet the user's high performance requirements for the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system.

It should be noted that there is no strict order of execution between the steps in the present application. As long as they conform to the logical order, these steps can be executed simultaneously or in a preset order. Figure 1 is only a schematic diagram and does not mean that this is the only execution order.

In the above embodiment, there is no limitation on how to perform step S101. In this embodiment, an optional method for determining the spectrum efficiency of uplink and downlink is provided, which includes the following steps:

Acquire a user received signal at a downlink user in a single time slot;

Determine a downlink spectrum efficiency that is achievable under a long block regime (LBR) based on a user received signal, and determine a downlink spectrum efficiency based on the achievable downlink spectrum efficiency, given a code length and ensuring a maximum decoding error probability for the downlink user;

Acquire an AP receive signal of the R-AP, and determine an uplink baseband signal of the R-AP according to the AP receive signal;

Based on the uplink baseband signal, the uplink spectrum efficiency is determined on the basis of ensuring the maximum decoding error probability of the uplink user.

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 signal can be directly obtained, and the corresponding signal data, interference data, noise data and channel data are obtained by parsing the received signal, and then the maximum uplink and downlink spectrum efficiency is calculated using these data. As an optional implementation, the obtained user received signal relationship can be expressed as:

Where y _D,k is the user received signal at the kth downlink user, D represents the downlink channel, is the downlink user set, is the uplink user set, U represents the uplink channel, h _D,k is the downlink channel of T-APs and the k-th downlink user, k' is the k'th downlink user, is the conjugate transpose of h _D,k. When the k'th downlink user is the same as the kth downlink user, indicates that the received signal data is signal data. When the k'th downlink user is different from the k'th downlink user, it indicates that the received interference data is interference data. w _D,k is the precoding vector of the k'th downlink user, w _D,k' is the precoding vector of the k'th downlink user, s _D,k ～CN(0,1), s _D,k is the data signal of the k'th downlink user, s _D,k' is the data signal of the k'th downlink user, h _IUI,j,k is the inter-user interference channel between the k'th downlink user and the j'th uplink user, s _U,j ～CN(0,1), s _U,j is the data signal of the j'th uplink user, p _U,j is the transmission power of the j'th uplink user, and n _D,k is the additive white Gaussian noise AWGN at the k'th downlink user receiver, with zero mean and variance of

After the user receiving signal is obtained, the user signal of the downlink user, the power consumption of the uplink user and various channel data can be obtained through the user receiving signal. Based on this, the signal to interference plus noise ratio SINR of the kth downlink user can also be calculated by calling the SINR signal calculation relationship; the SINR signal calculation relationship can be expressed as:

After determining the signal to interference plus noise ratio, the achievable downlink spectrum efficiency under long code can be further calculated based on the Shannon rate function. The achievable downlink spectrum efficiency can be expressed as:

Finally, for a given code length N=BT, in order to ensure the maximum decoding error probability ε _k of the kth downlink user, the downlink spectrum efficiency calculation formula can be used to calculate the maximum downlink spectrum efficiency; the downlink spectrum efficiency calculation formula can be expressed as:

Where r _D,k is the signal to interference plus noise ratio at the kth downlink user, w _D,k is the precoding vector of the kth downlink user, is the variance of the additive white Gaussian noise at the kth downlink user receiver, is the achievable downlink spectrum efficiency at the kth downlink user. rate, _RD,k is the downlink spectrum efficiency corresponding to the kth downlink user, Q ^-1 () is the inverse of the Gaussian Q function, ε _k is the maximum decoding error probability of the kth downlink user, N = BT, N is the given code length, B is the transmission bandwidth, T is the transmission time slot, and e is the natural logarithm. Among them, the Gaussian Q function can be expressed as

