A Reduced-Complexity Maximum Likelihood Detection with A Sub Optimal Ber Requirement
Sharan Mourya1, Amit Kumar Dutta2

1Sharan Mourya, Assistant Professor, Indian Institute of Technology, Kharagpur (Hyderabad), India.

2Amit Kumar Dutta, Research Associate, Indian Institute of Technology, Kharagpur (Hyderabad), India.

Manuscript received on 02 August 2022 | Revised Manuscript received on 10 August 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022. | PP: 1-7 | Volume-2 Issue-6 October 2022 | Retrieval Number: 100.1/ijdcn.F5025102622 | DOI: 10.54105/ijdcn.F5025.102622

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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 Nt transmit antennas employing M-ary modulation, the ML-MIMO detector requires MNt 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.

Keywords: Maximum Likelihood (ML) Detection, Multiple-input Multiple-output (MIMO)
Scope of the Article: Wireless Communication