Controlled Loosening-up (CLuP) -- achieving exact MIMO ML in polynomial time

TL;DR

This paper introduces a novel polynomial-time algorithm called CLuP that achieves exact maximum likelihood detection in MIMO systems, backed by theoretical foundations and numerical validation.

Contribution

The paper presents the Controlled Loosening-up (CLuP) method, a new approach based on Random Duality Theory, enabling polynomial-time exact MIMO ML detection.

Findings

Numerical experiments confirm the theoretical predictions.

CLuP achieves ML performance in polynomial time.

The approach is foundational for further research.

Abstract

In this paper we attack one of the most fundamental signal processing/informaton theory problems, widely known as the MIMO ML-detection. We introduce a powerful Random Duality Theory (RDT) mechanism that we refer to as the Controlled Loosening-up (CLuP) as a way of achieving the exact ML-performance in MIMO systems in polynomial time. We first outline the general strategy and then discuss the rationale behind the entire concept. A solid collection of results obtained through numerical experiments is presented as well and found to be in an excellent agreement with what the theory predicts. As this is the introductory paper of a massively general concept that we have developed, we mainly focus on keeping things as simple as possible and put the emphasis on the most fundamental ideas. In our several companion papers we present various other complementary results that relate to both, theoretical and practical aspects and their connections to a large collection of other problems and results that we have achieved over the years in Random Duality.