MEC energy saving and safety optimization method based on ISAC

Abstract

The invention discloses an MEC energy-saving and safety optimization method based on ISAC, which intelligently unloads a part of calculation tasks of a user to a base station, and enhances the safety of data transmission and meets the sensing requirement by using a radar signal function of a duplex base station without extra power consumption. Through task unloading, radar signal transmission and interference and an optimized resource allocation strategy, minimization of user energy consumption is achieved, and constraint conditions of processing delay, system safety and perception performance are met; variables in the optimization problem are mutually coupled, so that the optimization problem is non-convexity, an original problem is decomposed into four sub-problems by adopting a block coordinate descent method, and the sub-problems are alternately optimized to gradually approach to a local optimal solution; the method has the advantages that the security of data transmission is enhanced, the energy consumption of a user is reduced, and the application scene and function of edge calculation are expanded through the fusion of the ISAC technology.

Description

Technical Field

[0001] The present invention relates to an ISAC (communication perception integration) technology, and in particular to an ISAC (communication perception integration)-based MEC (mobile edge computing) energy saving and security optimization method, which takes into account that a base station (BS) can only obtain incomplete channel state information (CSI) in actual situations, and minimizes the energy consumption of users under the constraints of delay, perception and security performance. Background Art

[0002] With the rapid development of IoT technology and the surge in the number of IoT devices, IoT devices are increasingly being used in various fields. However, IoT devices are often limited by limited battery capacity and energy supply, making it difficult for them to cope with long-term, high-intensity data processing requirements. In order to reduce the energy consumption of IoT devices and extend their service life, mobile edge computing (MEC) technology has emerged. MEC technology offloads the computing tasks of IoT devices to MEC servers located at the edge of the network for processing, thereby reducing the computing burden of the IoT devices themselves and reducing their energy consumption. In addition, MEC servers usually have more powerful computing capabilities and more efficient energy utilization technologies, which can better cope with large amounts of data processing requirements, thereby further reducing the energy consumption of IoT devices.

[0003] MEC technology pushes computing power and applications to the edge of the network, greatly improving the user experience. However, given the broadcast nature of wireless communications, the signals transmitted during the offloading process are easily intercepted by potential eavesdroppers (Eves), which leads to serious information leakage risks. Physical layer security (PLS) is an information protection strategy based on information theory, which focuses on ensuring the security and integrity of data at the bottom layer of the communication system. Unlike traditional encryption technology, PLS does not rely on complex encryption algorithms or key management, but uses the characteristics of physical channels to provide secure communications. PLS technology can effectively protect the authenticity, integrity, privacy and confidentiality of communication data, providing reliable security protection for MEC.

[0004] ISAC technology deeply integrates traditional communication functions with perception functions to achieve efficient information transmission and accurate perception of the environment, so as to meet the future society's demand for intelligent and efficient communication services. During the unloading process of the MEC server, ISAC technology can monitor the surrounding environment in real time, discover potential security threats, and take corresponding protective measures to ensure the security of data transmission and processing. ISAC technology improves the perception capability and security of the MEC system by integrating perception and communication functions. Although PLS technology and ISAC technology provide new ideas and methods for improving MEC security, they still face some challenges and limitations in practical applications. These deficiencies need to be further explored in research in order to continuously improve and optimize these technologies and provide a more solid guarantee for the development of MEC. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an ISAC-based MEC energy saving and security optimization method, which can minimize the user's energy consumption while meeting the constraints of delay, perception and security performance.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a method for energy saving and security optimization of MEC based on ISAC, which is characterized by comprising the following steps:

[0007] Step 1: Establish an ISAC-based MEC system model, in which a full-duplex base station, several MEC servers, K single-antenna users, and a single-antenna eavesdropper are set up. The transmitter and receiver of the full-duplex base station are both equipped with uniform linear arrays with half-wavelength intervals. The full-duplex base station is connected to the MEC server. The single-antenna user offloads part of the computing tasks to the full-duplex base station. The MEC server performs the offloaded computing tasks for the single-antenna user. The single-antenna eavesdropper attempts to eavesdrop on the uplink offload signal transmitted by the single-antenna user to the receiver of the full-duplex base station; where K>1;

[0008] Step 2: Establish the communication model, radar perception model and offloading model as follows:

[0009] Communication model: All single-antenna users simultaneously offload their respective computing tasks to the full-duplex base station. Let the channel from the kth single-antenna user to the full-duplex base station be h k , let the uplink unloading signal transmitted by the kth single-antenna user to the receiving end of the full-duplex base station be d k , and d k The transmission power is p k , then the uplink unloading signal received by the receiving end of the full-duplex base station is expressed as in,

[0010] When the receiver of the full-duplex base station receives the uplink unloading signal, its transmitter sends a radar signal x to interfere with the single-antenna eavesdropper. Let the channel from the full-duplex base station to the single-antenna eavesdropper be h. be ;

