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

TL;DR

This paper proposes a new MIMO detection method that reduces computational complexity by targeting a sub-optimal BER, balancing performance and efficiency in practical systems.

Contribution

It introduces a detector design that relates BER to cost function, enabling complexity reduction for achieving desired sub-optimal BER levels.

Findings

Reduces ML detection complexity for sub-optimal BER requirements

Establishes a relation between BER and cost function in MIMO detection

Improves practical implementation efficiency of MIMO detectors

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.