An IRS-aided integrated base station computing offloading method

Abstract

The application relates to the technical field of radar and communication, and specifically discloses an integrated base station computing offloading method based on IRS assistance. First, in the environment of a high-performance edge server near a base station, with the aid of intelligent reflecting surface (IRS) technology, received data is offloaded to the server for centralized processing, and a radar detection and communication uplink joint receiving system is constructed. Then, under the constraints of receiver power, frequency band energy and IRS constant modulus, a computing offloading problem model is constructed to minimize the total energy consumption of the system as a radar point target, so that the receiving vector design is simultaneously optimized. Finally, the computing offloading problem model is decomposed, and the radar and communication receiving vectors and IRS phase matrices are solved respectively. The method realizes the overall performance improvement of each functional module, enhances the adaptability and anti-interference ability of the system in actual complex environments, and realizes the global optimal balance of communication rate, sensing accuracy, time delay and energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of radar and communication technology, and in particular to an integrated base station calculation offloading method based on IRS assistance. Background Technology

[0002] The integration of communication, sensing, and computing functions deeply merges these elements, achieving comprehensive improvements in spectrum efficiency, energy efficiency, and multi-dimensional resource utilization through resource sharing and collaborative optimization. As 6G networks evolve towards this integration, radar and communication systems face unprecedented challenges within a spectrum-sharing architecture. Competition for limited spectrum resources between the two systems significantly increases the complexity of spectrum allocation, particularly in the time-frequency-space-energy multi-dimensional resource domain. Furthermore, integrated nodes often lack efficient computing capabilities, thus posing a computational challenge to processing data received from communication radar.

[0003] Traditional separate optimization methods typically treat radar and communication systems as independent entities, optimizing them separately and neglecting their mutual influence and coupling relationships. However, in the integrated sensing, communication, and computing 6G network, radar and communication systems are closely connected across multiple dimensions, including spectrum, time, space, and energy. Optimization of one will affect the other. Therefore, a novel optimization method is needed that can comprehensively consider the interaction between radar and communication systems, achieving collaborative scheduling and dynamic optimization of multi-dimensional resources. Furthermore, the mutual interference between radar echoes and communication uplink signals, as well as the stringent requirements of edge computing tasks in energy-constrained environments, constitute the core contradiction between high-precision radar detection, high-quality communication services, and system energy consumption, restricting the improvement of overall system performance and posing new challenges to resource collaborative scheduling and dynamic optimization. Currently, researchers have proposed a multi-functional beamforming design framework integrating sensing, communication, and computing. By jointly optimizing the Cramer-Rao lower bound (CRB), the signal-to-interference-plus-noise ratio (SINR), and computing speed, it achieves efficient resource allocation. The proposed semidefinite relaxation (SDR) algorithm significantly improves radar point target estimation performance while guaranteeing a rank-one solution. Another researcher proposed an optimization scheme for mobile edge computing networks aimed at 6G ultra-reliable low-latency communication. This scheme, through the joint design of sensing, communication, and computing resources, minimizes the number of edge server deployments and end-to-end latency for communication users while meeting stringent latency and reliability requirements. It employs a two-stage optimization approach, combining short-term task offloading and bandwidth allocation optimization with long-term service deployment strategies. However, existing methods require pre-setting fixed weights or priorities and lack dynamic adjustment mechanisms, making it difficult to achieve a globally optimal balance between communication rate, sensing accuracy, latency, and energy efficiency in complex scenarios. The joint optimization framework involves high-dimensional non-convex problems, and existing algorithms based on SDR and similar methods have high computational complexity, making them difficult to scale to large-scale networks. Summary of the Invention

[0004] This invention provides an integrated base station computation offloading method based on IRS assistance, which solves the technical problem that existing methods are difficult to achieve a globally optimal balance of communication rate, sensing accuracy, latency and energy efficiency in complex scenarios.

[0005] To address the above technical problems, this invention provides an integrated base station computation offloading method based on IRS assistance, comprising the following steps:

[0006] S1. Construct a joint receiving system for radar detection and communication uplink. This system includes an integrated base station, a server, N communication users, one radar point target, and a smart reflective surface (IRS). When the radar point target is obstructed, it transmits data to the integrated base station through the smart reflective surface. Communication users transmit data directly to the integrated base station and also transmit data to the integrated base station through the smart reflective surface. The integrated base station is also subject to interference from K clutter signals. The integrated base station offloads communication computing and radar computing tasks to the server.

[0007] S2. Taking the minimization of total system energy consumption as the radar point target, and under the constraints of receiver power, frequency band energy, and IRS constant mode of the integrated base station, construct a calculation offloading problem model that jointly optimizes communication calculation offloading variables, radar calculation offloading variables, and IRS reflection coefficient variables.

[0008] S3. Solve the computational unloading problem model to obtain the optimal solutions for the communication computational unloading variables, radar computational unloading variables, and IRS reflection coefficient variables.

[0009] Furthermore, step S2 specifically includes the following steps:

[0010] Construct an energy minimization objective function that minimizes the total energy consumption of the system by optimizing communication computational offload variables and radar computational offload variables;

[0011] By incorporating the IRS reflection coefficient variable, the energy consumption minimization objective function is transformed into a weighted sum rate maximization objective function that maximizes the weighted sum of radar data offload rate and communication data offload rate.

