基于能量效率的双层非正交多址系统资源优化算法

该文针对双层非正交多址系统(NOMA)中基于能量效率的资源优化问题,该文提出基于双边匹配的子信道匹配方法和基于斯坦科尔伯格(Stackelberg)博弈的功率分配算法。首先将资源优化问题分解成子信道匹配与功率分配两个子问题,在功率分配问题中,将宏基站与小型基站层视作斯坦科尔伯格博弈中的领导者与追随者。然后将非凸优化问题转换成易于求解的方式,分别得到宏基站和小型基站层的功率分配。最后通过斯坦科尔伯格博弈,得到系统的全局功率分配方案。仿真结果表明,该资源优化算法能有效地提升双层NOMA系统的能量效率。     

基于能效的NOMA蜂窝车联网动态资源分配算法

在支持车与车直接通信(V2V)的非正交多址接入(NOMA)蜂窝网络场景下,针对V2V用户与蜂窝用户的干扰以及NOMA准则下的功率分配问题,该文提出一种基于能效的动态资源分配算法。该算法首先为了保证V2V用户的时延及可靠性同时满足蜂窝用户的速率需求,联合考虑子信道调度、功率分配和拥塞控制,建立了最大化系统能效的随机优化模型。其次,利用李雅普诺夫随机优化方法,通过控制可接入数据量保证队列稳定性以避免网络拥塞,并根据实时网络负载状态动态地进行资源调度,设计一种次优化子信道匹配算法获得用户调度方案,进一步,利用凸优化理论和拉格朗日对偶分解方法得到功率分配策略。最后,仿真结果表明,该文算法可以满足不同用户的服务质量(QoS)需求,并在保证网络稳定性前提下提高系统能效。     

可重构智能表面辅助的非正交多址接入网络鲁棒能量效率资源分配算法

为提高非正交多址接入(NOMA)网络的鲁棒性和系统能效(EE),考虑了不完美信道状态信息,该文提出一种可重构智能表面(RIS)辅助的NOMA网络鲁棒能效最大资源分配算法。考虑用户信干噪比(SINR)中断概率约束、基站的最大发射功率约束以及连续相移约束,建立了一个非线性的能效最大化资源分配模型。用Dinkelbach方法将分式形式的目标函数转换为线性的参数相减的形式,利用S-procedure方法将含有信道不确定性的SINR中断概率约束转换成确定性形式,利用交替优化算法将多变量耦合的非凸优化问题分解成多个凸优化子问题,最后用CVX对分解出的子问题进行求解。仿真结果表明,在EE方面,所提算法比无可重构智能表面(RIS)算法提高了7.4%。在SINR中断概率方面,所提算法比非鲁棒算法降低了85.5%。     

NOMA系统中利用共轭梯度法的功率分配方案

通过功率分配,5G通信的关键技术——非正交多址（NOMA）实现发射功率域的多用户复用,有效提高了频谱效率。不同的功率分配方案直接影响系统的吞吐量,针对NOMA下行链路现有功率分配算法存在的局部最优问题,提出了一种利用共轭梯度法的最优功率分配方案,采用共轭梯度法求解用户的加权和速率最大化的优化问题。现有理论证明,该方法可以收敛到全局最优解。仿真结果表明,该方法性能优于已有的固定功率分配（FPA）算法和分数阶发射功率分配（FTPA）算法,且此非正交多址（NOMA）系统性能明显优于正交多址（OMA）系统。      

基于能效最优准则的双跳中继网络功率分配算法

针对单天线多跳系统中的资源分配策略进行了研究,重点研究了基于能效最优的功率分配算法。所提算法以系统能效最大化为设计目标,以满足指定的系统服务质量(QoS, quality of service)为约束条件,建立了以源节点和中继节点发射功率为设计变量的数学优化模型。通过大信噪比区间近似等效,将原始的非凸优化问题转化为凸优化问题。再利用拉格朗日对偶函数凸优化算法,并借助于Lambert 函数,最终得到一种功率分配方案的闭合形式解,避免了采用交替迭代方法来求解最优化问题。相比传统以系统频谱效率最大化为目标的算法,所提算法能更好地提升系统整体能效,同时降低了功率分配算法的复杂度。     

多载波多用户下行NOMA系统的资源分配算法

非正交多址接入技术作为5G的候选技术之一受到了广泛关注。研究了以系统吞吐量优化为目标的多载波多用户NOMA系统下行链路的资源分配问题。在该问题的求解中,为了提高系统的吞吐量,子载波间采用线性注水算法,叠加用户间采用分数阶功率分配算法。同时,考虑了远近用户数目不等场景下能够调度更多的用户,在NOMA传输方案设计中引入时分的概念,将整个时间段t分为两个时隙,在不同时隙内实现不同远近用户分组的动态配对方案,从而在保证用户公平性的基础上,充分利用子信道资源,实现系统吞吐量的优化。仿真结果表明,对比于传统NOMA和OFDMA,提出的方法可以在相同的发射功率情况下传输更多的比特数。     

人工噪声辅助的物理层安全信号峰均功率比减低算法

人工噪声辅助的物理层安全通信系统采用人工噪声破坏窃听信道的方式提升系统安全信道容量是近年来物理层安全通信领域研究的经典系统模型之一。该文针对这一模型中发射信号存在高峰均功率比问题,利用噪声子空间提供的冗余度提出一种基于噪声子空间功率分配的峰均功率比降低算法。该算法通过分式规划、DC规划以及二次非凸等式约束松弛将非凸的峰均功率比优化问题转化为一系列的凸问题迭代求解。仿真结果表明在系统放大器存在一定线性范围的约束下,该文提出的算法能够有效降低人工噪声辅助的物理层安全通信系统发射信号的峰均功率比问题,达到提高系统中合法用户的通信性能的目的。     

基于NOMA的移动边缘计算系统公平能效调度算法

将移动边缘计算技术(MEC)与非正交多址技术(NOMA)结合,同时考虑公平性,该文研究了采用NOMA上行部分卸载的MEC系统公平能效问题。首先将基于公平函数的用户速率与功耗比值定义为公平能效函数,随后提出了两种公平能效调度准则下的能效调度算法,即最大化最小速率准则下DK-SCA算法及最大化系统能效准则下DK-SCALE算法,通过算法实现分别得到两种公平能效调度准则下用户最佳本地CPU处理频率及最佳传输功率。最后通过仿真表明,与基准方案相比,所提基于NOMA的部分卸载方案能够有效地将本地计算和基于NOMA的边缘卸载结合,达到最佳的公平能效性能。     

无人机辅助MEC系统中基于最优SIC顺序的能耗优化方案

在基于上行非正交多址接入（NOMA）的无人机（UAV）辅助移动边缘计算（MEC）系统中,NOMA的连续干扰消除（SIC）顺序已成为限制上行任务卸载链路传输性能的瓶颈,为降低系统能耗,对SIC顺序进行了讨论,提出了联合信道增益与任务时延约束的最优SIC顺序。在满足设备给定任务时延、设备最大发射功率约束以及UAV轨迹的约束下,基于最优SIC顺序提出了最小化系统能耗的问题。由于该问题是个复杂的非凸问题,采取交替优化的方法求解该优化问题,以实现功率分配和UAV轨迹的优化;利用匹配理论,提出了低复杂度算法来得到不同时隙的最优设备分组。仿真结果表明,与其他SIC顺序相比,最优SIC顺序能够在相同的任务时延约束下实现更小的系统能耗;所提的低复杂度设备分组算法能够得到最优设备分组。     

