基于能效的NOMA蜂窩車聯(lián)網(wǎng)動態(tài)資源分配算法
doi: 10.11999/JEIT190006
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1.
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.
重慶郵電大學(xué)移動通信技術(shù)重點實驗室 重慶 400065
Energy Efficiency Based Dynamic Resource Allocation Algorithm for Cellular Vehicular Based on Non-Orthogonal Multiple Access
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School of Communication and Information Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
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Key Laboratory of Mobile Communication Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China
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摘要:
在支持車與車直接通信(V2V)的非正交多址接入(NOMA)蜂窩網(wǎng)絡(luò)場景下，針對V2V用戶與蜂窩用戶的干擾以及NOMA準(zhǔn)則下的功率分配問題，該文提出一種基于能效的動態(tài)資源分配算法。該算法首先為了保證V2V用戶的時延及可靠性同時滿足蜂窩用戶的速率需求，聯(lián)合考慮子信道調(diào)度、功率分配和擁塞控制，建立了最大化系統(tǒng)能效的隨機(jī)優(yōu)化模型。其次，利用李雅普諾夫隨機(jī)優(yōu)化方法，通過控制可接入數(shù)據(jù)量保證隊列穩(wěn)定性以避免網(wǎng)絡(luò)擁塞，并根據(jù)實時網(wǎng)絡(luò)負(fù)載狀態(tài)動態(tài)地進(jìn)行資源調(diào)度，設(shè)計一種次優(yōu)化子信道匹配算法獲得用戶調(diào)度方案，進(jìn)一步，利用凸優(yōu)化理論和拉格朗日對偶分解方法得到功率分配策略。最后，仿真結(jié)果表明，該文算法可以滿足不同用戶的服務(wù)質(zhì)量(QoS)需求，并在保證網(wǎng)絡(luò)穩(wěn)定性前提下提高系統(tǒng)能效。
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關(guān)鍵詞:
- 非正交多址接入 /
- 蜂窩車聯(lián)網(wǎng) /
- 資源分配 /
- 李雅普諾夫優(yōu)化
Abstract:In the Non-Orthogonal Multiple Access (NOMA) based cellular network with Vehicle-to-Vehicle (V2V) communication, to mitigate the co-channel interference between V2V users and cellular users as well as the power allocation problem based on the NOMA principle, an energy efficiency dynamic resource allocation algorithm is proposed. Firstly, a stochastic optimization model is established to maximize the energy efficiency by considering subchannel scheduling, power allocation and congestion control, in order to guarantee the delay and reliability of V2V users while satisfying the rate of cellular users. Then, leveraging on the Lyapunov stochastic optimization method, the traffic queues can be stabilized by admitting as much traffic data as possible to avoid network congestion, and the radio resource can be allocated dynamically according to the real-time network traffic and thus a suboptimal subchannel matching algorithm is designed to obtain the user scheduling scheme. Furthermore, the power allocation policy is obtained by utilizing successive convex optimization theory and Lagrange dual decomposition method. Finally, the simulation results show that the proposed algorithm can improve the system energy efficiency and ensure the Quality of Service (QoS) requirements of different users and network stability.
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表 1 基于能效的動態(tài)資源分配算法
(1) 初始化控制參數(shù)$V$, NOMA用戶隊列${Q_i}(0) = 0$、虛擬隊列${Q_k}(0) = 0$、${H_i}(0) = 0$, ${\varGamma _i}(t)$, $R_k^{\min }$, $\forall k \in K,i \in I$； (2) 設(shè)置時隙長度${T_{\max }}$； (3) For $t = 0,1, ··· ,{T_{\max }} - 1,$ do； (4) 觀察該時隙每個NOMA用戶的隊列狀態(tài)${Q_i}(t)$以及虛擬隊列${Q_k}(t)$和${H_i}(t)$； (5) 計算輔助變量${\gamma _i}(t)$，然后根據(jù)式(18)和式(19)得到擁塞控制優(yōu)化解$\varGamma _i^*$； (6) 執(zhí)行表2求解優(yōu)化問題式(16)得到子信道調(diào)度策略$x_i^*,\alpha _k^*$； (7) 執(zhí)行表3求解問題式(21)得到優(yōu)化的功率分配方案$\{ p_1^{\rm{*}},p_2^{\rm{*}},···,p_{M{\rm{ - }}1}^{\rm{*}}\} $； (8) 根據(jù)下面公式分別更新下一時隙NOMA用戶的隊列狀態(tài)${Q_i}(t + 1)$，虛擬隊列狀態(tài)${Q_k}(t + 1)$和${H_i}(t + 1)$;
${Q_i}(t + 1) = \max \{ {Q_i}(t) + {\varGamma _i}(t) - {r_i}(t),0\} ,\;\;\forall i$, ${Q_k}(t + 1) = \max \{ {Q_k}(t) + R_k^{\min } - {r_k}(t),0\} ,\forall k$;
