同時透射反射可重構(gòu)智能表面賦能移動邊緣計算任務(wù)卸載研究

李斌; 楊冬東

doi:10.11999/JEIT240733

同時透射反射可重構(gòu)智能表面賦能移動邊緣計算任務(wù)卸載研究

doi: 10.11999/JEIT240733 cstr: 32379.14.JEIT240733

李斌^,,
楊冬東

南京信息工程大學(xué)計算機(jī)學(xué)院南京 210044

基金項目: 國家自然科學(xué)基金(62101277)

詳細(xì)信息

作者簡介:
李斌：男，副教授，研究方向為邊緣計算、無人機(jī)通信

楊冬東：男，碩士生，研究方向為智能反射面、移動邊緣計算

通訊作者:
李斌　bin.li@nuist.edu.cn

中圖分類號: TN929.5
計量
- 文章訪問數(shù): 205
- HTML全文瀏覽量: 66
- PDF下載量: 33
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-08-26
- 修回日期: 2024-12-24
- 網(wǎng)絡(luò)出版日期: 2024-12-31
- 刊出日期: 2025-02-28

Task Offloading for Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface-assisted Mobile Edge Computing

LI Bin^,,
YANG Dongdong

School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62101277)

摘要

摘要: 為彌補(bǔ)可重構(gòu)智能表面(RIS)半空間覆蓋和“乘性衰落”等不足，該文提出一種有源同時透射和反射可重構(gòu)智能表面(aSTAR-RIS)技術(shù)用于提升移動邊緣計算(MEC)卸載性能增益。首先，考慮MEC服務(wù)器計算資源、aSTAR-RIS能耗以及相移耦合約束，聯(lián)合設(shè)計任務(wù)卸載比例、計算資源配置、多用戶檢測矩陣(MUD)、aSTAR-RIS相移以及用戶上傳功率，建立一個多變量耦合的加權(quán)總時延最小化問題。然后，借助塊坐標(biāo)下降法(BCD)將原問題分解為兩個子問題，使用拉格朗日乘子法和罰項對偶分解法(PDD)交替優(yōu)化子問題。仿真結(jié)果表明，相較于無源STAR-RIS方案，所提aSTAR-RIS輔助MEC方案加權(quán)總時延降低了12.66%。
- 有源同時透射和反射可重構(gòu)智能表面 /
- 移動邊緣計算 /
- 計算卸載 /
- 資源分配
Abstract: Objective Mobile Edge Computing (MEC) is a distributed computing paradigm that brings computational resources closer to users, alleviating issues such as high latency and interference found in cloud computing. To enhance the offloading performance of MEC systems and promote green communication, Reconfigurable Intelligent Surface (RIS), a low-cost and easily deployable technology, offers a promising solution. RIS consists of numerous low-cost reflecting elements that can adjust phase shifts to alter the amplitude and phase of incident signals, thereby reconstructing the electromagnetic environment. This transforms traditional passive adaptation into active control. However, the signal reflected by RIS must pass through a two-stage cascaded channel, which is susceptible to multiplicative fading, leading to limited performance gains when direct links are unobstructed. To mitigate this, the concept of active RIS has been proposed, integrating signal amplification circuits into RIS elements, which not only reflect but also amplify signals, effectively overcoming this issue. Additionally, RIS can only transmit or reflect incident signals, limiting coverage to half-space: either the user and base station must be on the same side (reflecting RIS) or on opposite sides (transmitting RIS). This constraint limits deployment flexibility. To address this, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (STAR-RIS) is proposed, combining both transmission and reflection functions, where part of the signal is reflected to the same side, and the rest is transmitted to the opposite side. To address the challenges in practical RIS-assisted MEC systems, the active Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (aSTAR-RIS) is integrated into the MEC system to overcome geographic deployment constraints and effectively mitigate the effects of multiplicative fading. Methods Considering the computational resources available at the MEC server, the energy consumption of the aSTAR-RIS, and the phase shift coupling constraints, the task offloading ratio, computational resource allocation, Multi-User Detection (MUD) matrix, aSTAR-RIS phase shift, and transmission power are jointly optimized, resulting in a multivariable coupled weighted total latency minimization problem. To solve this problem, an iterative algorithm combining Block Coordinate Descent (BCD) and Penalty Dual Decomposition (PDD) algorithms is proposed. In each iteration, the original problem is decomposed into two subproblems: one for optimizing computational resource allocation and task offloading ratio, and the other for designing the aSTAR-RIS phase shift, MUD matrix, and transmission power. For the first subproblem, the Lagrange multiplier method is used to incorporate constraints into the objective function and enable efficient optimization. The optimal Lagrange multiplier and resource allocation are found using the bisection method. The second subproblem involves handling the fractional objective function using the weighted minimum mean square error algorithm. From the first-order conditions, the optimal MUD matrix is derived. For the aSTAR-RIS phase shift optimization, a non-convex phase shift coupling constraint is decoupled using the PDD algorithm. Results and Discussions And discussions as shown in (Fig. 2), with increasing iterations, the weighted total latency steadily decreases and stabilizes, validating the effectiveness of the proposed algorithm. A comparison with three benchmark schemes reveals that, although the proposed scheme converges more slowly, it achieves the lowest weighted total latency upon convergence, with a 12.66% reduction compared to the passive STAR-RIS scheme. This improvement is mainly due to the power amplification effect, which reduces the impact of multiplicative fading, thereby enhancing the received signal at the base station and reducing latency. As illustrated in (Fig. 3), the weighted total latency decreases as the number of aSTAR-RIS elements increases, allowing for more reflection paths and higher channel gain. For fewer elements, aSTAR-RIS shows a significant performance gain over STAR-RIS, but as the number of elements grows, the performance of both aSTAR-RIS and passive STAR-RIS converges, primarily due to thermal noise and power constraints. Moreover, compared to the benchmark scheme that optimizes for maximum rate, the proposed scheme shows significant advantages in reducing latency. As shown in (Fig. 4), when the aSTAR-RIS power overhead increases, the weighted total latency decreases, further showing the potential of aSTAR-RIS in improving communication performance via active amplification. Conclusions This paper investigates a task offloading scheme for an aSTAR-RIS-assisted MEC system, which optimizes the task offloading ratio, computational resource allocation, MUD matrix, aSTAR-RIS phase shift, and transmission power to minimize total user delay. The optimization problem is solved using an iterative approach, decomposing the problem into two subproblems and applying the Lagrange multiplier method, PDD, and BCD algorithms. Simulation results demonstrate that the proposed algorithm significantly outperforms benchmark schemes in terms of weighted total latency. The findings validate the effectiveness of aSTAR-RIS in MEC systems, highlighting its advantages over passive STAR-RIS in task offloading, resource optimization, and communication performance.

