A MA-enhanced mobile edge cloud computing network performance optimization method

Abstract

This invention belongs to the technical field of smart grid and vehicle-grid interaction. It discloses a method for optimizing mobile edge cloud computing network performance using a mobile edge computing (MA)-enhanced network. This method constructs a MA-enhanced WPB-MEC system to improve communication performance during vehicle-grid interaction scheduling optimization. The system calculates the total computational bits of the WPB-MEC system based on the transmit covariance matrix at the access point (AP), network time resource allocation, the transmit power and computation frequency of the wireless drivers (WDs), and the location of the mobile operators (MAs). A block coordinate descent algorithm is designed to optimize the time allocation scheme within a specified transmission block, the transmit covariance matrix at the AP, the computation frequency and transmit power of the wireless drivers (WDs), and the location of the mobile operators (MAs). This optimization problem is then formulated to maximize the system's total computational bits. This method significantly improves the network's computing power, enhances the communication capabilities of vehicle-grid interaction scheduling optimization, and improves communication performance during vehicle-grid interaction.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grid and vehicle-grid interaction, and specifically relates to a method for optimizing mobile edge cloud computing network performance using MA enhancement. Background Art

[0002] In recent years, electric vehicles (EVs) and other new energy vehicles (NEVs) have garnered increasing global attention for their energy-saving and environmentally friendly advantages. Driven by supportive policies implemented by various countries, the EV-related industry has experienced rapid growth. However, the highly random and volatile temporal and spatial distribution of EV charging loads significantly impacts the safety and stability of distribution network operations. Consequently, vehicle-grid interaction technologies based on ordered charging and discharging have emerged, revolutionizing the operation of smart grids. While mitigating the negative impacts of disorderly charging, EVs can also serve as mobile energy storage, enabling load shifting, peak shaving, frequency regulation, and backup services. During vehicle-grid interaction, intelligent terminals in substations send control commands to control their charging piles. Therefore, charging piles must possess reliable communication capabilities for information exchange with these intelligent terminals. Furthermore, efficient EV control strategies (control commands sent by these intelligent terminals) must be based on the perception of substation operating conditions and fully leverage the computing capabilities of these intelligent terminals in edge computing and cloud computing. Therefore, optimizing the information transmission performance during vehicle-grid interaction is of great significance for improving the accuracy and reliability of the orderly charging and discharging control strategy of electric vehicles and supporting the realization of vehicle-grid interaction.

[0003] However, the wireless devices (WDs) deployed in the vehicle-grid interaction process often suffer from limited battery capacity and computing power, which hinders the performance of the vehicle-grid interaction system. To address these issues, the concept of wireless powered and backscattering mobile edge computing (WPB-MEC) has emerged. It combines wireless power transfer (WPT) and backscatter-enhanced mobile edge computing (MEC) technologies. Specifically, in a WPB-MEC system, the MEC server performs data collection, transmission, and simple data processing for the WDs, and transmits data with low latency requirements to the cloud platform. WDs can passively offload tasks by backscattering signals from access points (APs), or actively offload tasks using a collect-then-offload protocol. The AP provides wireless energy transmission to the WDs to support their operation. While one WD is passively offloading its tasks, other WDs can simultaneously collect energy. However, in the current existing technology, the fixed position antennas (FPAs) equipped on APs and MEC servers cannot fully utilize their spatial degrees of freedom (DoF), resulting in low efficiency of wireless energy transmission and active and passive task offloading, which affects network performance.

[0004] Recently, a new antenna paradigm, the movable antenna (MA), has been proposed. It can be connected to its radio frequency (RF) chain via a flexible cable, enabling it to flexibly adjust its position within a given spatial area with the help of a stepper motor. However, there are still no reports on how to build MA-enhanced WPB-MEC networks. Furthermore, performance optimization methods for MA-enhanced WPB-MEC networks remain unknown. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a mobile edge cloud computing network performance optimization method enhanced by MA. Based on MAs, favorable channel conditions can be obtained, thereby making full use of DoFs to improve communication performance. During the vehicle-grid interaction process, the effective regulation of the orderly charging and discharging of electric vehicles depends on a reliable signal transmission network. The present invention aims to enhance the total computing bits of the network, thereby improving the efficiency of global information interaction during vehicle-grid interaction.

[0006] The method for optimizing mobile edge cloud computing network performance using MA enhancement according to the present invention comprises the following steps:

[0007] Step 1: Build an MA-enhanced WPB-MEC system, where the WPB-MEC system includes an access point (AP) with N movable antennas (MAs), an MEC server with M movable antennas (MAs), and K wireless devices (WDs).

[0008] The movable antennas MAs of the AP and MEC server are connected to their respective radio frequency RF chains through flexible cables, so that the MAs are in their respective two-dimensional areas C with a size of A×A. t and C r The position in can be moved;

[0009] The tasks of each WD are divisible. Each WD is equipped with a single fixed-position antenna FPA. Based on backscatter communication circuits and active RF circuits, each WD offloads the corresponding computing tasks to the MEC server in the form of passive task offloading or active task offloading.

[0010] Step 2: Based on the MA-enhanced WPB-MEC system, the total calculation bits of the WPB-MEC system are calculated according to the transmit covariance matrix at the AP, the network time resource allocation, the transmit power and calculation frequency of the WDs, and the location of the MAs;

[0011] Step 3: Design a block coordinate descent algorithm to optimize the time allocation scheme within a specified transmission block, the transmit covariance matrix at the AP, the calculation frequency of WDs, the transmit power, and the location of MAs, and establish an optimization problem to maximize the total computational bits of the system.