${H_i}(t + 1) = \max \{ {H_i}(t) - {\varGamma _i}(t) + {\gamma _i}(t),0\} ,\forall i$；(9) $t = t + 1$； (10) End； (11) 輸出優(yōu)化擁塞控制策略、頻譜和功率分配方案$\varGamma _i^*$, $x_i^*,\alpha _k^*$, $p_i^*,p_k^*$。 
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表 2 聯(lián)合次優(yōu)化子信道匹配算法
(1) 初始化${p_i},{p_k}$, ${Q_i}(0) = 0$, ${Q_k}(0) = 0$, ${H_i}(0) = 0$，初始化未分配子信道的NOMA和V2V用戶集$S_{{\rm{un}}}^C$, $S_{{\rm{un}}}^V$，復(fù)用同一信道的用戶集
${{U}} = \{ U_1,U_2,···,U_N\} $, ${\psi _n} = \varnothing $，用戶調(diào)度策略${{x}} = \varnothing ,{{\alpha}} = \varnothing $，分別構(gòu)造NOMA用戶和V2V用戶的信道增益矩陣，${{ H}_i} \triangleq {[|{h_{i,n}}|]_{I \times N}}$,
${{ H}_k} \triangleq {[|{h_{k,n}}|]_{K \times N}}$；(2) while ${S_{{\rm{un}}}}^C \ne \varnothing $&${S_{\rm{un}}}^V \ne \varnothing$ do； (3) for $n = 1:N$； (4) 從${{ H}_i}$中找到最大信道增益，將子信道$n$調(diào)度給用戶$i$，更新${{x}}$，并將矩陣中的第$i$行元素置0； (5) 更新${U_n} = {U_n} \cup u_n^i$ & $S_{{\rm{un}}}^C = S_{{\rm{un}}}^C\backslash u_n^i$； (6) end for； (7) for $n = 1:N$； (8) while ${N_{{U_n}}} < M$ do； (9) 分別從信道矩陣${{ H}_i}$和${{ H}_k}$ 中找到最大信道增益$|{h_{i,n}}|$和$|{h_{k,n}}|$； (10) if ${\rm{|}}{h_{i,n}}| > {h_{k,n}}|$； (11) 將子信道$n$分配給用戶$i$，更新${U_n} = {U_n} \cup u_i^n$； (12) else； (13) 將子信道$n$分配給用戶$k$，更新${U_n} = {U_n} \cup u_k^n$； (14) end if； (15) end while； (16) if ${N_{{U_n}}} = M$； (17) 計算用戶集${U_n}$復(fù)用在子信道$n$上的$\varphi (t)$，并將結(jié)果保存于${\psi _n}$ (18) 求解式(16)得到用戶調(diào)度的解$x_i^n,\alpha _k^n$以及被調(diào)度用戶集$u_n^C,u_n^V$，更新未調(diào)度用戶集$S_{un}^C = S_{un}^C\backslash u_n^C$ & $S_{un}^V = S_{un}^V\backslash u_n^V$，并將
信道矩陣${{ H}_i}$中的第$i$行置0，或?qū)?{{ H}_k}$中的第$k$行元素及第$n$列元素置0；(19) end if； (20) end for； (21) end while； (22) 輸出用戶調(diào)度策略${{x}},{{\alpha}} $。
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表 3 基于連續(xù)凸逼近和拉格朗日對偶的迭代功率優(yōu)化算法
(1) 初始化最大迭代次數(shù)${T_1}$及最大允許誤差${\xi _1}$，初始化${[{\tilde p_i}(t),{\tilde p_k}(t)]^0}$，迭代次數(shù)索引$t$； (2) while $g \le {T_1}$ or ${\rm{||}}\tilde \varphi ({[{\tilde p_i}(t),{\tilde p_k}(t)]^g}) - \tilde \varphi ({[{\tilde p_i}(t),{\tilde p_k}(t)]^{g - 1}})|| \le {\xi _1}$ do； (3) 根據(jù)迭代得到的${[{\tilde p_i}(t),{\tilde p_k}(t)]^g}$和$\tilde r_k^n$, $\tilde r_i^n$計算$c_k^n$ $d_k^n$ $c_i^n$ $d_i^n$，得到更新后的${{{c}}^g},{{q7j3ldu95}^g}$； (4) 求解優(yōu)化問題式(20)，更新當(dāng)前最優(yōu)解${[{\tilde p_i}(t),{\tilde p_k}(t)]^{{\rm{g + 1}}}}$并令$g = g + 1$； (5) end while； (6) 輸出連續(xù)凸逼近迭代后的優(yōu)化解$\tilde P(t) = {\left[ {{{\tilde p}_i}(t),\tilde p{}_k(t)} \right]^g}$； (7) 初始化最大迭代次數(shù)${N_1}$和${N_2}$及收斂條件${\varDelta _1}$和${\varDelta _2}$，初始化迭代索引$m = 0,n = 0$，初始化拉格朗日乘子${\nu ^0},{\lambda ^0},{\mu ^0},{\eta ^0}$,
${[{\tilde p_i}{(t)_m},\tilde p{}_k{(t)_m}]^0} = {[{\tilde p_i}{(t)_n},{\tilde p_k}{(t)_n}]^0} = {[{\tilde p_i}(t),{\tilde p_k}(t)]^g}$；(8) 觀察時隙$t$每個NOMA用戶的隊列狀態(tài)${Q_i}(t)$和虛擬隊列狀態(tài)${Q_k}(t)$, ${H_i}(t)$； (9) while $m < {N_1}$ or ${\rm{||} }\tilde \varphi ({[{\tilde p_i}{(t)_m},{\tilde p_k}{(t)_m}]^{m + 1} }) - \tilde \varphi ({[{\tilde p_i}{(t)_m},{\tilde p_k}{(t)_m}]^m})|| \ge {\varDelta _1}$ do； (10) while $n < {N_2}$ or $||{J^{n + 1} }(t) - {J^n}(t)|| \ge {\varDelta _2}$ do； (11) 將${\nu ^m},{\lambda ^m},{\mu ^m},{\eta ^m}$和${[{\tilde p_i}{(t)_n},{\tilde p_k}{(t)_n}]^n}$分別代入表達(dá)式(21)求導(dǎo)； (12) 通過KKT條件和二分搜索法求得功率分配${[{\tilde p_i}{(t)_n},{\tilde p_k}{(t)_n}]^{n + 1}}$，更新拉格朗日乘子； (13) $n = n + 1$； (14) end while； (15) $m = m + 1$； (16) end while；
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