As another optional implementation, the AP receiving signal of the zth R-AP may be expressed as:

where y _U,z is the AP received signal of the zth R-AP, H _IAI,z is the inter-antenna interference channel from the zth R-AP to T-APs, h _U,j,z is the uplink channel from the jth uplink user to the zth R-AP, and n _U,z is the zero-mean additive white Gaussian noise at the zth R-AP, with a variance of The IAI channel H _IAI,l,z is modeled as in represents the non-ideal channel between the lth T-AP and the zth R-AP, represents the corresponding estimated channel of the non-ideal channel between the lth T-AP and the zth R-AP, represents the channel estimation error of the non-ideal channel between the lth T-AP and the zth R-AP, and in, represents the M*M identity matrix, where M is a real number. is the residual error gain at the zth R-AP for the lth T-AP, which is used to represent the residual error gain of imperfect IAI elimination in the digital or analog domain.

After appropriate interference elimination, the baseband representation relationship can be first called to determine the uplink baseband signal of the zth R-AP; the baseband representation relationship can be expressed as:

in, set up is the combined vector used to demodulate the jth uplink user data signal s _U,j at the zth R-AP. Then the noise calculation formula is called to calculate the signal to interference plus noise ratio of the jth uplink user at the CPU; the noise calculation formula can be expressed as:

The jth uplink user interference plus noise power γ _U,j can be expressed as:

The achievable uplink spectrum efficiency of the jth uplink user can be expressed as Similarly, for a given channel code block length N, in order to ensure that the maximum decoding error probability of the jth uplink user does not exceed ε _j , the maximum uplink spectrum efficiency can be calculated by calling the uplink spectrum efficiency calculation formula; the uplink spectrum efficiency calculation formula can be expressed as:

In the formula, is the uplink baseband signal, is the channel estimation error of the non-ideal channel from the zth R-AP to T-APs; r _U,j is the signal to interference plus noise ratio of the jth uplink user at the CPU, γ _U,j is the interference plus noise power at the jth uplink user, is the combined vector for demodulating the jth decoded uplink user data signal at the zth R-AP, H represents the conjugate transpose; R _U,j is the uplink spectrum efficiency corresponding to the jth uplink user, is the achievable uplink spectrum efficiency of the jth uplink user, ε _{j is} the maximum decoding error probability of the jth uplink user. Q ^-1 () is the inverse of the Gaussian Q function, and N is the given code length.

Based on the above embodiment, the uRLLC system weighted and spectral efficiency is maximized under the transmission power and QoS constraints, and the transceivers {w _D,k ,u _U,j ,p _U,j } are jointly optimized. That is, the uplink and downlink transceivers and user transmission power are jointly optimized by calling the uRLLC system optimization relation, and the uRLLC system optimization relation can be expressed as:

In the formula, is the set of {w _D,k ,u _U,j ,p _U,j }, where {w _D,k ,u _U,j ,p _U,j } represents the parameters corresponding to the transceiver, w _D,k is the precoding vector of the k-th downlink user, u _U,j is the combined vector used to demodulate the data signal of the j-th uplink user, D represents the downlink channel, is the downlink user set, is the uplink user set, U represents the uplink channel, α _D,k is the spectrum efficiency weight of the k-th downlink user, α _U,j is the spectrum efficiency weight of the j-th uplink user, _RD,k is the downlink spectrum efficiency corresponding to the k-th downlink user, RU _,j is the uplink spectrum efficiency corresponding to the j-th uplink user, w _D,l,k is the precoding vector of the k-th downlink user in the l-th T-AP, _PD,l is the power consumption budget of the l-th T-AP, PU,j is the power consumption (i.e., transmission power consumption) of the j-th uplink user, PU _, _j is the power consumption budget of the j-th uplink user, C1 is the power consumption constraint of the k-th downlink user, C2 is the power consumption constraint of the j-th uplink user; C3 is the QoS constraint of the k-th downlink user, C4 is the QoS constraint of the j-th uplink user, _RD,min,k is the minimum value of the QoS constraint of the k-th downlink user, and _RU,min,j is the minimum value of the QoS constraint of the j-th uplink user.