[0012] Furthermore, the set of K wireless devices WDs is {WD-1, WD-2, ..., WD-K}, the duration of the transmission block in the system is T seconds, and TDMA is used to divide the passive task offloading and active task offloading into K sub-phases respectively. In the kth sub-phase of the passive offloading phase of 10 seconds, WD-k offloads part of the tasks to the MEC server in the form of passive offloading based on the backscatter circuit; The subscript k corresponds to the kth sub-stage and also corresponds to the kth WD. The superscript b indicates the passive unloading stage. At the same time, the remaining WDs collect energy from the AP to increase the total amount of energy collected; The kth sub-phase of the active unloading phase of seconds, The superscript a in the middle represents the active offloading stage. WD-k uses the collected energy to actively offload another part of the tasks to the MEC server. Specifically:

[0013] 1) In the passive task offloading phase, the AP transmits an energy signal to achieve passive offloading and wireless energy transmission (WPT), specifically:

[0014] Let W k for The covariance matrix of the transmission at the AP during the period is: For passive offloading, the calculation bit of WD-k offloading is expressed as:

[0015]

[0016] in, B is the channel bandwidth, ρ is the reflection coefficient, is the noise power spectral density, ξ represents the performance gap caused by using actual modulation in the passive unloading phase;

[0017] In the passive unloading phase, WPT is performed on WD-k; for WD-k, a portion of its received power ρtr(B k W k ) is used to implement Passive unloading during the period, and the remaining power (1-ρ)tr(B k W k ) is converted into direct current and stored in the battery of WD-k;

[0018] During the entire passive unloading phase, except for Except for the corresponding sub-phase, WD-k continues to collect energy from the AP. According to the nonlinear energy harvesting model, the energy obtained by WD-k during the passive unloading phase is:

[0019]

[0020] in, represents the energy collected by WD-k in the kth sub-stage of the passive unloading phase, represents the duration of the kth sub-phase of the passive unloading phase, represents the duration of the i-th sub-phase of the passive unloading phase, i≠k; represents the received power of WD-k in the i-th sub-phase of the passive unloading phase, i≠k; represents the logic function in the energy harvesting circuit of WD-k, P sat is the saturation power, a k and b k are the control parameters of the energy harvesting circuit of WD-k;

[0021] 2) In the active task offloading phase, let p k is the transmission power of WD-k, WD-k is The calculation bits of active offloading during the period are expressed as:

[0022]

[0023] Let E c,k represents a constant circuit energy consumption, then the energy consumption of WD-k in the active unloading stage is expressed as

[0024] 3) The calculation bits of WD-k calculated locally are:

[0025]

[0026] Among them, f k represents the calculation frequency of WD-k, represents the computational complexity of the task at WD-k; the energy required to perform local computation at WD-k is Indicates that, where ε k is the effective capacitance coefficient of the on-chip processor located at WD-k.

[0027] Furthermore, the channel model in the WPB-MEC system is as follows:

[0028] Assume t n =[x n ,y n ],r m =[x m ,y m ] represent the nth MA position of the AP and the mth MA position of the MEC server, respectively, where x n ,y n is the horizontal and vertical coordinates of the nth MA position of AP, x m ,y m is the horizontal and vertical coordinates of the mth MA position of the MEC server; the set of MAs positions on the AP and MEC server is expressed as

[0029] The number of transmission channel paths between AP and WD-k, and between WD-k and MEC server are respectively and To express it, then the nth MA position of WD-k and the lth reference point between the AP t The signal propagation phase difference of the paths is expressed as:

[0030]

[0031] Where, and Respectively represent the lth t The elevation and azimuth angles of the transmission paths;

[0032] Channel vector from AP to WD-k Expressed as:

[0033]

[0034] is the transmitting field response matrix, is the corresponding vector of the transmission field of the nth MA in the AP, λ is the wavelength of the carrier, j is the imaginary number sign, is the path response vector between AP and WD-k;

[0035] Similarly, the channel vector from WD-k to the MEC server is Expressed as:

[0036]

[0037] in represents the corresponding path response vector, is the receiving field response matrix of the MEC server, is the corresponding vector of the mth receiving field in the MEC server, Indicates the lth distance between the mth MA location of the MEC server and the reference point r The signal propagation phase difference of the paths is and They are the first r The elevation and azimuth angles of the receiving paths,

[0038] Furthermore, we construct an optimization problem, specifically:

[0039] According to the location of MAs in AP and MEC server and Channel vector from AP to WD-k Channel vector from WD-k to MEC server The transmit covariance matrix at AP is W = {W k ,k∈K}, the transmission power of WDs p={p k ,k∈K}, calculate frequency f={f k ,k∈K}, we establish the problem of maximizing the total computational bits of the network, P0, which can be expressed as:

[0040]

[0041] C8: ||t n -t n′||2≥D,1≤n≠n'≤N

[0042] C9: ||r m -r m′ ||2≥D,1≤m≠m'≤N

[0043] Where P H is the maximum transmit power of the AP, is the maximum calculation frequency of WD-k, D is the minimum distance between any two MAs; ||·||2 represents the Euclidean norm.