[0011] The single-antenna eavesdropper attempts to eavesdrop on the uplink unloading signals transmitted by all single-antenna users to the receiving end of the full-duplex base station. Let the channel from the kth single-antenna user to the single-antenna eavesdropper be g k , then the signal y eavesdropped by the single antenna eavesdropper e Expressed as The signal-to-interference-to-noise ratio γ of the signal from the kth single-antenna user eavesdropped by the single-antenna eavesdropper e,k Expressed as Among them, n e Indicates power is The noise, || is the modulus operator, p i represents the uplink offload signal d transmitted by the i-th single-antenna user to the receiving end of the full-duplex base station i Transmission power, g i represents the channel from the i-th single-antenna user to the single-antenna eavesdropper, the superscript “H” represents the conjugate transpose operation, Q represents the covariance matrix of the radar signal x, It means to find the mathematical expectation;

[0012] Radar perception model: The receiving end of the full-duplex base station receives the radar echo signal. Suppose the transmission steering vector a of the full-duplex base station is t (θ0) is Assume that the receiving steering vector a of the full-duplex base station is r (θ0) is Then the signal y received by the receiving end of the full-duplex base station b Expressed as Among them, θ0 represents the angle of the single-antenna eavesdropper relative to the full-duplex base station, e is a natural constant, j is the imaginary part, and N t N is the number of elements in the uniform linear array with half-wavelength spacing configured at the transmitting end of a full-duplex base station. r represents the number of elements in the half-wavelength-spaced uniform linear array equipped at the receiving end of the full-duplex base station, the superscript “T” represents the transpose operation, ξ represents the complex path loss coefficient, and H SI represents the self-interference channel from the uniform linear array with half-wavelength spacing at the transmitting end of the full-duplex base station to the uniform linear array with half-wavelength spacing at the receiving end, n b Indicates power is Noise;

[0013] Assume that the signal y received by the receiving end of the full-duplex base station is b Apply a set of modulo 1 communication receive beams To recover the uplink unloading signal transmitted by the single-antenna user to the receiving end of the full-duplex base station, the signal-to-interference-noise ratio γ of the signal from the kth single-antenna user received by the receiving end of the full-duplex base station is b,k Expressed as Assume that a radar receiving beam u with a modulus of 1 is used to capture the radar echo signal reflected by a single-antenna eavesdropper. Then, the signal-to-interference-to-noise ratio γ of the radar echo signal received by the receiving end of the full-duplex base station is echo Expressed as Among them, w k represents the communication receiving beam of the receiving end of the full-duplex base station receiving the signal from the kth single-antenna user, h i represents the channel from the ith single-antenna user to the full-duplex base station, B is the introduced intermediate quantity, B = A(θ0) + H SI , A(θ0) is the introduced intermediate quantity, N r The identity matrix of order;

[0014] Unloading model: The unloading model is modeled as a partial unloading model. Assume that the kth single-antenna user has a total of D k bit calculation task, assuming that the kth single-antenna user will k D k The bit calculation task is offloaded to the full-duplex base station. Let the number of CPU cycles required for each bit calculation task be F k , then the delay calculated locally by the kth single-antenna user is Expressed as The energy consumption of the k-th single-antenna user local calculation is Expressed as The kth single-antenna user will be k D k The time required to offload the bit calculation task to the full-duplex base station Expressed as The kth single-antenna user will be k D k The energy consumption required to offload the bit computing task to the full-duplex base station Expressed as The time required for the MEC server to perform the offloaded computing task for the kth single-antenna user Expressed as Then, considering that the single-antenna user and the MEC server take parallel processing, the data processing delay of the kth single-antenna user is T k Expressed as The total energy consumption E of the kth single-antenna user k Expressed as Among them, α krepresents the offloading ratio of the computing task of the kth single-antenna user, α k ∈(0,1), F k The unit is cycles / bit, f k represents the local computing power of the kth single-antenna user, f k The unit is cycles / s, Indicates the CPU energy efficiency coefficient at a single-antenna user. represents the channel bandwidth, R b,k represents the achievable rate of the kth single-antenna user, R b,k =log2(1+γ b,k ), represents the computing power allocated by the full-duplex base station to the kth single-antenna user, The unit is cycles / s;

[0015] Step 3: By jointly optimizing the radar receiving beam u, the set w of communication receiving beams, the covariance matrix Q of the radar signal x, the set α of the offloading ratio, and the set of local computing capabilities of all single-antenna users The set of computing power allocated by a full-duplex base station to all single-antenna users Minimize the maximum energy consumption between single-antenna users and construct an optimization problem, which can be described as:

[0016]

[0017] in, represents the set of optimization variables, T max represents the maximum data processing delay of a single-antenna user, Γ b,min represents the minimum signal-to-interference-to-noise ratio of the full-duplex base station receiving signal, Γ e,max represents the maximum signal-to-interference-to-noise ratio of the eavesdropper’s eavesdropping signal with a single antenna, Γ min represents the minimum signal-to-interference-noise ratio of the radar echo signal, Tr() represents the trace of the matrix, P max represents the maximum transmit power of a full-duplex base station, f max represents the maximum local computing capacity of a single-antenna user, It represents the maximum computing capacity allocated by a full-duplex base station to a single-antenna user;