In order to determine the target spectrum efficiency through joint optimization, the present application also provides two different methods to solve the problem of uRLLC system weighting and spectrum efficiency, which may include the following:

As an optional implementation manner, this embodiment can jointly optimize the uplink and downlink transceivers by calling the concave-convex algorithm optimization relationship, and the concave-convex algorithm optimization relationship can be expressed as:

in,

C14:

C15:

C17:

C19:

C25:

C26: C28:

C31:

C32:

C34: C38:

C39: C40: C41:

In the formula, is a set, t _D,k , t _U,j , ρ _D,k , ρ _U,j , η _U,j,j′ 、 χ _U,j 、τ _U,j,k,l,z 、 ν _U,j,j′ 、 ξ _U,j 、 are auxiliary variables, h _IUI,j,k is the inter-user interference channel between the kth downlink user and the jth uplink user, is the variance of the additive white Gaussian noise at the kth downlink user receiver, N is the code length, w _D,k is the precoding vector of the kth downlink user, p _U,j is the power consumption of the jth uplink user, is the precoding vector value of w _D,k′ at the nth iteration, k′ is the k′th downlink user, _HD,k is the intermediate parameter, hD _,k is the downlink channel between T-APs and the kth downlink user, is the conjugate transpose of h _D,k , represents the first preset function, j′ is the j′th uplink user, and similarly, if j′=j, it means that signal data is received, and if j′≠j, it means that interference data is received. represents the second preset function, for The value at the nth iteration, is the conjugate transpose of h _{D,l,k ,} where h _D,l,k is the channel vector from the lth T-AP to the kth downlink user, is the value of ρ _D,k at the nth iteration, is the conjugate transpose of u _U,j , u _U,j is the combined vector used to demodulate the data signal of the jth uplink user, is the value of u _U,j at the nth iteration, is the value of p _U,j at the nth iteration, is the variance of the additive white Gaussian noise at the zth R-AP, u _U,j,z is the receiver vector of the zth R-AP processing the jth uplink user, is the value of u _U,j,z at the nth iteration, H _U,j′ is the intermediate parameter, p _U,j′ is the power consumption of the j′th uplink user, is the value of ξ _U,j at the nth iteration, is the conjugate transpose of h _U,j′ , is the value at the nth iteration, is the value of w _D,l,k at the nth iteration, w _D,l,k is the precoding vector from the lth T-AP to the kth downlink user, I _LM is the identity matrix of dimension LM*LM, LM＝L*M, L and M are both real numbers, for The value of I _ZM at the nth iteration is The identity matrix of dimension ZM*ZM, ZM=Z*M, Z and M are both real numbers, is the value of ν _U,j,j′ at the nth iteration, for The value at the nth iteration, f represents the third preset function, and e is the natural logarithm. The first preset function Second preset function The third preset functions f are all user-defined functions, and the expressions of the user-defined functions are as follows:

Where a＞0, c＞0 represent two scalar variables, b represents a variable in vector form, and A represents a matrix variable; n represents the number of loops, and c ⁽ⁿ⁾ and b ⁽ⁿ⁾ represent the current feasible points of c and b in the nth loop, respectively. It means taking the real part.

Specifically, this embodiment adopts a CCCP-based method (CCCP is a monotonically decreasing global optimization method) to solve the above-mentioned problem of joint optimization design of uplink and downlink transceivers. By introducing auxiliary variables {t _D,k ≥0}, {t _U,j ≥0}, {ρ _D,k ≥0} and {ρ _U,j ≥0}, the joint optimization problem is equivalently converted into:

s.t.C1，C2

C5:

C6:

C7:

C8:

C9:

C10:

in, Although the objective function and constraints C1, C2, C5, and C6 are already approximately convex functions, the new problem after transformation is still non-convex due to the existence of downlink constraints C7, C9 and uplink constraints C8 and C10. In order to transform the original problem from non-convex to convex, we first introduce the following inequality:

D1:

D2:

D3:

Among them, a＞0, c＞0 represent two scalar variables, b represents a variable in vector form, and A represents a matrix variable; n represents the number of loops, and c ⁽ⁿ⁾ and b ⁽ⁿ⁾ represent the current feasible points of c and b in the nth loop respectively.