[0044] Furthermore, the total computation bit maximization problem (P0) is solved, including:

[0045] Step 3-1: Given Solve the problem to obtain the suboptimal solution t of variables t, p, and f * 、p * 、f * ;

[0046] Step 3-2, given {t,p,f} and By introducing the auxiliary variable β k 、z k,i and c k , obtain the suboptimal solution W of W through continuous convex approximation * ;

[0047] Step 3-3, given {t, p, f} and W, use GA-PSO algorithm to solve the problem and obtain and Suboptimal solution and

[0048] Step 3-4: Repeat steps 3-1 to 3-3 until the total computational bits of the network tend to a fixed value, and obtain the suboptimal solution to the final optimization problem. * 、p * 、f * 、W * 、 and

[0049] Furthermore, step 3-1 is specifically as follows:

[0050] In a given Finally, the total network computation bit maximization problem P0 is rewritten as problem P1, which is expressed as:

[0051]

[0052] in as well as The maximum total computation bits are obtained when each WD exhausts its available energy, so C1 is updated as:

[0053]

[0054] Will Defined in Rewritten as:

[0055]

[0056] Based on the above formula, the P1 problem is rewritten as the convex optimization problem P1.1, which is expressed as:

[0057]

[0058] Solve the convex optimization problem P1.1 using the CVX tool and obtain the corresponding suboptimal solution t * 、p * .

[0059] Furthermore, step 3-2 is specifically as follows:

[0060] Given {t,p,f}, by introducing auxiliary variables β k 、z k，i and c k , rewrite the network's total computational bit maximization problem P0 into problem P2, problem P2 is expressed as, problem P2 is expressed as:

[0061]

[0062] C8: ||t n -t n′ ||2≥D,1≤n≠n'≤N,

[0063] C9: ||r m -r m′ ||2≥D, 1≤m≠m'≤N,

[0064] Among them, the variable β k =q k / (E k -E c,k ) represents the energy distribution between WD-k’s active offloading and local computation; β k (E k -E c,k ) and (1-β k )(E k -E c,k ) represent the energy allocated to active offloading and local computing, respectively;

[0065] Based on this, the number of bits calculated for active offloading is:

[0066]

[0067] The number of bits calculated locally is:

[0068]

[0069] By and Substituting into problem P2, problem P2 can be rewritten as problem P2.1, expressed as:

[0070]

[0071] C8: ||t n -t n′ ||2≥D,1≤n≠n'≤N

[0072] C9: ||r m -r m ′||2≥D,1≤m≠m'≤N;

[0073] because The non-convexity of the above problem still exists due to the coupling of W and Two related sub-problems, and solve them separately;

[0074] Next, given {t,p,f} and And by introducing the auxiliary variable z k，i and c k , simplifying Problem P2.1 to Problem P2.2, expressed as:

[0075]

[0076] C10:z k，i ≥exp(-a k (tr(B k W k )-b k )),

[0077] C11:c k ≥exp(-a k ((1-ρ)tr(B k W k )-b k )),

[0078] where z k，i and c k It is an auxiliary variable introduced to solve the non-convexity of the objective function.

[0079] Based on this, Rewrite as This is with z k,i and c k Related non-convex problems;

[0080] By applying the continuous convex approximation SCA method, we get E k The approximate value of is:

[0081]

[0082] in, and are z in the sth iteration respectively. k，i and c k Based on this, problem P2.2 becomes a convex problem, and the CVX toolbox is used to solve it, and the corresponding suboptimal solution W is obtained. * .

[0083] Furthermore, steps 3-4 are specifically as follows:

[0084] Given {t,p,f} and W, reduce Problem P2.1 to Problem P2.3:

[0085]

[0086] C8: ||t n -t n′ ||2≥D, 1≤n≠n'≤N,

[0087] C9:||r m -r m′ ||2≥D, 1≤m≠m'≤N;

[0088] Use GA-PSO algorithm to solve the problem. First, initialize J particles, whose positions are Speed ​​is Specifically, the particles The position of represents a possible solution to Problem P2.3, where particle Based on their personal best position and the global optimal position among all particles And update its position; in the t-th iteration of the GA-PSO algorithm, the particle The speed and position are modified as follows:

[0089]

[0090] Among them, c1 and c2 are the personal learning factor and the global learning factor respectively; τ1 and τ2 are two parameters generated uniformly in [0,1]; ω is the inertia weight that controls the convergence performance and is updated as:

[0091]

[0092] where ω max and ω min It limits the range of ω; d1 and d2 are control factors; is a constant, indicating the maximum number of iterations; to ensure the constraints and That is, the solution obtained should be within the feasible region, and the following mapping operations are performed:

[0093]

[0094] in, express The i-th element of ;

[0095] In order to evaluate the impact of position optimization on the total computation bits, the fitness function is defined as:

[0096]

[0097] Where, use Represents the total computation bits; is the location set of MAs pairs that violate C8 and C9 on AP and MEC servers respectively; express The cardinality of τ>0 is a large penalty parameter to ensure the constraints C8 and C9, otherwise will be less than zero;

[0098] By evaluating the particles The fitness of

[0099] and Update the individual and global best positions of the particles until convergence.

[0100] Furthermore, the GA-PSO algorithm includes crossover and mutation operations. The crossover probability p in the operation c and mutation probability p m Greater than a random number , the crossover and mutation operations are re-executed as follows:

[0101]

[0102] in and Representing the Random numbers in crossover and mutation, and represent the jth and lth crossover particles randomly selected at fixed crossover positions, respectively. Indicates the mutation selected at the i-th MA position particle;

[0103] Check whether the updated solution satisfies constraints C6 and C7; if not, perform crossover and mutation operations again.