[0018] Step 4: Use the block coordinate descent method to decouple the optimization problem into four manageable sub-problems, and iteratively optimize the sub-problems to obtain the local optimal solution, as follows:

[0019] Step 4.1: Let iter represent the number of iterations, and the initial value of iter is 1; initialize u, w, Q, α,

[0020] Step 4.2: The first subproblem is about optimizing u and w. Given Q, α, and Optimize u and w in the case of maximizing γ echo and γ b,k Get u and w k The closed-form solution is denoted by u o and Among them, Q, α, and is the initialization value, Q, α, and Corresponding to the Q obtained at the iter-1th iteration o , α o , and

[0021] The second sub-problem is about optimizing Q. Given u, w, α, and Optimize Q in the case of continuous convex approximation and S-Procedure, convert the second subproblem into a convex problem, and then use CVX to solve the convex problem to obtain the solution of Q o ; where the given u and w correspond to the u obtained at the iterth iteration o and α given when iter=1, and is the initialization value, α is given when iter≠1, and Corresponding to the α obtained at the iter-1th iteration o , and

[0022] The third sub-problem is about optimizing α. Given u, w, Q, and Optimize α under the condition of α, and directly obtain the solution of α through the linear programming problem o ; where the given u, w and Q correspond to the u obtained at the iterth iteration o , and Q o , given when iter = 1 and Initialization value, given when iter≠1 and Corresponding to the iter-1th iteration and

[0023] The fourth sub-question is about optimization and Subproblem, optimize given u, w, Q, α and Directly through the linear programming problem to obtain Solution and Solution Among them, the given u, w, Q, α corresponds to the u obtained at the iterth iteration o , Q o and α o ;

[0024] Step 4.3: Calculate the iterth iteration Then compare the iter-th iteration Subtract the iter-1th iteration The difference between the obtained value and the judgment threshold value, if the former is smaller than the latter, the iteration ends and u,w,Q,α, their respective local optimal solutions; if the former is greater than or equal to the latter, set iter = iter + 1 and return to step 4.2 to continue execution; where iter = 1 is the iter-1th iteration The initialization value 1 is set, and the "=" in iter=iter+1 is the assignment symbol.

[0025] In step 2, h k Modeled as the Rice channel model, Where ρ represents the Rice factor, represents the line-of-sight link from the kth single-antenna user to the full-duplex base station, represents the non-line-of-sight link from the kth single-antenna user to the full-duplex base station, It follows a complex Gaussian distribution with mean 0 and variance 1.

[0026] In step 2, it is assumed that a full-duplex base station can only obtain partial channel state information in actual situations, and a bounded CSI error model is used to describe the imperfect channel state information, then: in, represents the estimated channel from the full-duplex base station to the single-antenna eavesdropper, δ be Indicates h be The channel error, || || is the binary norm operator, ε be is the threshold value, μ represents the error percentage, represents the estimated channel from the kth single-antenna user to the single-antenna eavesdropper, δ k Indicates g k The channel error, || is the modulus operator, ε k is the threshold value,

[0027] and Both are modeled as Rice channel models.

[0028] Where ρ represents the Rice factor, represents the line-of-sight link from a full-duplex base station to a single-antenna eavesdropper, represents a non-line-of-sight link from a full-duplex base station to a single-antenna eavesdropper, represents the line-of-sight link from the kth single-antenna user to the single-antenna eavesdropper, represents the non-line-of-sight link from the kth single-antenna user to the single-antenna eavesdropper, and They all follow a complex Gaussian distribution with mean 0 and variance 1.

[0029] Compared with the prior art, the advantages of the present invention are:

[0030] 1) The method of the present invention utilizes the full-duplex characteristics of the full-duplex base station, so that it can simultaneously transmit radar signals and receive unloaded data, i.e., uplink unloaded signals, in the same frequency band without the need for additional spectrum resources, thereby significantly improving spectrum efficiency and resource utilization and reducing the cost of the MEC system.

[0031] 2) The method of the present invention uses the reflection characteristics of radar signals, and the full-duplex base station can effectively sense potential eavesdroppers in the case of imperfect channel state information (CSI). This capability significantly improves the physical layer security of the MEC system, making it difficult for eavesdroppers to hide their existence. While sensing the eavesdropper, the full-duplex base station can use radar signals to actively interfere with the eavesdropper, reducing the signal-to-noise ratio of the eavesdropper receiving the unloaded data, thereby effectively preventing the unloaded data from being eavesdropped. This dual protection measure greatly enhances the security of data transmission.

[0032] 3) The method of the present invention adopts a partial offloading solution for the MEC system and cleverly integrates the ISAC technology to significantly improve the offloading rate. This flexible approach avoids unnecessary data transmission and computing resource consumption, thereby reducing the energy consumption of user devices. The integration of ISAC technology also expands the application scenarios and functions of edge computing. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of the overall implementation process of the method of the present invention;

[0034] Figure 2 It is a simple schematic diagram of the ISAC-based MEC system model;

[0035] Figure 3 is the maximum transmission power P of the method of the present invention and the secure MEC-ZF method and ISAC-MEC method used for comparison in a full-duplex base station max The comparison curve of the average eavesdropping rate when changes;

[0036] Figure 4 The method of the present invention and the comparison method k =f max Program and The maximum energy consumption between single-antenna users in the scheme is constrained by the data processing delay of the single-antenna user (T k ≤T max )The impact of changes. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below with reference to the accompanying drawings.