(1) Downstream constraint approximation: Process downstream constraints C9 and C7 in order. First, Can Further simplification, constraint C9 can be equivalently transformed into:

C11:

Introducing auxiliary variables C11 can be further written as:

C12:

C13:

Using inequality D1, C12 and C13 can be approximated as follows:

C14:

C15:

in,

Next, introduce another auxiliary variable Constraint C7 is rewritten as the following two equations:

C16:

C17:

It can be found that constraint C17 has been rewritten as a convex constraint, but C16 is still a non-convex constraint, so through some simple changes, C16 is rewritten as follows:

C18:

Using inequality D2, C18 can finally be approximated as:

C19:

(2) Uplink constraint approximation: Uplink constraints C8 and C10 are similar to downlink constraints. First, constraint C10 is approximated. After some simple operations, we get:

Furthermore, the inequality can be rewritten as:

C20:

Introduce a series of auxiliary variables for constraint C20 Each variable satisfies:

C21:

C22:

C23:

C24:

The above four constraints C21-C24 are all non-convex. Let’s deal with the simpler C23 and C24 first. First, make some simple inequality transformations on constraint C23 to get Then, using the inequality D3 approximation, we get:

C25:

Similarly, applying inequality D2 to approximate C24, we obtain:

C26:

After completing the approximation of constraints C23 and C24, continue to approximate C21 and C22. Through mathematical inequality transformation, constraint C21 is rewritten as:

C27:

It can be found that both sides of the C27 inequality are non-convex and non-concave functions. For Function Approximately, we can get:

Among them, ξ _U,j is an auxiliary variable introduced, and {ξ _U,j ≥0}, is the value of ξ _U,j at the nth iteration, In addition, when When , the inequality takes the equal sign. Dividing both sides of C27 by p _U,j′ and applying inequality D1 and C27, we can obtain:

C28:

in,

After completing the approximate processing of constraint C21, a new auxiliary variable {τ _U,j,k,l,z ≥0} is introduced for constraint C22, so constraint C22 can be equivalently converted into the following two equations:

C29:

C30:

Then use inequality D2 to approximate C29 and C30, and we get:

C31:

C32:

At this point, the convex approximation of constraint C10 has been completed, and constraint C8 is processed; C8 can be equivalently replaced by the following two inequalities:

C33:

C34:

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

C35:

C36:

C37:

C38:

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

C39:

C40:

C41:

In order to further reduce the complexity of the CCCP-based algorithm proposed in the above embodiment, this embodiment also provides another solution to the joint optimization problem, which can be called a hybrid ZF-MRT beamforming algorithm. This method can jointly optimize the uplink and downlink transceivers by calling the hybrid algorithm optimization relationship. The hybrid algorithm optimization relationship can be:

s.t.C3,C45,C46,C47,C48,C53,C54,C59,C60,C61,C62,C64,C65,C66,C67

in, C45: C46: C47:

C48:

C53:

C54:

C59:

C60:

C61:

C62:

C64:

C65:

C66:

C67:
Ψ,Ω,d _k′,k , are auxiliary variables, is the merging factor variable, p _{D,l,k is} the power transmitted from the lth T-AP to the kth downlink user when hybrid beamforming is used, is the value of p _D,l,k at the nth iteration, is the R-AP set, for The value at the nth iteration, is the value of p _U,j at the nth iteration, p _U,j is the power consumption of the jth uplink user, represents the second preset function, is the value of d _k′,k at the nth iteration, for The value at the nth iteration, represents the first preset function, u _U,j is a combined vector for demodulating the data signal of the jth uplink user, is the value of u _U,j at the nth iteration, is the conjugate transpose of u _U,j , is the value of u _U,j,z at the nth iteration, u _U,j,z is the receiver vector of the zth R-AP processing the jth uplink user, h _U,j′ is the uplink channel between the R-APs and the j′th uplink user, H _U,j and H _U,j′ are intermediate parameters, for The value at the nth iteration, p _U,j′ is the power consumption of the j′th uplink user, for At the nth iteration, I _ZM is the identity matrix of dimension ZM*ZM. Similarly, ZM＝Z*M, where Z and M are both real numbers. is the variance of the additive white Gaussian noise at the zth R-AP, for The value at the nth iteration, for The value at the nth iteration, e is the natural logarithm.

