A Method for Minimizing Energy Consumption in a RIS-Assisted Communication System

Abstract

The present invention relates to a method for minimizing energy consumption of a RIS-assisted communication system, which belongs to the field of B5G cellular communications. Taking into account user communication rate constraints and RIS discrete phase shift constraints, and with minimizing total system power consumption as the optimization goal, a system model is established for a RIS-assisted multi-user multi-input single-output communication system with hardware damage; the original problem is transformed into an equivalent convex optimization problem using the S process and the successive convex approximation method; and the convex optimization problem is solved based on a semidefinite relaxation method and an alternating optimization algorithm. The present invention can ensure good robustness and reduce base station transmission power. The deployment of RIS effectively alleviates the impact of channel uncertainty and non-ideal hardware, and is suitable for actual communication scenarios where the transceiver has hardware damage such as I / Q imbalance and amplifier nonlinearity.

Description

Technical Field

[0001] The present invention belongs to the field of B5G cellular communications and relates to a method for minimizing energy consumption of a RIS-assisted communication system. Background Art

[0002] With the continuous advancement of communication technology, wireless communication networks are becoming increasingly dense. However, the impact of obstruction and channel uncertainty is becoming increasingly prominent. Reconfigurable Intelligence Surfaces (RIS) are a key technology to address these issues. By intelligently adjusting the phase shift of RIS, they can dynamically improve the wireless propagation environment and actively reconfigure end-to-end wireless links, introducing additional degrees of freedom to maximize system performance.

[0003] Due to the passive nature of RIS, perfect channel state information is difficult to obtain. Traditional beamforming design methods for multi-user multiple-input single-output (MU-MISO) communication systems often ignore the impact of channel uncertainty on system performance. Furthermore, objective factors such as amplifier nonlinearity and I / Q imbalance can cause changes in the receiver's operating mode or state. For example, in multi-antenna systems, communication equipment manufacturers use inexpensive components to reduce hardware costs, which increases the likelihood of hardware impairments. However, existing transmitter calibration schemes and receiver compensation methods cannot completely eliminate the impact of hardware impairments on the system. Therefore, robust beamforming design in RIS-assisted MU-MISO communication system scenarios with channel uncertainty and hardware impairments is extremely challenging. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method for minimizing energy consumption of a RIS-assisted communication system. In view of the problems of distortion noise and channel uncertainty, the present invention considers the user communication rate constraint, the discrete phase shift constraint of RIS, and the maximum transmission power constraint of each base station. With the minimization of the system base station transmit power as the optimization goal, a system model is established and solved for the RIS-assisted MU-MISO communication system with hardware impairments.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for minimizing energy consumption in a RIS-assisted communication system is proposed. Taking user communication rate constraints and RIS discrete phase shift constraints into consideration, minimizing total system power consumption is optimized. A system model is established for a RIS-assisted multi-user multiple-input single-output (MU-MISO) communication system with hardware impairments. The original problem is transformed into an equivalent convex optimization problem using the S-process and successive convex approximation method. The convex optimization problem is solved using a semidefinite relaxation method and an alternating optimization algorithm.

[0007] Furthermore, the method specifically comprises the following steps:

[0008] S1: Initialize system parameters and set the convergence accuracy of the iterative method Iterative initialization;

[0009] S2: Given the diagonal phase shift matrix of RIS, calculate the beamforming matrix from the base station to user k. At this time, the base station transmit power

[0010] S3: Fix the beamforming matrix from the base station to user k and calculate the diagonal phase shift matrix of RIS;

[0011] S4: Update the RIS diagonal phase shift matrix and calculate Calculation, n represents the nth iteration;

[0012] S5: Determine whether the power consumption of the MU-MISO communication system has converged; if so, calculate and output the optimal beamforming matrix from the base station to the user, the optimal diagonal phase shift matrix of the RIS, and the minimum total power consumption of the MU-MISO communication system, and then end; otherwise, proceed to step S6;

[0013] S6: Determine whether the current number of iterations is greater than the maximum number of iterations. If so, output the optimal beamforming matrix from the base station to the user, the optimal diagonal phase shift matrix of the RIS, and the minimum base station transmit power of the MU-MISO communication system, and then end. Otherwise, enter the next iteration and return to step S2.

