無人機輔助的非正交多址反向散射通信系統(tǒng)max-min速率優(yōu)化算法
doi: 10.11999/JEIT221210
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重慶郵電大學通信與信息工程學院 重慶 400065
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成都理工大學計算機與網(wǎng)絡(luò)安全學院 成都 610059
基金項目: 國家自然科學基金(61701064, 62271094)，四川省區(qū)域創(chuàng)新合作項目(2022YFQ0017)，重慶市教委科學技術(shù)研究項目(KJZD-K202200501)，重慶市博士后研究項目(2021XM3082)，中國博士后科學基金(2022MD723725)
Max-min Rate Optimization Algorithm for Non-Orthogonal Multiple Access Backscatter Communication System Assisted by Unmanned Aerial Vehicles
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School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
Funds: The National Natural Science Foundation of China (61701064,62271094), The Sichuan Regional Innovation Cooperation Project (2022YFQ0017), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200501), Chongqing Postdoctoral Research Project (2021XM3082), China Postdoctoral Science Foundation (2022MD723725)
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摘要: 無人機(UAV)、非正交多址(NOMA)和反向散射通信(BC)相結(jié)合，可以滿足熱點地區(qū)高容量需求，提高通信質(zhì)量。該文提出一種無人機輔助的NOMA反向散射通信系統(tǒng)最小速率最大化資源分配算法?？紤]無人機發(fā)射功率、能量收集、反射系數(shù)、傳輸速率以及連續(xù)干擾消除(SIC)解碼順序約束，建立基于系統(tǒng)最小速率最大化的資源分配模型。首先利用塊坐標下降將原問題分解為無人機發(fā)射功率優(yōu)化、反射系數(shù)優(yōu)化和無人機位置與SIC解碼順序聯(lián)合優(yōu)化3個子問題，然后使用反證法給出無人機最優(yōu)發(fā)射功率，再用變量替換法和連續(xù)凸逼近將剩余子問題進一步轉(zhuǎn)化為凸優(yōu)化問題進行求解。仿真結(jié)果表明，所提算法在系統(tǒng)和速率與用戶公平性之間具有較好折中。Abstract: The combination of Unmanned Aerial Vehicle (UAV), Non-Orthogonal Multiple Access (NOMA), and Backscatter Communication (BC) can meet the high capacity demand and improve the communication quality in hotspots. A max-min rate optimization algorithm is proposed for UAV-assisted NOMA-based backscatter communication systems. Specifically, a resource allocation model is developed to maximize systems’ minimum rate under the UAV transmit power, energy harvesting, reflection coefficient, transmission rate, and Successive Interference Cancellation (SIC) decoding order constraints. The original problem is divided into three subproblems: UAV transmit power optimization, reflection coefficient optimization, and joint optimization of UAV position and SIC decoding order optimization, which are handled by block coordinated decent method. Then, the UAV’s optimal transmit power optimization subproblem is solved by contradiction. Furthermore, the remaining subproblems are solved by convex optimization with variable substitution and successive convex approximation methods. Finally, the simulation results show that the proposed algorithm has obtained a good tradeoff between the systems’ sum rate and users’ fairness.
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算法1 最小速率最大化資源分配算法 初始化：max-min速率$R_{\max - \min }^0$，內(nèi)層迭代次數(shù)$ l = 0 $，外層迭
代次數(shù)$ t = 0 $，懲罰參數(shù)$\mu = {\mu _0}$，步長$\gamma = {\gamma _0}$；無人機最大發(fā)射
功率$ {P_{\max }} $，$ {{\mathbf{q}}^0},{{\mathbf{A}}^0},{{\mathbf{B}}^0},{{\mathbf{C}}^0},{{\mathbf{U}}^0},{{\mathbf{G}}^0} $；max-min速率收斂精度
$ {\varsigma _1} $，懲罰收斂精度$ {\varsigma _2} $，外層最大迭代次數(shù)為$ {T_{\max }} $；(1) repeat (2) repeat (3) 根據(jù)給定的$ {{\mathbf{q}}^l} $和$ {{\mathbf{A}}^l} $利用凸優(yōu)化內(nèi)點法求解問題式(9)得
到反射系數(shù)$ {{\mathbf{R}}^*} $；(4) 根據(jù)$ {{\mathbf{R}}^*},{{\mathbf{B}}^l},{{\mathbf{C}}^l},{{\mathbf{U}}^l},{{\mathbf{G}}^l} $利用凸優(yōu)化內(nèi)點法求解問題
式(22)得到無人機位置$ {{\mathbf{q}}^*} $和SIC解碼順序$ {{\mathbf{A}}^*} $；(5) 更新$ l = l + 1 $； (6) until $ \left| {R_{\max - \min }^{l + 1} - R_{\max - \min }^l} \right| < {\varsigma _1} $； (7) if $ \max \left\{ {{\varphi _{nm}}} \right\} \gt {\varsigma _2} $ (8) 更新$ \mu = \gamma \mu $； (9) else (10) 更新$ t = t + 1 $； (11) end if (12) until $t \ge {T_{\max } }$。 
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