超密集網(wǎng)絡(luò)中基于移動(dòng)邊緣計(jì)算的任務(wù)卸載和資源優(yōu)化
doi: 10.11999/JEIT180592
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重慶郵電大學(xué)通信與信息工程學(xué)院 ??重慶 ??400065
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2.
美國(guó)德克薩斯州拉瑪爾大學(xué)電子工程系 ??美國(guó) ??77710
Computing Offloading and Resource Optimization in Ultra-dense Networks with Mobile Edge Computation
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1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Department of Electronic Engineering, Lamar University, TX 77710, USA
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摘要:
移動(dòng)邊緣計(jì)算(MEC)通過在無線網(wǎng)絡(luò)邊緣為用戶提供計(jì)算能力，來提高用戶的體驗(yàn)質(zhì)量。然而，MEC的計(jì)算卸載仍面臨著許多問題。該文針對(duì)超密集組網(wǎng)(UDN)的MEC場(chǎng)景下的計(jì)算卸載，考慮系統(tǒng)總能耗，提出卸載決策和資源分配的聯(lián)合優(yōu)化問題。首先采用坐標(biāo)下降法制定了卸載決定的優(yōu)化方案。同時(shí)，在滿足用戶時(shí)延約束下采用基于改進(jìn)的匈牙利算法和貪婪算法來進(jìn)行子信道分配。然后，將能耗最小化問題轉(zhuǎn)化為功率最小化問題，并將其轉(zhuǎn)化為一個(gè)凸優(yōu)化問題得到用戶最優(yōu)的發(fā)送功率。仿真結(jié)果表明，所提出的卸載方案可以在滿足用戶不同時(shí)延的要求下最小化系統(tǒng)能耗，有效地提升了系統(tǒng)性能。
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關(guān)鍵詞:
- 超密集組網(wǎng) /
- 移動(dòng)邊緣計(jì)算 /
- 計(jì)算卸載 /
- 資源分配
Abstract:Mobile Edge Computing (MEC) improves the quality of users experience by providing users with computing capabilities at the edge of the wireless network. However, computing offloading in MEC still faces some problems. In this paper, a joint optimization problem of offloading decision and resource allocation is proposed for the computation offloading problem in Ultra-Dense Networks (UDN) with MEC. To solve this problem, firstly, the coordinate descent method is used to formulate the optimization scheme for the offloading decision. Meanwhile, the improved Hungarian algorithm and greedy algorithm are used to allocate the channels to meet the user’s delay requirements. Finally, the problem of minimizing energy consumption is converted into a problem of minimizing power. Then it is converted into a convex optimization problem to get the user’s optimal transmission power. Simulation results show that the proposed scheme can minimize the energy consumption of the system while satisfying the users’ different delay requirements, and improve effectively the performance of the system.
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表 1 任務(wù)卸載和資源分配算法
輸入：用戶數(shù)$N$，${t_n} = {\rm{(}}{w_n},{d_n}{\rm{,}}T_n^{\ {\rm{max}}}{\rm{)}}$，${f^c}$，初始卸載決定${{{A}}^0}$。 初始化：$l \leftarrow 0$， Repeat $l \leftarrow l + 1$ for $n = 1{\rm{ : }}N$ 根據(jù)式(13)得到${{{A}}^{l - 1}}{\rm{(}}n{\rm{)}}$； 采用改進(jìn)的匈牙利算法和貪婪算法得到子信道分配矩陣${{{C}}_{{N_c} \times K}}$； 根據(jù)凸優(yōu)化問題P3采用內(nèi)點(diǎn)法求解得到每個(gè)子信道上最優(yōu)的發(fā)
送功率$p_n^k$；根據(jù)式(12)計(jì)算$Q_n^l$； end $q_l^* \leftarrow {\rm{ma}}{{\rm{x}}_{n = 1, \cdots ,N}}Q_n^l$和$n_l^* \leftarrow {\rm{arg ma}}{{\rm{x}}_{n = 1, \cdots ,N}}Q_n^l$； 更新${{{A}}^l} \leftarrow {{{A}}^{l - 1}}\left( {n_l^*} \right)$； Until $q_l^* \le 0$； 輸出：卸載決定矩陣${{{A}}^{\rm{*}}}$，信道分配矩陣${{C}}_{{N_c} \times K}^{\rm{*}}$，功率分配矩陣${{{P}}^{\rm{*}}}$。 
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表 2 仿真參數(shù)
參數(shù) 取值 子信道帶寬$B$ 0.2 MHz 子信道個(gè)數(shù) 20 用戶最大發(fā)送功率${P_{\max }}$ 23 dBm 空閑時(shí)電路功率消耗${P^i}$ 10 mW 背景噪聲功率${\omega _0}$ –100 dBm 用戶的計(jì)算能力$f_n^l$ 0.1～1 GHz/周期 計(jì)算任務(wù)的大小${d_n}$ 400～1200 kB 需要的CPU周期${w_n}$ 0.2～1 GHz 用戶容忍最大時(shí)延$T_n^{\ \max }$ 1～4 s MEC的計(jì)算能力${f^c}$ 4 GHz/周期
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