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A novel compressed sensing ultra‐wideband channel estimation method based on non‐convex optimization
Author(s) -
Wang Weidong,
Yang Junan,
Zhang Chun
Publication year - 2015
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.2680
Subject(s) - compressed sensing , ultra wideband , computer science , norm (philosophy) , channel (broadcasting) , convex optimization , optimization problem , mathematical optimization , wideband , algorithm , function (biology) , regular polygon , telecommunications , mathematics , electronic engineering , geometry , evolutionary biology , political science , law , biology , engineering
Summary Due to the low power spectral density and complicated transfer propagation of ultra‐wideband (UWB) signal, it is important to estimate UWB channel accurately. But it is difficult to sample UWB signals directly due to their wider band width. However, compressed sensing (CS) theory provides a feasible way through lower sampling speed. Common CS‐UWB channel estimation methods adopt convex optimization, non‐sparse or non‐restricted form. In order to strengthen the restriction on sparsity of the reconstructed channel vector, a non‐convex optimization method is proposed in this paper to estimate UWB channel. Proposed method sets the objective function as a non‐convex optimization model using l p –norm. This model is combined as a convex function to approximate the objective function and reconstruct the UWB channel vector iteratively. Because l p –norm is closer to l 0 –norm than l 1 and l 2 –norm, its restriction on sparsity of objective vector is stricter. The simulation results show that this method can enhance reconstruction performance compared with existing CS‐UWB channel estimation methods. Copyright © 2013 John Wiley & Sons, Ltd.