A Design of Low-Projection SCMA Codebooks for Ultra-Low Decoding Complexity in Downlink IoT Networks
Qu Luo, Zilong Liu, Gaojie Chen, Pei Xiao, Yi Ma, Amine Maaref

TL;DR
This paper introduces a novel low-projection SCMA codebook design tailored for ultra-low decoding complexity in downlink IoT networks, achieving significant complexity reduction while maintaining good error performance.
Contribution
The paper proposes a low-projection codebook design using golden angle modulation for SCMA, significantly reducing decoding complexity in IoT networks.
Findings
Decoding complexity reduced by at least 97%
Achieved one-shot decoding convergence
Maintained large minimum Euclidean distance
Abstract
This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Wireless Communication Technologies · graph theory and CDMA systems · Advanced Wireless Communication Techniques
