基于M2M终端组的比例公平资源分配研究

基于M2M终端组的资源分配策略可以有效提高M2M通信中的资源利用率.本文研究了M2M通信系统中基于组的保证不同组间传输速率的比例公平资源分配问题,提出了一种次优的能够保证不同终端组间传输速率比例公平的资源分配算法,并通过仿真对算法的性能进行了验证,给出了速率最大化算法与终端组比例公平算法的性能比较.     

认知无线电下行链路中的OFDMA资源分配算法

摘 要： 本文提出一种适用于下行认知无线电系统的正交频分多址接入资源分配算法,在总发射功率、误比特率和对授权用户的干扰受限的条件下最大化系统信息传输速率.本算法分两步实现：首先通过比较各认知用户在各子载波上单位信号发送条件下接收信号的信干噪比实现子载波分配;然后利用凸优化理论求解非负实数域内的比特数和功率值的最优解,并将其调整为符合实际系统需要的比特数和功率值,实现比特和功率分配.仿真结果表明,相比传统的基于频谱空洞的资源分配算法,本算法可以提供显著的系统信息传输速率增益.     

Low complexity resource allocation and scheduling algorithms for downlink SDMA systems

The downlink zero-forcing beamforming strategy in the case of random packet arrivals is investigated. Under this setting, the relevant fairness criterion is the stabilization of all buffer queues which guarantees a bounded average delay for all users. It has been shown that allocating resources to maximize a queue-length-weighted sum of the rates is a stabilizing policy. However, the high complexity of user selection and the feasible rates determination for optimal scheme may prevent the real-time scheduling operation. Two low complexity algorithms are provided taking the channel state, queue state and orthogonality into account. In particular, the authors pick the first user with the largest product between channel gain and queuing length, and select the remaining users to construct candidate user set based on the greedy user selection method or channel orthogonal user selection method. Then, the power and rate allocation for the selected users are implemented based on the modified water-filling method. The complexity of the proposed algorithms is analyzed. The average delay and average throughput are studied in homogeneous scenarios and heterogeneous scenarios, respectively. Simulation results show that the proposed algorithms can take full advantage of the multi-user diversity gain and provide average delay (or throughput) and fairness improvement compared with channel-aware-only schemes.     

OFDMA分布式无线接入网络中基于共享标准的资源分布研究

Distributed radio access network (DRAN) is a novel wireless access architecture and can solve the problem of the available spectrum scarcity in wireless communications. In this paper, we investigate resource allocation for the downlink of OFDMA DRAN. Unlike previous exclusive criterion based algorithms that allocate each subcarrier to only one user in the system, the proposed algorithms are based on shared criterion that allow each subcarrier to be allocated to multiple users through different antennas and to only one user through same antenna. First, an adaptive resource allocation algorithm based on shared criterion is proposed to maximize total system rate under each user’s minimal rate and each antenna’s maximal power constraints. Then we improve the above algorithm by considering the influence of the resource allocation scheme on single user. The simulation results show that the shared criterion based algorithm provide much higher total system rate than that of the exclusive criterion based algorithm at the expense of the outage performance and the fairness, while the improved algorithm based on shared criterion can achieve a good tradeoff performance.     

一种基于粒子群算法的DC-NOMA系统遍历容量优化方案

为提升设备到设备（Device-to-Device,D2D）辅助的协作非正交多址接入（Non-orthogonal Multiple Access,NOMA）中继系统的性能,以最大化系统遍历容量为目标建立优化模型,利用粒子群优化（Particle Swarm Optimization,PSO）算法设计最优功率分配策略,求出每个用户的最佳功率分配因子,从而得到系统遍历容量的最优值。仿真结果表明,所提出的基于PSO的功率分配方案不仅能提高系统容量,还可以降低用户的中断概率。     

电力线通信系统中基于改进NSGA-II的自适应资源分配

 分析电力线通信系统在各种约束下,多用户多业务在多子载波上自适应资源分配的多层多目标模型。基于快速的非支配分类遗传算法II,提出改进的功率或速率自适应的资源分配多目标和单目标优化遗传算法。在使用多目标遗传算法获得实时用户资源分配的所有Pareto非劣解后,由每个Pareto解计算系统的剩余资源,再采用单目标遗传算法把剩余资源分给非实时用户,最后从所有的资源分配方案中寻找全局最优方案。在典型电力线信道环境下仿真结果表明,本文算法其性能更好且能更好地满足多用户资源分配的多目标要求。     