[0012] Define the constraints, including receiver power constraints, frequency band energy constraints, and IRS constant mode constraints;

[0013] Based on the approach of solving the Rayleigh quotient problem, the radar data unloading rate function and the communication data unloading rate function in the weighted sum rate maximization objective function are simplified to obtain the optimized overall objective function;

[0014] A computational offloading problem model is obtained, which aims to optimize the overall objective function and, under constraints, jointly optimizes the computational offloading variables of communication, radar, and IRS reflection coefficient.

[0015] Furthermore, the receiver power constraint is that the norm of the power vector of each communication receiving filter is 1, and the norm of the power vector of the radar receiving filter is 1; the frequency band energy constraint is that the energy of the radar receiver must be less than the radar receiving energy threshold in each restricted sub-band of the radar, and the energy of the communication receiver must be less than the communication receiving energy threshold in the specified restricted sub-band of the communication; the IRS constant mode constraint is that the mode of the reflection coefficient of each reflection element is 1.

[0016] Furthermore, step S3 specifically includes the following steps:

[0017] S31. Transform the computational offloading problem model into a radar optimization subproblem that optimizes only the radar computational offloading variables, a communication optimization subproblem that optimizes only the communication computational offloading variables, and a reflection coefficient optimization subproblem that optimizes only the IRS reflection coefficient variables.

[0018] S32. Iteratively solve the radar optimization subproblem, the communication optimization subproblem, and the reflection coefficient optimization subproblem.

[0019] Furthermore, in step S31, the unloading variables and IRS reflection coefficient variables are calculated through fixed communication, the constants in the unloading problem model are eliminated, and the objective function is transformed by using the properties of matrix determinants, first-order Taylor expansion, and equivalent real-value transformation to obtain the radar optimization subproblem.

[0020] Furthermore, in the iterative solution process of step S32, the Lagrange alternating direction multiplier method is used to solve the radar optimization subproblem.

[0021] Furthermore, in step S31, by fixing the radar calculation unloading variables and IRS reflection coefficient variables, retaining the corresponding constraints and objective function, and using the block coordinate descent method to solve separately, by using first-order Taylor expansion, and by introducing an equivalent real-value transformation to perform an equivalent transformation on the objective function, the communication optimization subproblem is obtained.

[0022] Furthermore, in the iterative solution process of step S32, the Lagrange alternating direction multiplier method is used to solve the communication optimization subproblem.

[0023] Furthermore, in step S31, by fixing the radar calculation unloading variables and the communication calculation unloading variables, the corresponding constraints and objective functions are retained, and the objective function is equivalently transformed by continuous convex approximation, first-order Taylor expansion, and matrix transformation to obtain the IRS reflection coefficient optimization subproblem.

[0024] Furthermore, the objective function of the unloading problem model is to maximize... B r SINR is the bandwidth of the radar system. r B is the received signal-to-interference-plus-noise ratio (SINR) of the receiving filter designed for radar target points. c,n SINR is the bandwidth of communication user n. c,n λ is the received signal-to-interference-plus-noise ratio of the receiving filter designed for communication user n. c,n Let λ be the proportion of the unloaded tasks for communication user n. r For the proportion of radar unloading tasks, there are

[0025] This invention provides an integrated base station computation offloading method based on IRS assistance. First, in a high-performance edge server environment near the base station, intelligent reflective surface (IRS) technology is used to offload received data to the server for centralized processing, constructing a joint receiving system for radar detection and communication uplink. Then, under receiver power constraints, frequency band energy constraints, and IRS constant mode constraints, a computation offloading problem model is constructed with minimizing the total system energy consumption as the radar point target to simultaneously optimize the receiver vector design. Finally, the computation offloading problem model is decomposed. The radar and communication receiver vectors are relaxed using first-order Taylor expansions and then solved alternately using the Alternating Direction Penalty (ADPM) algorithm. The IRS phase matrix is ​​replaced by the upper bound problem of a second-order function, followed by simplification and element-wise solution using the Block Coordinate Descent (BCD) algorithm. The introduction of IRS optimizes the channel environment, improves communication quality, and effectively enhances the joint performance of the system. This method achieves overall performance improvement for each functional module, enhances the system's adaptability and anti-interference capability in complex real-world environments, and achieves a globally optimal balance between communication rate, sensing accuracy, latency, and energy efficiency. Attached Figure Description

[0026] Figure 1 This is an architecture diagram of the radar detection and communication uplink joint receiving system provided in an embodiment of the present invention;

[0027] Figure 2 This is a graph showing the change of performance index values ​​with the number of iterations provided in this embodiment of the invention;

[0028] Figure 3 This is a comparison curve of the impact of IRS on system performance provided in the embodiments of the present invention;

[0029] Figure 4 This is a graph showing the change of the objective function value as a function of CNR, provided in an embodiment of the present invention.

[0030] Figure 5This is a graph showing the change of the communication objective function with the radar interference-to-noise ratio provided in the embodiments of the present invention;

[0031] Figure 6 This is a graph showing the change of radar target function with communication interference-to-noise ratio provided in an embodiment of the present invention. Detailed Implementation

[0032] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are given for illustrative purposes only and should not be construed as limiting the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0033] This invention provides an integrated base station computation offloading method based on IRS assistance, comprising the following steps:

[0034] S1. Construct a joint receiving system for radar detection and communication uplink. This system includes an integrated base station (base station node), a server, N communication users, one radar point target, and a smart reflector (IRS). When the radar point target is obstructed, it transmits data to the integrated base station through the smart reflector. Communication users transmit data directly to the integrated base station and also transmit data to the integrated base station through the smart reflector. The integrated base station is also subject to interference from K clutter signals. The integrated base station offloads communication computing and radar computing tasks to the server.

