面向用户体验的多小区混合非正交多址接入网络资源分配方法

该文研究了多小区混合非正交多址接入(MC-hybrid NOMA)网络的资源分配。为满足异构用户的服务体验,以最大化全网综合平均意见评分(MOS)累加和为目标,考虑基站选择、信道接入和功率资源分配的联合优化问题,该文提出一种用户、基站和信道3方的2阶段转移匹配算法,并根据用户MOS进行子信道功率优化。仿真结果表明所提多小区混合NOMA网络资源分配方案能有效提升全网用户服务体验和公平性。     

面向用户体验的多小区混合非正交多址接入网络资源分配方法

该文研究了多小区混合非正交多址接入(MC-hybrid NOMA)网络的资源分配.为满足异构用户的服务体验,以最大化全网综合平均意见评分(MOS)累加和为目标,考虑基站选择、信道接入和功率资源分配的联合优化问题,该文提出一种用户、基站和信道3方的2阶段转移匹配算法,并根据用户MOS进行子信道功率优化.仿真结果表明所提多小区混合NOMA网络资源分配方案能有效提升全网用户服务体验和公平性.     

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。     

分层异构网络中基于斯坦克尔伯格博弈的资源分配算法

分层异构网络中家庭基站与宏基站之间往往存在干扰,如何分配资源以获得高谱率和高容量、保证用户性能一直是研究的重点。为了解决这个问题,本文提出了一种异构蜂窝网络中基于斯坦克尔伯格博弈的家庭基站与宏基站联合资源分配算法,算法首先基于图论的分簇算法对家庭基站和宏用户进行分簇和信道分配,以减少家庭基站之间的同层干扰和家庭基站层与宏蜂窝网络的跨层干扰;然后建立了联合家庭基站发射功率以及宏用户接入选择的斯坦克尔伯格博弈,推导出达到纳什均衡时的家庭基站发射功率的表达式,并据此为宏用户选择合适的接入策略。仿真结果表明,该算法能够有效地提高宏用户的信干噪比（SINR）,家庭用户的性能也得到改善。      

异构蜂窝网络中基于能效的非正交多址接入下行功率分配算法

该文针对应用非正交多址接入(NOMA)技术的异构蜂窝网络,在考虑层间层内干扰的情况下,提出一种能效最大化的功率分配算法。该算法主要包括两部分,一部分为子信道内用户功率分配因子的求解,主要利用差分优化的方法,迭代求解。另一部分为子信道间的功率分配,主要利用凹凸程序法将原有的非凸问题简化为可解的凸问题,最后利用拉格朗日求解法得出功率最优解。仿真结果表明该算法有良好的迭代性,且新算法表明利用NOMA技术得到的系统能效较利用正交技术得到的系统能效提高了至少44%以上。     

基于信道定价的无线虚拟网络资源分配策略:匹配/Stackelberg分层博弈

针对分层匹配博弈不能跟踪信道变化以及循环迭代收敛慢等问题,该文提出一种基于信道定价的无线虚拟网络资源分配策略:匹配/Stackelberg分层博弈.分别以基于流带宽的用户满意度、系统带宽及切片功率作为报酬函数建立3级联合优化模型,并采用匹配/Stackelberg分层博弈求解.在博弈下层,定义移动虚拟网络操作者(MVNOs)m—切片n对mn及其与用户(UEs)的1对1匹配博弈以代替UEs与MVNOs的多对1匹配,对mn定义基于信道平均信息的切片功率价格,加速上、下一致收敛并使UEs适应信道选择最优mn,证明均衡点存在并给出了低复杂度的分布式拒绝-接收算法;在博弈上层,基于UEs与mn已匹配关系,形成基础资源提供者(InPs)与mn的Stackelberg博弈,给出了基于局部信道信息的功率定价和分配策略,使系统效用及频谱效率基于信道最优.最后定义了双层循环稳定条件及过程.仿真表明,该策略在信道跟踪、频谱效率、效用方面均优于随机定价的匹配/Stackelberg分层博弈以及传统分层匹配博弈.     

