Robust Energy Efficiency Optimization Algorithm for NOMA-based D2D Communication With Simultaneous Wireless Information and Power Transfer
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摘要: 针对频谱短缺、基站负荷过高、通信系统功耗较大等问题，考虑不完美的信道状态信息，该文提出一种基于非正交多址接入的无线携能(SWIPT)D2D网络鲁棒能效(EE)最大化资源分配算法(SREA)。考虑用户的服务质量约束以及最大发射功率约束，基于随机信道不确定性建立鲁棒能效最大化资源分配模型。利用Dinkelbach和变量替换方法，将原NP-hard问题转换为确定性的凸优化问题，通过拉格朗日对偶理论求得解析解。仿真结果表明，所提算法在保证蜂窝用户通信质量的同时，能够有效提高D2D用户的能效性和鲁棒性能。Abstract: In order to resolve the problems of spectrum shortage, large power consumption, and excessive load at base stations, a Simultaneous Wireless Information and Power Transfer (SWIPT)-based Robust Energy Efficiency (EE) Algorithm (SREA) with imperfect channel state information is proposed to maximize the total EE in Non-Orthogonal Multiple Access (NOMA) assisted Device-to-Device (D2D) networks. Considering the users' Quality of Service (QoS) constraints and maximum transmit power constraints, a robust EE maximization-based resource allocation model is established based on random channel uncertainties. Moreover, the original NP-hard problem is transformed into a deterministic convex optimization problem by using Dinkelbach’s method and the variable substitution method. And the analytical solutions are obtained through Lagrange dual theory. Simulation results demonstrated that the proposed algorithm can effectively improve the system EE and the robustness of D2D users while ensuring the communication quality of cellular users.
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表 1 系统参数
参数 含义 $N$ D2D用户数量 $\theta $ 能量收集效率系数 $P_{}^{\max }$ 基站的最大发射功率 ${M_k}$ 资源块$k$上的蜂窝用户数量 $P_n^{\max }$ D2D用户$n$的最大发射功率 $h_n^k$ D2D用户$n$在资源块$k$上的信道增益 $q_n^k$ D2D用户$n$在资源块$k$上的发射功率 $h_i^{k,C}$ 基站到蜂窝用户$i$在资源块$k$上的信道增益 $g_n^{k,B}$ 基站到D2D用户$n$在资源块$k$上的信道增益 $K$ 资源块数量 $\tau $ 中断概率门限 $M$ 蜂窝用户总数量 ${\sigma ^2}$ 接收机的背景噪声功率 $P_c^D$ D2D用户的电路功率消耗 $R_i^{k,\min }$ 蜂窝用户$i$的最小数据速率 $p_i^k$ 基站通过资源块$k$分配给蜂窝用户$i$的发射功率 $g_{n,i}^k$ D2D用户$n$到蜂窝用户$i$在资源块$k$上的信道增益 $g_{d,n}^k$ D2D用户$d$到D2D用户$n$在资源块$k$上的信道增益 
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表 2 鲁棒资源分配算法
初始化系统参数$N$, ${M_k}$, $M$, $K$, $P_c^D$, ${\sigma ^2}$, $\theta $, $\tau $, $R_n^{\min }$, $R_i^{k,\min }$, $P_n^{\max }$, $P_{}^{\max }$, $d$；给定$p_n^k$, $x_n^k$, $\eta $, $\rho _{n,1}^k$, $\rho _{n,2}^k$；外层迭代次数$t = 0$；定义算法
收敛精度$\varpi $和$\varsigma $，外层最大迭代次数$T$；(1) While $\left| {\dfrac{ {\tilde R(t)} }{ {\tilde P(t)} } - \eta (t - 1)} \right| > \varpi$和$t < T$, do (2) 初始化迭代步长和拉格朗日乘子，定义内层最大迭代次数$L$，初始化$l = 0$； (3) While $\left| {q_n^k(l) - q_n^k(l - 1)} \right| > \varsigma $和$l < L$，do (4) For $m = 1:1:M$ (5) For $n = 1:1:N$ (6) For $k = 1:1:K$ (7) 根据式(26)计算$T_n^k$,更新$x_n^k$； (8) 根据式(29)计算$\alpha _n^k$； (9) 计算$\rho _{n,1}^k$,$\rho _{n,2}^k$, $q_n^k$的最优值； (10) 根据式(30)—式(34)更新拉格朗日乘子； (11) End For (12) End For (13) End For (14) 更新 $l = l + 1$； (15) End While
(16) 更新$\eta (t) = \dfrac{ {\tilde R(t)} }{ {\tilde P(t)} }$, $t = t + 1$；(17) End While
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