QoE-Based Resource Allocation for Multi-Cell NOMA Networks
QoE-Based Resource Allocation for Multi-Cell NOMA Networks
复制标题
多小区 NOMA 网络基于 QoE 的资源分配
DOI：
10.1109/twc.2018.2855130
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发表时间：
2018-09-01
影响因子：
10.4
通讯作者：
Nallanathan, Arumugam
中科院分区：
文献类型：
--
作者：
Cui, Jingjing;Liu, Yuanwei;Nallanathan, Arumugam
体验质量（QoE）是第五代（5G）无线通信系统中的重要指标。针对多小区多载波非正交多址接入（MC-NOMA）网络中用户-基站（BS）关联、子信道分配和功率分配问题，研究了MC-NOMA网络中的资源分配问题。一个优化问题制定的目标是最大化的总和平均意见得分（MOS）的用户在网络中。为了解决具有挑战性的混合整数规划问题，我们首先将其分解为两个子问题，这两个子问题的特征分别是组合变量和连续变量。对于组合子问题，提出了一个三维匹配问题来建模用户，基站和子信道之间的关系。然后，提出了一个两步的方法来获得一个次优解。对于连续的功率分配子问题，采用分支定界法求解。此外，一个低复杂度的次优方法的基础上，逐次凸逼近技术的发展，以达到良好的计算复杂性和最优性的折衷。仿真结果表明：1）所提出的NOMA网络能够在QoE方面优于传统的正交多址接入网络，以及2）所提出的用于总和MOS最大化的算法可以实现相对于总和速率最大化方案的显著公平性改进。
Quality of experience (QoE) is an important indicator in the fifth generation (5G) wireless communication systems. For characterizing user-base station (BS) association, subchannel assignment, and power allocation, we investigate the resource allocation problem in multi-cell multicarrier non-orthogonal multiple access (MC-NOMA) networks. An optimization problem is formulated with the objective of maximizing the sum mean opinion scores (MOSs) of users in the networks. To solve the challenging mixed integer programming problem, we first decompose it into two subproblems, which are characterized by combinational variables and continuous variables, respectively. For the combinational subproblem, a 3-D matching problem is proposed for modeling the relation among users, BSs, and subchannels. Then, a two-step approach is proposed to attain a suboptimal solution. For the continuous power allocation subproblem, the branch and bound approach is invoked to obtain the optimal solution. Furthermore, a low complexity suboptimal approach based on successive convex approximation techniques is developed for striking a good computational complexity-optimality tradeoff. Simulation results reveal that: 1) the proposed NOMA networks is capable of outperforming conventional orthogonal multiple access networks in terms of QoE and 2) the proposed algorithms for sum-MOS maximization can achieve significant fairness improvement against the sum-rate maximization scheme.