[0038] The present invention proposes an ISAC-based MEC energy saving and security optimization method, which offloads part of the computing tasks of single-antenna users to full-duplex base stations, which are calculated by MEC servers. The full-duplex base station not only receives the uplink offload signal from the single-antenna user, but also interferes with and senses the potential single-antenna eavesdropper (Eve) by transmitting radar signals, so as to achieve secure MEC and perception requirements without consuming additional power. The goal of the method of the present invention is to minimize the total energy consumption of single-antenna users while meeting the constraints of processing delay, system security and perception performance. Since the variables in the optimization problem are coupled with each other, the optimization problem is non-convex. The block coordinate descent (BCD) method is used to decompose the original problem into four sub-problems, and these sub-problems are optimized alternately to gradually approach the local optimal solution.

[0039] The present invention is a method for optimizing energy saving and security of MEC based on ISAC, and its overall implementation flow chart is as follows: Figure 1 As shown, it includes the following steps:

[0040] Step 1: Establish an ISAC-based MEC system model, such as Figure 2As shown, in the model, a full-duplex base station (FD BS), several MEC servers, K single-antenna users, and a single-antenna eavesdropper (Eve) are set. The transmitting end and the receiving end of the full-duplex base station are equipped with a uniform linear array (ULA) with a half-wavelength interval. The full-duplex base station is connected to the MEC server. The single-antenna user offloads part of the computing task to the full-duplex base station. The MEC server performs the offloaded computing task for the single-antenna user. The single-antenna eavesdropper attempts to eavesdrop on the uplink offload signal transmitted by the single-antenna user to the receiving end of the full-duplex base station; wherein, K>1, and K=4 is taken in this specific embodiment.

[0041] Step 2: Establish the communication model, radar perception model and offloading model as follows:

[0042] Communication model: All single-antenna users simultaneously offload their respective computing tasks to the full-duplex base station. Let the channel from the kth single-antenna user to the full-duplex base station be h k , let the uplink unloading signal transmitted by the kth single-antenna user to the receiving end of the full-duplex base station be d k , and d k The transmission power is p k In this specific embodiment, p k =0.5W, then the uplink unloading signal received by the receiving end of the full-duplex base station is expressed as in,

[0043] In this specific embodiment, in step 2, h k Modeled as the Rice channel model, Where ρ represents the Rice factor, and here ρ=10, represents the line-of-sight link from the kth single-antenna user to the full-duplex base station, represents the non-line-of-sight link from the kth single-antenna user to the full-duplex base station, It obeys a complex Gaussian distribution with mean 0 and variance 1, that is,

[0044] When the receiver of the full-duplex base station receives the uplink unloading signal, its transmitter sends a radar signal x to interfere with the single-antenna eavesdropper to improve the security performance of the MEC system based on ISAC. Suppose the channel from the full-duplex base station to the single-antenna eavesdropper is h. be .

[0045] The single-antenna eavesdropper attempts to eavesdrop on the uplink unloading signals transmitted by all single-antenna users to the receiving end of the full-duplex base station. Let the channel from the kth single-antenna user to the single-antenna eavesdropper be g k , then the signal y eavesdropped by the single antenna eavesdropper e Expressed as The signal to interference and noise ratio (SINR) γ of the signal from the kth single-antenna user eavesdropped by the single-antenna eavesdropper e,k Expressed as Among them, n e Indicates power is In this specific embodiment, || is the modulus operator, p i represents the uplink offload signal d transmitted by the i-th single-antenna user to the receiving end of the full-duplex base station i Transmission power, g i represents the channel from the i-th single-antenna user to the single-antenna eavesdropper, the superscript “H” represents the conjugate transpose operation, Q represents the covariance matrix of the radar signal x, It means to find the mathematical expectation.

[0046] In this specific embodiment, in step 2, it is assumed that a full-duplex base station can only obtain partial channel state information (CSI) in actual situations, and a bounded CSI error model is used to describe the imperfect channel state information, then: in, represents the estimated channel from the full-duplex base station to the single-antenna eavesdropper, δ be Indicates h be The channel error, |||| is the two-norm operator, ε be is the threshold value, μ represents the error percentage, and in this specific embodiment, μ=0.1. represents the estimated channel from the kth single-antenna user to the single-antenna eavesdropper, δ k Indicates g k The channel error, || is the modulus operator, ε k is the threshold value,

[0047] The above, and Both are modeled as Rice channel models. Where ρ represents the Rice factor, and here ρ=10, represents the line-of-sight link from a full-duplex base station to a single-antenna eavesdropper, represents a non-line-of-sight link from a full-duplex base station to a single-antenna eavesdropper, represents the line-of-sight link from the kth single-antenna user to the single-antenna eavesdropper, represents the non-line-of-sight link from the kth single-antenna user to the single-antenna eavesdropper, and All obey a complex Gaussian distribution with a mean of 0 and a variance of 1, that is,

