MIMO decoding based on stochastic reconstruction from multiple projections

TL;DR

This paper introduces the Tomographic Least Squares Decoder (TLSD), a novel algorithm for MIMO decoding that reconstructs integer-valued parameters from multiple projections, offering improved accuracy, convergence guarantees, and probabilistic outputs.

Contribution

The paper presents a new decoding algorithm based on stochastic reconstruction from multiple projections, outperforming existing sub-optimal methods and providing probabilistic information.

Findings

TLSD achieves better decoding accuracy than other sub-optimal algorithms.

The algorithm guarantees convergence unlike iterative methods such as belief propagation.

Simulations demonstrate the effectiveness of TLSD in MIMO decoding scenarios.

Abstract

Least squares (LS) fitting is one of the most fundamental techniques in science and engineering. It is used to estimate parameters from multiple noisy observations. In many problems the parameters are known a-priori to be bounded integer valued, or they come from a finite set of values on an arbitrary finite lattice. In this case finding the closest vector becomes NP-Hard problem. In this paper we propose a novel algorithm, the Tomographic Least Squares Decoder (TLSD), that not only solves the ILS problem, better than other sub-optimal techniques, but also is capable of providing the a-posteriori probability distribution for each element in the solution vector. The algorithm is based on reconstruction of the vector from multiple two-dimensional projections. The projections are carefully chosen to provide low computational complexity. Unlike other iterative techniques, such as the belief propagation, the proposed algorithm has ensured convergence. We also provide simulated experiments comparing the algorithm to other sub-optimal algorithms.