Browsing by Author "Hamila, R."
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Item Exploiting Sparsity in Amplify-and-Forward Broadband Multiple Relay Selection(Institute of Electrical and Electronics Engineers Inc., 2019-05-01) Hamila, R.; Al-Dhahir, Neofal; Foufou, S.; 113149196515374792028 (Al-Dhahir, N); Al-Dhahir, NeofalCooperative communication has attracted significant attention in the last decade due to its ability to increase the spatial diversity order with only single-antenna nodes. However, most of the techniques in the literature are not suitable for large cooperative networks such as device-to-device and wireless sensor networks that are composed of a massive number of active devices, which significantly increases the relay selection complexity. Therefore, to solve this problem and enhance the spatial and frequency diversity orders of large amplify and forward cooperative communication networks, in this paper, we develop three multiple relay selection and distributed beamforming techniques that exploit sparse signal recovery theory to process the subcarriers using the low complexity orthogonal matching pursuit algorithm (OMP). In particular, by separating all the subcarriers or some subcarrier groups from each other and by optimizing the selection and beamforming vector(s) using OMP algorithm, a higher level of frequency diversity can be achieved. This increased diversity order allows the proposed techniques to outperform existing techniques in terms of bit error rate at a lower computation complexity. A detailed performance-complexity tradeoff, as well as Monte Carlo simulations, are presented to quantify the performance and efficiency of the proposed techniques. © 2013 IEEE.Item Impact of Passive and Active Security Attacks on MIMO Smart Grid Communications(Institute of Electrical and Electronics Engineers Inc.) El Shafie, Ahmed; Chihaoui, H.; Hamila, R.; Al-Dhahir, Naofal; Gastli, A.; Ben-Brahim, L.; 0000-0002-7315-8242 (El Shafie, A); El Shafie, Ahmed; Al-Dhahir, NaofalWe consider multiple source nodes (consumers) communicating wirelessly their energy demands to the meter data-management system (MDMS) over the subarea gateway(s). We quantify the impact of passive and active security attacks on the reliability and security of the wireless communications system and the energy-demand estimation error cost in dollars incurred by the utility. We adopt a multiple-input multiple-output (MIMO) multiantenna-eavesdropper wiretap channel model. To secure the MIMO wireless communication system, the legitimate nodes generate artificial noise signals to mitigate the effect of the passive eavesdropping security attacks. Furthermore, we propose a redundant gateway design where multiple gateways are assumed to coexist in each subarea to forward the consumers’ energy-demand messages. We quantify the redundant designs impact on the communication reliability between the consumers and the MDMS and on the energy-demand estimation error cost.Item Joint Frame Synchronization and Channel Estimation: Sparse Recovery Approach and USRP Implementation(Institute of Electrical and Electronics Engineers Inc., 2019-03-25) Ozdemir, O.; Anjinappa, C. K.; Hamila, R.; Al-Dhahir, Naofal; Guvenc, I.; 113149196515374792028 (Al-Dhahir, N); Al-Dhahir, NaofalCorrelation-based techniques used for frame synchronization can suffer significant performance degradation over multi-path frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error between the transmitted and the equalized symbols compared to the conventional method. © 2013 IEEE.Item Sparsity-Based Joint NBI and Impulse Noise Mitigation in Hybrid PLC-Wireless Transmissions(Institute of Electrical and Electronics Engineers Inc.) Elgenedy, Mahmoud; Mokhtar, M.; Hamila, R.; Bajwa, W. U.; Ibrahim, A. S.; Al-Dhahir, Naofal; 0000-0003-3402-5188 (Elgenedy, M); 113149196515374792028 (Al-Dhahir, N); Elgenedy, Mahmoud; Al-Dhahir, NaofalWe propose a new sparsity-aware framework to model and mitigate the joint effects of narrow-band interference (NBI) and impulsive noise (IN) in hybrid powerline and unlicensed wireless communication systems. The proposed mitigation techniques, based on the principles of compressive sensing (CS), exploit the inherent (non-contiguous or contiguous) sparse structures of NBI and IN in the frequency and time domains, respectively. For the non-contiguous NBI and IN, we develop a multi-level orthogonal matching pursuit recovery algorithm that exploits prior knowledge about the sparsity level at each receive antenna and powerline to further reduce computational complexity without performance loss. In addition, for the non-contiguous asynchronous NBI scenario, we investigate the application of time-domain windowing to enhance the NBI’s sparsity and, hence, improve the NBI mitigation performance. For the contiguous NBI and IN scenario, we estimate the NBI and IN signals by modeling their burstiness as block-sparse vectors with and without prior knowledge of the bursts’ boundaries. Moreover, we show how to exploit the spatial correlations of the NBI and IN across the receive antennas and powerlines to convert a non-contiguous NBI and IN problem to a block-sparse estimation problem with much lower complexity. Furthermore, we investigate a Bayesian linear minimum mean square error based approach for estimating both non-contiguous and contiguous NBI and IN based on their second-order statistics to further improve the estimation performance. Finally, our numerical results illustrate the superiority of the joint processing of our proposed NBI and IN sparsity-based mitigation techniques compared to separate processing of the wireless and powerline received signals.