基于时延线阵列的毫米波NOMA系统混合预编码设计和功率分配

针对传统毫米波非正交多址接入（NOMA）系统中高精度移相器复杂度高、功耗大的问题,将可进行连续相位调制的低复杂度、低功耗的时延线阵列引入毫米波NOMA系统,研究了该系统能效和谱效。首先,提出一种改进K-means算法对用户进行分组,每组用户采用NOMA技术传输信息;然后,设计了一种基于开关控制时延线阵列的低复杂度混合模拟数字预编码;最后,构建了一个在用户服务质量和系统总功率约束下优化发送功率的能效最大化问题,并提出一种基于Dinkelbach算法和交替优化的两层迭代算法。仿真结果表明,与传统基于移相器的结构相比,所提方案的系统能效和谱效分别提高了32.3%和10.7%。     

Joint resource optimization for nonorthogonal multiple access-enhanced scalable video coding multicast in unmanned aerial vehicle-assisted radio-access networks

A joint resource-optimization scheme is investigated for nonorthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast in unmanned aerial vehicle (UAV)-assisted radio-access networks (RANs). This scheme allows a ground base station and UAVs to simultaneously multicast successive video layers in SVC with successive interference cancellation in NOMA. A video quality-maximization problem is formulated as a mixed-integer nonlinear programming problem to determine the UAV deployment and association, RAN spectrum allocation for multicast groups, and UAV transmit power. The optimization problem is decoupled into the UAV deployment–association, spectrum-partition, and UAV transmit-power–control subproblems. A heuristic strategy is designed to determine the UAV deployment and association patterns. An upgraded knapsack algorithm is developed to solve spectrum partition, followed by fast UAV power fine-tuning to further boost the performance. The simulation results confirm that the proposed scheme improves the average peak signal-to-noise ratio, aggregate video-reception rate, and spectrum utilization over various baselines.     

Power allocation scheme for maximizing spectrum efficiency and energy efficiency tradeoff for multi-cluster NOMA system

In this paper, a power allocation to maximize tradeoff between spectrum efficiency (SE) and energy efficiency (EE) is considered for the downlink non-orthogonal multiple access (NOMA) system with arbitrarily clusters and arbitrarily users, where the subcarriers of clusters are mutually orthogonal to each other. Specifically, an optimization problem of maximizing SE-EE tradeoff is formulated by optimizing power allocation among users under the constraints of user rate requirements. Then, the optimization problem is decomposed into a group of sub-problems with the aim of maximizing SE-EE tradeoff for each cluster, which is solved by using bisection method and monotonicity of function. Finally, the power allocation optimization problem among users is transformed into that between clusters, and a two steps inter-cluster power allocation algorithm is developed to solve this problem. Simulation results show that SE-EE tradeoff of the proposed scheme is better than that of the existing schemes.     

基于动态反馈与功率分配的干扰对齐方法

在多用户MIMO系统中,信道状态的精确性严重影响干扰对齐技术的性能。该文针对信道状态信息(CSI)有限反馈导致的干扰泄露问题,提出一种基于动态反馈与功率分配的干扰对齐方法。首先从理论上分析了系统和速率与信道状态反馈比特分配和功率分配之间的关系,得到了在动态反馈和功率分配条件下系统和速率的解析表达式;在此基础上,以系统容量提升为目标,对反馈比特分配和功率分配优化问题进行建模;并根据信道的准静态特性利用库恩塔克条件(KKT)对该问题进行求解,得到功率和反馈比特分配方案。仿真结果表明,与单独考虑信道状态动态反馈条件下的干扰对齐技术相比,提出的方法能够有效减少干扰泄漏强度,提高系统和速率。     

Dynamic Resource Allocation in Non-orthogonal Multiple Access Using Weighted Maximin Fairness Strategy for a UAV Network

Non-orthogonal multiple access (NOMA) with power domain multiplexing and successive interference cancellation (SIC) is one of the promising technologies for future wireless communication. The performance of NOMA is highly dependent on resource allocation such as power allocation and channel assignment. In this paper, we investigate the power allocation (PA) scheme, to optimize the weighted maximin fairness (MMF) for 2-user and 3-user clusters. We utilize the particle swarm optimization (PSO) based algorithm for power allocation due to its promising behavior. Application area of NOMA is becoming broader, then, we considered a cellular network, assisted by an unmanned aerial vehicle (UAV) as the base station (BS) which is integrated with the NOMA system. The PA for weighted MMF problem in NOMA is non-convex, it is difficult to find out the optimal solution directly. Simulation results show the performance of PSO-based algorithm in different adaptive weights and its convergence characteristics. We have also shown that the rate and fairness tradeoff using weighted maximin fairness. Numerical results compare the performance of NOMA and orthogonal multiple access (OMA) and prove the significance of the proposed algorithm.

