Massive Coded-NOMA for Low-Capacity Channels: A Low-Complexity Recursive Approach
Massive Coded-NOMA for Low-Capacity Channels: A Low-Complexity Recursive Approach
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DOI：
10.1109/tcomm.2021.3064327
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发表时间：
2021-06-01
影响因子：
8.3
通讯作者：
Mahdavifar, Hessam
中科院分区：
文献类型：
--
作者：
Jamali, Mohammad Vahid;Mahdavifar, Hessam
在本文中，我们提出了一个低复杂度的递归方法，大规模和可扩展的码域非正交多址接入（NOMA）与新兴的低容量的情况下的应用。本文中的问题定义的灵感来自下一代无线网络的三个主要要求。首先，所提出的方案在低容量机制中特别有益，这在诸如物联网（IoT）和大规模机器类型通信（mMTC）之类的最感兴趣的实际场景中是重要的。其次，我们采用码域NOMA有效地共享用户之间的稀缺的公共资源。最后，所提出的递归方法使码域NOMA与低复杂度的检测算法，可扩展的用户数量，以满足大规模连接的要求。为此，我们提出了一种新的编码和解码方案的码域NOMA因式分解的模式矩阵的基础上，分配可用的资源元素给用户，作为几个较小的因子矩阵的克罗内克积。结果，在发射机侧的模式矩阵设计和在接收机侧的混合符号的检测都可以在具有比整个模式矩阵小得多的维度的矩阵上执行。因此，这导致检测的复杂性和延迟的显著降低。我们提出了一般情况下的因子矩阵的检测算法。所提出的算法涉及到几个递归，每个涉及到一定的方程组对应于一定的因子矩阵。然后，我们的系统性能的平均和速率，延迟和检测复杂度。我们的延迟和复杂性分析证实了我们提出的方案在实现大模式矩阵方面的优越性。此外，我们的平均和速率的数值结果表明，该方案提供了更好的性能相比，简单的码域NOMA具有相当的复杂性，特别是在低容量的制度。
In this paper, we present a low-complexity recursive approach for massive and scalable code-domain nonorthogonal multiple access (NOMA) with applications to emerging low-capacity scenarios. The problem definition in this paper is inspired by three major requirements of the next generations of wireless networks. Firstly, the proposed scheme is particularly beneficial in low-capacity regimes which is important in practical scenarios of utmost interest such as the Internet-of-Things (IoT) and massive machine-type communication (mMTC). Secondly, we employ code-domain NOMA to efficiently share the scarce common resources among the users. Finally, the proposed recursive approach enables code-domain NOMA with low-complexity detection algorithms that are scalable with the number of users to satisfy the requirements of massive connectivity. To this end, we propose a novel encoding and decoding scheme for code-domain NOMA based on factorizing the pattern matrix, for assigning the available resource elements to the users, as the Kronecker product of several smaller factor matrices. As a result, both the pattern matrix design at the transmitter side and the mixed symbols' detection at the receiver side can be performed over matrices with dimensions that are much smaller than the overall pattern matrix. Consequently, this leads to significant reduction in both the complexity and the latency of the detection. We present the detection algorithm for the general case of factor matrices. The proposed algorithm involves several recursions each involving certain sets of equations corresponding to a certain factor matrix. We then characterize the system performance in terms of average sum rate, latency, and detection complexity. Our latency and complexity analysis confirm the superiority of our proposed scheme in enabling large pattern matrices. Moreover, our numerical results for the average sum rate show that the proposed scheme provides better performance compared to straightforward code-domain NOMA with comparable complexity, especially at low-capacity regimes.