[0104] The beneficial effects described in the present invention are as follows: the present invention deploys MAs on APs and MEC servers to fully utilize spatial DoFs, thereby improving the energy transmission efficiency from APs to WDs and the task offloading efficiency from WDs to MEC servers; based on the MA enhancement scheme, an efficient time slot scheduling scheme is constructed to fully utilize the characteristics of active and passive task offloading, thereby improving the amount of collected energy and the number of offloaded tasks; furthermore, a network performance optimization problem is defined, and the problem is solved using the designed block coordinate descent algorithm, thereby achieving network performance optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 This is a diagram of a WPB-MEC system model supporting MA provided by an embodiment;

[0106] Figure 2 It is the convergence behavior curve of GA-PSO algorithm;

[0107] Figure 3 It is a graph showing the relationship between the total calculated bits and the AP transmit power;

[0108] Figure 4 It is a graph of the relationship between the total computation bits and the number of WDs;

[0109] Figure 5 It is a graph of the relationship between the total computation bits and the number of MAs;

[0110] Figure 6 is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0111] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0112] like Figure 6 As shown, the present invention relates to a method for optimizing mobile edge cloud computing network performance enhanced by MA in the field of smart grid and vehicle-to-vehicle interaction, comprising the following steps:

[0113] Step 1: Build the MA-enhanced WPB-MEC system;

[0114] Step 2: Based on the MA-enhanced WPB-MEC system, the total calculation bits of the WPB-MEC system are calculated according to the transmit covariance matrix at the AP, the network time resource allocation, the transmit power and calculation frequency of the WDs, and the location of the MAs;

[0115] Step 3: Design a block coordinate descent algorithm to optimize the time allocation scheme within a specified transmission block, the transmit covariance matrix at the AP, the calculation frequency of WDs, the transmit power, and the location of MAs, and establish an optimization problem to maximize the total computational bits of the system.

[0116] The WPB-MEC system model supporting MA used in the embodiment of the present invention is shown in FIG. Figure 1 The WPB-MEC system includes an access point AP with N movable antennas MAs, an MEC server with M movable antennas MAs, and K wireless devices WDs. The set of K wireless devices WDs is {WD-1, WD-2, …, WD-K}, where k and K are pre-set values, k represents the index value of WDs, and k≤K; each WD is an energy-constrained device and is equipped with a single fixed-position antenna FPA.

[0117] Each WD can backscatter the incident signal from the AP to achieve passive offloading from WDs to the MEC server. In addition, it can also implement a collection and offloading protocol to actively offload its tasks to the MEC server using the previously collected energy. Considering the bit-by-bit independence and arbitrary splitting of the tasks of WDs, a partial offloading strategy is adopted. The MAs of the AP and MEC server are connected to their respective RF chains using flexible cables, so that the MAs are located in their respective two-dimensional (2D) areas C of size A×A. t and C r The location in the network can be moved to establish a more efficient transmission connection.

[0118] The duration of the transmission block in the system is T seconds, which includes four execution phases: passive unloading, active unloading, edge computing phase and result feedback phase. In order to avoid interference, TDMA is used to divide the passive unloading and active unloading phases into K sub-phases respectively. In the kth sub-phase of the passive offloading phase of 10 seconds, WD-k backscatters part of its tasks to the MEC server, where At the same time, the remaining WDs can harvest energy from the AP to increase the total amount of harvested energy. In the kth sub-phase of the active offloading phase of 10 seconds, WD-k actively offloads part of its tasks to the MEC server by utilizing its harvested energy.

[0119] The channel model in the system is as follows:

[0120] Assume t n =[x n ,y n ],r m =[x m ,y m ] represent the nth MA position of the AP and the mth MA position of the MEC server, respectively, where x n 、y n is the horizontal and vertical coordinates of the nth MA position of AP, x m 、y m is the horizontal and vertical coordinates of the mth MA position of the MEC server. The set of MAs positions on the AP and MEC server is expressed as

[0121] The number of transmission channel paths between AP and WD-k, and between WD-k and MEC server are respectively and To express it, WD-k is the first position between the nth MA position and the AP reference point. The signal propagation phase difference of the paths is expressed as:

[0122]

[0123] Where, and Respectively represent the lth t The elevation and azimuth angles of the transmission paths. The channel vector from AP to WD-k Expressed as

[0124]

[0125] is the transmitting field response matrix, λ is the carrier wavelength, is the path response vector between AP and WD-k.

[0126] Similarly, the channel vector from WD-k to the MEC server is Expressed as

[0127]

[0128] in represents the corresponding path response vector, is the receiving field response matrix of the MEC server, and They are the first rThe elevation and azimuth angles of the receiving paths.

[0129] The propagation model in the system is as follows:

[0130] 1) Passive offloading stage: In this stage, the AP transmits energy signals to achieve passive offloading and WPT. k for During the period, the AP transmits the covariance matrix. For passive offloading, the calculation bit of WD-k offloading is expressed as:

[0131]

[0132] in, B is the channel bandwidth, ρ is the reflection coefficient, is the noise power spectral density, and ξ represents the performance gap caused by using actual modulation in the passive offloading phase.

[0133] In the passive unloading phase, WPT can be performed on WD-k. For WD-k, a portion of its received power (ρtr(B k W k )) is used to implement Passive unloading during the period, and the remaining power ((1-ρ)tr(B k W k )) is converted into DC power and stored in the battery of WD-k. Except for the corresponding sub-phase, WD-k can always collect energy from AP. According to the nonlinear energy harvesting model, the energy obtained by WD-k in the passive unloading phase is:

[0134]

[0135] in,

[0136] P sat is the saturation power, a k and b k are the control parameters of the energy harvesting circuit of WD-k;

[0137] 2) Active unloading stage: Set p k is the transmission power of WD-k. The calculation bits of active offloading during the period are expressed as:

[0138]

[0139] Let E c,krepresents a constant circuit energy consumption, then the energy consumption of WD-k in the active unloading stage is expressed as

[0140] 3) Local calculation: The calculation bits of WD-k calculated locally are:

[0141]

[0142] Among them, f k represents the calculation frequency of WD-k, represents the computational complexity of the task at WD-k. The energy required to perform local computation at WD-k is Indicates that, where ε k is the effective capacitance coefficient of the on-chip processor located at WD-k.