[0048] Radar perception model: The receiving end of the full-duplex base station receives the radar echo signal to achieve target perception of the single-antenna eavesdropper. Suppose the transmission steering vector a of the full-duplex base station is t (θ0) is Assume that the receiving steering vector a of the full-duplex base station is r (θ0) is Then the signal y received by the receiving end of the full-duplex base station b Expressed as Among them, θ0 represents the angle of the single-antenna eavesdropper relative to the full-duplex base station, e is a natural constant, e = 2.71…, j is the imaginary part, π = 3.14…, N t N is the number of elements in the uniform linear array with half-wavelength spacing configured at the transmitting end of a full-duplex base station. r represents the number of elements in the half-wavelength-spaced uniform linear array equipped at the receiving end of the full-duplex base station. In this specific embodiment, N is taken t =16, N r = 16, the superscript “T” indicates the transposition operation, ξ indicates the complex path loss coefficient, H SI represents the self-interference (SI) channel from the transmitting end of the full-duplex base station equipped with a uniform linear array with half-wavelength spacing to the receiving end equipped with a uniform linear array with half-wavelength spacing, n b Indicates power is In this specific embodiment,

[0049] Assume that the signal y received by the receiving end of the full-duplex base station is b Apply a set of modulo 1 communication receive beams To recover the uplink unloading signal transmitted by the single-antenna user to the receiving end of the full-duplex base station, the signal-to-interference-plus-noise ratio (SINR) γ of the signal from the kth single-antenna user received by the receiving end of the full-duplex base station is b,k Expressed as Assume that a radar receiving beam u with a modulus of 1 is used to capture the radar echo signal reflected by a single-antenna eavesdropper. Then, the signal-to-interference-to-noise ratio (SINR) γ of the radar echo signal received by the receiving end of the full-duplex base station is echo Expressed as Among them, w k represents the communication receiving beam of the receiving end of the full-duplex base station receiving the signal from the kth single-antenna user, h i represents the channel from the ith single-antenna user to the full-duplex base station, B is the introduced intermediate quantity, B = A(θ0) + H SI , A(θ0) is the introduced intermediate quantity, I Nr N r The identity matrix of order.

[0050] Unloading model: The unloading model is modeled as a partial unloading model. Assume that the kth single-antenna user has a total of D k bit calculation task, in this specific embodiment D k is [35,45]Kbit. In order to reduce energy consumption, assume that the kth single-antenna user sets α k D k The bit calculation task is offloaded to the full-duplex base station. Let the number of CPU cycles required for each bit calculation task be F k In this specific embodiment, F k =1000 cycles / bit, then the delay calculated locally by the kth single-antenna user is Expressed as The energy consumption of the k-th single-antenna user local calculation is Expressed as The kth single-antenna user will be k D k The time required to offload the bit calculation task to the full-duplex base station Expressed as The kth single-antenna user will be k D k The energy consumption required to offload the bit computing task to the full-duplex base station Expressed as The time required for the MEC server to perform the offloaded computing task for the kth single-antenna user Expressed as Then, considering that the single-antenna user and the MEC server take parallel processing, the data processing delay of the kth single-antenna user is T k Expressed as The total energy consumption E of the kth single-antenna user k Expressed as Among them, α k represents the offloading ratio of the computing task of the kth single-antenna user, α k ∈(0,1), F k The unit is cycles / bit, f k represents the local computing power of the kth single-antenna user, f k The unit is cycles / s, represents the CPU energy efficiency coefficient at a single-antenna user. In this specific embodiment represents the channel bandwidth. In this specific embodiment R b,k represents the achievable rate of the kth single-antenna user, R b,k =log2(1+γ b,k ), represents the computing power allocated by the full-duplex base station to the kth single-antenna user, The unit is cycles / s, and max is the maximum value function.

[0051] Step 3: By jointly optimizing the radar receiving beam u, the set w of communication receiving beams, the covariance matrix Q of the radar signal x, the set α of the offloading ratio, and the set of local computing capabilities of all single-antenna users The set of computing power allocated by a full-duplex base station to all single-antenna users Minimize the maximum energy consumption between single-antenna users and construct an optimization problem, which can be described as:

[0052]