[0014] Furthermore, in step S1, the system parameters include the number of intelligent metasurface (Reconfigurable Intelligence Surface, RIS) reflective elements L, the number of base station antennas M, the number of users K, the channel estimation vector from the base station to user k The cascade channel estimation vector from the base station to the RIS and then to user k is The total circuit power consumption value P of the base station C , base station power amplification factor ξ, base station maximum transmit power threshold P max , the minimum transmission rate threshold of user k The radius of the CSI uncertainty set from the base station to user k The radius of the CSI uncertainty set from the base station to the RIS and then to user k Base station hardware damage factor ρ t , hardware damage factor ρ at user k r,k , the noise power at user k Maximum number of iterations N max .

[0015] Furthermore, the beamforming matrix V from the base station to user k in step S2 is k for:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] C4:Rank(V k )=1.

[0022] in, v k Beamforming vector from base station to user k; λ, r k , α k and β k is the slack variable; r k is the achievable rate for user k, is the minimum transmission rate threshold of user k; Tr(·) is the trace of the matrix; is the channel estimation vector from the base station to user k, is the concatenated channel estimation vector from the base station to the RIS and then to user k, ρ t is the hardware damage factor of the base station, ρ r,k is the hardware damage factor at user k, is the noise power at user k, θ is the vector of diagonal elements of the RIS phase shift matrix, I is the identity matrix, X ≥ 0 means the matrix X is a semi-positive matrix, 0 is an all-zero matrix, is the radius of the uncertainty set of the two channels from the base station to user k, represents the radius of the CSI uncertainty set from the base station to the RIS and then to user k, represents the radius of the CSI uncertainty set from the base station to user k; is the total system power consumption, vec(X) means vectorizing the matrix X, taking each column of the matrix X and forming a column vector, diag(·) means diagonalization, (·) H is the conjugate transpose of the matrix, (·) T is the transpose of the matrix, |·| is the absolute value, ||·|| is the Euclidean criterion of the vector, and Rank(·) is the rank of the matrix.

[0023] Furthermore, the diagonal phase shift matrix of the RIS in step S3 is calculated according to the following formula:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] C5:Rank(E)=1,

[0031] C6:0<q≤1.

[0032] in, E=θθ H , is a vector of diagonal elements of the RIS phase shift matrix, each θ l It can only take d finite values, which are equally distributed in [0,2π); the RIS phase shifter set uses Indicates that θ l The collection of is the number of phase shift levels, m is the number of bits required to represent d phase shift levels, Representation matrix The modulus of the element in the lth row and lth column of is q;

[0033] Further, in step S5, the method for determining whether the transmission power of the MU-MISO communication system base station is converged is as follows: The optimal beamforming matrix from the base station to the user is based on Calculate and use Gaussian randomization to obtain the optimal diagonal phase shift vector θ of RIS *, solved to the discrete phase of RIS by projection theorem System base station minimum transmission power

[0034] The beneficial effects of the present invention are: the present invention can ensure good robustness and reduce the base station transmission power. The actual interruption probability of the scheme of the present invention is 89.73% lower than that of the existing algorithm, which reduces the base station transmission power by 7.9%. The deployment of RIS effectively alleviates the impact of channel uncertainty and non-ideal hardware, and is suitable for actual communication scenarios where the transceiver has hardware damage such as I / Q imbalance and amplifier nonlinearity.

