OFDMA系统中基于多目标优化的无线资源分配算法

本文提出了OFDMA系统中基于多目标优化的无线资源分配算法.针对不同业务的QoS要求,建立多目标优化模型,并提出一种多业务分级分配算法通过确定优先级、引入合适的参数获得次优解.仿真结果表明,该算法不但能够保证不同业务的QoS要求,而且能够兼顾相同业务的用户之间的公平性.     

混合NOMA/OFDMA可见光通信系统资源分配算法

为了进一步降低混合非正交多址/正交频分复用多址（NOMA/OFDMA）技术的多用户多小区可见光通信系统的误码率,提出了一种功率和子载波的联合分配算法。该算法以最小化系统误符号率为优化目标,实现了子小区间载波和用户功率的联合最优分配。首先,理论推导出系统的误符号率表达式;然后,使用Matlab软件进行仿真,模拟可见光通信系统模型,并借助遗传算法得到最优的分配结果;最后,将该算法与现有的算法进行了对比。仿真结果表明,所提算法的误符号率性能更优。     

一种新型的OFDMA比例公平资源分配算法

针对已有算法对系统容量和高公平性兼顾较差的情况,提出了一种满足公平性的系统容量最大化资源分配算法。在子载波分配中通过建立信道效率控制模型,给当前用户分配信道效率最高的子载波,将信道增益低于门限值的子载波重新分配,改进了最大化最小(max-min)用户速率模型。在功率分配中将系统模型转化成用注水线表示的数学模型,首先求解各用户的注水线,再求解各用户的功率分配,保证了用户间比例公平性。两种信噪比情形下的仿真和分析表明,整个方案计算复杂度稍低,系统容量获得较大提升,并且用户间的公平性始终为1。     

基于不完备CSI的OFDMA公平资源分配算法

针对在信道状态信息不完备时,很难兼顾系统容量和公平性这一问题,通过引入公平松弛因子,提出一种公平性可调的遍历容量最大化资源分配算法。为了降低计算复杂度,在利用对偶优化方法求解拉格朗日算子过程中,提出同层循环迭代的搜索方式以代替传统的内外层循环迭代方式。通过仿真和分析表明,该算法在满足公平性的同时实现了遍历容量最大化,且计算复杂度明显降低。     

OFDMA系统跨层资源分配算法

OFDM具有高频谱利用率和抗多径衰落的优点,已被公认为第三代移动通信系统长期演进标准以及第四代移动通信系统的核心技术。OFDMA是基于OFDM的一种多用户接入技术,在OFDMA系统中,各用户在不同的子载波上同时传输数据。主要研究了OFDMA系统中的跨层资源分配算法。详细分析了保证速率比例公平的非实时业务跨层资源分配算法,仿真结果说明该算法能够较好地保证用户之间的公平性,并能够获得较大的系统吞吐量和较小的业务延时。     

OFDM系统中一种多用户自适应资源分配算法

针对OFDMA系统中各个用户传输速率受限的情况下,最大化系统容量问题,提出了一种资源分配的实现算法.该算法首先根据每个用户发送的比特数目要求确定用户需要占用的子载波数,将最优的若干个子信道分配给用户后,通过比较各个用户内部子信道之间的差异调整被几个用户同时占用的子信道.最后用赋初值的贪心算法为各个用户分配比特、功率.仿真结果表明,该算法复杂度较低,系统容量较大,且实现了用户之间的公平性.     

基于goodput优化的OFDMA系统资源分配算法

资源分配是OFDMA系统资源调度中的一个重要研究问题。针对OFDMA下行链路信道知识获取的现实误差问题,提出了一种基于实际吞吐量优化的资源分配算法。重点研究了功率和数据率在用户之间的分配问题,以往的很多分配算法通常是假设下行链路发射端具有理想信道信息,这与实际状况不符,同时根据部分信道信息得到的速率分配不被真实的信道支持,会造成传输的中断事件。算法考虑了信道估计的误差问题,将实际吞吐量goodput作为分配算法优化的依据。仿真结果表明,该算法获得了较好的多用户分集增益和实际吞吐量性能。     

基于差分进化算法的多用户OFDM自适应资源分配

针对多用户正交频分复用系统自适应资源分配问题,提出一种改进的子载波和基于差分进化算法的功率自适应分配算法.该算法首先在均等功率下进行子载波分配,然后通过添加约束条件检测改进步骤,改进差分进化算法,并采用该算法根据设置的兼顾用户公平性与系统容量的目标函数,全局寻优实现用户间的功率分配.仿真结果表明,算法在低算法复杂度及兼顾用户公平性的情况下实现了较高的系统容量提升,证明其有效性.     

