@inproceedings{0a947ccbf3854496877b28f1715a4fee,
title = "Leveraging the ℓ1-LS criterion for OFDM sparse wireless channel estimation",
abstract = "In order to exploit the inherent sparsity of an OFDM multipath wireless channel, this paper presents a novel estimation technique based on the ℓ1-LS criterion that relies on the selection of the optimal regularization parameter. This term is proven in the paper to be intimately related to the Signal to Noise Ratio (SNR) and Sparsity Degree (ρ) of the channel. Therefore, in order to take full advantage of the ℓ1-LS framework, an ad-hoc technique for estimating blindly the SNR and ρ of the problem is also introduced. The performance of the novel algorithm is compared to a number of reference techniques. Two standard measures, namely the Mean Square Error (MSE) of the estimated channel and the Symbol Error Rate (SER) of the OFDM system validate the robustness of the proposed algorithm. Indeed, this novel technique achieves better estimation along with better spectrum efficiency.",
keywords = "channel estimation, Least Squares (LS) algorithm, Orthogonal Frequency Division Multiplexing (OFDM), Sparsity, ℓ1-norm",
author = "Fatimah Al-Ogaili and Hadeel Elayan and Leen Alhalabi and Abdullah Al-Shabili and Bilal Taha and Luis Weruaga and Shihab Jimaa",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 11th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015 ; Conference date: 19-10-2015 Through 21-10-2015",
year = "2015",
month = dec,
day = "4",
doi = "10.1109/WiMOB.2015.7348050",
language = "British English",
series = "2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "845--849",
booktitle = "2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015",
address = "United States",
}