[0053] in, min is the minimum function, st means "subject to...", represents the set of optimization variables, T max represents the maximum data processing delay of a single-antenna user, Γ b,min represents the minimum signal-to-interference-to-noise ratio of the full-duplex base station receiving signal, Γ e,max represents the maximum signal-to-interference-to-noise ratio of the eavesdropper’s eavesdropping signal with a single antenna, Γ min represents the minimum signal-to-interference-noise ratio of the radar echo signal, Tr() represents the trace of the matrix, P max represents the maximum transmit power of a full-duplex base station, f max represents the maximum local computing capacity of a single-antenna user, represents the maximum computing capacity allocated by the full-duplex base station to a single-antenna user, and constraint C1 ensures that T k No more than T max , constraint C2 guarantees γ b,k Greater than Γ b,min , constraint C3 guarantees γ e,k Less than Γ e,max , Γ b,min Greater than Γ e,max Ensure safety performance and constrain C4 to ensure γ echo More than Γ min , constraint C5 ensures that the transmission power of the full-duplex base station does not exceed P max , constraint C8 guarantees the computing power requirement of a single-antenna user, and constraint C9 guarantees the computing power requirement of a full-duplex base station allocated to a single-antenna user. In this specific embodiment, T max =0.1s,Γ b,min =30dB, Γ e,max =0.2dB,Γ min =3dB, P max =5W, f max=2×10 8 cycles / s,

[0054] Step 4: Due to the high coupling of variables, the optimization problem is non-convex. The optimization problem is decoupled into four tractable sub-problems using block coordinate descent (BCD) and the sub-problems are iteratively optimized to obtain the local optimal solution, as follows:

[0055] Step 4.1: Let iter represent the number of iterations, and the initial value of iter is 1; initialize u, w, Q, α,

[0056] Step 4.2: The first subproblem is about optimizing u and w. Given Q, α, and In order to satisfy the constraints C1-C3 and reduce energy consumption, we optimize u and w. echo and γ b,k Get u and w k The closed-form solution is denoted by u o and Among them, Q, α, and is the initialization value, Q, α, and Corresponding to the Q obtained at the iter-1th iteration o , α o , and

[0057] The second sub-problem is about optimizing Q. Given u, w, α, and In the case of optimizing Q, due to R b,k Non-convexity and γ e,k The second subproblem is difficult to solve due to the imperfect channel state information in , so we use continuous convex approximation (SCA) and S-Procedure to convert the second subproblem into a convex problem, and then use CVX to solve the convex problem to obtain the solution of Q o ; where the given u and w correspond to the u obtained at the iterth iteration o and α given when iter=1, and is the initialization value, α is given when iter≠1, and Corresponding to the α obtained at the iter-1th iteration o , and

[0058] Here, continuous convex approximation (SCA) and S-Procedure processing are both existing technologies. SCA solves R b,k The non-convexity of γ is solved by S-Procedure e,k The non-convexity of .

[0059] The third sub-problem is about optimizing α. Given u, w, Q, and Optimize α under the condition of α, and directly obtain the solution of α through the linear programming problem o ; where the given u, w and Q correspond to the u obtained at the iterth iteration o , and Q o , given when iter = 1 and Initialization value, given when iter≠1 and Corresponding to the iter-1th iteration and

[0060] The fourth sub-question is about optimization and Subproblem, optimize given u, w, Q, α and Directly through the linear programming problem to obtain Solution and Solution Among them, the given u, w, Q, α corresponds to the u obtained at the iterth iteration o , Q o and α o .

[0061] Step 4.3: Calculate the iterth iteration Then compare the iter-th iteration Subtract the iter-1th iteration The difference between the obtained value and the judgment threshold value, if the former is smaller than the latter, the iteration ends and u,w,Q,α, their respective local optimal solutions; if the former is greater than or equal to the latter, set iter = iter + 1 and return to step 4.2 to continue execution; where iter = 1 is the iter-1th iteration The initialization value is set to 1, and the judgment threshold is 10 -6, the “=” in iter=iter+1 is the assignment symbol.

[0062] The feasibility and effectiveness of the method of the present invention are further illustrated by the following simulation.

[0063] Figure 3 The maximum transmission power P of the method of the present invention and the security MEC-ZF method and ISAC-MEC method for comparison in full-duplex base stations are given. max The comparison curve of the average eavesdropping rate when changes. The calculation formula of the average eavesdropping rate is: Figure 3 In the secure MEC-ZF method, the full-duplex base station transmits at the maximum power P max The radar signal is transmitted, and the covariance matrix Q of the radar signal is set by zero forcing (ZF) technology to effectively eliminate the self-interference in the system; the ISAC-MEC method refers to the full-duplex base station transmitting radar signals only for sensing targets without considering the eavesdropping rate constraint. Figure 3 It can be seen that as the maximum transmission power P of the full-duplex base station max As the value of increases, the average eavesdropping rate decreases, and the average eavesdropping rate of the method of the present invention is lower than that of the secure MEC-ZF method, which shows that the method of the present invention has a significant effect in improving communication security.

[0064] Figure 4 The method of the present invention and the comparison method are given. k =f max Program and The maximum energy consumption between single-antenna users in the scheme is constrained by the data processing delay of the single-antenna user (T k ≤T max )The impact of changes. Figure 4 In, f k =f max The solution refers to the maximum computing power f used by each single antenna user max ; The solution is that the full-duplex base station evenly distributes computing power to K single-antenna users. Figure 4 It can be seen that the total energy consumption of a single-antenna user decreases as the data processing delay constraint of the single-antenna user is relaxed, and the method of the present invention is significantly better than other solutions under the ultra-low delay requirement.