OFDM系统中自适应资源分配算法的研究

在无线资源日益紧缺和信道条件日益恶化的背景下,通过动态资源分配可以获得较高的频谱利用率和更高的QoS保证,以适应未来无线宽带业务的发展.首先介绍了链路自适应和多用户分集,在此基础上重点研究了在不同优化目标下单天线OFDM与多天线OFDM的资源分配算法,并对其优缺点进行了分析,最后展望了动态资源分配的发展趋势.     

认知无线电系统中基于OFDM的一种资源分配方案

文章在已知主用户干扰门限的基础上,对认知用户的子载波和功率进行合理分配;阐述了使认知用户系统容量达到最大的最优功率分配算法,给出了易于实现的次优算法,并与传统功率分配算法进行比较。仿真结果表明,在给出不同干扰门限的条件下,认知用户采用最优算法比传统算法获得的发射功率要高,并且次优算法的性能接近最优算法。     

Resource allocation scheme for orthogonal frequency division multiple access networks based on cooperative game theory

Cooperative game theory can be applied to orthogonal frequency division multiple access (OFDMA) networks for fair resource allocation. In this work, we consider a comprehensive cross‐layer framework including physical and medium access control layer requirements. We apply two cooperative games, nontransferable utility (NTU) game and transferable utility (TU) game, to provide fairness in OFDMA networks. In NTU game, fairness is achieved by defining appropriate objective function, whereas in TU game, fairness is provided by forming the appropriate network structure. For NTU game, we analyze the Nash bargaining solution as a solution of NTU game taking into account channel state information and queue state information. In a TU game, we show that coalition among subcarriers to jointly provide rate requirements leads to better performance in terms of power consumption. The subcarrier's payoff is determined according to the amount of payoff which that subcarrier brings to the coalition by its participation. We show that although NTU and TU games are modeled as rate adaptive and margin adaptive problems, respectively, both solutions provide a fair distribution of resources with minimum fairness index of 0.8. Copyright © 2012 John Wiley & Sons, Ltd.     

Dynamic subcarrier and power allocation based on cooperative game theory in symmetric cooperative OFDMA networks

In order to improve the efficiency and fairness of radio resource utilization,a scheme of dynamic cooperative subcarrier and power allocation based on Nash bargaining solution(NBS-DCSPA) is proposed in the uplink of a three-node symmetric cooperative orthogonal frequency division multiple access(OFDMA) system.In the proposed NBS-DCSPA scheme,resource allocation problem is formulated as a two-person subcarrier and power allocation bargaining game(SPABG) to maximize the system utility,under the constraints of each user’s maximal power and minimal rate,while considering the fairness between the two users.Firstly,the equivalent direct channel gain of the relay link is introduced to decide the transmission mode of each subcarrier.Then,all subcarriers can be dynamically allocated to the two users in terms of their selected transmission mode.After that,the adaptive power allocation scheme combined with dynamic subcarrier allocation is optimized according to NBS.Finally,computer simulation is conducted to show the efficiency and fairness performance of the proposed NBS-DCSPA scheme.     