Specifically, in this embodiment, two merging factor variables are introduced: and make in U _D,k represents The orthogonal basis of the null space, the link gain can be expressed as:

make and Therefore, G _k′,k can be rewritten as:

When hybrid beamforming is used, the power transmitted by the lth AP to the kth user can be expressed as:

in, and

Then the new problem using the hybrid beamforming algorithm is written as follows:

s.t.C3,

C42:

C43:

C44:

in,

in, is the interference plus noise power of the jth uplink user. In order to calculate this new problem, a new variable can be introduced This variable satisfies And zoom Make or because So G _k′,k and p _D,l,k can be reformulated as:

in,

Therefore, the original problem can be transformed into:

s.t.C3,

C45:

C46:

C47:

C48:

C49:

C50:

C51:

C52:

in, and is an auxiliary variable, and Since the objective function and constraints C49, C50, C51, and C52 are non-convex, the new problem needs further convex approximation. Similar to the above embodiment, the CCCP method can be used to obtain convex approximations of constraints C49, C50, C51, and C52 respectively.

(1) First, constraints C49 and C51 are processed. By introducing auxiliary variables {d _{k′, k} ≥ 0, Ψ ≥ 0, Ω ≥ 0}, the left side of constraint C49 can be transformed into:

in,

Will Approximately as follows:

in, Therefore, constraint C49 can be approximated as:

C53:

Further process C51. After moving the term, constraint C51 becomes Substituting the scaling inequality D2 into the equation, we get:

C54:

(2) Continue to process constraints C50 and C52. First, C50 can be rewritten as:

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

C55:

C56:

C57:

C58:

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

C59:

C60:

C61:

C62:

in, By to update.

Next, we process constraint C52 and introduce a new auxiliary variable. C52 This is transformed into the following two inequalities:

C63:

C64:

Since C63 is a non-convex expression, auxiliary variables need to be introduced again This is transformed into the following inequality:

The above three equations can be transformed into:

C65:

C66:

C67:

In order to verify the effectiveness of the method provided by this embodiment, that is, compared with the traditional CCFD and HD solutions, the system performance can be improved. This embodiment also carried out a series of verification tests. Figure 3 proves that the technical solution proposed in this embodiment has good convergence. The NAFD in Figure 3 corresponds to the technical solution of this application for joint optimization of uplink and downlink transceivers based on the CCCP algorithm. (For ease of description, referred to as NAFD), the Hybrid in FIG3 corresponds to the technical solution of the present application for joint optimization of uplink and downlink transceivers based on the hybrid beamforming algorithm of CCCP (for ease of description, referred to as Hybrid), the horizontal axis of FIG3 represents the number of iterations, and the vertical axis represents the spectrum efficiency. It can be seen from FIG3 that NAFD can obtain the best steady-state performance, followed by Hybrid. FIG4 shows the variation of spectrum efficiency with the number of users K=J when M=2, L=Z=8, Δ=-10dB, _PD,l =30dBm, where Δ is the self-interference residual noise power. It can be seen from FIG4 that the method provided in this embodiment, namely NAFD and Hybrid, has better performance than CCFD and TDD (Time Division Duplex) solutions, and when the number of users is less than 4, the spectrum efficiency SE increases rapidly with the number of users, because the system performance is better when T-AP is much larger than R-AP, but when the number of users is greater than a certain value, the interference between users is enhanced and SE increases slowly. Figure 5 shows the relationship between SE and T-AP power constraint. For NAFD and Hybrid, the increase in T-AP power constraint can provide more spectrum gain. Figure 6 shows that the spectrum efficiency obtained by NAFD and Hybrid will increase with the improvement of IAI cancellation when M = 2, L = Z = 8, _PD,l = 30dBm, and K = J = 4. When Δ≤-15dB, the SE of the three schemes (NAFD, Hybrid, and CCFD) will not be affected by IAI suppression. When Δ≥10dB, the spectrum efficiency obtained by NAFD and Hybrid is lower than that of the TDD scheme. Figure 7 shows that when M = 2, Δ = -10dB, K = J = 4, _PD,l = 30dBm, and L = Z, the channel gain increases because the interference is easier to suppress. FIG8 shows the relationship between SE and the number of antennas M. When L=Z=6, Δ=-10dB, K=J=4, and _PD,l =30dBm, the technical solutions provided in this embodiment can obtain better spectrum efficiency.