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圖 1 aSTAR-RIS輔助MEC系統(tǒng)模型

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圖 2 加權(quán)總時延迭代收斂圖

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圖 3 各方案時延與反射單元數(shù)量關(guān)系

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圖 4 不同功率約束下加權(quán)總時延與反射單元數(shù)量關(guān)系

下載: 全尺寸圖片幻燈片

1 求解最優(yōu)任務(wù)卸載比和MEC計算資源分配算法

初始化優(yōu)化變量，${n_1} = 0$，收斂閾值$ {\varepsilon }_{1}={10}^{-4} $
步驟1　利用式(3)計算${R_{\tau ,i}}$，根據(jù)式(9)對${{\boldsymbol{\alpha}} ^{({n_1})}}$進(jìn)行更新；
步驟2　利用二分法求得${\mu ^{({n_1})}}$，根據(jù)式(12)計算${{\boldsymbol{f}}^{\text{e}}}^{({n_1})}$；
步驟3　計算$ {\varepsilon }^{({n}_{1})} $，若$ {\varepsilon }^{({n}_{1})}\ge {\varepsilon }_{1} $且${n_1} \le n_1^{\max }$，令${n_1} = {n_1} + 1$，　回到步驟1；
步驟4　輸出$({{\boldsymbol{\alpha}} ^},{{\boldsymbol{f}}^{\text{e}}}^)$