[0143] Optimize the network time allocation scheme, the transmit covariance matrix at the AP, the calculation frequency and transmit power of WDs, and the location of MAs. Because the optimal solution is difficult to obtain, the corresponding process for obtaining a suboptimal solution includes:

[0144] According to the location of MAs in AP and MEC server and Channel vector from AP to WD-k Channel vector from WD-k to MEC server The transmit covariance matrix at AP is W = {W k ,k∈K}, the transmission power of WDs p={p k ,k∈K}, calculate frequency f={f k ,k∈K}, we establish the problem of maximizing the total computational bits of the network, P0, which can be expressed as:

[0145]

[0146]

[0147] C8:||t n -t n′ ||2≥D,1≤n≠n'≤N

[0148] C9: ||r m -r m′ ||2≥D,1≤m≠m'≤Nwhere P H is the maximum transmit power of the AP, is the maximum calculation frequency of WD-k, D is the minimum distance between any two MAs; ‖‖·‖2 represents the Euclidean norm;

[0149] Solving the total computational bit maximization problem (P0) includes:

[0150] Step 3-1: Given Solve the problem to obtain the suboptimal solution t of variables t, p, and f * 、p * 、f * ;

[0151] Step 3-2, given {t,p,f} and By introducing the auxiliary variable β k 、z k，i and c k , obtain the suboptimal solution W of W through continuous convex approximation * ;

[0152] Step 3-3, given {t, p, f} and W, use GA-PSO algorithm to solve the problem and obtain and Suboptimal solution and

[0153] Step 3-4: Repeat steps 3-1 to 3-3 until the total computational bits of the network tend to a fixed value, and obtain the suboptimal solution to the final optimization problem. * 、p * 、f * 、W * 、 and

[0154] Among them, step 3-1 is specifically: optimize the calculation time, transmission power, and calculation frequency of WDs under the conditions of the given AP's transmission covariance matrix, the MAs position on the AP, and the MAs position on the MEC server, that is, fix Obtain the suboptimal solution t of variables t, p, and f by solving the problem * 、p * 、f * The specific process includes:

[0155] In a given Finally, the total network computation bit maximization problem P0 is rewritten as problem P1, which is expressed as:

[0156]

[0157] in as well as The maximum total computation bits are obtained when each WD exhausts its available energy. Therefore, C1 is updated as:

[0158]

[0159] Will Defined in Rewritten as:

[0160]

[0161] Based on the above formula, the P1 problem can be rewritten as the convex optimization problem P1.1. Problem P1.1 is expressed as:

[0162]

[0163] Solve the convex optimization problem P1.1 using the CVX tool and obtain the corresponding suboptimal solution t * 、p * .

[0164] Step 3-2 is as follows: Optimize the AP's transmit covariance matrix under the given WDs calculation time, transmit power, calculation frequency, MAs location on the AP, and MAs location on the MEC server, that is, fix {t, p, f} and Optimize W. By introducing auxiliary variables β k 、z k,i and c k , rewrite the network's total computational bit maximization problem P0 into problem P2, which is expressed as:

[0165]

[0166] C8:||t n -t n′ ||2≥D, 1≤n≠n′≤N,

[0167] C9:||r m -r m′ ||2≥D, 1≤m≠m'≤N,

[0168] Among them, the variable β k =q k / (E k -E c,k ) represents the energy distribution between WD-k’s active offloading and local computation; β k (E k -E c,k ) and (1-β k )(E k -E c,k ) represent the energy allocated to active offloading and local computing, respectively.

[0169] Based on this, the number of bits calculated for active offloading is:

[0170]

[0171] The number of bits calculated locally is:

[0172]

[0173] By and Substituting into problem P2, problem P2 can be rewritten as problem P2.1, expressed as:

[0174]

[0175] C8: ||t n -t n′ ||2≥D,1≤n≠n'≤N

[0176] C9: ||r m -r m′ ||2≥D, 1≤m≠m'≤N;

[0177] because The non-convexity of the above problem still exists due to the coupling of W and Two related sub-problems, and solve them separately;

[0178] Next, given {t,p,f} and And by introducing the auxiliary variable z k，i and c k , simplifying Problem P2.1 to Problem P2.2, expressed as:

[0179]

[0180] C10:z k，i ≥exp(-a k (tr(B k W k )-b k )),

[0181] C11:c k ≥exp(-a k ((1-ρ)tr(B k W k )-b k )),

[0182] where z k，i and c k It is an auxiliary variable introduced to solve the non-convexity of the objective function.

[0183] Based on this, Can be rewritten as This is with z k，i and c k Related non-convex problems. By applying SCA, we get Ek The approximate value of in, and are z in the sth iteration respectively. k，i and c k Based on this, problem P2.2 becomes a convex problem, which can be solved using the CVX toolbox to obtain the corresponding suboptimal solution W*.

[0184] Step 3-3 is as follows: Optimize the MAs position on the AP and the MAs position on the MEC server under the given WDs calculation time, transmission power, calculation frequency, and AP transmission covariance matrix, that is, fix {t, p, f} and W optimization Rewrite the network's total computational bit maximization problem P2.1 into P2.3, and the problem P2.3 is expressed as:

[0185]

[0186] C8: ||t n -t n′ ||2≥D, 1≤n≠n'≤N,

[0187] C9:||r m -r m′ ||2≥D,1≤m≠m'≤N;

[0188] Considering that the solution space of problem P2.3 is very large, the GA-PSO algorithm is used to solve the problem. In the GA-PSO algorithm, first initialize J particles, whose positions are Speed ​​is Specifically, the particles The position of represents a possible solution to Problem P2.3, where particle Based on their personal best position and the global optimal position among all particles And update its position; in the t-th iteration of the GA-PSO algorithm, the particle The speed and position are modified as follows:

[0189]

[0190] Among them, c1 and c2 are the personal learning factor and the global learning factor respectively; τ1 and τ2 are two parameters generated uniformly in [0,1]; ω is the inertia weight that controls the convergence performance and is updated as:

[0191]

[0192] where ω max and ω minIt limits the range of ω; d1 and d2 are control factors; is a constant, indicating the maximum number of iterations; to ensure the constraints and That is, the solution obtained should be within the feasible region, and the following mapping operations are performed:

[0193]

[0194] in, express The i-th element of ;

[0195] In order to evaluate the impact of position optimization on the total computation bits, the fitness function is defined as:

[0196]

[0197] Where, use Represents the total computation bits; is the location set of MAs pairs that violate C8 and C9 on AP and MEC servers respectively; express The cardinality of τ>0 is a large penalty parameter to ensure the constraints C8 and C9, otherwise will be less than zero.

[0198] By evaluating the particles The fitness of

[0199] and Update the individual and global best positions of the particles until convergence.

[0200] The GA-PSO algorithm includes crossover and mutation operations. Specifically, when the The crossover probability p in the operation c and mutation probability p m Greater than a random number , the crossover and mutation operations are re-executed as follows:

[0201]

[0202] in and Representing the Random numbers in crossover and mutation, and represent the jth and lth crossover particles randomly selected at fixed crossover positions, respectively. Indicates the mutation selected at the i-th MA position After completing these operations, check whether the updated solution satisfies the constraints C6 and C7. If not, perform crossover and mutation operations again.

[0203] The method of the present invention is described below with reference to specific experiments.

[0204] Consider a two-dimensional simulation environment, where the locations of the AP and MEC server are set to (-15m, 0) and (15m, 0), respectively, and the WDs are evenly distributed in a circle with a center of (0, 0) and a radius of 10m. The mobile area of ​​the MAs on the AP and MEC server is set to a square area of ​​[-3λ / 2, 3λ / 2] × [-3λ / 2, 3λ / 2], where λ = 0.1m. The number of sending paths and receiving paths is set to the same, that is, Assume q k and Each element in Where C0 = -10dBm represents large-scale fading with a reference distance of 1m, α = 2.8 is the path loss exponent, and d represents the distance between two nodes. Other parameters are expressed as follows: H =30dBm, T=1s, N=M=10, K=4, B=0.1MHz, ρ=0.8, ξ=0.0316, E c,k =10 -10 J, D = 0.5λ, A = 3λ, a k =1500, b k =0.0022, P sat =0.024, ε k =10 -26 Watt / Hz 3 , J=300, c1=c2=1.4, d1=0.2, d2=7, ω max =0.9,ω min =0.4, p c =0.7, p m =0.3,τ=100000.

[0205] For performance comparison, the MA scheme based on the GA-PSO algorithm is compared with the following reference schemes: i) MA scheme based on the particle swarm optimization (PSO) algorithm: the positions of MAs in the MA scheme are optimized using the standard PSO algorithm; ii) FPA scheme: all antenna positions are fixed, and necessary parameter optimization is considered; iii) full offload scheme: for the invented MA scheme, WDs can only perform their tasks remotely through the MEC server; iv) full local scheme: WDs can only use their local processors to compute their tasks.

[0206] Figure 2 The relationship curve of the total computation bits of the system as the number of iterations changes is shown. Figure 2 As shown in Figure 2, as the number of iterations increases, the overall computational bit performance obtained by the GA-PSO algorithm is significantly better than that of the standard PSO algorithm. This is because the GA-PSO algorithm implements crossover and mutation operations, and has strong global search capabilities and particle diversity.

[0207] Figure 3 The graph shows how the total system computation bits vary with the AP's maximum transmit power. Clearly, increasing transmit power improves the total computation bits for all schemes. Compared to the MA scheme using the PSO algorithm, the MA scheme using the GA-PSO algorithm achieves a higher total computation bits, demonstrating that the GA-PSO algorithm can improve global search capabilities and avoid falling into local optimal solutions. Compared to the FPA scheme, using MAs on the AP and MEC server ensures better system performance. This is because MAs can create favorable channel conditions between APs and WDs, and between WDs and MEC servers.

[0208] Figure 4 The graph shows how the total computational bits of the system vary with the number of WDs. As K increases, the total computational bits of all schemes improve. The performance gap between the schemes using the GA-PSO and PSO algorithms widens with the increase in WDs, further demonstrating the superiority of the GA-PSO algorithm over the PSO algorithm. Furthermore, compared to the full offload scheme and the fully local scheme, the hybrid offload scheme using the GA-PSO algorithm improves the total computational bits by 34.6% and 286.6%, respectively.

[0209] Figure 5 The relationship between the total system computing bits and the number of MAs is shown in the figure. It is obvious that the number of MAs has a positive impact on the total system computing bits. This is because more MAs can achieve higher antenna gain from WDs to AP / MEC servers. Figure 3 and Figure 4 ,The total computation bit achieved by the GA-PSO algorithm is the largest.,Simulation results show that the use of MAs in the WPB-MEC system,of the present invention can greatly improve the performance of the MEC network.

[0210] The above description is only a preferred embodiment of the present invention and is not intended to further limit the present invention. All equivalent changes made using the contents of the present invention description and drawings are within the scope of protection of the present invention.

Claims

1. A method for optimizing mobile edge cloud computing network performance enhanced by MA, characterized in that: The following steps are involved: Step 1: Build an MA-enhanced WPB-MEC system, where the WPB-MEC system includes an access point (AP) with N movable antennas (MAs), an MEC server with M movable antennas (MAs), and K wireless devices (WDs). The movable antennas MAs of the AP and MEC server are connected to their respective radio frequency RF chains through flexible cables, so that the MAs are in their respective two-dimensional areas C with a size of A×A. t and C r The position in can be moved; The tasks of each WD are divisible. Each WD is equipped with a single fixed-position antenna FPA. Based on backscatter communication circuits and active RF circuits, each WD offloads the corresponding computing tasks to the MEC server in the form of passive task offloading or active task offloading. Step 2: Based on the MA-enhanced WPB-MEC system, the total calculation bits of the WPB-MEC system are calculated according to the transmit covariance matrix at the AP, the network time resource allocation, the transmit power and calculation frequency of the WDs, and the location of the MAs; Step 3: Design a block coordinate descent algorithm to optimize the time allocation scheme within a specified transmission block, the transmit covariance matrix at the AP, the calculation frequency of WDs, the transmit power, and the location of MAs, and establish an optimization problem to maximize the total computational bits of the system.

2. The method for optimizing mobile edge cloud computing network performance based on MA enhancement according to claim 1, characterized in that: The set of K wireless devices WDs is {WD-1, WD-2, ..., WD-K}. The duration of the transmission block in the system is T seconds. TDMA is used to divide the passive task offloading and active task offloading into K sub-phases respectively. In the kth sub-phase of the passive offloading phase of 10 seconds, WD-k offloads part of the tasks to the MEC server in the form of passive offloading based on the backscatter circuit; The subscript k corresponds to the kth sub-stage and also corresponds to the kth WD. The superscript b indicates the passive unloading stage. At the same time, the remaining WDs collect energy from the AP to increase the total amount of energy collected; The kth sub-phase of the active unloading phase of seconds, The superscript a in the middle represents the active offloading stage. WD-k uses the collected energy to actively offload another part of the tasks to the MEC server. Specifically: 1) In the passive task offloading phase, the AP transmits an energy signal to achieve passive offloading and wireless energy transmission (WPT), specifically: Let W k for The covariance matrix of the transmission at the AP during the period is: For passive offloading, the calculation bit of WD-k offloading is expressed as: in, B is the channel bandwidth, ρ is the reflection coefficient, is the noise power spectral density, ξ represents the performance gap caused by using actual modulation in the passive unloading phase; In the passive unloading phase, WPT is performed on WD-k; for WD-k, a portion of its received power ρtr(B k W k ) is used to implement Passive unloading during the period, and the remaining power (1-ρ)tr(B k W k ) is converted into direct current and stored in the battery of WD-k; is the channel vector from WD-k to the MEC server; The channel vector from AP to WD-k; During the entire passive unloading phase, except for Except for the corresponding sub-phase, WD-k continues to collect energy from the AP. According to the nonlinear energy harvesting model, the energy obtained by WD-k during the passive unloading phase is: in, represents the energy collected by WD-k in the kth sub-stage of the passive unloading phase, represents the duration of the kth sub-phase of the passive unloading phase, represents the duration of the i-th sub-phase of the passive unloading phase, i≠k; represents the received power of WD-k in the i-th sub-phase of the passive unloading phase, i≠k; represents the logic function in the energy harvesting circuit of WD-k, P sat is the saturation power, a k and b k are the control parameters of the energy harvesting circuit of WD-k; 2) In the active task offloading phase, let p k is the transmission power of WD-k, WD-k is The calculation bits of active offloading during the period are expressed as: Let E c,k represents a constant circuit energy consumption, then the energy consumption of WD-k in the active unloading stage is expressed as 3) The calculation bits of WD-k calculated locally are: Among them, f k represents the calculation frequency of WD-k, represents the computational complexity of the task at WD-k; the energy required to perform local computation at WD-k is Indicates that, where ε k is the effective capacitance coefficient of the on-chip processor located at WD-k.

3. The method for optimizing network performance of mobile edge cloud computing enhanced by MA according to claim 2, characterized in that: The channel model in the WPB-MEC system is as follows: Assume t n =[x n ,y n ],r m =[x m ,y m ] represent the nth MA position of the AP and the mth MA position of the MEC server, respectively, where x n ,y n is the horizontal and vertical coordinates of the nth MA position of AP, x m ,y m is the horizontal and vertical coordinates of the mth MA position of the MEC server; the set of MAs positions on the AP and MEC server is expressed as The number of transmission channel paths between AP and WD-k, and between WD-k and MEC server are respectively and To express it, then the nth MA position of WD-k and the lth reference point between the AP t The signal propagation phase difference of the paths is expressed as: Where, and Respectively represent the lth t The elevation and azimuth angles of the transmission paths; Channel vector from AP to WD-k Expressed as: is the transmitting field response matrix, is the corresponding vector of the transmission field of the nth MA in the AP, λ is the wavelength of the carrier, j is the imaginary number sign, is the path response vector between AP and WD-k; Similarly, the channel vector from WD-k to the MEC server is Expressed as: in represents the corresponding path response vector, is the receiving field response matrix of the MEC server, is the corresponding vector of the mth receiving field in the MEC server, Indicates the lth distance between the mth MA location of the MEC server and the reference point r The signal propagation phase difference of the paths is and They are the first r The elevation and azimuth angles of the receiving paths, 4. The method for optimizing network performance of mobile edge cloud computing enhanced by MA according to claim 3, characterized in that: Construct the optimization problem, specifically: Based on the location of MAs in AP and MEC server and Channel vector from AP to WD-k Channel vector from WD-k to MEC server The transmit covariance matrix at AP is W = {W k ,k∈K}, the transmission power of WDs p={p k ,k∈K}, calculate frequency f={f k ,k∈K}, we establish the problem of maximizing the total computational bits of the network, P0, which can be expressed as: C8:||t n -t n′ ||2≥D,1≤n≠n'≤N C9:||r m -r m′ ||2≥D,1≤m≠m'≤N Where P H is the maximum transmit power of the AP, is the maximum calculation frequency of WD-k, D is the minimum distance between any two MAs; ||·||2 represents the Euclidean norm.

5. The method for optimizing network performance of mobile edge cloud computing enhanced by MA according to claim 4, characterized in that: Solve the total computational bit maximization problem P0, including: Step 3-1: Given Solve the problem to obtain the suboptimal solution t of variables t, p, and f * 、p * 、f * ; Step 3-2, given {t,p,f} and By introducing the auxiliary variable β k 、z k,i and c k , obtain the suboptimal solution W of W through continuous convex approximation * ; Step 3-3, given {t, p, f} and W, use GA-PSO algorithm to solve the problem and obtain and Suboptimal solution and Step 3-4: Repeat steps 3-1 to 3-3 until the total computational bits of the network tend to a fixed value, and obtain the suboptimal solution to the final optimization problem. * 、p * 、f * 、W * 、 and 6. The method for optimizing mobile edge cloud computing network performance using MA enhancement according to claim 5, characterized in that: Step 3-1 is as follows: In a given Finally, the total network computation bit maximization problem P0 is rewritten as problem P1, which is expressed as: in as well as The maximum total computation bits are obtained when each WD exhausts its available energy, so C1 is updated as: Will Defined in Rewritten as: Based on the above formula, the P1 problem is rewritten as the convex optimization problem P1.1, which is expressed as: Solve the convex optimization problem P1.1 using the CVX tool and obtain the corresponding suboptimal solution t * 、p * .

7. The method for optimizing network performance of mobile edge cloud computing enhanced by MA according to claim 6, characterized in that: Step 3-2 is as follows: Given {t,p,f}, by introducing auxiliary variables β k 、z k,i and c k , rewrite the network's total computational bit maximization problem P0 into problem P2, problem P2 is expressed as, problem P2 is expressed as: C8:||t n -t n′ ||2≥D,1≤n≠n'≤N, C9:||r m -r m′ ||2≥D,1≤m≠m'≤N, Among them, the variable β k =q k / (E k -E c,k ) represents the energy distribution between WD-k’s active offloading and local computation; β k (E k -E c,k ) and (1-β k )(E k -E c,k ) represent the energy allocated to active offloading and local computing, respectively; Based on this, the number of bits calculated for active offloading is: The number of bits calculated locally is: By and Substituting into problem P2, problem P2 can be rewritten as problem P2.1, expressed as: C8:||t n -t n′ ||2≥D,1≤n≠n'≤N C9:||r m -r m′ ||2≥D,1≤m≠m'≤N; because The non-convexity of the above problem still exists due to the coupling of W and Two related sub-problems, and solve them separately; Next, given {t,p,f} and And by introducing the auxiliary variable z k,i and c k , simplifying Problem P2.1 to Problem P2.2, expressed as: C10:z k，i ≥exp(-a k (tr(B k IN k )-b k )), C11:c k ≥exp(-a k ((1-ρ)tr(B k W k )-b k )), where z k,i and c k It is an auxiliary variable introduced to solve the non-convexity of the objective function. Based on this, Rewrite as This is with z k,i and c k Related non-convex problems; By applying the continuous convex approximation SCA method, we get E k The approximate value of is: in, and are z in the sth iteration respectively. k,i and c k Based on this, problem P2.2 becomes a convex problem, and the CVX toolbox is used to solve it, and the corresponding suboptimal solution W is obtained. * .

8. The method for optimizing network performance of mobile edge cloud computing enhanced by MA according to claim 7, characterized in that: Steps 3-4 are as follows: Given {t,p,f} and W, reduce Problem P2.1 to Problem P2.3: C8:||t n -t n′ ||2≥D,1≤n≠n'≤N, C9:||r m -r m′ ||2≥D,1≤m≠m'≤N; Use GA-PSO algorithm to solve the problem. First, initialize J particles, whose positions are Speed ​​is Specifically, the particles The position of represents a possible solution to Problem P2.3, where particle Based on their personal best position and the global optimal position among all particles And update its position; in the t-th iteration of the GA-PSO algorithm, the particle The speed and position are modified as follows: Among them, c1 and c2 are personal learning factor and global learning factor respectively; and are two parameters uniformly generated in [0,1]; ω is the inertia weight that controls the convergence performance and is updated as: where ω max and ω min It limits the range of ω; d1 and d2 are control factors; is a constant, indicating the maximum number of iterations; to ensure the constraints and That is, the solution obtained should be within the feasible region, and the following mapping operations are performed: in, express The i-th element of ; In order to evaluate the impact of position optimization on the total computation bits, the fitness function is defined as: Where, use Represents the total computation bits; is the location set of MAs pairs that violate C8 and C9 on AP and MEC servers respectively; express The cardinality of is a large penalty parameter to ensure constraints on C8 and C9, otherwise will be less than zero; By evaluating the particles The fitness of and Update the individual and global best positions of the particles until convergence.

9. The method for optimizing network performance of mobile edge cloud computing enhanced by MA according to claim 8, characterized in that: GA-PSO algorithm includes crossover and mutation operations. The crossover probability p in the operation c and mutation probability p m Greater than a random number , the crossover and mutation operations are re-executed as follows: in and Representing the Random numbers in crossover and mutation, and represent the jth and lth crossover particles randomly selected at fixed crossover positions, respectively. Indicates the mutation selected at the i-th MA position particle; Check whether the updated solution satisfies constraints C6 and C7; if not, perform crossover and mutation operations again.

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