Low-complexity soft-output quantum-assisted multi-user detection for direct-sequence spreading and slow subcarrier-hopping aided SDMA-OFDM systems

Abstract

Low-complexity sub-optimal Multi-User Detectors (MUD) are widely used in multiple access communication systems for separating users, since the computational complexity of the Maximum Likelihood (ML) detector is potentially excessive for practical implementation. Quantum computing may be invoked in the detection procedure, by exploiting its inherent parallelism for approaching the ML MUD’s performance at a substantially reduced number of Cost Function (CF) evaluations. In this contribution, we propose a Soft-Output (SO) Quantum-assisted MUD achieving a near-ML performance and compare it to the corresponding SO Ant Colony Optimization (ACO) MUD. We investigate rank deficient Direct-Sequence Spreading (DSS) and Slow Subcarrier-Hopping aided (SSCH) Spatial Division Multiple Access (SDMA) Orthogonal Frequency Division Multiplexing (OFDM) systems, where the number of users to be detected is higher than the number of receive antenna elements used. We show that for a given complexity budget, the proposed SODHA QMUD achieves a better performance. We also propose an adaptive hybrid SO-ML / SO-DHA MUD, which adapts itself to the number of users equipped with the same spreading sequence and transmitting on the same subcarrier. Finally, we propose a DSS-based uniform SSCH scheme, which improves the system’s performance by 0:5 dB at a BER of 105, despite reducing the complexity required by the MUDs employed.

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Identifiers

ORCID for Soon Xin Ng: orcid.org/0000-0002-0930-7194

ORCID for Lajos Hanzo: orcid.org/0000-0002-2636-5214

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