[0035] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0037] Figure 1 This is a flow chart of the method for minimizing energy consumption of the RIS-assisted communication system according to the present invention;

[0038] Figure 2 It is the base station transmission power convergence diagram of the method of the present invention;

[0039] Figure 3 This is the robustness diagram of the method of the present invention. DETAILED DESCRIPTION

[0040] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0041] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0042] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0043] See also Figures 1 to 3 The present invention provides a method and device for minimizing energy consumption based on a RIS-assisted communication system, considering the downlink communication scenario of a RIS-assisted MU-MISO communication system. In the network, there is a base station with M antennas serving K single-antenna macrocellular users, a RIS with L passive reflective elements, and a user set and a RIS phase shifter set respectively. and Indicates that, and use represents the phase shift matrix of RIS, where θ l is the phase shift of the lth phase shifter, diag(·) is the diagonalization operator, and is defined as is a vector of diagonal elements of the matrix Θ, considering an ideal RIS reflection amplitude model, where each θ l Assume that it is fixed and a discrete RIS reflection phase shift model where each θ l It can only take d finite values, which are equally distributed in [0,2π). Then θ l The set of is given by: is the number of phase shift levels, and m is the number of bits required to represent d phase shift levels. Our goal is to minimize the total base station energy consumption of the communication system under the constraints of inter-user interference, user minimum rate, and RIS phase shift. Therefore, this optimization problem can be formulated by jointly optimizing the beamforming matrix and the diagonal phase shift matrix:

[0044] P1:

[0045]

[0046]

[0047] Among them, R k =log2(1+γ k ) represents the transmission rate of user k, represents the signal-to-interference-and-noise ratio of user k, represents the interference noise power,

[0048]

[0049]

[0050] in, Indicates that user k receives interference from other users, n t It represents the distortion noise vector caused by the hardware damage of the back-end circuit of each transmitting antenna of the base station, and is proportional to the signal power at the antenna, with a mean of zero and a variance of Φ, Φ = ρ t diag(q1,q2,...,q M ), the diagonal elements of the base station transmit covariance matrix are (q1,q2,...,q M ), represents the received distortion noise variance at user k, represents the variance of the amplified thermal noise at user k, represents the minimum rate of user k, represents the total power consumption of the system, ξ represents the power amplification factor, v k represents the beamforming vector from the base station to user k, P C represents the circuit power consumption of the system, ρ t represents the hardware damage factor of the base station, ρ r,k represents the hardware damage factor at user k.

[0051] In order to overcome the impact of imperfect CSI, a bounded CSI error model is considered. Based on the uncertainty of channel constraints, the uncertainty set can be expressed as:

[0052]

[0053] Among them, h BU represents the actual interference channel estimation vector from the base station to user k, G represents the actual interference channel estimation vector from the base station to RIS and then to user k; Δh BU represents the actual interference channel estimation error from the base station to user k, ΔG represents the actual interference channel estimation error from the base station to RIS and then to user k; ||·|| FIndicates that the Frobenius norm of the matrix is obtained; represents the radius of the CSI uncertainty set from the base station to the RIS and then to user k, represents the radius of the CSI uncertainty set from the base station to user k.

[0054] C1 is the minimum communication rate guaranteed for each user, C2 is the RIS discrete phase shift constraint, and C3 is the maximum transmission power constraint of the base station.

[0055] Because v k and Θ in R k Medium coupling, using SCA to R k Processing:

[0056]

[0057]

[0058]

[0059] Among them, α k and β k is a slack variable, (2) is non-convex. Based on Taylor expansion, (2) can be linearized as:

[0060]

[0061] in, is the slack variable, and They are and The previous iteration.

[0062] P1 can be restated as:

[0063] P2:

[0064]

[0065]

[0066] C3:(3)-(5)

[0067] To handle channel uncertainty, the equivalent estimated channel matrix and estimation error are defined as:

[0068]

[0069] First, bring (1) into (3), and define To organize:

[0070]

[0071] Among them, step a is because a H Ba=Tr{Baa H} remain equal, step b because remains unchanged. Combined with (6), (7) can be transformed into the following quadratic form:

[0072]

[0073] in,

[0074] Lemma 1 (S-process): Define the function in, At this time, if and only if holds, there exists λ≥0 such that:

[0075] Using Lemma 1 to further transform (8), we can obtain:

[0076]

[0077] Similarly, (4) can be transformed into the following quadratic form:

[0078]

[0079] in,

[0080] Using Lemma 1 to further transform (10), we can obtain:

[0081]

[0082] definition The original optimization problem can be converted into the following form:

[0083] P3:

[0084]

[0085]

[0086] C4:Rank(V k )=1.