[0035] S2. Taking the minimization of total system energy consumption as the radar point target, and under the constraints of receiver power, frequency band energy, and IRS constant mode of the integrated base station, construct a computational offloading problem model that jointly optimizes communication computational offloading variables and radar computational offloading variables.

[0036] S3. Solve the computational unloading problem model to obtain the optimal solutions for the communication computational unloading variables and the radar computational unloading variables.

[0037] This invention first utilizes Intelligent Reflective Surface (IRS) technology in a high-performance edge server environment near the base station to offload received data to a centralized server for processing, constructing a joint receiving system for radar detection and communication uplink. Then, under receiver power constraints, frequency band energy constraints, and IRS constant mode constraints, a computational offloading problem model is constructed with minimizing the total system energy consumption as the radar point target to optimize the receiver vector design. Finally, the computational offloading problem model is solved to obtain the offloading allocation of radar and communication data. The introduction of IRS optimizes the channel environment, improves communication quality, and effectively enhances the joint performance of the system. This method achieves an overall performance improvement for each functional module, enhances the system's adaptability and anti-interference capability in complex real-world environments, and achieves a globally optimal balance between communication rate, sensing accuracy, latency, and energy efficiency.

[0038] The following provides a more detailed explanation of each step.

[0039] (1) Step S1: Constructing the system model

[0040] In the receiving scenario of a uniform linear array base station, considering that the base station is equipped with M receiving / transmitting antennas, its tasks include not only receiving uplink data transmission from single-antenna communication users, but also detecting the echo signal of a radar point target. Specifically, there are N communication users and a single radar point target. However, in practical applications, the radar echo signal is often affected by K clutter interferences, thus placing higher demands on signal processing and radar point target detection. To solve the problem of difficult direct-line detection of radar point targets due to factors such as building obstruction, this invention further introduces intelligent reconfigurable surface-assisted radar point target detection, while utilizing its beamforming characteristics to improve the service quality of communication users. The IRS has L independent reflective elements, which can be used for fine-tuning of the signal in a multipath environment, thereby enhancing the energy of the radar point target signal and suppressing interference, improving the overall detection performance and communication quality of the system. A specific scenario diagram is shown below. Figure 1 As shown.

[0041] In theoretical modeling, it is assumed that the received signal is known. For each communication user n, the set of data pulses transmitted in the uplink is denoted as... Where P represents the set of pulse counts, This represents the set of complex numbers. Meanwhile, for radar point target echo signals, it is denoted as... These two types of signals together constitute the base station received signal. The introduction of IRS adds additional degrees of freedom to the signal transmission channel, allowing for more flexible control of the signal's phase and amplitude. This enables effective separation and joint detection of multi-radar point target signals, constructing the received signal model as follows:

[0042]

[0043] Among them, the first term y r It refers to the signals received by the radar, including radar target detection signals and clutter reflection signals. It is the radar target detection signal receiving and transmitting steering matrix. It is the path loss coefficient of the radar target signal. and These are the radar point targets in the direction of the radar target. The receiving guidance vector and the reflecting guidance vector, It is the channel matrix from the intelligent reflective surface to the communication user n. The diagonal matrix associated with the intelligent reflective surface is defined as follows: Consistent with the definition of a uniform linear array, Let be the reflection coefficient of the l-th reflecting unit, and diag{} be a diagonal matrix. It is the receive and transmit steering matrix for clutter reflection signals. It is the path loss coefficient of active clutter interference. and These are radar point targets with respect to clutter interference directions. The receiving steering vector and the reflecting steering vector are consistent with the definition of a uniform linear array. The superscripts T and H represent the matrix transpose and conjugate transpose, respectively.

[0044] The second term y c It is the uplink signal of the communication user, in which It is the channel vector from communication user n to the base station. It is the channel vector from the intelligent reflective surface to the communication user n. It is the path loss coefficient for communication user signals. x n For communication user n, there is a set of data pulses transmitted on the uplink.

[0045] The third term n v It is a noise term, specifically additive white Gaussian noise, with a power of δ. 2 .

[0046] For radar functionality, a normalized receiver filter is introduced at the receiver, and its received signal-to-interference-plus-noise ratio is:

[0047]

[0048] f r This is the weight vector of the radar receiver filter.

[0049] For the communication function, a corresponding receiving filter is designed for each communication user n, and its received signal-to-interference-plus-noise ratio is:

[0050]

[0051] f c,n Let n be the receive filter vector of the nth communication user. Let be the expectation operator, representing the statistical mean of the random variable.

