RIS-assisted cellular-removal large-scale MIMO energy efficiency improvement method and system

Abstract

The invention relates to the technical field of wireless communication, and discloses an RIS-assisted cellular-removal large-scale MIMO energy efficiency improvement method and system, and the method comprises the steps: building an RIS-assisted cellular-removal large-scale MIMO system under a Rician fading channel condition; based on the constructed MIMO system, channel estimation is carried out through a linear minimum mean square error method; based on the constructed MIMO system, deducing a closed expression of system energy efficiency based on a zero-forcing detector; constructing an energy efficiency optimization problem formula based on the closed expression; and according to the optimization problem formula, designing an alternate optimization algorithm to optimize the energy efficiency. The system corresponds to the method. By adopting the RIS-assisted honeycomb-removed large-scale MIMO energy efficiency improvement method and system provided by the invention, the energy efficiency performance of the system can be remarkably improved, and a foundation is laid for improving the performance of the MIMO system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and particularly relates to a RIS-aided de-cell large-scale MIMO energy efficiency improvement method and system. BACKGROUND

[0002] With the rapid development of mobile communication technology, people's demand for wireless communication systems is growing, not only requiring higher data transmission rate, but also expecting lower energy consumption and more reliable communication connection. Large-scale MIMO (Multiple Input Multiple Output, a wireless communication technology that uses multiple antennas at the transmitting end and receiving end to improve data transmission rate and system capacity) technology has become one of the key technologies of 5G and future 6G communication systems due to its significant advantages in improving spectral efficiency, enhancing communication capacity and improving signal coverage. However, under the traditional cellular network architecture, large-scale MIMO systems face many challenges, especially in energy efficiency improvement.

[0003] In the traditional cellular network architecture, the base station needs to provide services for multiple users at the same time, and each user equipment communicates with the base station through a wireless channel. Due to the complexity of the wireless channel, such as multipath effect, shadow fading, etc., the signal will be attenuated and distorted during transmission, which not only affects the communication quality, but also causes energy waste. In order to overcome these challenges, the base station usually needs to increase the transmission power to ensure the reliable transmission of the signal, which undoubtedly increases the energy consumption of the system. In addition, the base station in the large-scale MIMO system needs to be equipped with a large number of antennas, and the hardware cost of these antennas and the complexity of signal processing also bring a huge burden to the system. In recent years, RIS technology as a new emerging wireless communication technology has received widespread attention. RIS is a reconfigurable electromagnetic surface composed of a large number of low-power and low-cost reflecting elements, which can dynamically adjust its reflection coefficient according to the needs, thereby actively regulating the wireless channel. By reasonably designing the reflection coefficient of RIS, the propagation characteristics of the wireless channel can be effectively improved, the strength and quality of the signal can be enhanced, and the energy loss in the signal transmission process can be reduced, providing a new idea and method for solving the energy efficiency problem in large-scale MIMO systems.

[0004] In existing research, although some work has explored RIS-aided massive MIMO systems, most research is still based on traditional cellular network architecture and does not fully exploit the potential of RIS. In actual communication scenarios, there are problems such as imperfect channel state information and hardware damage, which will affect network performance and further increase the complexity of system design and optimization. In addition, these studies mainly focus on channel estimation, beamforming and other technologies, and lack systematic optimization strategies for overall energy efficiency improvement. Therefore, how to use RIS to assist massive MIMO systems in a de-cellular network architecture to achieve more efficient energy efficiency improvement is a problem to be solved. SUMMARY

[0005] According to the technical problems proposed above, the present application proposes a RIS-aided de-cellular massive MIMO energy efficiency improvement method and system. By using the linear minimum mean square error method to obtain imperfect channel state information, and combining with the zero-forcing reception scheme, a closed-form expression of the system energy efficiency is derived, and an alternating optimization algorithm is further designed to jointly optimize the power control coefficient and the phase offset of the RIS, which significantly improves the energy efficiency performance of the system and lays a foundation for improving the system performance.

[0006] To achieve the above purpose, the present application discloses the following technical solutions:

[0007] In a first aspect, the present application discloses a RIS-aided de-cellular massive MIMO energy efficiency improvement method, which comprises:

[0008] Step S1: establishing a RIS-aided de-cellular massive MIMO system under the condition of Rayleigh fading channel;

[0009] Step S2: based on the constructed MIMO system, performing channel estimation by using the linear minimum mean square error method;

[0010] Step S3: based on the constructed MIMO system, deriving a closed-form expression of the system energy efficiency based on the zero-forcing detector;

[0011] Step S4: based on the closed-form expression, constructing an energy efficiency optimization problem;

[0012] Step S5: according to the optimization problem, designing an alternating optimization algorithm to optimize the energy efficiency.

[0013] As a preferred, the step S1 comprises:

[0014] constructing a de-cellular massive MIMO system composed of m distributed access points, r RIS and u single-antenna users;

[0015] All access points and RISs in the MIMO system are interconnected with a central processor through a backhaul link, and based on a data sharing transmission strategy, the access points share channel information to cooperatively allocate resources, and the access points and RISs cooperatively optimize reflection phase shift and beamforming, and all access points and RISs synchronize data with the CPU to realize joint channel estimation and signal detection;

[0016] Each access point of the MIMO system is equipped with N antennas, and each RIS is configured with j passive reflecting elements;

[0017] The MIMO system is configured with a time division duplex mode, wherein the time-frequency resources are divided into multiple coherence intervals, each coherence interval includes τ c symbols, and τ p symbols are used for pilot signals;

[0018] Define the set of access points as M = {1, 2,..., m}, the set of users as U = {1, 2,..., u}, the set of RISs as R = {1, 2,..., r}, the set of passive reflecting elements of a single RIS as J = {1, 2,..., j}, and the total element set of all RISs as Q = {1, 2,..., RJ}.

