2011/11/24 by Carles Navarro Manchón, Gunvor E. Kirkelund, Manchón, Carles Navarro +9 · 2 citations
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Techniques #Error Correcting Code Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (stat.ML) #cs.IT #math.IT #stat.ML
paper · pdf · doi:10.48550/arxiv.1111.5848
arxiv created 2011/11/24 · openalex publication_date 2011/11/24 · arxiv updated 2011/11/28 · openalex created_date 2025/10/24 · openalex updated_date 2026/08/01
Iterative information processing, either based on heuristics or analytical frameworks, has been shown to be a very powerful tool for the design of efficient, yet feasible, wireless receiver architectures. Within this context, algorithms performing message-passing on a probabilistic graph, such as the sum-product (SP) and variational message passing (VMP) algorithms, have become increasingly popular. In this contribution, we apply a combined VMP-SP message-passing technique to the design of receivers for MIMO-ODFM systems. The message-passing equations of the combined scheme can be obtained from the equations of the stationary points of a constrained region-based free energy approximation. When applied to a MIMO-OFDM probabilistic model, we obtain a generic receiver architecture performing iterative channel weight and noise precision estimation, equalization and data decoding. We show that this generic scheme can be particularized to a variety of different receiver structures, ranging from high-performance iterative structures to low complexity receivers. This allows for a flexible design of the signal processing specially tailored for the requirements of each specific application. The numerical assessment of our solutions, based on Monte Carlo simulations, corroborates the high performance of the proposed algorithms and their superiority to heuristic approaches.