[0052] (2) Step S2: Constructing a computational unloading problem model

[0053] This method takes minimizing the total system energy consumption as the original objective function. Energy consumption optimization needs to simultaneously consider the radar offload energy consumption E. r Energy consumption for communication offloading (E c,n (This represents the offloading energy consumption of communication user n), and a trade-off is made between the two to achieve the overall energy consumption E. T Minimize. Assume the computational task data volume for a single communication user n is D. c,n Its data unloading rate is B c,n Let D be the bandwidth of communication user n; and let D be the computational data volume of the radar detection mission. r The radar data offloading rate is B r The bandwidth of the radar system is given. To further characterize the task offloading process, the proportion of offloaded tasks for communication user n, λ, is introduced. c,n The ratio of radar unloading tasks to λ r These measures the allocation of communication and radar computing tasks, respectively, so the total system energy consumption can be modeled as follows: Where P c and P r These are the power of the communication radar receiver, and they satisfy the constraints. The original energy consumption minimization objective function can be equivalently transformed into maximizing the weighted sum rate, i.e.:

[0054]

[0055] In terms of constraint design, considering the power limitation of the receiving filter, to ensure that the power of the communication and radar receivers is constrained and the system operates stably, the power of the communication receiving filter is set to |||f. c,n Given that ||| = 1, n = 1, ..., N, the power of the radar receiving filter is |||f. r ‖‖=1, || || is the norm of the vector. Furthermore, to effectively suppress interference at the receiver, the receiver needs to be optimized and constrained in both the spatial and frequency domains. Assume the system has X invalid receiving frequency bands, meaning signals within these bands should be strictly suppressed. Using... and Let represent the upper and lower frequency limits of the x-th invalid receiving frequency band, respectively. The radar receiver should then set appropriate suppression weights within these frequency bands to reduce the interference from non-radar point target signals. Furthermore, the received energy of the m-th radar receiving filter can be expressed as:

[0056]

[0057] f is the radar receiving filter, f r,m (p) represents the receiving filter weight of the m-th radar receiving antenna at the p-th pulse time, f r,m Let Ξ be the filter weight vector for the m-th radar receiving antenna. r,x,m Let be the energy constraint matrix of the radar receiver on the xth invalid frequency band and the mth filter.

[0058] Ξ r,x (p1, p2) represents the (p1, p2)th element of the frequency domain energy constraint matrix at the radar receiver.

[0059]

[0060] Among them, there are In high-priority communication scenarios, to ensure high-quality service for communication users, it is necessary to suppress interference using signal correlation and limit the spectrum of non-radar point target signals outside a specific frequency band to reduce out-of-band interference. Assume that each communication user n needs to suppress interference signals within a specific frequency band and sets up an invalid receiving frequency band to limit the influence of irrelevant signals. Let... and Let represent the normalized frequency range and upper and lower bounds of the v-th interference suppression band for communication user n, respectively. Within this frequency band, the power of all non-radar point target signals should be suppressed as much as possible to avoid interfering with the communication data reception of communication user n. In this context, the received energy of the m-th communication receiving filter for the n-th communication user can be modeled as follows:

[0061]

[0062] f c,n,m (p) represents the receiving filter weights of the nth communication user and the mth receiving antenna at time p, f c,n,m Let Ξ be the filter vector for the nth communication user and the mth receiving antenna. c,n,v,m Let be the energy constraint matrix of the m-th receiving filter of user n in the v-th interference suppression frequency band in the communication system.

[0063] Ξ c,n,v,m (p3, p4) represents the (p3, p4)th element of the frequency domain energy constraint matrix at the communication receiver.

[0064]

[0065] Similarly, there are

[0066] To improve radar detection and communication data reception performance, it is necessary to reasonably constrain the received energy in each frequency band, meet energy control requirements, optimize spectrum utilization, and reduce interference. For an M-dimensional array filter, the total received energy at the radar receiver is defined as... Similarly, the total received energy at the communication receiver is defined as... I M This is an identity matrix of size M×M. Specifically, it is stipulated that within each restricted sub-band of the radar, the energy must be less than the radar's received energy threshold ν. r Within the specified communication restriction sub-band, the energy must be less than the communication reception energy threshold ν. c That is, the following constraints must be met:

[0067]

[0068] Furthermore, to enhance the system's sensing and communication performance in complex wireless environments, a passive intelligent reflective surface is introduced to dynamically regulate channel states and improve signal propagation characteristics. Since the IRS is composed of passive reflective elements, its phase modulation capability is limited by physical implementation; therefore, it must satisfy the constant modulus constraint, i.e., for the reflection coefficient of the l-th reflective element, we have… || indicates modulo.

[0069] When jointly optimizing radar echo reception and communication reception, the system needs to solve a non-convex optimization problem. Therefore, the objective function of the radar part is simplified using the normalization characteristics of the signal and filter, thereby reducing the solution complexity. Specifically, the radar signal processing undergoes the following optimization transformation steps:

[0070]

[0071] in, H b,n =h b,n +G H Θ H h b,n I MPLet be an identity matrix of size M×P, and Tr() be the trace of the matrix. This optimization problem can be reduced to the Generalized Rayleigh Quotient Problem (GRQP), whose objective is to maximize the signal-to-interference-plus-noise ratio (SNR) of the system under given constraints. Based on the theory of Generalized Eigenvalue Decomposition (GEVD), the optimal solution for s can be obtained using the Maximum Eigenvalue Criterion (MEC). in, Next, substituting the optimal receive vector s into the original optimization problem and deriving the numerator, we can simplify it to:

[0072]

[0073] Therefore, the numerator as a whole equals 1. For the denominator:

[0074]

[0075] Ultimately, the expression can be written as According to the matrix determinant lemma, for any matrix Q and vector x, we have det(I+Qxx). H )=1+x H Qx, where I is the identity matrix, i.e., for the radar target function, we have:

[0076]

[0077] det() is the determinant of a matrix.