面向6G的新型多址与波形技术

随着互联网终端海量接入,传统正交多址接入（orthogonal multiple access,OMA）技术接入效率低, 5G NR 系统面临拥塞及高时延问题,并且在高速场景下,基于正交频分复用（orthogonal frequency-division multiplexing,OFDM）的系统由于多普勒效应性能严重恶化。为满足6G在高速移动场景下低时延、高可靠、海量接入需求,首先,结合正交时频空间（orthogonal time frequency space,OTFS）和图样分割多址接入（pattern division multiple access,PDMA）技术,提出了一种OTFS-PDMA联合方案;然后,推导PDMA传输码字在时延-多普勒（delay-Doppler,DD）域采用不同分配方式的系统输入-输出关系;最后,提出了一种基于期望传播算法（expectation propagation algorithm,EPA）的低复杂度接收机。仿真结果表明,OTFS-PDMA较传统的OTFS-OMA技术能够显著提升误码率性能;对于规则码本,不同码字分配方案性能相似,而对于非规则码本,发送信号采用集中式扩频优于离散式扩频,且对于离散式扩频,PDMA 扩频信号沿多普勒轴分配,系统可取得较好性能;此外EPA接收机性能优于其他传统接收机。     

基于博弈论的OFDMA系统多小区功率协调分配算法

提出了一种用于正交频分多址(OFDMA)通信系统下行链路的多小区功率协调分配算法,协调相邻小区在同频子信道上的发送功率,抑制小区间干扰,提高信道容量.将博弈论引入到多小区功率分配中,给出基于定价的多小区非合作功率分配博弈模型,并分析了该博弈的纳什均衡解的存在性和惟一性,给出了该模型的分布式求解算法.通过仿真分别给出了单小区场景与多小区场景下算法性能,讨论了定价因子对系统性能的影响.仿真结果表明,提出的多小区功率协调分配算法可以充分利用无线资源,提高系统吞吐量,降低平均发送功率.     

Cross-layer resource allocation for QoS guarantee with finite queue constraint in multirate OFDMA wireless networks

Efficient radio resource allocation is essential to provide quality of service （QoS） for wireless networks. In this article, a cross-layer resource allocation scheme is presented with the objective of maximizing system throughput, while providing guaranteed QoS for users. With the assumption of a finite queue for arrival packets, the proposed scheme dynamically a/locates radio resources based on user＇s channel characteristic and QoS metrics derived from a queuing model, which considers a packet arrival process modeled by discrete Markov modulated Poisson process （dMMPP）, and a multirate transmission scheme achieved through adaptive modulation. The cross-layer resource allocation scheme operates over two steps. Specifically, the amount of bandwidth allocated to each user is first derived from a queuing analytical model, and then the algorithm finds the best subcarrier assignment for users. Simulation results show that the proposed scheme maximizes the system throughput while guaranteeing QoS for users.     

机器类设备基于不同时延需求分组的随机接入方法

针对大规模机器类通信中拥塞导致的时延敏感设备时延高和接入成功率低的问题,提出将小区中设备按时延要求分组,对不同组设备引入不同的退避模型,分析时延敏感设备的时延和吞吐量,按照不同组中设备的时延需求动态分配前导数目,同时通过调整接入类限制因子实现吞吐量的优化。仿真结果表明,在给定时延敏感设备的时延限制条件时,与统一退避的机制对比,所提分组机制的时延敏感设备能够满足时延要求,并且提高了接入效率。     

NOMA物联网下多UAV辅助MEC系统的资源分配

为解决偏远地区或突发灾害等场景中的物联网(Internet of Things, IoT)设备的任务计算问题,构建了一个非正交多址接入(Non-orthogonal Multiple Access, NOMA)-IoT(NOMA-IoT)下多无人机(Unmanned Aerial Vehicle, UAV)辅助的NOMA多址边缘计算(Multiple Access Edge Computing, MEC)系统。该系统中设备的计算能耗、卸载能耗和MEC服务器计算能耗直接受同信道干扰、计算资源和发射功率的影响,可通过联合优化卸载策略、计算资源和发射功率最小化系统加权总能耗。根据优化问题的非凸性和复杂性,提出了一种有效的迭代算法解决：首先,对固定卸载策略,计算资源和发射功率分配问题可通过连续凸逼近转化为可解的凸问题;其次,对固定计算资源和发射功率,利用联盟形成博弈解决卸载策略问题,以最小化IoT设备之间的同信道干扰。仿真结果表明,较OMA接入方式,NOMA接入方式减少本地计算能耗、卸载能耗及计算能耗约20%;较无卸载策略方法,包含卸载策略方法在减少系统加权总能耗方面效果较为明显。