[0019] As a preferred, the step S1 further comprises:

[0020] Assuming that the channel between the kth RIS and all users is a line-of-sight scenario, the channel matrix G RU,k from the kth RIS to the u users is represented as:

[0021]

[0022] wherein diag(·) is a diagonal matrix operation, β RU,lk is the path loss factor of the kth RIS and the tth user, is a deterministic LoS channel matrix, and the dimension of the matrix is j x u.

[0023] As a preferred, the step S1 further comprises:

[0024] The channel G AR,ik between the ith access point and the kth RIS is represented as:

[0025]

[0026] wherein β AR,ik is the path loss factor of the ith access point and the kth RIS, K AR,ik is the Rician factor, is the line-of-sight component, for the non-line-of-sight component, whose elements are subject to independent and identically distributed complex Gaussian distribution CN(0, 1);

[0027] the channel G between the ith access point and the tth user AU,i is expressed by using the Rice fading as:

[0028]

[0029] where β AU,it is the path loss factor between the ith access point and the tth user, K AU,it is the Rice factor, is the line-of-sight component, for the non-line-of-sight component, whose elements are subject to independent and identically distributed complex Gaussian distribution CN(0, 1).

[0030] As a preference, the step S1 further comprises:

[0031] the aggregated channel matrix G between the ith access point and all users i is expressed by:

[0032]

[0033] where θ k = [θ k,1 , θ k,2 ,..., θ k,j ] T is the phase shift vector of the kth RIS, θ k,l ∈ [0, 2Π) is the phase shift coefficient of the 1st element of the kth RIS, diag(θ k ) represents diagonalization of θ k , and the phase shift vector of all RIS is defined as

[0034] As a preference, the step S2 comprises:

[0035] S21, within each coherence interval, all users send orthogonal pilot signals to m access points as S = [s1; s2;...; s U ], where the pilot sequence of the user t is s t , the pilot length τ p , and the pilot sequence satisfies S H S = I U , I U is the U-order unit matrix, S H is the conjugate transpose of the pilot signal corresponding matrix; the pilot signal Y p,i received by the ith access point is:

[0036]

[0037] where p p is the transmit power of the pilot for the user, is the additive white Gaussian noise at the i-th access point, whose elements follow a complex Gaussian distribution CN(0, σ 2 ); G i is the aggregate channel matrix between the i-th access point and all users;

[0038] S22, for estimating the aggregate channel, multiplying the pilot signal received by the i-th access point by to obtain:

[0039]

[0040] S23, using the linear minimum mean square error method to estimate the aggregate channel between the i-th access point and u users

[0041]

[0042] where is the covariance matrix of G i , is the covariance matrix of ;

[0043] S24, defining the estimated channel between the i-th access point and the t-th user as , and the channel estimation error as The estimated channel and the channel estimation error are statistically independent, and where and are the covariance matrices of the estimated channel and the channel estimation error, respectively.

[0044] As a preferred, the step S3 comprises:

[0045] S31, in the uplink data transmission phase, u users send signals X, and the signal received by the i-th access point is:

[0046]

[0047] where p u is the maximum uplink data transmit power, G i is the aggregate channel matrix between the i-th access point and all users, η = [η1, η2,..., η U ] T is the power control coefficient, is the noise at the i-th access point, whose elements follow a complex Gaussian distribution CN(0, σ 2 ); the i-th access point uses a zero-forcing detector, and the detection matrix is and A i G i = IU;

[0048] S32, after the CPU processing of all access point signals, the detection signal is:

[0049] S33, the detection signal of the tth user is:

[0050]

[0051] wherein, (A i G i ) ts is the tth row s th column element of matrix A i G i ;

[0052] S34, the achievable rate R t of the tth user is:

[0053]

[0054] wherein, denotes statistical expectation, is the conjugate of χ t ;

[0055] S35, the system energy efficiency EE is the ratio of total rate to total energy consumption:

[0056]

[0057] wherein, W is the transmission bandwidth, P AP is the fixed circuit power consumption of the access point, P RIS is the fixed circuit power consumption of the RIS, P User is the fixed circuit power consumption of the user;

[0058] S36, through matrix transformation, the closed-form expression of the system energy efficiency EE is rewritten as:

[0059]

[0060] wherein, det(·) is the determinant operation, is the channel covariance matrix of the ith access point, C 6,it is the (N-1) × (N-1) dimensional submatrix of C 5,i after removing the tth row and the tth column, and det(C 6,it ) is the corresponding minor.

[0061] As a preferred, the step S4 comprises:

[0062] S41, the energy efficiency optimization problem is expressed as:

[0063]

[0064] wherein the optimization variable is: (the phase shift vector of all RISs, dimension Qx1) θ k is the phase shift vector of the kth RIS, θ k,l is the phase shift coefficient of the lth element of the kth RIS; η t is the power control coefficient;

[0065] The constraint conditions are respectively the value range of the power control coefficient and the value range of the RIS phase shift coefficient;

[0066] S42, when the power control coefficient is fixed, the RIS phase shift optimization sub-problem is:

[0067]

[0068] S43, the expression of solving the RIS phase shift optimization sub-problem by using the gradient ascent method, the gradient of the objective function is:

[0069]

[0070] wherein, is the gradient vector of the system energy efficiency EE with respect to θ; C 5,i is the channel covariance matrix of the ith access point;

[0071] S44, when the optimization variable is fixed, the power control coefficient optimization sub-problem is:

[0072] S45, auxiliary variables y = [y1,...,y U ] T and Z are introduced, the power control coefficient optimization sub-problem is converted into a convex problem, and η, y and Z are iteratively optimized; wherein y t ≤ R t (η t ), R t is the achievable rate of the tth user, (which is a function of η t ),

[0073] S46, S42 and S44 are alternately executed until convergence.

