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

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 paper is inspired by three major requirements of the next generations wireless networks. Firstly, proposed scheme particularly beneficial regimes which important practical scenarios utmost interest such as Internet-of-Things (IoT) machine-type communication (mMTC). Secondly, employ NOMA efficiently share scarce common resources among users. Finally, enables detection algorithms that are number users satisfy connectivity. To end, propose novel encoding decoding based on factorizing pattern matrix, assigning available resource elements users, Kronecker product several smaller factor matrices. As result, both matrix design at transmitter side mixed symbols' receiver can be performed over matrices dimensions much than overall matrix. Consequently, leads significant reduction complexity latency detection. We algorithm general case involves recursions each involving certain sets equations corresponding then characterize system performance terms average sum rate, latency, complexity. Our analysis confirm superiority our enabling large Moreover, numerical results rate show provides better compared straightforward comparable complexity, especially regimes.