Claims

1. A method for optimizing energy saving and security of MEC based on ISAC, characterized in that The following steps are involved: Step 1: Establish an ISAC-based MEC system model, in which a full-duplex base station, several MEC servers, K single-antenna users, and a single-antenna eavesdropper are set up. The transmitter and receiver of the full-duplex base station are both equipped with uniform linear arrays with half-wavelength intervals. The full-duplex base station is connected to the MEC server. The single-antenna user offloads part of the computing tasks to the full-duplex base station. The MEC server performs the offloaded computing tasks for the single-antenna user. The single-antenna eavesdropper attempts to eavesdrop on the uplink offload signal transmitted by the single-antenna user to the receiver of the full-duplex base station; where K>1; Step 2: Establish the communication model, radar perception model and offloading model as follows: Communication model: All single-antenna users simultaneously offload their respective computing tasks to the full-duplex base station. Let the channel from the kth single-antenna user to the full-duplex base station be h k , let the uplink unloading signal transmitted by the kth single-antenna user to the receiving end of the full-duplex base station be d k , and d k The transmission power is p k , then the uplink unloading signal received by the receiving end of the full-duplex base station is expressed as in, When the receiver of the full-duplex base station receives the uplink unloading signal, its transmitter sends a radar signal x to interfere with the single-antenna eavesdropper. Let the channel from the full-duplex base station to the single-antenna eavesdropper be h. be ; The single-antenna eavesdropper attempts to eavesdrop on the uplink unloading signals transmitted by all single-antenna users to the receiving end of the full-duplex base station. Let the channel from the kth single-antenna user to the single-antenna eavesdropper be g k , then the signal y eavesdropped by the single antenna eavesdropper e Expressed as The signal-to-interference-to-noise ratio γ of the signal from the kth single-antenna user eavesdropped by the single-antenna eavesdropper e,k Expressed as Among them, n e Indicates power The noise, || is the modulus operator, p i represents the uplink offload signal d transmitted by the i-th single-antenna user to the receiving end of the full-duplex base station i Transmission power, g i represents the channel from the i-th single-antenna user to the single-antenna eavesdropper, the superscript "H" represents the conjugate transpose operation, Q represents the covariance matrix of the radar signal x, It means to find the mathematical expectation; Radar perception model: The receiving end of the full-duplex base station receives the radar echo signal. Suppose the transmission steering vector a of the full-duplex base station is t (θ0) is Assume that the receiving steering vector a of the full-duplex base station is r (θ0) is Then the signal y received by the receiving end of the full-duplex base station b Expressed as Among them, θ0 represents the angle of the single-antenna eavesdropper relative to the full-duplex base station, e is a natural constant, j is the imaginary part, and N t N is the number of elements in the uniform linear array with half-wavelength spacing configured at the transmitting end of a full-duplex base station. r represents the number of elements in the half-wavelength-spaced uniform linear array equipped at the receiving end of the full-duplex base station, the superscript "T" represents the transpose operation, ξ represents the complex path loss coefficient, and H SI represents the self-interference channel from the uniform linear array with half-wavelength spacing at the transmitting end of the full-duplex base station to the uniform linear array with half-wavelength spacing at the receiving end, n b Indicates power is Noise; Assume that the signal y received by the receiving end of the full-duplex base station is b Apply a set of modulo 1 communication receive beams To recover the uplink unloading signal transmitted by the single-antenna user to the receiving end of the full-duplex base station, the signal-to-interference-noise ratio γ of the signal from the kth single-antenna user received by the receiving end of the full-duplex base station is b,k Expressed as Assume that a radar receiving beam u with a modulus of 1 is used to capture the radar echo signal reflected by a single-antenna eavesdropper. Then, the signal-to-interference-to-noise ratio γ of the radar echo signal received by the receiving end of the full-duplex base station is echo Expressed as Among them, w k represents the communication receiving beam of the receiving end of the full-duplex base station receiving the signal from the kth single-antenna user, h i represents the channel from the ith single-antenna user to the full-duplex base station, B is the introduced intermediate quantity, B = A(θ0) + H SI , A(θ0) is the introduced intermediate quantity, N r The identity matrix of order; Unloading model: The unloading model is modeled as a partial unloading model. Assume that the kth single-antenna user has a total of D k bit calculation task, assuming that the kth single-antenna user will k D k The bit calculation task is offloaded to the full-duplex base station. Let the number of CPU cycles required for each bit calculation task be F k , then the delay calculated locally by the kth single-antenna user is Expressed as The energy consumption of the k-th single-antenna user local calculation is Expressed as The kth single-antenna user will be k D k The time required to offload the bit calculation task to the full-duplex base station Expressed as The kth single-antenna user will be k D k The energy consumption required to offload the bit computing task to the full-duplex base station Expressed as The time required for the MEC server to perform the offloaded computing task for the kth single-antenna user Expressed as Then, considering that the single-antenna user and the MEC server take parallel processing, the data processing delay of the kth single-antenna user is T k Expressed as The total energy consumption E of the kth single-antenna user k Expressed as Among them, α k represents the offloading ratio of the computing task of the kth single-antenna user, α k ∈(0,1), F k The unit is cycles / bit, f k represents the local computing power of the kth single-antenna user, f k The unit is cycles / s, Indicates the CPU energy efficiency coefficient at a single-antenna user. represents the channel bandwidth, R b,k represents the achievable rate of the kth single-antenna user, R b,k =log2(1+γ b,k ), represents the computing power allocated by the full-duplex base station to the kth single-antenna user, The unit is cycles / s; Step 3: By jointly optimizing the radar receiving beam u, the set w of communication receiving beams, the covariance matrix Q of the radar signal x, the set α of the offloading ratio, and the set of local computing capabilities of all single-antenna users The set of computing power allocated by a full-duplex base station to all single-antenna users Minimize the maximum energy consumption between single-antenna users and construct an optimization problem, which can be described as: in, represents the set of optimization variables, T max represents the maximum data processing delay of a single-antenna user, Γ b,min represents the minimum signal-to-interference-to-noise ratio of the full-duplex base station receiving signal, Γ e,max represents the maximum signal-to-interference-to-noise ratio of the eavesdropper’s eavesdropping signal with a single antenna, Γ min represents the minimum signal-to-interference-noise ratio of the radar echo signal, Tr() represents the trace of the matrix, P max represents the maximum transmit power of a full-duplex base station, f max represents the maximum local computing capacity of a single-antenna user, It represents the maximum computing capacity allocated by a full-duplex base station to a single-antenna user; Step 4: Use the block coordinate descent method to decouple the optimization problem into four manageable sub-problems, and iteratively optimize the sub-problems to obtain the local optimal solution, as follows: Step 4.1: Let iter represent the number of iterations, and the initial value of iter is 1; initialize Step 4.2: The first subproblem is about optimizing u and w. Given Q, α, and Optimize u and w in the case of maximizing γ echo and γ b,k Get u and w k The closed-form solution is denoted by u o and Among them, Q, α, and is the initialization value, Q, α, and Corresponding to the Q obtained at the iter-1th iteration o , α o , and The second sub-problem is about optimizing Q. Given u, w, α, and Optimize Q in the case of continuous convex approximation and S-Procedure, convert the second subproblem into a convex problem, and then use CVX to solve the convex problem to obtain the solution of Q o ; where the given u and w correspond to the u obtained at the iterth iteration o and α given when iter=1, and is the initialization value, α is given when iter≠1, and Corresponding to the α obtained at the iter-1th iteration o , and The third sub-problem is about optimizing α. Given u, w, Q, and Optimize α under the condition of α, and directly obtain the solution of α through the linear programming problem o ; where the given u, w and Q correspond to those obtained at the iterth iteration and Q o , given when iter = 1 and Initialization value, given when iter≠1 and Corresponding to the iter-1th iteration and The fourth sub-question is about optimization and Subproblem, optimize given u, w, Q, α and Directly through the linear programming problem to obtain Solution and Solution Among them, the given u, w, Q, α corresponds to the u obtained at the iterth iteration o , Q o and α o ; Step 4.3: Calculate the iterth iteration Then compare the iter-th iteration Subtract the iter-1th iteration The difference between the obtained value and the judgment threshold value, if the former is smaller than the latter, the iteration ends and the result is their respective local optimal solutions; if the former is greater than or equal to the latter, set iter = iter + 1 and return to step 4.2 to continue execution; where iter = 1 is the iter-1th iteration The initialization value 1 is set, and the "=" in iter=iter+1 is the assignment symbol.