[0074] As a preferred, the step S5 comprises:

[0075] S51, initialize the parameter t = 0, and the initial phase shift vector θ(0) Initial power control coefficient η (0) ;

[0076] S52. Initialize the parameters for the accelerated gradient ascent algorithm: t AGA =0, θ AGA (0) =θ (0) v (0) =θ (0) , σ (0) =1;

[0077] S53, fix the power control coefficient vector η for the t-th iteration. (t) Update the gradient using the gradient of the objective function.

[0078] S54. Set step size α using backtracking search. AGA ,renew

[0079] S55, Update the momentum term υ (t+1) =θ AGA (t+1) +(t AGA / (t AGA +3))(θ AGA (t+1) -θ AGA (t) Step size parameter σ (t+1) =σ (t) / 2;

[0080] S56, If |f(θ) AGA (t+1) )-f(θ AGA (t) )|<10 -5 Output θ (t+1) =θ AGA (t+1) And execute S57; otherwise, t AGA =t AGA +1, return to S53;

[0081] S57. Initialize the second transformation parameters: t QT =0, η QT (0) =η (t) y (0) Z (0) S58. Fix the phase shift vector η of the t-th iteration. QT (t) ,pass Update y (t+1) , where C 5,iC is the channel covariance matrix of the ith access point 6,it C is the channel covariance matrix of the ith access point 5,i (N-1)×(N-1) dimensional sub-matrix after removing the t-th row and the t-th column;

[0082] S59, fix η QT (t) and y (t+1) , by updating Z (t+1) , where W is the transmission bandwidth, P total is the total energy consumption of the system;

[0083] S510, fix Z (t+1) , solve the convex problem to update η QT (t+1) ;

[0084] S511, if |f(η QT (t+1) )-f(η QT (t) |<10 -5 , output η (t+1) =η QT (t+1) and perform S512; otherwise, t QT =t QT +1, return to S58;

[0085] S512, if |f(θ (t+1) ,η (t+1) )-f(θ (t) ,η (t) |<10 -5 , output θ (t+1) and η (t+1) ; otherwise, t=t+1, return to S52.

[0086] In a second aspect, the application discloses a RIS-aided de-cell large-scale MIMO energy efficiency improvement system, which applies the RIS-aided de-cell large-scale MIMO energy efficiency improvement method as described above, and the system comprises:

[0087] A system construction module, which establishes a RIS-aided de-cell large-scale MIMO system under the condition of a Rician fading channel;

[0088] A channel estimation module, which performs channel estimation by a linear minimum mean square error method based on the constructed MIMO system;

[0089] An efficiency expression module, which deduces a closed-form expression of system energy efficiency when a zero-forcing detector is used based on the constructed MIMO system;

[0090] a problem optimization module, based on the closed-form expression, constructs an energy efficiency optimization problem;

[0091] an efficiency optimization module, according to the optimization problem, designs an alternating optimization algorithm to optimize energy efficiency.

[0092] Compared with the prior art, the RIS-assisted de-cell large-scale MIMO energy efficiency improvement method and system has the following beneficial effects:

[0093] 1. The RIS-assisted de-cell large-scale MIMO energy efficiency improvement method and system provided by the application adopts minimum linear mean square error channel estimation to estimate the aggregated channel in the imperfect channel state information (CSI) scenario. In order to eliminate inter-user interference, a ZF detector is used at the access point end to detect the received user signal, and an alternating optimization algorithm is designed according to the optimization problem to optimize the RIS phase shift and user end power allocation respectively, so as to improve the system energy efficiency. Simulation results verify the accuracy of the closed-form expression, and prove that the algorithm can maintain high system performance under imperfect CSI conditions, and has significant improvement in energy efficiency compared with the traditional scheme.

[0094] 2. The RIS-assisted de-cell large-scale MIMO energy efficiency improvement method and system provided by the application, the de-cell large-scale MIMO technology connects all distributed access points to the CPU through the backhaul link, and then provides services for all users, effectively solves the problem of inter-cell interference, and significantly improves the communication service quality of multi-cell users. Combining RIS with de-cell large-scale MIMO technology can fully exert the advantages of both to improve the overall system performance at a lower cost. BRIEF DESCRIPTION OF DRAWINGS

[0095] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0096] Figure 1 The flow chart of the RIS-assisted de-cell large-scale MIMO energy efficiency improvement method provided by the embodiment of the application;

[0097] Figure 2 The model structure diagram of the de-cell large-scale MIMO system suitable for the RIS-assisted de-cell large-scale MIMO energy efficiency improvement method of the application;

[0098] Figure 3A structural block diagram of a RIS-assisted de-cell large-scale MIMO energy efficiency improvement system provided by an embodiment of the present application is shown in the figure.

[0099] Figure 4 A diagram showing the relationship between uplink pilot transmission power and energy efficiency under random phase shift and full power conditions is provided by an embodiment of the present application.