The embodiment of the present invention also provides a corresponding device for the spectrum efficiency determination method of the non-cellular massive MIMO uRLLC system based on NAFD, which further makes the method more practical. The device can be described from the perspective of functional modules and hardware. The spectral efficiency determination device of the NAFD-based cellular-free massive MIMO uRLLC system provided in the embodiment is introduced. The spectral efficiency determination device of the NAFD-based cellular-free massive MIMO uRLLC system described below and the spectral efficiency determination method of the NAFD-based cellular-free massive MIMO uRLLC system described above can be referenced to each other.

From the perspective of functional modules, see FIG9 , which is a structural diagram of a spectral efficiency determination device for a cellular-free massive MIMO uRLLC system based on NAFD provided by an embodiment of the present invention in a specific implementation manner, and the device may include:

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

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 the uplink and downlink users, and determine the target spectrum efficiency by maximizing the uplink and downlink weighted and spectrum efficiencies.

The functions of various functional modules of the spectrum efficiency determination device of the NAFD-based non-cellular massive MIMO uRLLC system in the embodiment of the present invention can be specifically implemented according to the method in the above-mentioned method embodiment, and the specific implementation process can refer to the relevant description of the above-mentioned method embodiment, which will not be repeated here.

The spectral efficiency determination device of the NAFD-based non-cellular massive MIMO uRLLC system mentioned above is described from the perspective of functional modules. Furthermore, the present application also provides an electronic device, which is described from the perspective of hardware. Figure 10 is a structural schematic diagram of an electronic device provided in an embodiment of the present application under one implementation. As shown in Figure 10, the electronic device includes a memory 100 for storing a computer program; a processor 101 for implementing the steps of the spectral efficiency determination method of the NAFD-based non-cellular massive MIMO uRLLC system mentioned in any of the above embodiments when executing the computer program.

In some embodiments, the electronic device may further include a display screen 102, an input/output interface 103, a communication interface 104 or a network interface, a power supply 105, and a communication bus 106. For ease of representation, FIG10 only uses one thick line, but does not mean that there is only one bus or one type of bus.

Those skilled in the art will appreciate that the structure shown in FIG. 10 does not limit the electronic device and may include more or fewer components than shown in the figure, for example, may also include a sensor 107 for implementing various functions.

The functions of the functional modules of the electronic device described in the embodiment of the present invention can be specifically implemented according to the method in the above method embodiment. The specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

From the above, it can be seen that the embodiments of the present invention can effectively improve the performance of the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system to meet the high performance requirements of users for the NAFD-based non-cellular massive MIMO ultra-high reliability and low latency system.

It can be understood that if the spectrum efficiency determination method of the NAFD-based non-cellular massive MIMO uRLLC system in the above embodiment is implemented in the form of 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 the present application is essentially 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, and the computer software product is stored in a storage medium to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), electrically erasable programmable ROM, registers, hard disks, multimedia cards, card-type memories (such as SD or DX memories, etc.), magnetic memories, removable disks, CD-ROMs, magnetic disks or optical disks, and other media that can store program codes.