2. The method for optimizing MEC energy saving and security based on ISAC according to claim 1, characterized in that In step 2, h k Modeled as the Rice channel model, Where ρ represents the Rice factor, represents the line-of-sight link from the kth single-antenna user to the full-duplex base station, represents the non-line-of-sight link from the kth single-antenna user to the full-duplex base station, It follows a complex Gaussian distribution with mean 0 and variance 1.

3. The method for optimizing MEC energy saving and security based on ISAC according to claim 1, characterized in that In step 2, it is assumed that a full-duplex base station can only obtain partial channel state information in actual situations, and a bounded CSI error model is used to describe the imperfect channel state information, then: in, represents the estimated channel from the full-duplex base station to the single-antenna eavesdropper, δ be Indicates h be The channel error, || || is the binary norm operator, ε be is the threshold value, μ represents the error percentage, represents the estimated channel from the kth single-antenna user to the single-antenna eavesdropper, δ k Indicates g k The channel error, | | is the modulus operator, ε k is the threshold value, 4. The method for optimizing MEC energy saving and security based on ISAC according to claim 3 is characterized in that and Both are modeled as Rice channel models. Where ρ represents the Rice factor, represents the line-of-sight link from a full-duplex base station to a single-antenna eavesdropper, represents a non-line-of-sight link from a full-duplex base station to a single-antenna eavesdropper, represents the line-of-sight link from the kth single-antenna user to the single-antenna eavesdropper, represents the non-line-of-sight link from the kth single-antenna user to the single-antenna eavesdropper, and They all follow a complex Gaussian distribution with mean 0 and variance 1.

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