[0100] Figure 5 A diagram showing energy efficiency performance comparison under four different phase shift and power control schemes is provided by an embodiment of the present application. DETAILED DESCRIPTION

[0101] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0102] In this document, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent to such a process, method, article or device. Without more limitations, the elements defined by the statement "comprising" do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0103] The present embodiment provides, in a first aspect, a RIS-assisted de-cell large-scale MIMO energy efficiency improvement method as shown in the figure, which comprises: Figure 1

[0104] Step S1: Establish a RIS-assisted de-cell large-scale MIMO system under the condition of Rayleigh fading channel;

[0105] Step S2: Based on the constructed MIMO system, perform channel estimation by linear minimum mean square error method;

[0106] Step S3: Based on the constructed MIMO system, derive a closed-form expression of system energy efficiency when based on a zero-forcing detector;

[0107] Step S4: Based on the closed-form expression, construct an energy efficiency optimization problem formula;

[0108] Step S5: According to the optimization problem formula, design an alternating optimization algorithm to optimize energy efficiency.

[0109] In the present embodiment, the step S1 comprises: ​

[0110] A de-cellular massive MIMO system consisting of m distributed access points (APs), r RISs and u single-antenna users is constructed, and a schematic diagram of an exemplary MIMO system structure is shown as Figure 2 ;

[0111] All APs and RISs in the MIMO system are interconnected with a central processor through backhaul links, and based on a data sharing transmission strategy, the channel information between APs is shared to cooperatively allocate resources, the APs and RISs are cooperatively optimized for reflection phase shift and beamforming, and all APs and RISs are synchronized with the CPU for joint channel estimation and signal detection;

[0112] Each AP in the MIMO system is equipped with N antennas, and each RIS is configured with j passive reflecting elements;

[0113] The MIMO system is configured with a time division duplex mode, in which the time-frequency resources are divided into multiple coherence intervals, each coherence interval includes τ c symbols, of which τ p symbols are used for pilot signals;

[0114] Define the set of APs as M = {1, 2,..., m}, the set of users as U = {1, 2,..., u}, the set of RISs as R = {1, 2,..., r}, the set of passive reflecting elements of a single RIS as J = {1, 2,..., j}, and the total element set of all RISs as Q = {1, 2,..., RJ}.

[0115] Assuming that the channel between the kth RIS and all users is in a line-of-sight scenario, the channel matrix G RU,k from the kth RIS to the u users is represented as:

[0116]

[0117] where diag(·) is a diagonal matrix operation, β RU,lk is the path loss factor of the kth RIS and the tth user, is a deterministic LoS channel matrix, and the dimension of the matrix is j x u.

[0118] The channel G AR,ik between the ith AP and the kth RIS is represented by a Rician fading as:

[0119]

[0120] where β AU,it is the path loss factor of the ith AP and the tth user, K AU,it is the Rician factor (the strength ratio of the line-of-sight component and the non-line-of-sight component). For the line-of-sight component (dimension N×j, corresponding to the number of access point antennas N and the number of elements in a single RIS), For non-line-of-sight components, all elements follow an independent and identically distributed complex Gaussian distribution CN(0,1).

[0121] The channel G between the i-th access point and the t-th user AU,i Ricean fading is represented as follows:

[0122]

[0123] Where, β AU,it K is the path loss factor between the i-th access point and the t-th user. AU,it Rice factor, For the line-of-sight component (dimension N×1 corresponding to the number of access point antennas N), For non-line-of-sight components, all elements follow an independent and identically distributed complex Gaussian distribution CN(0,1).

[0124] The aggregation channel matrix G between the i-th access point and all users i The expression is:

[0125]

[0126] Where, θ k =[θ k,1 ,θ k,2 ,...,θ k,j ] T Let θ be the phase shift vector of the k-th RIS (dimension j×1, corresponding to the number of components j in a single RIS). k,l ∈[0,2π) represents the phase shift coefficient of the l-th element (l∈J) of the k-th RIS, and diag(θ) k ) indicates that θ k Diagonalization (to obtain a j×j diagonal matrix), defining the phase shift vector of all RIS as... (Dimension Q×1 corresponds to the total number of elements Q).

[0127] In this embodiment, step S2 includes:

[0128] S21. Within each coherent interval, all users send orthogonal pilot signals to m access points as S = [s1; s2; ...; s...]. U ], where the pilot sequence for user t (t∈U) is s t pilot length τ p And the pilot sequence satisfies S H S = I U I U Let S be a U-order identity matrix.H The pilot signal is the conjugate transpose of the matrix corresponding to the pilot signal; the pilot signal Y received by the i-th access point (i∈M) is... p,i for:

[0129]

[0130] Where, p p For user pilot transmit power, Additive white Gaussian noise at the i-th access point (dimension N×τ) p Corresponding to the number of antennas N at the access point and the pilot length τ p Its elements follow a complex Gaussian distribution CN(0,σ). 2 );G i This is the aggregated channel matrix between the i-th access point and all users;

[0131] S22. To estimate the aggregation channel, the pilot signal received by the i-th access point is compared with... Multiplying together, we get:

[0132]

[0133] S23. Estimate the aggregation channel between the i-th access point and u users using the linear minimum mean square error method.

[0134]

[0135] in, For G i The covariance matrix (dimension N×U), for The covariance matrix (dimension N×U);

[0136] S24. Define the estimated channel between the i-th access point and the t-th user as... ( The channel estimation error is (column t, dimension N×1). (Dimension N×1), the estimated channel and the channel estimation error are statistically independent, and in and These are the covariance matrices of the estimated channel and the channel estimation error, respectively.

