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Massive Coded-NOMA for Low-Capacity Channels A Low-Complexity Recursive Approach

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Document pages: 18 pages

Abstract: In this paper, we present a low-complexity recursive approach for massive andscalable code-domain nonorthogonal multiple access (NOMA) with applications toemerging low-capacity scenarios. The problem definition in this paper isinspired by three major requirements of the next generations of wirelessnetworks. Firstly, the proposed scheme is particularly beneficial inlow-capacity regimes which is important in practical scenarios of utmostinterest such as the Internet-of-Things (IoT) and massive machine-typecommunication (mMTC). Secondly, we employ code-domain NOMA to efficiently sharethe scarce common resources among the users. Finally, the proposed recursiveapproach enables code-domain NOMA with low-complexity detection algorithms thatare scalable with the number of users to satisfy the requirements of massiveconnectivity. To this end, we propose a novel encoding and decoding scheme forcode-domain NOMA based on factorizing the pattern matrix, for assigning theavailable resource elements to the users, as the Kronecker product of severalsmaller factor matrices. As a result, both the pattern matrix design at thetransmitter side and the mixed symbols detection at the receiver side can beperformed over matrices with dimensions that are much smaller than the overallpattern matrix. Consequently, this leads to significant reduction in both thecomplexity and the latency of the detection. We present the detection algorithmfor the general case of factor matrices. The proposed algorithm involvesseveral recursions each involving certain sets of equations corresponding to acertain factor matrix. We then characterize the system performance in terms ofaverage sum rate, latency, and detection complexity. Our latency and complexityanalysis confirm the superiority of our proposed scheme in enabling largepattern matrices.

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