下載: 導(dǎo)出CSV

2 MUD矩陣、aSTAR-RIS相移和用戶上傳功率交替優(yōu)化算法

初始化優(yōu)化變量，${n_2} = 0$，收斂閾值$ \zeta ={\varepsilon }_{2}={\varepsilon }_{3}={10}^{-4} $, 　$\rho = 10$
步驟1　根據(jù)式(20)更新$ {{\boldsymbol{W}}^{({n_2})}} $；
步驟2　解決問題式P2.5更新$\{ {{\boldsymbol{\theta}} _{\text{t}}}^{({n_2})},{{\boldsymbol{\theta}} _{\text{r}}}^{({n_2})}\} $；
步驟3　更新$\left\{ {\tilde {\boldsymbol{\psi}} _{\text{t}}^{({n_2})},\tilde{\boldsymbol{ \psi}} _{\text{r}}^{({n_2})}} \right\}$和$\left\{ {{{\tilde {\boldsymbol{\beta}} }_{\text{t}}}^{({n_2})},{{\tilde {\boldsymbol{\beta }}}_{\text{r}}}^{({n_2})}} \right\}$；
步驟4　解決問題式P2.8更新$ {{\boldsymbol{p}}^{({n_2})}} $；
步驟5　更新輔助變量${{\boldsymbol{\varphi}} ^{({n_2})}}$；
步驟6　計算$ {\varepsilon }^{\left({n}_{2}\right)} $，若$ {\varepsilon }^{\left({n}_{2}\right)} \gt {\varepsilon }_{2} $，且${n_2} \le n_2^{{\text{max}}}$，令　${n_2} = {n_2} + 1$，回到步驟1；
步驟7　更新$ {{\boldsymbol{\lambda}} ^{({n_2})}} $, $ {{\boldsymbol{\xi}} ^{({n_2})}} $，若$ \left\|{\lambda }_{k}^{({n}_{2})}{R}_{k}^{({n}_{2})}-1\right\| \gt {\varepsilon }_{2} $或　$ \left\|{\xi }_{k}^{({n}_{2})}{R}_{k}^{({n}_{2})}-{\varpi }_{k}{\alpha }_{k}{L}_{k}\right\| \gt {\varepsilon }_{2} $，令${n_2} = {n_2} + 1$，回到步驟1；
步驟8　若$ \upsilon \le \zeta $, $ {{\boldsymbol{\eta}} _{\,\tau }} = {{\boldsymbol{\eta}} _{\,\tau }} + \dfrac{1}{\rho }({\tilde {\boldsymbol{\theta}} _\tau } - {{\boldsymbol{\theta}} _\tau }) $，否則設(shè)置$ \rho = c\rho $；
步驟9　$ \zeta = 0.9\upsilon $，若$ \upsilon \gt {\varepsilon }_{3} $，令${n_2} = 0$，回到步驟1；
輸出 $ \left( {{{\boldsymbol{W}}^},{{\boldsymbol{\theta}} _{\text{t}}}^,{{\boldsymbol{\theta}} _{\text{r}}}^,{{\boldsymbol{p}}^}} \right) $

下載: 導(dǎo)出CSV

3 整體算法

初始化優(yōu)化變量，${n_3} = 0$，收斂閾值$ \varepsilon ={10}^{-4} $
步驟1　根據(jù)算法1，給定$ {{\boldsymbol{W}}^{({n_3} - 1)}} $, ${{\boldsymbol{\theta}} _{\text{t}}}^{({n_3} - 1)}$, ${{\boldsymbol{\theta}} _{\text{r}}}^{({n_3} - 1)}$, 　$ {{\boldsymbol{p}}^{({n_3} - 1)}} $優(yōu)化${{\boldsymbol{\alpha}} ^{({n_3})}}$, ${{\boldsymbol{f}}^{\text{e}}}^{({n_3})}$；
步驟2 根據(jù)算法2，給定${{\boldsymbol{\alpha}} ^{({n_3})}}$, ${{\boldsymbol{f}}^{\text{e}}}^{({n_3})}$優(yōu)化$ {{\boldsymbol{W}}^{({n_3})}} $, ${{\boldsymbol{\theta }}_{\text{t}}}^{({n_3})}$, 　${{\boldsymbol{\theta}} _{\text{r}}}^{({n_3})}$, ${{\boldsymbol{p}}^{({n_3})}}$；
步驟3 計算$ {\varepsilon }^{\left({n}_{3}\right)} $，若$ {\varepsilon }^{\left({n}_{3}\right)} \gt \varepsilon $且$ {n_3} \le n_3^{\max } $，令${n_3} = {n_3} + 1$，　回到步驟1；
輸出：$\left( {{{\boldsymbol{\alpha}} ^},{{\boldsymbol{f}}^{\text{e}}}^,{{\boldsymbol{W}}^},{{\boldsymbol{\theta}} _{\text{t}}}^,{{\boldsymbol{\theta}} _{\text{r}}}^,{{\boldsymbol{p}}^}} \right)$

下載: 導(dǎo)出CSV

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