     

Energy-efficient power allocation for NOMA heterogeneous networks with imperfect CSI

In this paper, an optimal user power allocation scheme is proposed to maximize the energy efficiency for downlink non-orthogonal multiple access (NOMA) heterogeneous networks (HetNets). Considering channel estimation errors and inter-user interference under imperfect channel state information (CSI), the energy efficiency optimization problem is formulated, which is non-deterministic polynomial (NP)-hard and non-convex. To cope with this intractable problem, the optimization problem is converted into a convex problem and address it by the Lagrangian dual method. However, it is difficult to obtain closed-form solutions since the variables are coupled with each other. Therefore, a Lagrangian and sub-gradient based algorithm is proposed. In the inner layer loop, optimal powers are derived by the sub-gradient method. In the outer layer loop, optimal Lagrangian dual variables are obtained. Simulation results show that the proposed algorithm can significantly improve energy efficiency compared with traditional power allocation algorithms.     

Joint resource allocation for hybrid NOMA-assisted MEC in 6G networks

Multi-access Edge Computing (MEC) is an essential technology for expanding computing power of mobile devices, which can combine the Non-Orthogonal Multiple Access (NOMA) in the power domain to multiplex signals to improve spectral efficiency. We study the integration of the MEC with the NOMA to improve the computation service for the Beyond Fifth-Generation (B5G) and the Sixth-Generation (6G) wireless networks. This paper aims to minimize the energy consumption of a hybrid NOMA-assisted MEC system. In a hybrid NOMA system, a user can offload its task during a time slot shared with another user by the NOMA, and then upload the remaining data during an exclusive time duration served by Orthogonal Multiple Access (OMA). The original energy minimization problem is non-convex. To efficiently solve it, we first assume that the user grouping is given, and focuses on the one group case. Then, a multilevel programming method is proposed to solve the non-convex problem by decomposing it into three subproblems, i.e., power allocation, time slot scheduling, and offloading task assignment, which are solved optimally by carefully studying their convexity and monotonicity. The derived solution is optimal to the original problem by substituting the closed expressions obtained from those decomposed subproblems. Furthermore, we investigate the multi-user case, in which a close-to-optimal algorithm with low-complexity is proposed to form users into different groups with unique time slots. The simulation results verify the superior performance of the proposed scheme compared with some benchmarks, such as OMA and pure NOMA.     

Secrecy sum rate optimization in MISO nonorthogonal multiple access systems with orthogonal space‐time block coding

In this paper, we investigate the secrecy sum rate optimization problem for a multiple‐input single‐output (MISO) nonorthogonal multiple access (NOMA) system with orthogonal space‐time block codes (OSTBC). This system consists of a transmitter, two users, and a potential eavesdropper. The transmitter sends information by orthogonal space‐time block codes. The transmitter's precoder and the power allocation scheme are designed to maximize achievable secrecy sum rate subject to the power constraint at the transmitter and the minimum transmission rate requirement of the weak user. We consider two cases of the eavesdropper's channel condition to obtain positive secrecy sum rate. The first case is the eavesdropper's equivalent channel is the weakest, and the other is the eavesdropper's equivalent channel between the strong user and weak user. For the former case, we employ the constrained concave convex procedure (CCCP)‐based iterative algorithm with one‐dimensional search. While for the latter, we adopt the method of alternating optimization (AO) between precoder and power allocation. We solve a semidefinite programming to optimize the precoder and drive a closed‐form expression of power allocation. The simulation results obtained by our method demonstrate the superiority of our proposed scheme.