Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
From an error rate performance perspective, maximum likelihood (ML) detection is the preferred detection method for multiple-input multiple-output (MIMO) communication systems. However, for high transmission rates a straight forward exhaustive search implementation suffers from prohibitive complexity. The K-best algorithm provides close-to-ML bit error rate (BIER) performance, while its circuit complexity is reduced compared to an exhaustive search. In this paper, a new VLSI architecture for the implementation of the K-best algorithm is presented. Instead of the mostly sequential processing that has been applied in previous VLSI implementations of the algorithm, the presented solution takes a more parallel approach. Further-more, the application of a simplified norm is discussed. The implementation in an ASIC achieves up to 424 Mbps throughput with an area that is almost on par with current state-of-the-art implementations.
Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla