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arXiv · 2208.05194

A Reduced-Complexity Maximum-Likelihood Detection with a sub-optimal BER Requirement

Sharan Mourya·Amit Kumar Dutta
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Abstract

Maximum likelihood (ML) detection is an optimal signal detection scheme, which is often difficult to implement due to its high computational complexity, especially in a multiple-input multiple-output (MIMO) scenario. In a system with $N_t$ transmit antennas employing $M$-ary modulation, the ML-MIMO detector requires $M^{N_t}$ cost function (CF) evaluations followed by a search operation for detecting the symbol with the minimum CF value. However, a practical system needs the bit-error ratio (BER) to be application-dependent which could be sub-optimal. This implies that it may not be necessary to have the minimal CF solution all the time. Rather it is desirable to search for a solution that meets the required sub-optimal BER. In this work, we propose a new detector design for a SISO/MIMO system by obtaining the relation between BER and CF which also improves the computational complexity of the ML detector for a sub-optimal BER.

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Sharan Mourya, Amit Kumar Dutta. 2022-08-10. A Reduced-Complexity Maximum-Likelihood Detection with a sub-optimal BER Requirement. https://doi.org/10.54105/ijdcn.f5025.102622

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