[0137] In this embodiment, step S3 includes:

[0138] S31. During the uplink data transmission phase, u users send signals X (X ~ CN(0, I_U), dimension U×1), and the signal received by the i-th access point is:

[0139]

[0140] where p u is the maximum uplink data transmission power, G i is the aggregated channel matrix between the i-th access point and all users, η = [η1, η2,..., η U ] T is the power control coefficient (0 ≤ η_t ≤ 1, dimension U × 1, t ∈ U), is the noise at the i-th access point (dimension N × 1), whose elements follow a complex Gaussian distribution CN(0, σ 2 ); the i-th access point uses a zero-forcing detector, and the detection matrix is A i G i = IU (to ensure the elimination of multi-user interference);

[0141] S32, after the CPU processes all access point signals, the detection signal is:

[0142] S33, the detection signal of the t-th user is:

[0143]

[0144] where (A i G i ) ts is the element of the t-th row and s-th column of the matrix A i G i (t ≠ s is the interference term);

[0145] S34, the achievable rate R t of the t-th user is:

[0146]

[0147] where denotes the statistical expectation, is the conjugate of χ t ;

[0148] S35, the system energy efficiency EE is the ratio of the total rate to the total energy consumption:

[0149]

[0150] where W is the transmission bandwidth, P User , P AP is the fixed circuit power consumption of the access point, P AP = m · P AP,per (P AP,per is the fixed circuit power consumption of a single access point); P RIS is the fixed circuit power consumption of the RIS, PRIS = r · P RIS,per (P RIS,per = r · P User = u · P User = u · P User,per = u · P User,per = u · P

[0151] S36, through matrix transformation, the closed-form expression of system energy efficiency EE is rewritten as:

[0152]

[0153] where det(·) is the determinant operation, is the channel covariance matrix of the ith access point (corresponding to the number of access point antennas N), C 6,it is the channel covariance matrix of the ith access point (corresponding to the number of access point antennas N), C 5,i is the (N-1) × (N-1) dimensional submatrix after removing the t-th row and t-th column of C 6,it , and det(C 6,it ) is the corresponding minor.

[0154] In this embodiment, the step S4 comprises:

[0155] S41, the energy efficiency optimization problem is expressed as:

[0156]

[0157] where the optimization variable is: (Phase shift vector of all RISs, dimension Q × 1) θ k is the phase shift vector of the kth RIS, θ k,l is the phase shift coefficient of the first element of the kth RIS; η t is the power control coefficient;

[0158] The constraint conditions are the value range of the power control coefficient (0 ≤ η t ≤ 1, to ensure that the actual power does not exceed p u ) and the value range of the RIS phase shift coefficient (0 ≤ θ k,l < 2∏, physical constraint of passive elements);

[0159] S42, when the power control coefficient is fixed, the RIS phase shift optimization sub-problem is:

[0160]

[0161] S43, the expression of the RIS phase shift optimization sub-problem is solved by using the gradient ascent method, and the gradient of the objective function is:

[0162]

[0163] where, is the gradient vector of system energy efficiency EE with respect to θ (dimension Q x 1, each element corresponds to the partial derivative of θ k,l ); C 5,i is the channel covariance matrix of the ith access point (N x N dimension, derived from channel estimation results);

[0164] S44, fixing the optimization variable, the power control coefficient optimization sub-problem is:

[0165] S45, introducing auxiliary variables y = [y1,..., y U ] T (dimension U x 1, y t is the rate auxiliary variable of the tth user) and Z (scalar, auxiliary variable of the total rate), the power control coefficient optimization sub-problem is converted into a convex problem, and η, y and Z are iteratively optimized; wherein y t ≤ R t (η t ), R t is the achievable rate of the tth user, (is a function of η t ), (ensuring that Z is the lower bound of the total rate);

[0166] S46, alternately performing S42 (accelerated gradient ascent method to obtain θ) and S44 (quadratic transformation to obtain η) until convergence (the difference in energy efficiency between adjacent two iterations is less than 10 -5 ).

[0167] In the embodiment, the step S5 comprises:

[0168] S51, initializing parameters t = 0, RIS initial phase shift vector θ (0) (dimension Q x 1), and initial power control coefficient η (0) (dimension U x 1);

[0169] S52, initializing the accelerated gradient ascent algorithm parameters: t AGA = 0 (iteration counter), θ AGA (0) = θ (0) (initial phase shift), υ (0) = θ (0) (momentum term), σ (0) = 1 (step size parameter);

[0170] S53, fixing the power control coefficient vector η (t) of the tth iteration, updating the gradient

[0171] S54, set step size a with backtracking line search AGA , update

[0172] S55, update momentum term v (t+1) = θ AGA (t+1) + (t AGA / (t AGA + 3)) (θ AGA (t+1) - θ AGA (t) ), step size parameter s (t+1) = s (t) / 2;

[0173] S56, if |f (θ AGA (t+1) ) - f (θ AGA (t) | <10 -5 , output θ (t+1) = θ AGA (t+1) and perform S57; otherwise, t AGA = t AGA + 1, return to S53;

[0174] S57, initialize quadratic transformation parameters: t QT = 0, η QT (0) = η (t) , y (0) (initial auxiliary variable), Z (0) (initial auxiliary variable);