认知型飞蜂窝网络中的子信道分配与功率控制

为了解决宏蜂窝与飞蜂窝构成的两层异构网络上行干扰与资源分配问题,提出了一种在认知型飞蜂窝的双层异构网中结合子信道分配和功率控制进行资源分配的框架。通过对异构网中跨层干扰问题进行分析与建模,将求解最优子信道分配矩阵和用户发射功率矩阵作为干扰管理问题的解决方法。模型中认知型飞蜂窝网络子信道和飞蜂窝网络用户构成非合作博弈,双方利用效用函数最优值进行匹配,构成初始信道分配矩阵;再由接入控制器根据接入条件从初始信道分配矩阵中筛选用户,并优化接入用户的发射功率矩阵,得到最优子信道分配矩阵和功率矩阵。仿真结果表明,优化框架提高了双层异构网络中飞蜂窝网络用户的吞吐量和接入率,降低了异构网中跨层干扰。     

面向船联网的NOMA系统用户公平性研究

非正交多址技术(Non-orthogonal Multiple Access, NOMA)有助于提升船联网的连接数量和频谱效率。通过研究公平性约束下船联网NOMA系统的能量效率优化问题,设计基于速率方差的用户公平性因子,并提出了一种公平性约束下NOMA系统的能量效率优化策略。首先,假设基站到各个簇的发射功率相同且簇内用户数固定,以最大化能量效率作为用户分簇的目标,利用遗传算法对用户分簇方案进行优化;其次,在满足最小公平因子的条件下对簇内用户间的功率分配进行优化。仿真结果表明该优化策略能提高能量效率且用户公平性得到了保证。     

基于信道定价的无线虚拟网络资源分配策略:匹配/Stackelberg分层博弈

针对分层匹配博弈不能跟踪信道变化以及循环迭代收敛慢等问题,该文提出一种基于信道定价的无线虚拟网络资源分配策略:匹配/Stackelberg分层博弈。分别以基于流带宽的用户满意度、系统带宽及切片功率作为报酬函数建立3级联合优化模型,并采用匹配/Stackelberg分层博弈求解。在博弈下层,定义移动虚拟网络操作者(MVNOs)m—切片n对m_n及其与用户(UEs)的1对1匹配博弈以代替UEs与MVNOs的多对1匹配,对\begin{document}${m_n}$\end{document}定义基于信道平均信息的切片功率价格,加速上、下一致收敛并使UEs适应信道选择最优,证明均衡点存在并给出了低复杂度的分布式拒绝-接收算法;在博弈上层,基于UEs与已匹配关系,形成基础资源提供者(InPs)与的Stackelberg博弈,给出了基于局部信道信息的功率定价和分配策略,使系统效用及频谱效率基于信道最优。最后定义了双层循环稳定条件及过程。仿真表明,该策略在信道跟踪、频谱效率、效用方面均优于随机定价的匹配/Stackelberg分层博弈以及传统分层匹配博弈。     

基于能量采集异构蜂窝网络的功率分配算法研究

针对能量采集异构蜂窝网络,由于能量到达和信道状态的随机性导致离线功率分配算法只能取得理论最优,本文提出了一种在线功率分配算法.算法在每个时隙开始时,基站控制器通过能量判别选出满足开启条件的小蜂窝基站,然后采用基于拉格朗日乘子的两层迭代算法对所选择的小蜂窝基站分配发射功率,能够实际最大化系统在每个时隙的能效.仿真表明在满足基站开启条件的情况下,所提算法可以为密集异构网络提供更高的能量效率.该算法适用于信道状态和能量状态不可预测的网络.     

Stackelberg game for backhaul resource allocation in the two-tier LTE femtocell networks

As one promising technology for indoor coverage and service offloading from the conventional cellular networks, femtocells have attracted considerable attention in recent years. However, most of previous work are focused on resource allocation during the access period, and the backhaul involved resource allocation is seriously ignored. The authors studied the backhaul resource allocation in the wireless backhaul based two-tier heterogeneous networks （HetNets）, in which cross-tier interference control during access period is jointly considered. Assuming that the macrocell base station （MBS） protects itself from interference by pricing the backhaul spectrum allocated to femtocells, a Stackelberg game is formulated to work on the joint utility maximization of the macrocell and femtocells subject to a maximum interference tolerance at the MBS. The closed-form expressions of the optimal strategies are obtained to characterize the Stackelberg equilibriums for the proposed games, and a backhaul spectrum payment selection algorithm with guaranteed convergence is proposed to implement the backhaul resource allocation for femtocell base stations （FBSs）. Simulations are presented to demonstrate the Stackelberg equilibrium （SE） is obtained by the proposed algorithm and the proposed scheme is effective in backhaul resource allocation and macrocell protection in the spectrum-sharing HetNets.     