[0078] Similarly, the expression for the signal-to-interference-plus-noise ratio (SINR) in communication can first be transformed into:

[0079]

[0080] Solving this Rayleigh quotient problem yields the optimal x. n for Among them, C c,n It is the interference plus noise covariance matrix of the nth communication user, I P Let P be the identity matrix of size P×P. Substituting this into the original equation, the uplink signal-to-noise ratio function for communication user n can be simplified to:

[0081]

[0082] Ultimately, the overall objective function for system optimization can be rewritten as:

[0083]

[0084] (3) Step S3: Solve the unloading problem model

[0085] The optimization problem shown in Equation (16) changes from minimizing computational offloading energy consumption to maximizing the weighted sum of radar detection and communication service quality. The objective function includes radar and communication components. To satisfy system constraints, power normalization, frequency band energy constraints, and IRS constant mode constraints are introduced. The overall problem is non-convex, and we will now consider dividing the original problem into three subproblems for iterative solution.

[0086] 1) Optimize radar receiver filter

[0087] fixed Eliminating the constants transforms the optimization problem into:

[0088]

[0089] The objective function is transformed using the properties of matrix determinants to simplify the solution process. The optimization objective is then reconstructed into a new expression. Among them O r (f r ) is about the optimization variable f r The function can more clearly reflect the structural properties of the optimization problem. Furthermore, using C(f)... r Represent C as f r The function, because C(f) r )and All are related to the optimization variable f r Since it exhibits joint convexity, it can be linearly approximated using a first-order Taylor expansion to construct a convex approximation problem, thereby reducing the non-convexity of the problem and the solution complexity. Assume that in the current iteration, the solution of the previous iteration is... Based on the differential rule of complex matrices, it is in The Taylor expansion form of the nearby expression is:

[0090]

[0091] in, To take the real part of the complex number. Using the idea of ​​the Majorization-Minimization (MM) substitution algorithm, since the terms affecting concavity and convexity after Taylor expansion have negative values, maximizing the original problem can be transformed into:

[0092]

[0093] in, Frequency band restriction matrix Ξr,x As a symmetric positive definite matrix, it possesses favorable numerical properties and can be decomposed using the Cholesky decomposition method, i.e., finding a lower triangular matrix U. r,x Make Introducing an equivalent real-valued transformation converts complex-valued variables into equivalent real-valued expressions, i.e., using f r,R ,Ω r,R ,κ r,R ,Ξ r,x,R to represent f r ,Ω r ,κ r ,Ξ r,x The real-valued form of the expression allows the optimization problem to be reformulated over the real number field. Ultimately, it is transformed into an equivalent real-valued optimization problem:

[0094]

[0095] Its Lagrange function can be expressed as:

[0096]

[0097] Where, {μ 1,x}, μ2 is the Lagrange multiplier vector associated with the constraints, {υ 1,v},υ2 represents the penalty coefficient in the augmented Lagrange function. Within the iterative framework of the alternating direction multiplier method, if the current iteration is q1, the variable update rule can be implemented through the following steps:

[0098] ① Update f r,R

[0099] Only retain the constraints and the part of the objective function related to f. r,R The related terms mean that the original Lagrange problem can be remodeled into the following form:

[0100]

[0101] Construct its about f r,R The gradient expression can be analytically obtained by establishing the stationary point equation and setting it to 0, resulting in the updated solution:

[0102]

[0103] ②Update b

[0104] By imposing constraints and eliminating terms unrelated to b from the objective function, the original Lagrange problem can be reformulated as follows:

[0105]

[0106] Its closed-form solution can be given by the following equation:

[0107]

[0108] in

[0109] ③ Update {c x}

[0110] For any x = 1, ..., X, the original problem can be expressed as:

[0111]

[0112] Its closed-form solution can be expressed as:

[0113]

[0114] in

[0115] ④ Update {μ 1,x} and μ2

[0116] The Lagrange parameters have been updated as follows:

[0117]

[0118] ⑤ Update {υ 1,x} and υ2

[0119] The penalty parameters are updated as follows:

[0120]

[0121] in, It is a number slightly larger than 1, used to speed up algorithm convergence. as well as

[0122] 2) Optimize the communication receiving filter

[0123] During this stage, fixed Subsequently, we focused on optimizing {f c,n By retaining the corresponding constraints and objective function, we can obtain the new optimization objective as follows:

[0124]

[0125] First, for each receiving filter for user n, the N terms of the objective function and the constraints are all one-to-one and only related to the optimization variable f. c,n They are self-related and independent of each other, therefore they can be solved separately using the block coordinate descent method, that is, for any f in the set... c,n The objective function can be transformed into:

[0126]

[0127] Similar to the objective function transformation in the radar section, irrelevant terms are eliminated, and the problem is equivalently transformed into a problem to be solved. in, It is the value of the previous iteration, O c,n (f c,n ) is the weighted sum rate function of communication users, which, after first-order Taylor expansion, yields the substitution function as:

[0128]

[0129] in, The linear terms do not affect concavity or convexity, and the original objective function for maximizing communication can be further transformed into:

[0130]

[0131] in, For a positive semidefinite symmetric matrix Ξ c,n,v A matrix E can be found. c,n,v satisfy At the same time, use f c,n,R ,Ω c,n,R , Ξ c,n,v,R f c,n ,Ω c,n , Ξ c,n,v In real-valued form, for the nth user, the final optimization objective can be expressed as:

[0132]

[0133] For primal optimization problems with constraints, an augmented Lagrangian function can be constructed by introducing dual variables. Its mathematical form is defined as:

[0134]