[0175] S58, fix phase shift vector η QT (t) of the tth iteration, update y (t+1) by , where C 5,i is the channel covariance matrix of the ith access point, C 6,it is the (N-1) × (N-1) dimensional submatrix of C 5,i after removing the tth row and tth column;

[0176] S59, fix η QT (t) and y (t+1) , update Z (t+1) by , where W is the transmission bandwidth, and P total is the total energy consumption of the system;

[0177] S510, fix Z(t+1) Solving the convex problem and updating η QT (t+1) ;

[0178] S511, If ​​|f(η) QT (t+1) )-f(η QT (t) )|<10 -5 Output η (t+1) =η QT (t+1) And execute S512; otherwise, t QT =t QT +1, return to S58;

[0179] S512, If |f(θ) (t+1) ,η (t+1) )-f(θ (t) ,η (t) )|<10 -5 Output θ (t+1) and η (t+1) Otherwise, t = t + 1, and return to S52.

[0180] This embodiment provides a second aspect as follows: Figure 3 The RIS-assisted decellularized massive MIMO energy efficiency improvement system shown applies the RIS-assisted decellularized massive MIMO energy efficiency improvement method described above. The system includes:

[0181] The system building module establishes a RIS-assisted decellularized massive MIMO system under Ricean fading channel conditions.

[0182] The channel estimation module, based on the constructed MIMO system, performs channel estimation using the linear minimum mean square error method;

[0183] The efficiency expression module derives a closed-form expression for the system energy efficiency based on the constructed MIMO system and the zero-forcing detector.

[0184] The problem optimization module constructs an energy efficiency optimization problem expression based on the closed-form expression;

[0185] The efficiency optimization module designs an alternating optimization algorithm to optimize energy efficiency based on the optimization problem equation.

[0186] It should be noted that the RIS-assisted de-celled massive MIMO energy efficiency improvement system of the embodiment corresponds to the RIS-assisted de-celled massive MIMO energy efficiency improvement method described above, and therefore, the parts not fully described in the RIS-assisted de-celled massive MIMO energy efficiency improvement system of the embodiment (including but not limited to specific implementation means and technical effects, etc.) can be correspondingly referred to the relevant description in the RIS-assisted de-celled massive MIMO energy efficiency improvement method described above, which will not be repeated here.

[0187] In order to verify the effectiveness of the scheme of the present application, the following simulation experiment is carried out:

[0188] The specific simulation parameters of the RIS-assisted de-celled massive MIMO network are as follows:

[0189] For the RIS-assisted de-celled massive MIMO system, the simulation scenario is set in a circular area with (110m, 0m) as the center, a radius of 5m, and a height of 1.5m, K=5 users are distributed therein. M=3 base stations are configured with N antennas, and are deployed at positions (0m, -15m, 15m), (0m, 0m, 15m) and (0m, 15m, 15m). L=3 intelligent metasurfaces are deployed on the building surface, and are deployed at positions (100m, -15m, 25m), (100m, 0m, 25m) and (100m, 15m, 25m). The large-scale path loss factor is and, where d represents the distance between the kth RIS and the ith AP, the ith user and the kth RIS, and the tth user and the ith AP, respectively. Other simulation parameters include p p = 30dBm, τ p = 5, p u = 50dBm. In the random phase shift scheme, the phase shift vector of the RIS is set to 0≤θ k,l <2Π, and in the full power control scheme, the base station power control coefficient is 1.

[0190] Figure 4The relationship between uplink pilot transmission power and energy efficiency under random phase shift and full power conditions is plotted. First, from the simulation results, the consistency of the closed-form expression and the theoretical simulation formula can be seen, verifying the accuracy of the energy efficiency closed-form expression. Second, as the pilot transmission power increases, the system energy efficiency also increases, but when the pilot transmission power reaches a certain threshold, the increase in system energy efficiency gradually slows down, indicating that increasing the pilot transmission power within a certain range can improve system energy efficiency and increase channel estimation accuracy, but excessive pilot power will not bring additional gains. Finally, under different pilot transmission powers, increasing the number of AP antennas and RIS reflection units can improve system energy efficiency. However, in actual system design, the impact of system parameters needs to be considered, and a balance needs to be sought between performance improvement and energy consumption cost to achieve optimal resource allocation.

[0191] Figure 5 The energy efficiency performance of four different phase shift and power control schemes is compared, including the random phase shift and full power scheme, the full power and accelerated gradient ascent algorithm scheme, the random phase shift and convex optimization algorithm scheme, and the alternating optimization algorithm scheme. The simulation results show that although the accelerated gradient ascent algorithm and the convex optimization algorithm can improve the system energy efficiency to a certain extent when the number of RIS units and the number of AP antennas is different, the use of the alternating optimization algorithm can more comprehensively optimize the system parameters, thereby maximizing the system energy efficiency, providing an important theoretical basis and practical guidance for system energy efficiency enhancement.

[0192] In summary, the simulation results verify the accuracy of the system energy efficiency theoretical expression derived by the proposed scheme, and the alternating optimization algorithm proposed significantly outperforms other schemes by jointly optimizing system parameters to maximize energy efficiency. In addition, increasing the number of AP antennas and RIS units can systematically improve energy efficiency. The algorithm has convergence, effectiveness and robustness. Therefore, in actual system design, the threshold effect of pilot power, the reasonable configuration of hardware resources, and the use of efficient joint optimization strategies such as alternating optimization algorithm need to be considered to achieve the optimal balance between performance improvement and energy consumption cost.