Resource allocation in NOMA heterogeneous network based on MEC

Aiming at maximizing the cache revenue,a joint user association and power allocation algorithm was proposed for the matching of base stations with users and power allocation in a NOMA heterogeneous network with caches,a NOMA joint optimization algorithm which was achieved by combing with message passing and DC programming was proposed.First,the constraints were incorporated into the objective function,and the user association result was obtained by calculating the marginal of the message passing between the function node and the variable node in the new optimization problem.Then,the original optimization problem was transformed into the form of the difference between two convex functions,and allocate power resources through DC programming.Finally,the final user association and power allocation results are obtained through iterative calculations.Simulation results prove that the proposed algorithm effectively improves network performance.     

Energy efficiency optimization algorithm for heterogeneous NOMA network based on imperfect CSI

In order to improve the suppression capability of parametric perturbation and energy efficiency (EE) of heterogeneous networks (HetNets),a robust resource allocation algorithm was proposed to maximize system EE for reducing cross-tier interference power in non-orthogonal multiple access (NOMA) based HetNets.Firstly,the resource optimization problem was formulated as a mixed integer and nonlinear programming one under the constraints of the interference power of macrocell users,maximum transmit power of small cell base station (BS),resource block assignment and the quality of service (QoS) requirement of each small cell user.Then,based on ellipsoid bounded channel uncertainty models,the original problem was converted into the equivalent convex optimization problem by using the convex relaxation method,Dinkelbach method and the successive convex approximation (SCA) method.The analytical solutions were obtained by using the Lagrangian dual approach.Simulation results verifiy that the proposed algorithm had better EE and robustness by comparing it with the existing algorithm under perfect channel state information.     

Optimal user selection and power allocation for revenue maximization in non‐orthogonal multiple access systems

A novel algorithm for joint user selection and optimal power allocation for Stackelberg game‐based revenue maximization in a downlink non‐orthogonal multiple access (NOMA) network is proposed in this study. The condition for the existence of optimal solution is derived by assuming perfect channel state information (CSI) at the transmitter. The Lagrange multiplier method is used to convert the revenue maximization problem into a set of quadratic equations that are reduced to a regular chain of expressions. The optimal solution is obtained via a univariate iterative procedure. A simple algorithm for joint optimal user selection and power calculation is presented and exhibits extremely low complexity. Furthermore, an outage analysis is presented to evaluate the performance degradation when perfect CSI is not available. The simulation results indicate that at 5‐dB signal‐to‐noise ratio (SNR), revenue of the base station improves by at least 15.2% for the proposed algorithm when compared to suboptimal schemes. Other performance metrics of NOMA, such as individual user‐rates, fairness index, and outage probability, approach near‐optimal values at moderate to high SNRs.     

A matching game framework for users clustering and resource allocation with wireless power transfer in a CR-NOMA network

This paper investigates the resource allocation in a massively deployed user cognitive radio enabled non-orthogonal multiple access (CR-NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one-to-one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale-Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied.     

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.

     

QoS-based optimal and fair resource allocation for energy-efficiency uplink NOMA networks

The energy-efficiency(EE) optimization problem was studied for resource allocation in an uplink single-cell network, in which multiple mobile users with different quality of service (QoS) requirements operate under a non-orthogonal multiple access (NOMA) scheme. Firstly, a multi-user feasible power allocation region is derived as a multidimensional body that provides an efficient scheme to determine the feasibility of original channel and power assignment problem. Then, the size of feasible power allocation region was first introduced as utility function of the subchannel-user matching game in order to get high EE of the system and fairness among the users. Moreover, the power allocation optimization to the EE maximization is proved to be a monotonous decline function. The simulation results show that compared with the conventional schemes, the network connectivity of the proposed scheme is significantly enhanced and besides, for low rate massive connectivity networks, the proposed scheme obtains performance gains in the EE of the system.     

Power allocation for downlink multiuser hybrid NOMA‐OMA systems: An auction game approach

Nonorthogonal multiple access (NOMA) is viewed as one of the key enabling candidate for the fifth‐generation systems. The effectiveness of such networks heavily relies on the power allocation. This paper addresses the problem of power allocation in a downlink multiuser hybrid NOMA‐orthogonal multiple access (OMA) network, where NOMA is integrated into OMA. Users with strong channel conditions are paired up with the users having weak channel conditions based on a random mechanism. Further, user pairs compete in an auction game for the transmit power being sold by the base station. Bids are placed iteratively by each user pair such that it maximizes their own utility. The existence of a unique Nash equilibrium has been proved theoretically. Simulation results show that the proposed scheme achieves higher average sum rate of users in comparison with that of the existing algorithms.