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首页> 外文期刊>Communications, IET >Radio resource allocation for heterogeneous traffic in GFDM-NOMA heterogeneous cellular networks
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Radio resource allocation for heterogeneous traffic in GFDM-NOMA heterogeneous cellular networks

机译:GFDM-NOMA异构蜂窝网络中用于异构业务的无线电资源分配

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摘要

In this study, the authors consider the downlink radio resource allocation for heterogeneous traffic in generalised frequency division multiplexing (GFDM)-non-orthogonal multiple access (NOMA) based heterogeneous cellular networks. In this scheme, multiple number of users can be allocated on each subcarrier. Two types of traffic are considered, elastic and streaming. The problem of maximising the weighted sum-rate of elastic users is addressed subject to streaming users minimum rate in addition to subcarrier and transmit power constraints. This problem is a non-convex NP-hard optimisation problem. To solve this problem, the authors divide it into two subproblems, subcarrier allocation and power allocation then an iterative algorithm is proposed. Subcarrier allocation is updated by solving an integer linear program, where a successive convex approximation approach is adopted to transform the power allocation subproblem to a sequence of convex subproblems, using one of the three methods, successive convex approximation for low ComplExity, arithmetic-geometric mean approximation (AGMA) and difference of two concave functions to find the power allocation optimal solutions. Numerical experiments show that the proposed algorithms can improve the system performance. Furthermore, they show that AGMA can achieve a sum-rate near to the global optimal solution, at the expense of more computational time.
机译:在这项研究中,作者考虑了基于通用频分复用(GFDM)-非正交多路访问(NOMA)的异构蜂窝网络中异构业务的下行链路无线电资源分配。在该方案中,可以在每个子载波上分配多个用户。考虑两种流量,弹性流量和流媒体。除了子载波和发射功率约束之外,还以流用户最小速率为前提,解决了使弹性用户的加权总速率最大化的问题。此问题是非凸NP硬优化问题。为了解决这个问题,作者将其分为两个子问题:子载波分配和功率分配,然后提出了一种迭代算法。通过求解整数线性程序来更新子载波分配,其中采用连续凸近似方法将功率分配子问题转换为凸子问题序列,使用以下三种方法之一:低ComplExity的连续凸逼近,算术几何均数近似(AGMA)和两个凹函数的差来找到功率分配的最优解。数值实验表明,该算法可以提高系统性能。此外,他们表明,AGMA可以以接近整体最优解的速度求和,但要花费更多的计算时间。

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