[0135] Where {μ 3,v},μ4 and {υ 3,v},υ4 are the Lagrange multiplier vectors, which are penalty parameters for balancing the objective function and constraints. Under the iterative framework of the alternating direction multiplier method, the core process of the q2th iteration can be described in detail as follows:

[0136] ① Update f c,n,R

[0137] By eliminating constraints and the objective function related to f c,n,R Irrelevant terms, the original communication optimization problem can be reformulated as the following function:

[0138]

[0139] For f c,n,R By differentiating the equation and setting it to 0, the solution for this iteration can be analytically obtained as follows:

[0140]

[0141] ②Updated d

[0142] By removing constraints and terms in the objective function that are irrelevant to d, the original Lagrange problem can be simplified to the following form:

[0143]

[0144] Its closed-form solution can be given by the following equation:

[0145]

[0146] in,

[0147] ③ Update {e v}

[0148] For any v = 1, ..., V, the original problem can be expressed as:

[0149]

[0150] Its closed-form solution can be expressed as:

[0151]

[0152] in,

[0153] ④ Update {μ 3,v} and μ4

[0154] The Lagrange parameters have been updated as follows:

[0155]

[0156] ⑤ Update {υ 3,v} and υ4

[0157] The penalty parameters are updated as follows:

[0158]

[0159] Where 0 < ι1 < 1, 0 < ι3 < 1, and ι2, ι4 are numbers slightly larger than 1, used to speed up algorithm convergence.

[0160]

[0161] 3) Optimize the phase of the intelligent reflective surface

[0162] In this stage, fix f r ,{f c,n}optimization Only keep and With the relevant constraints, the original optimization problem is transformed into:

[0163]

[0164] Consider using a continuous convex approximation process, based on Where M represents any matrix. Given the value from the previous iteration, the linear terms on both the left and right sides of the equation maintain consistent concavity and convexity. Utilizing this property, by eliminating the constant term, the objective function can be further simplified to:

[0165]

[0166] in, and This is the derivative of the first-order corresponding term between the radar target point and the communication user n. It is worth noting that the transformed optimization objective function exhibits good convexity in its structure, where the first term is jointly convex with respect to (C(Θ), A0(Θ)), and the second term is convex with respect to (C... c,n (Θ),H b,n (Θ) is also jointly convex. To further simplify the optimization problem and improve the convergence of the algorithm, a first-order Taylor expansion is used to linearize the objective function, constructing a feasible convex optimization subproblem. Assume... Given the value obtained in the previous iteration, we expand the internal structure of the first term of the objective function and use complex matrix differential theory to perform a local linear approximation of the function, ensuring that the new optimization problem maintains convexity in each iteration. Then, we retain the terms related to Θ, i.e.:

[0167]

[0168] in, and variables During the optimization process, the first term of problem (46) does not contain Θ and can be ignored. The second term contains Θ and can be expanded and simplified as follows: Using the commutative property of diagonal matrices, Θg = diag(g)θ, where Θ = diag(θ), it can ultimately be transformed into:

[0169]

[0170] in The constant term can be omitted.

[0171] Using the commutative property of traces, Ψ r,2 A0(Θ) can ultimately be converted to:

[0172]

[0173] in, T2=ΘGΨ r,2 G H The first term in the communication section can be simplified similarly to:

[0174]

[0175] in, These are terms irrelevant to the variable and can be ignored.

[0176] Next, we simplify the part of the communication section containing the 4th-order term Θ. Using a second-order relaxation technique, we replace the 4th-order term Θ with an appropriate quadratic form, ensuring that it still closely approximates the original function while maintaining the convexity constraint:

[0177]

[0178] During the optimization iteration process It is set to the calculation result of the previous iteration, while matrix ss H The largest eigenvalues ​​form the identity matrix M s Since the objective function involves multiple variables, those related to A0 are retained, while the first term, being a constant diagonal matrix, can be ignored. Similarly, the third term, being a constant matrix obtained from the previous iteration, can also be omitted, thus reducing computational complexity. Therefore:

[0179]

[0180] Using the diagonal property of Θ, in yes The element in the i-th row, It is the i-th row of matrix G. Similarly, we have Substituting into the original expression, we have By changing the order of integration, we obtain g i It is the conjugate transpose of the i-th row of matrix G, which will It can be extracted to obtain definition If θ is the element in the i-th row and j-th column of Λ, then it can be simplified to: θ H Λθ. The last term can be simplified to Tr(Ψ) using the commutative property of the trace.c,n,2 G H Θ H h i,n )=Tr(Θ H h i,n Ψ c,n,2 G H )=θ H ψ, where ψ is h i,n Ψ c,n,2 G H The vector consisting of the diagonal elements, i.e., ψ = [(h i,n Ψ c,n,2 G H ) 1,1 ,(h i,n Ψ c,n,2 G H ) 2,2 ,...,(h i,n Ψ c,n,2 G H ) LP,LP Ultimately, the expression can be transformed into:

[0181]

[0182] in, In addition, consider Due to the constraints, problem (46) remains non-convex and requires further simplification. Consider using the BCD algorithm to process each intelligent reflective surface element sequentially. Optimize. First, for the first item... For a fixed l, the summation is divided into AND and . The relevant parts and Irrelevant constant terms: When i = l and k = l, we have When i = l and k ≠ l, we have When i ≠ l and k = l, we have When i≠l and k≠l, it can be considered a constant. Because Therefore, there is Since the constant value can be ignored, and since E is a Hermitian matrix, that is... We can simplify and combine the results to obtain Adding linear terms gives and Ultimately, it can be simplified to Then contains The objective function part (ignoring the constant term) can be written as: in Then you can get but Because of |a l If | > 0, then to minimize this expression, we need φ. l-θ l =π (mod 2π), which gives θ. l =φ l -π gives the solution:

[0183] The effects of the present invention will now be verified.