[0193] In the embodiments provided by the present application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following: application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. For software implementation, the procedures described herein can be implemented with associated hardware to perform the procedures described herein. In implementation, the procedures described above can be stored in a computer readable storage medium or transmitted as one or more instructions or code on a computer readable storage medium. The computer readable storage medium includes a computer storage medium and a communication medium, and the communication medium includes any medium that facilitates transfer of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a computer. The computer readable storage medium can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage medium, or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

[0194] Finally, it should be noted that the above-described embodiments are merely exemplary of the application and should not be used in a limiting sense. Rather, the description is intended to cover any alternatives, modifications, and equivalents, which can be made to the embodiments described herein, by those skilled in the art in the spirit and scope of the present application.

Claims

1. A RIS-assisted method for improving the energy efficiency of decellularized large-scale MIMO, characterized in that, The method includes: Step S1: Establish a RIS-assisted decellularized massive MIMO system under Ricean fading channel conditions; Step S2: Based on the constructed MIMO system, channel estimation is performed using the linear minimum mean square error method; Step S3: Based on the constructed MIMO system, derive a closed-form expression for the system energy efficiency based on the zero-forcing detector; Step S4: Based on the closed-form expression, construct the energy efficiency optimization problem formula; Step S5: Based on the optimization problem, design an alternating optimization algorithm to optimize energy efficiency.

2. The RIS-assisted decellularized large-scale MIMO energy efficiency improvement method according to claim 1, characterized in that, Step S1 includes: Construct a decellularized massive MIMO system consisting of m distributed access points, r RIS, and u single-antenna users; In this MIMO system, all access points and RIS are interconnected with the central processing unit via a backhaul link. Based on a data sharing transmission strategy, channel information is shared among access points to coordinate resource allocation. The access points and RIS are jointly optimized for reflection phase shift and beamforming. All access points and RIS synchronize data with the CPU to achieve joint channel estimation and signal detection. Each access point of the MIMO system is equipped with N antennas, and each RIS is configured with j passive reflective elements; The MIMO system is configured in time-division duplex mode, where time-frequency resources are divided into multiple coherent intervals, each coherent interval including τ. c There are symbols, where τ p One symbol is used for the pilot signal; Define the set of access points as M = {1,2,...,m}, the set of users as U = {1,2,...,u}, the set of RIS as R = {1,2,...,r}, the set of passive reflective elements of a single RIS as J = {1,2,...,j}, and the total set of elements of all RIS as Q = {1,2,...,RJ}.

3. The RIS-assisted decellularized large-scale MIMO energy efficiency improvement method according to claim 1, characterized in that, Step S1 further includes: Assuming the channel between the k-th RIS and all users is a line-of-sight scenario, then the channel matrix G from the k-th RIS to the u-th user is... RU,k Represented as: Where diag(·) is a diagonal matrix operation, β RU,lk Let be the path loss factor between the k-th RIS and the t-th user. The deterministic LoS channel matrix has dimensions j×u.

4. The RIS-assisted decellularized large-scale MIMO energy efficiency improvement method according to claim 1, characterized in that, Step S1 further includes: The channel G between the i-th access point and the k-th RIS AR,ik Ricean fading is represented as follows: Where, β AU,it K is the path loss factor between the i-th access point and the t-th user. AU,it Rice factor, For the line-of-sight component, For non-line-of-sight components, all elements follow an independent and identically distributed complex Gaussian distribution CN(0,1); The channel G between the i-th access point and the t-th user AU,i Ricean fading is represented as follows: Where, β AU,it K is the path loss factor between the i-th access point and the t-th user. AU,it Rice factor, For the line-of-sight component, For non-line-of-sight components, all elements follow an independent and identically distributed complex Gaussian distribution CN(0,1).

5. The RIS-assisted decellularized large-scale MIMO energy efficiency improvement method according to claim 1, characterized in that, Step S1 further includes: The aggregation channel matrix G between the i-th access point and all users i The expression is: Where, θ k =[θ k,1 ,θ k,2 ,...,θ k,j ] T Let θ be the phase shift vector of the k-th RIS. k,l ∈[0,2η) represents the phase shift coefficient of the l-th element of the k-th RIS, and diag(θ) k ) indicates that θ k Diagonalization, defining the phase shift vector of all RIS as 6. The RIS-assisted decellularized large-scale MIMO energy efficiency improvement method according to claim 1, characterized in that, Step S2 includes: S21. Within each coherent interval, all users send orthogonal pilot signals to m access points as S = [s1; s2; ...; s...]. U ], where the pilot sequence for user t is s t pilot length τ p And the pilot sequence satisfies S H S = I U I U S is a U-order identity matrix. H The pilot signal is the conjugate transpose of the matrix corresponding to the pilot signal; the pilot signal Y received by the i-th access point is... p,i for: Where, p p For user pilot transmit power, The additive white Gaussian noise at the i-th access point has elements that follow a complex Gaussian distribution CN(0,σ). 2 );G i This is the aggregated channel matrix between the i-th access point and all users; S22. To estimate the aggregation channel, the pilot signal received by the i-th access point is compared with... Multiplying together, we get: S23. Estimate the aggregation channel between the i-th access point and u users using the linear minimum mean square error method. in, For G i The covariance matrix, for The covariance matrix; S24. Define the estimated channel between the i-th access point and the t-th user as... The channel estimation error is The estimated channel and the channel estimation error are statistically independent, and in and These are the covariance matrices of the estimated channel and the channel estimation error, respectively.

