2013/04/28 by Renu Jose, Jose, Renu, Sooraj K. Ambat +4
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1304.7434
7 pages, 4 figures, under review in AEU - International Journal of Electronics and Communications (Elsevier) (paper id-AEUE-D-12-00625)
arxiv created 2013/04/28 · openalex publication_date 2013/04/28 · arxiv updated 2013/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Low complexity joint estimation of synchronization impairments and channel in a single-user MIMO-OFDM system is presented in this letter. Based on a system model that takes into account the effects of synchronization impairments such as carrier frequency offset, sampling frequency offset, and symbol timing error, and channel, a Maximum Likelihood (ML) algorithm for the joint estimation is proposed. To reduce the complexity of ML grid search, the number of received signal samples used for estimation need to be reduced. The conventional channel estimation methods using Least-Squares (LS) fail for the reduced sample under-determined system, which results in poor performance of the joint estimator. The proposed ML algorithm uses Compressed Sensing (CS) based channel estimation method in a sparse fading scenario, where the received samples used for estimation are less than that required for an LS based estimation. The performance of the estimation method is studied through numerical simulations, and it is observed that CS based joint estimator performs better than LS based joint estimator