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IEEE Vehicular Technology Conference

Quantum-Native Formulation of Maximum Likelihood Detection in Random Access Channel with Overloaded MIMO

Hyoga Iizumi, Naoki Ishikawa, Shunsuke Uehashi, Kota Nakamura, Akira Kurita, Masao Oga

Published Oct 19, 2025• 0 citations
Publisher

Abstract

In this paper, we propose a quantum-native formulation of maximum likelihood detection in random access channel with overloaded multiple-input multiple-output (MIMO), where a number of user terminals share the same channel resource and asynchronously transmit signals. Classical low-complexity linear detectors suffer from significant performance degradation in this scenario, while the exhaustive-search-based maximum likelihood detection achieves the optimal performance but induces an exponential increase in time complexity. To strike this fundamental tradeoff, we formulate the maximum likelihood detection as a binary optimization problem and solve it via the Grover adaptive search (GAS), a quantum exhaustive search algorithm offering quadratic speedup. We further introduce a search space reduction technique using quantum W states and controlled-H gates, which substantially decreases complexity. In addition, we investigate the effects of efficient parameter settings in GAS. Our simulation results show that the proposed detector achieved the optimal detection performance while reducing the query complexity, thereby highlighting its potential as a solution for future quantum-accelerated overloaded MIMO systems.

DOI
10.1109/vtc2025-fall65116.2025.11310783

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We propose a new quantum-assisted maximum likelihood detection (MLD) scheme for generalized spatial modulation (GSM) systems. To that end, we first present a novel MLD formulation for GSM, and then describe a corresponding novel method to prepare the initial state for an associated quantum-computing Grover adaptive search (GAS) algorithm. Simulation results are presented to show that our approach achieves optimal performance and reduces query complexity compared both to the classical MLD and to earlier quantum search methods, which do not achieve an advantage over classical MLD for some parameter settings.