[0087] Since the constraint C4 in P3 is non-convex, the variable V k The problem is difficult to solve due to the coupling of Θ and the discrete phase of constraint C2. An alternating optimization method is used to iteratively update V k and Θ.

[0088] First, the RIS phase shift matrix is fixed and the base station transmit matrix is solved.

[0089] P3 can be rewritten as:

[0090]

[0091] By utilizing SDR, the constraint C4:Rank(V k )=1. Therefore, the resulting semidefinite programming problem can be solved efficiently using CVX tools. Finally, by using the Gaussian randomization method, we can get k Obtaining a suboptimal solution

[0092] Then, the base station transmit matrix is fixed and the RIS phase shift matrix is solved.

[0093] Define E=θθ H ,but

[0094] Will and Substitute (9) and (11) for updates, and scale the discrete phase to a continuous phase constraint. The original problem is transformed into:

[0095] P6:

[0096]

[0097]

[0098]

[0099] C5:Rank(E)=1.

[0100] It can be seen that E is completely unrelated to the objective function, and the objective function does not necessarily decrease during the iteration process. Therefore, the problem is equivalently converted to the following form for solution:

[0101] P7:

[0102]

[0103]

[0104] C5:0<q≤1.

[0105] This equivalent relaxation subproblem is also convex. The solution E of P6 and the solution of P7 are The relationship between is given by: This subproblem is also a convex semidefinite programming problem, which can be effectively solved using CVX tools. Through SDR technology, P7 can remove constraint C5. At this time, it cannot be guaranteed that the problem is a rank-one problem. In this case, Gaussian randomization method can be used to obtain a high-quality solution θ * .

[0106] Then the RIS discrete phase is solved by the projection theorem, which is as follows:

[0107]

[0108] Iterative robust beamforming algorithms such as Figure 1 .

[0109] The application effect of the present invention is described in detail below with reference to simulation.

[0110] 1) Simulation conditions

[0111] Assume that there is a 4-antenna base station, 2 single-antenna users, and a RIS with 4 reflectors in the communication system. The base station coordinates are (0,0), the RIS coordinates are (50,10), and the coordinates of user 1 and user 2 are (70,0) and (70,5), respectively. Assume that the channel model includes large-scale fading and small-scale fading. The large-scale fading is PL = -30-10αlg(d)dB, where α is the path loss exponent and β is the link distance in meters. Assume The small-scale fading in follows the Rayleigh fading distribution. For the bounded CSI error model, define ||Δh BU,k || 2 ≤(δ BU,k ) 2 and in represents the radius of the CSI uncertainty set from the base station to the RIS and then to user k. Denotes the radius of the CSI uncertainty set from the base station to user k. Definition is the maximum normalized estimation error. The path loss index from the base station to the RIS is α BI =2.2, RIS to user path loss exponent α IU =2, base station to user path loss index α BU =4. Other simulation parameters are given in Table 1:

[0112] Table 1

[0113]