OFDMA协同通信系统子载波和功率联合分配算法

针对OFDMA协同通信系统资源分配仅考虑平均功率下的子载波分配,中继存在未用功率情况,研究子载波分配后中继剩余功率分配问题。提出一种既满足业务QoS需求又兼顾用户间公平性的子载波和功率联合分配算法,并设计一种基于二分法的功率注水分配方案。测试表明,该算法能在满足业务QoS需求及用户公平性的同时,提升系统容量。     

基于非合作博弈的OFDMA无线多跳中继网络上行链路资源分配算法

研究了基于OFDMA多址技术的无线多跳中继网络上行链路资源分配问题。首先,在最大发射功率等约束条件下,建立了多小区OFDMA无线多跳中继网络上行链路的资源分配优化模型。将非合作博弈论和定价机制引入后,该优化问题可转化为在每个子信道上独立地进行功率分配。基于非合作博弈的功率分配模型中的纳什均衡点的存在性和唯一性得到了证明,并给出了具体的分布式求解算法。仿真结果表明,所提算法能在大幅减少系统总发射功率的情况下,有效地提升系统吞吐量,达到较高的能效比。     

改进的基于合作博弈的资源分配和接入控制策略

为了提高频谱共享的有效性和公平性,提出了一个基于合作博弈的认知网络频谱共享 模型和接入控制策略。博弈规则为在满足各用户最小收益的前提下,认知用户进行相互合作 ,并最大化总体收益;接入控制策略以满足服务质量需求为约束条件,并引入惩罚函数,迫 使认知用户按照其实际需求竞争频谱资源。仿真结果表明：提出的合作博弈模型和接入策略 提高了认知用户的总体收益和公平性,改善了系统中认知用户的满意度。     

Fusion of artificial intelligence and game theory for resource allocation in non-orthogonal multiple access-assisted device-to-device cooperative communication

Device-to-device (D2D) communication offers a low-cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non-orthogonal multiple access (NOMA)-based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.     

基于博弈论的多小区OFDMA系统资源分配

立足于限制条件下实现多小区OFDMA系统容量最大或总传输功率最小的优化问题,比较分析了近年来提出的多种基于博弈论的资源分配算法。分析了纳什均衡点存在和唯一的条件,通过引入定价机制或虚拟裁判机制,使得到的解收敛于纳什均衡点。最后,探讨了联合中继节点或MIMO技术的多小区OFDMA系统资源分配算法,该算法能有效提高频谱效率,将成为未来研究的热点。     

Optimal resource allocation scheme for cognitive radio networks with relay selection based on game theory

In this paper,we present a non-transferable utility coalition graph game(NTU-CGG) based resource allocation scheme with relay selection for a downlink orthogonal frequency division multiplexing(OFDMA) based cognitive radio networks to maximize both system throughput and system fairness.In this algorithm,with the assistance of others SUs,SUs with less available channels to improve their throughput and fairness by forming a directed tree graph according to spectrum availability and traffic demands of SUs.So this scheme can effectively exploit both space and frequency diversity of the system.Performance results show that,NTU-CGG significantly improves system fairness level while not reducing the throughput comparing with other existing algorithms.     

Stackelberg game theoretic pricing algorithm for bandwidth allocation in cooperative access

Cooperative access among user devices by sharing wireless access bandwidth opens a new paradigm in heterogeneous networks.However,how to stimulate cooperative relay nodes forwarding service data for others and allocating corresponding bandwidth to support it are two key issues in the cooperative access.This paper proposes a Stackelberg game based framework which is benefit participants including relay nodes and client nodes.This framework generalizes the pricing based bandwidth allocation algorithm by the Stackelberg game model,which optimizes the profit of the cooperative relay nodes while guaranteeing the bandwidth requirements of client nodes.We transform the profit maximization problem into a convex problem and solve it using the convex optimization method.The simulation results demonstrate that the proposed framework and corresponding algorithms outperform the bidding weight proportional fairness and fixed value bandwidth allocation ones significantly.     

大规模MIMO OFDMA下行系统能效资源分配算法

针对大规模多输入多输出（MIMO）正交频分多址(OFDMA)下行移动通信系统,提出了一种基于能效最优的资源分配算法。所提算法在采用迫零(ZF)预编码的情况下,以最大化系统能效的下界为准则,同时考虑每个用户的最低速率要求,通过调整带宽分配、功率分配和基站天线数分配来优化能效函数。首先根据优化条件提出了一种迭代算法确定每个用户的带宽分配,然后利用分数规划的性质并采用凸优化方法,通过联合调整基站端的发射天线数和用户的发射功率来优化能效函数。仿真结果表明,所提算法在较少迭代次数的同时能够取得较好的系统能效性能和吞吐量性能。