
arXiv: 2301.00933
This paper studies a downlink system that combines orthogonal-time-frequency-space (OTFS) modulation and sparse code multiple access (SCMA) to support massive connectivity in high-mobility environments. We propose a cross-domain receiver for the considered OTFS-SCMA system which efficiently carries out OTFS symbol estimation and SCMA decoding in a joint manner. This is done by iteratively passing the extrinsic information between the time domain and the delay-Doppler (DD) domain via the corresponding unitary transformation to ensure the principal orthogonality of errors from each domain. We show that the proposed OTFS-SCMA detection algorithm exists at a fixed point in the state evolution when it converges. To further enhance the error performance of the proposed OTFS-SCMA system, we investigate the cooperation between downlink users to exploit the diversity gains and develop a distributed cooperative detection (DCD) algorithm with the aid of belief consensus. Our numerical results demonstrate the effectiveness and convergence of the proposed algorithm and show an increased spectral efficiency compared to the conventional OTFS transmission.
Signal Processing (eess.SP), FOS: Computer and information sciences, Distributed cooperation, Computer Science - Information Theory, Information Theory (cs.IT), Multiple access, 620, 004, State evolution, Non-orthogonal multiple access (NOMA), Orthogonal time frequency space (OTFS), FOS: Electrical engineering, electronic engineering, information engineering, Sparse code multiple access (SCMA), Electrical Engineering and Systems Science - Signal Processing
Signal Processing (eess.SP), FOS: Computer and information sciences, Distributed cooperation, Computer Science - Information Theory, Information Theory (cs.IT), Multiple access, 620, 004, State evolution, Non-orthogonal multiple access (NOMA), Orthogonal time frequency space (OTFS), FOS: Electrical engineering, electronic engineering, information engineering, Sparse code multiple access (SCMA), Electrical Engineering and Systems Science - Signal Processing
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