[0114] 2) Simulation results

[0115] In this embodiment, Figure 2 The robustness diagram of the iterative method of this example is given. Figure 3The transmission power diagram of the iterative method of this example is given. Figure 2 It shows that as the channel error δ increases, the interruption probability of users of all methods also increases, and at the same δ value, the user interruption probability of the method of the present invention is the smallest, which proves that the method of the present invention has good robustness. Figure 3 It shows that as the channel error δ increases, the transmission power of all methods increases. The transmission power of the method of the present invention is slightly higher than that of the non-robust method. However, by adding the RIS reflection unit, the transmission power of the base station can be reduced. Figure 2 and Figure 3 The experimental results show that although the method of the present invention sacrifices a certain amount of transmission power, it ensures good robustness.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for minimizing energy consumption of a RIS-assisted communication system, characterized by: Considering the user communication rate constraints and the discrete phase shift constraints of the RIS, and taking the minimization of the total system power consumption as the optimization goal, a system model is established for a RIS-assisted multi-user multi-input single-output communication system with hardware impairments. The original problem is transformed into an equivalent convex optimization problem using the S-process and the successive convex approximation method. The convex optimization problem is solved using the semidefinite relaxation method and the alternating optimization algorithm. The method specifically comprises the following steps: S1: Initialize system parameters and set the convergence accuracy of the iterative method Iterative initialization; S2: Given the diagonal phase shift matrix of RIS, calculate the beamforming matrix from the base station to user k. At this time, the base station transmit power S3: Fix the beamforming matrix from the base station to user k and calculate the diagonal phase shift matrix of RIS; S4: Update the RIS diagonal phase shift matrix and calculate n represents the nth iteration; S5: Determine whether the power consumption of the MU-MISO communication system has converged; if so, calculate and output the optimal beamforming matrix from the base station to the user, the optimal diagonal phase shift matrix of the RIS, and the minimum base station transmit power of the MU-MISO communication system, and then end; otherwise, proceed to step S6; S6: Determine whether the current number of iterations is greater than the maximum number of iterations; if so, output the optimal beamforming matrix from the base station to the user, the optimal diagonal phase shift matrix of the RIS, and the minimum base station transmit power of the MU-MISO communication system, and then end; otherwise, enter the next iteration and return to step S2; In step S1, the system parameters include the number of intelligent metasurface RIS reflective elements L, the number of base station antennas M, the number of users K, the channel estimation vector from the base station to user k The cascade channel estimation vector from the base station to the RIS and then to user k is The total circuit power consumption value P of the system C , base station power amplification factor ξ, base station maximum transmit power threshold P max , the minimum transmission rate threshold of user k The radius of the CSI uncertainty set from the base station to user k The radius of the CSI uncertainty set from the base station to the RIS and then to user k Base station hardware damage factor ρ t , hardware damage factor ρ at user k r,k , the noise power at user k Maximum number of iterations N max ; The beamforming matrix V from the base station to user k in step S2 k for: in, v k Beamforming matrix from base station to user k; λ, r k , α k and β k is the slack variable; r k is the achievable rate for user k, is the minimum transmission rate threshold of user k; Tr(·) is the trace of the matrix; is the channel estimation vector from the base station to user k, is the concatenated channel estimation vector from the base station to the RIS and then to user k, ρ t is the hardware damage factor of the base station, ρ r,k is the hardware damage factor at user k, is the noise power at user k, θ is the vector of diagonal elements of the RIS phase shift matrix, I is the identity matrix, X ≥ 0 means the matrix X is a semi-positive matrix, 0 is an all-zero matrix, is the radius of the uncertainty set of the two channels from the base station to user k, represents the radius of the CSI uncertainty set from the base station to the RIS and then to user k, represents the radius of the CSI uncertainty set from the base station to user k; is the total system power consumption, vec(X) means vectorizing the matrix X, taking each column of the matrix X and forming a column vector, diag(·) means diagonalization, (·) H is the conjugate transpose of the matrix, (·) T is the transpose of the matrix, |·| is the absolute value, ||·|| is the Euclidean criterion of the vector, and Rank(·) is the rank of the matrix; The diagonal phase shift matrix of the RIS in step S3 is calculated according to the following formula: in, E=θθ H , is a vector of diagonal elements of the RIS phase shift matrix, each θ l It can only take d finite values, which are equally distributed in [0,2π); the RIS phase shifter set uses Indicates that θ l The collection of d = 2 y is the number of phase shift levels, y represents the number of bits required for d phase shift levels, Representation matrix The modulus of the element in the lth row and lth column of is q; 2. The method for minimizing energy consumption of a RIS-assisted communication system according to claim 1, characterized in that: In step S5, the method for determining whether the transmission power of the MU-MISO communication system base station is converged is as follows: The optimal beamforming matrix from the base station to the user is based on Calculate and use Gaussian randomization to obtain the optimal diagonal phase shift vector θ of RIS * , solved to the discrete phase of RIS by projection theorem Minimum total system power consumption

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