7. The RIS-assisted decellularized large-scale MIMO energy efficiency improvement method according to claim 1, characterized in that, Step S3 includes: S31. During the uplink data transmission phase, u users send signal X, and the signal received by the i-th access point is: Where, p u For maximum uplink data transmission power, G i Let η be the aggregated channel matrix between the i-th access point and all users, where η = [η1, η2, ..., η]. U ] T This is the power control coefficient. Let CN(0,σ) be the noise at the i-th access point, whose elements follow a complex Gaussian distribution. 2 The i-th access point uses a zero-forcing detector, and the detection matrix is... And A i G i =I U ; S32. After all access point signals are processed by the CPU, the detected signal is: S33, The detection signal of the t-th user is: Among them, (A) i G i ) ts Let matrix A i G i The element in the t-th row and s-th column; S34, the reachable rate R of the t-th user t for: in, Indicates statistical expectation. For x t The conjugate; S35. System energy efficiency EE is the ratio of total rate to total energy consumption: Where W is the transmission bandwidth. P AP P is the fixed circuit power consumption of the access point. RIS For the fixed circuit power consumption of RIS, P User For the user's fixed circuit power consumption; S36. Through matrix transformation, the closed-form expression for the system energy efficiency EE is rewritten as: Here, det(·) performs determinant operations. Let C be the channel covariance matrix of the i-th access point. 6,it C 5,i After removing the (N-1)×(N-1) dimensional submatrix from the t-th row and t-th column, det(C 6,it ) represents the corresponding cofactor.

8. The RIS-assisted decellularized large-scale MIMO energy efficiency improvement method according to claim 1, characterized in that, Step S4 includes: S41. The energy efficiency optimization problem is expressed as follows: The optimization variables are: (The phase shift vectors of all RIS, dimension Q×1)θ k Let θ be the phase shift vector of the k-th RIS. k,1 η is the phase shift coefficient of the first element of the k-th RIS; t This refers to the power control coefficient; The constraints are the range of values ​​for the power control coefficient and the range of values ​​for the RIS phase shift coefficient, respectively. S42. With a fixed power control coefficient, the RIS phase shift optimization subproblem is: S43. Solve the expression for the RIS phase-shift optimization subproblem using the gradient ascent method. The gradient of the objective function is: in, Let C be the gradient vector of the system energy efficiency EE with respect to θ; 5,i Let be the channel covariance matrix of the i-th access point; S44. When the optimization variables are fixed, the subproblem of power control coefficient optimization is: S45. Introduce auxiliary variables y = [y1,...,y U ] T The power control coefficient optimization subproblem is transformed into a convex problem by iteratively optimizing η, y, and Z; where y t ≤R t (η t ), R t Let t be the reachable rate of the t-th user. S46. Alternately execute S42 and S44 until convergence.

9. The RIS-assisted decellularized large-scale MIMO energy efficiency improvement method according to claim 8, characterized in that, Step S5 includes: S51. Initialize parameter t = 0, RIS initial phase shift vector θ (o) Initial power control coefficient η (o) ; S52. Initialize the parameters for the accelerated gradient ascent algorithm: t AGA =0, θ AGA (o) =θ (o) υ (o) =θ (o) , σ (o) =1; S53, fix the power control coefficient vector η for the t-th iteration. (t) Update the gradient using the gradient of the objective function. S54. Set step size α using backtracking search. AGA ,renew S55, Update the momentum term υ (t+1) =θ AGA (t+1) +(t AGA / (t AGA +3))(θ AGA (t1) -θ AGA (t) Step size parameter σ (t+1) =σ (t) / 2; S56, If |f(θ) AGA (t+1) )-f(θ AGA (t) ) | <10 -5 Output θ (t+1) =θ AGA (t+1) And execute S57; otherwise, t AGA =t AGA +1, return to S53; S57. Initialize the second transformation parameters: t QT =0, η QT (o) =η (t) y (o) Z (o) ; S58. Fix the phase shift vector η of the t-th iteration. QT (t) ,pass Update y (t+1) , where C 5,i Let C be the channel covariance matrix of the i-th access point. 6,it C 5,i The (N-1)×(N-1) dimensional submatrix after removing the t-th row and t-th column; S59, Fixed η QT (t) and y (t+1) ,pass Update Z (t+1) Where W is the transmission bandwidth, P total This represents the total energy consumption of the system. S510, Fixed Z (t+1) Solving the convex problem and updating η QT (t+1) ; S511, If ​​|f(η) QT (t+1) )-f(η QT (t) )|<10 -5 Output η (t+1) =η QT (t+1) And execute S512; otherwise, t QT =t QT +1, return to S58; S512, If |f(θ) (t+1) ,η (t+1) )-f(θ (t) ,η (t) )|<10 -5 Output θ (t+1) and η (t+1) Otherwise, t = t + 1, and return to S52.

10. A RIS-assisted decellularized massive MIMO energy efficiency improvement system, employing the RIS-assisted decellularized massive MIMO energy efficiency improvement method as described in any one of claims 1-9, characterized in that, The system includes: The system building module establishes a RIS-assisted decellularized massive MIMO system under Ricean fading channel conditions. The channel estimation module, based on the constructed MIMO system, performs channel estimation using the linear minimum mean square error method; The efficiency expression module derives a closed-form expression for the system energy efficiency based on the constructed MIMO system and the zero-forcing detector. The problem optimization module constructs an energy efficiency optimization problem expression based on the closed-form expression; The efficiency optimization module designs an alternating optimization algorithm to optimize energy efficiency based on the optimization problem equation.

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