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

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

The above is a detailed introduction to the spectrum efficiency determination method, device, electronic device and readable storage medium of a NAFD-based non-cellular massive MIMO uRLLC system provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

A method for determining the spectrum efficiency of a cellular-free massive MIMO uRLLC system based on NAFD, characterized by comprising:

Based on the inter-user interference channels of uplink and downlink users, the channels from uplink users to R-APs, the channels from downlink users to T-APs, the transmission power of uplink users, and system noise, the uplink spectrum efficiency is determined on the basis of 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 uplink and downlink transceivers are jointly optimized to determine the target spectrum efficiency by maximizing the uplink and downlink weighted and spectrum efficiency.
The method according to claim 1, characterized in that the determining of the downlink spectrum efficiency comprises:

Acquire a user received signal at a downlink user in a single time slot;

The achievable downlink spectrum efficiency under the long code is determined based on the user received signal, and based on the achievable downlink spectrum efficiency, the downlink spectrum efficiency is determined on the basis of a given code length and ensuring the maximum decoding error probability of the downlink user.
The method according to claim 1, characterized in that the determining of the uplink spectrum efficiency comprises:

Acquire an AP receive signal of the R-AP, and determine an uplink baseband signal of the R-AP according to the AP receive signal;

Based on the uplink baseband signal, the uplink spectrum efficiency is determined on the basis of ensuring the maximum decoding error probability of the uplink user.
The method according to claim 2, characterized in that the downlink spectrum efficiency is:

in,

Where R D,k is the downlink spectrum efficiency corresponding to the kth downlink user, is the achievable downlink spectrum efficiency at the kth downlink user, r D,k is the signal to interference plus noise ratio at the kth downlink user, h D,k is the downlink channel between T-APs and the kth downlink user, is the conjugate transpose of h D,k ; w D,k is the precoding vector of the kth downlink user, p U,j is the transmission power of the jth uplink user, h IUI,j,k is the inter-user interference channel between the kth downlink user and the jth uplink user, is the variance of the additive white Gaussian noise at the k-th downlink user receiver, Q -1 () is the inverse of the Gaussian Q function, ε k is the maximum decoding error probability of the k-th downlink user, N is the given code length, e is the natural logarithm, is the downlink user set, is the uplink user set, and k' is the k'th downlink user.
The method according to claim 3, characterized in that the uplink spectrum efficiency is:

in,

Where R U,j is the uplink spectrum efficiency corresponding to the jth uplink user, is the achievable uplink spectrum efficiency of the jth uplink user, r U,j is the signal to interference plus noise ratio of the jth uplink user at the CPU, ε j is the maximum decoding error probability of the jth uplink user, p U,j is the transmission power of the jth uplink user, and γ U,j is the signal to interference plus noise ratio of the jth uplink user at the CPU. The interference plus noise power, is the combined vector used to demodulate the j-th uplink user data signal at the z-th R-AP, H represents the conjugate transpose; h U,j,z is the uplink channel from the j-th uplink user to the z-th R-AP.
The method according to any one of claims 1 to 5, characterized in that the uRLLC system optimization relation is called to jointly optimize the uplink and downlink transceivers, and the uRLLC system optimization relation is:

In the formula, is the set {w D,k ,u U,j ,p U,j }, w D,k is the precoding vector of the k-th downlink user, u U,j is the combining vector used to demodulate the data signal of the j-th uplink user, p U,j is the power consumption of the j-th uplink user, is the downlink user set, is the uplink user set, α D,k is the spectrum efficiency weight of the k-th downlink user, α U,j is the spectrum efficiency weight of the j-th uplink user, RD ,k is the downlink spectrum efficiency corresponding to the k-th downlink user, RU ,j is the uplink spectrum efficiency corresponding to the j-th uplink user, w D,l,k is the precoding vector of the k-th downlink user in the l-th T-AP, PD,l is the power consumption budget of the l-th T-AP, PU,j is the power consumption budget of the j-th uplink user, C1 is the power consumption constraint of the k-th downlink user, C2 is the power consumption constraint of the j-th uplink user; C3 is the QoS constraint of the k-th downlink user, C4 is the QoS constraint of the j-th uplink user, RD,min,k is the minimum value of the QoS constraint of the k-th downlink users, and RU,min,j is the minimum value of the QoS constraint of the j-th uplink user.
The method according to claim 6 is characterized in that the concave-convex algorithm optimization relational expression is called to jointly optimize the uplink and downlink transceivers, and the concave-convex algorithm optimization relational expression is:

in,

In the formula, is a set, t D,k , t U,j , ρ D,k , ρ U,j , η U,j,j′ 、 χ U,j 、τ U,j,k,l,z 、 ν U,j,j′ 、 ξ U,j 、 are auxiliary variables, h IUI,j,k is the inter-user interference channel between the kth downlink user and the jth uplink user, is the variance of the additive white Gaussian noise at the k-th downlink user receiver, N is the code length, w D,k is the precoding vector of the k-th downlink user, is the precoding vector value of w D,k′ at the nth iteration, h D,k is the downlink channel between T-APs and the kth downlink user, represents the first preset function, j′ is the j′th uplink user, represents the second preset function, h D,l,k is the channel vector from the lth T-AP to the kth downlink user, is the variance of the additive white Gaussian noise at the zth R-AP, u U,j,z is the receiver vector of the zth R-AP processing the jth uplink user, H U,j′ is an intermediate parameter, is the residual error gain at the zth R-AP for the lth T-AP, I LM is the unit matrix of dimension LM*LM, I ZM is the unit matrix of dimension ZM*ZM, f represents the third preset function, and e is the natural logarithm.
The method according to claim 6 is characterized in that a hybrid algorithm optimization relation is called to jointly optimize the uplink and downlink transceivers, and the hybrid algorithm optimization relation is:

in,
Ψ,Ω,d k′,k , are auxiliary variables, is the merging factor variable, p D,l,k is the power transmitted from the lth T-AP to the kth downlink user when hybrid beamforming is used, is the R-AP set, represents the second preset function, represents the first preset function, u U,j is used for is the combined vector for demodulating the data signal of the jth uplink user, u U,j,z is the receiver vector of the zth R-AP processing the jth uplink user, h U,j′ is the uplink channel between the R-APs and the j′th uplink user, H U,j and H U,j′ are both intermediate parameters, p U,j′ is the power consumption of the j′th uplink user, is the residual error gain at the zth R-AP for the lth T-AP, I ZM is the identity matrix of dimension ZM*ZM, is the variance of the additive white Gaussian noise at the zth R-AP, and e is the natural logarithm.
A spectral efficiency determination device for a cellular-free massive MIMO uRLLC system based on NAFD, characterized by comprising:

A spectrum efficiency determination module, used to determine the uplink and downlink spectrum efficiencies based on ensuring the maximum decoding error probability of the uplink and downlink users according to the inter-user interference channels of the uplink and downlink users, the channels from the uplink users to the R-APs, the channels from the downlink users to the T-APs, the transmission power of the uplink users, and the system noise;

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 the uplink and downlink users, and determine the target spectrum efficiency by maximizing the uplink and downlink weighted and spectrum efficiency.
An electronic device, characterized in that it comprises a processor and a memory, wherein the processor is used to implement the steps of the method for determining the spectrum efficiency of a NAFD-based non-cellular massive MIMO uRLLC system as described in any one of claims 1 to 8 when executing a computer program stored in the memory.
A readable storage medium, characterized in that a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the steps of the method for determining the spectrum efficiency of a NAFD-based non-cellular massive MIMO uRLLC system are implemented as described in any one of claims 1 to 8.