[0184] In the system model of this invention, a radar-communication integrated ULA base station configured with an edge server is considered. Its transmit / receive array has M=6 elements, and the number of elements in the linear intelligent reflective surface is L=10. The base station detects radar point targets through the reflective surface. The transmitted signal is assumed to be a 7-bit Barker code. Simultaneously, two single-antenna communication users N request communication services from the base station via the uplink. The communication duration is consistent with the radar pulse, P=7. Furthermore, it is assumed that K=2 active clutter relay radar signals interfere with the base station. In the spectrum-limited scenario, it is assumed that there is X=1 radar receiving frequency band restriction area with a normalized frequency range of [0.6, 0.7], and V=1 communication receiving frequency band restriction area with a range of [0.3, 0.4]. The spectrum restriction threshold for both is 10. -1 In the initial setup, radar and communication tasks were considered equally important, i.e., λ r =0.5,λ c,1 =λ c,2 =0.25. Assume noise power δ 2 The communication channel loss factor is set to 0.01. Radar channel loss coefficient set to The active clutter channel loss factor is set to

[0185] This invention first studies the influence of penalty parameters on the convergence of optimization problems. By adjusting the penalty parameters, we can analyze their impact on the convergence speed and the final optimized value, thereby exploring suitable parameter selection so that the optimization algorithm can converge quickly and achieve a good trade-off between radar and communication performance. Figure 2 This is a curve showing how the performance index value of the present invention changes with the number of iterations. From... Figure 2 It can be seen that the choice of penalty parameter has a significant impact on the convergence speed and final performance of the optimization process. When the penalty parameter is set to 500, the optimization algorithm converges quickly in the first few iterations, and the overall objective value stabilizes relatively quickly, indicating that the optimization efficiency under this parameter is high, and a good balance can be achieved between radar and communication performance. However, as the penalty parameter increases to 1000 and 1500, although convergence still occurs eventually, the final convergence value is lower than before. In addition, the convergence speed decreases significantly, especially in the initial stage, where the optimization process becomes slower, indicating that a larger penalty parameter may lead to a lag in the constraint convergence process.

[0186] The role of intelligent reflective surfaces in joint radar communication systems was then explored, and the impact on radar detection and communication transmission was analyzed by comparing the system performance with and without IRS. Figure 3 This is a comparison curve showing the impact of IRS on system performance. Figure 3 The results show that the presence of an IRS improves both radar and communication performance compared to the absence of an IRS. This indicates that the IRS enhances target echoes by modulating the reflected signals, making radar detection more accurate and stable. It also demonstrates that the introduction of an IRS can optimize the channel environment, improve communication quality, and effectively improve the joint performance of the system, thus verifying its technical advantages in spectrum sharing scenarios.

[0187] Next, this invention explores the changes in system performance from two dimensions: carrier noise ratio (CNR) and interference noise ratio (INR). In practical scenarios, INR is usually limited by the actual power of the transmitter, so this invention sets the research range of INR to 0-20dB; while active interference often relays the target signal with similar or higher power after detecting it, thus extending the range of CNR to 0-30dB. Figure 4 This is a curve showing the change of the objective function value as a function of CNR. From... Figure 4 As can be seen, the performance of each optimization algorithm differs significantly in terms of objective function value as CNR increases. Specifically, the proposed algorithm maintains the highest and most stable objective function value as CNR increases, effectively mitigating the negative impact of environmental degradation on system performance. The Alternating Direction Multiplier (ADMM) scheme, however, exhibits a decline in overall performance, showing slightly less robustness than the proposed algorithm. Furthermore, the SDR algorithm's performance further deteriorates, reflecting its insufficient adaptability in high-interference environments. These comparative results verify the effectiveness of the proposed scheme in resisting clutter interference, demonstrating not only an overall performance improvement across all functional modules but also enhanced system adaptability in complex real-world environments.

[0188] Finally, different interference-to-noise ratios are achieved by adjusting the loss coefficients of the radar channel and the communication channel respectively. Figure 5 This is a graph showing the change of the communication objective function with the radar interference-to-noise ratio. Figure 6 This is a graph showing the change of radar target function with communication interference-to-noise ratio. From Figure 5 and Figure 6 It can be seen that as the INR increases, the performance of all three optimization algorithms declines to varying degrees on both communication and radar targets. Specifically, Figure 5 The analysis of the changes in the communication objective function value as radar interference intensity increases shows that the proposed method has the strongest anti-radar interference capability and the slowest performance degradation, while the ADMM algorithm performs poorly and the SDR algorithm performs the worst. Figure 6The changes in radar target function value as the intensity of communication interference increases were analyzed. It can be seen that when the interference intensity is not high, the performance of the three algorithms is similar, and the proposed algorithm has certain advantages. However, as the interference-to-noise ratio increases, the performance of SDR and ADMM algorithms drops sharply, while the proposed algorithm still maintains strong anti-interference ability, which verifies the robustness of the proposed algorithm in dealing with interference.

