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Resource Allocation for MU-MIMO Non-Orthogonal Multiple Access (NOMA) System with Interference Alignment

机译:具有干扰对齐的mU-mImO非正交多址(NOma)系统的资源分配

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摘要

Non-orthogonal multiple access (NOMA) has attracted a lot of attention recently due to its superior spectral efficiency and could play a vital role in improving the capacity of future networks. This paper considers resource allocation for a downlink, multi-user (MU) MIMO-NOMA system that aims at maximizing the sum rate with interference alignment (IA) technique. Using singular ecomposition value (SVD) based IA, we propose IA based NOMA system in which a number of users are grouped together while the others are aligned to the null space as interference. The targeted group of users employ NOMA with a low complexity hierarchical power allocation scheme for sum rate maximization. In addition, an optimization problem is formulated to maximize the sum rate under the total power and proportional fairness constraints. A low complexity sub-optimal solution for two-user scenario is obtained and then extended tothe multi-user case by a hierarchical pairing scheme. Another approach is proposed to allocate the transmission power of each user using an iterative subgradient method. Simulation results show that the proposed schemes provide better performance than an existing scheme and perform close to the optimal one. In addition, the simulation scenario considers the case where twousers share the data streams while performing IA as compared to the case where all users are sharing it without IA. Simulation results verify that applying IA with NOMA could improve the achievable sum rate and offers simplicity in terms of successive interference cancellation (SIC) application.
机译:非正交多路访问(NOMA)由于其出色的频谱效率而最近引起了很多关注,并可能在提高未来网络的容量方面起着至关重要的作用。本文考虑了下行链路多用户(MU)MIMO-NOMA系统的资源分配,该系统旨在通过干扰对齐(IA)技术最大化总速率。使用基于奇异组成值(SVD)的IA,我们提出了一个基于IA的NOMA系统,其中将多个用户分组在一起,而其他用户则作为干扰而与零位对齐。目标用户群采用具有低复杂度分层功率分配方案的NOMA来实现总和率最大化。另外,提出了一个优化问题,以在总功率和比例公平约束下最大化总和率。获得了用于两用户场景的低复杂度次优解决方案,然后通过分层配对方案将其扩展到多用户情况。提出了另一种使用迭代次梯度方法来分配每个用户的传输功率的方法。仿真结果表明,所提出的方案具有比现有方案更好的性能,并且性能接近最佳方案。另外,与所有用户都在不使用IA的情况下共享数据的情况相比,仿真方案考虑了两个用户在执行IA时共享数据流的情况。仿真结果证明,将IA与NOMA配合使用可以提高可实现的和速率,并在连续干扰消除(SIC)应用方面提供了简便性。

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