[0189] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for integrated base station calculation offloading based on IRS assistance, characterized in that, Including the following steps: S1. Construct a joint receiving system for radar detection and communication uplink, which includes an integrated base station, server, One communication user, one radar point target, and one intelligent reflective surface (IRS). The radar point target transmits data to the integrated base station via the intelligent reflective surface when obstructed. The communication user transmits data directly to the integrated base station and also transmits data via the intelligent reflective surface. The integrated base station is also subject to... Interference from individual clutter signals; Integrated base stations offload communication and radar computing tasks to servers; S2. Taking the minimization of total system energy consumption as the radar point target, and under the constraints of receiver power, frequency band energy, and IRS constant mode of the integrated base station, construct a calculation unloading problem model that jointly optimizes communication calculation unloading variables, radar calculation unloading variables, and IRS reflection coefficient variables; Step S2 specifically includes the following steps: Construct an energy minimization objective function that minimizes the total energy consumption of the system by optimizing communication computational offload variables and radar computational offload variables; By incorporating the IRS reflection coefficient variable, the energy consumption minimization objective function is transformed into a weighted sum rate maximization objective function that maximizes the weighted sum of radar data offload rate and communication data offload rate. Define the constraints, including receiver power constraints, frequency band energy constraints, and IRS constant mode constraints; Based on the approach of solving the Rayleigh quotient problem, the radar data unloading rate function and the communication data unloading rate function in the weighted sum rate maximization objective function are simplified to obtain the optimized overall objective function; A computational offloading problem model is obtained, which aims to achieve the overall objective function and, under constraints, jointly optimizes the computational offloading variables of communication, radar, and IRS reflection coefficients. ， in, For communication users The receiving filter vector; This is the radar receiver filter weight vector; For the first The reflection coefficient of each IRS reflective unit; 'st' means to maximize, and 's' means to satisfy. This represents the proportion of radar unloading tasks. For communication users The proportion of uninstallation tasks, ; For the bandwidth of the radar system; Let be the determinant of the matrix; for identity matrix This refers to the number of base station antennas. The number of pulses; The interference plus noise covariance matrix at the radar receiver; This is the receiving and transmitting guidance matrix for radar target detection signals; For communication users bandwidth; For communication users The interference plus noise covariance matrix; For communication users The equivalent channel matrix to the base station; For communication users In the Energy constraint matrix for each interference suppression frequency band; The energy threshold for communication reception; For radar in the Energy constraint matrix for one invalid receiving frequency band; The radar receive energy threshold; This represents the total number of IRS reflective units; This represents the total number of frequency bands for communication interference suppression. This represents the total number of invalid radar reception frequency bands. Let be the norm of the vector; Indicates modulo; the superscript H indicates conjugate transpose; S3. Solve the computational offloading problem model to obtain the optimal solutions for the communication computational offloading variables, radar computational offloading variables, and IRS reflection coefficient variables; Step S3 specifically includes the following steps: S31. Transform the computational offloading problem model into a radar optimization subproblem that optimizes only the radar computational offloading variables, a communication optimization subproblem that optimizes only the communication computational offloading variables, and a reflection coefficient optimization subproblem that optimizes only the IRS reflection coefficient variables. By fixing the computational unloading variables and IRS reflection coefficient variables, the constants in the computational unloading problem model are eliminated. Then, by utilizing the properties of matrix determinants, employing first-order Taylor expansion, and introducing equivalent real-valued transformations, an equivalent transformation of the objective function is performed, resulting in a radar optimization subproblem. Alternatively, by fixing the radar computational unloading variables and IRS reflection coefficient variables, retaining the corresponding constraints and objective function, and using the block coordinate descent method for separation and solution, employing first-order Taylor expansion, and introducing equivalent real-valued transformations, an equivalent transformation of the objective function is performed, resulting in a communication optimization subproblem. S32. Iteratively solve the radar optimization subproblem, the communication optimization subproblem, and the reflection coefficient optimization subproblem.

2. The integrated base station calculation offloading method based on IRS assistance according to claim 1, characterized in that: The receiver power constraint is that the norm of the power vector of each communication receiving filter is 1, and the norm of the power vector of the radar receiving filter is 1. The frequency band energy constraint is that the energy of the radar receiver must be less than the radar receiving energy threshold in each restricted sub-band of the radar, and the energy of the communication receiver must be less than the communication receiving energy threshold in the specified restricted sub-band of the communication. The IRS constant mode constraint is that the mode of the reflection coefficient of each reflection element is 1.

3. The integrated base station calculation and offloading method based on IRS assistance according to claim 1, characterized in that: In the iterative solution process of step S32, the Lagrange alternating direction multiplier method is used to solve the radar optimization subproblem.

4. The integrated base station calculation and offloading method based on IRS assistance according to claim 1, characterized in that: In the iterative solution process of step S32, the Lagrange alternating direction multiplier method is used to solve the communication optimization subproblem.

5. The integrated base station calculation and offloading method based on IRS assistance according to claim 1, characterized in that: In step S31, by fixing the radar calculation unloading variables and the communication calculation unloading variables, retaining the corresponding constraints and objective function, and using continuous convex approximation, first-order Taylor expansion, and matrix transformation to perform equivalent transformation on the objective function, the IRS reflection coefficient optimization subproblem is obtained.

6. A method for integrated base station calculation and offloading based on IRS assistance according to any one of claims 1 to 5, characterized in that, The objective function of the unloading problem model is to maximize , It is the received signal-to-interference-plus-noise ratio of the receiving filter designed for radar target points. It is for communication users The received signal-to-interference-plus-noise ratio of the designed receiving filter.

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