Communications
N. Danesh; M. Sheikhan; B. Mahboobi
Abstract
Background and Objectives: To achieve significant throughput, interference alignment (IA) is an encouraging technique for wireless interference networks. In this study, we design an aligned beamformer based on the interference leakage minimization (ILM) method to reduce the interference power for a ...
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Background and Objectives: To achieve significant throughput, interference alignment (IA) is an encouraging technique for wireless interference networks. In this study, we design an aligned beamformer based on the interference leakage minimization (ILM) method to reduce the interference power for a multiple-input multiple-output interference channel (MIMO-IC).Methods: To deal with the non-convexity of ILM problem, we used a non-convex programming method (i.e., difference of convex [DC]). In this way, the interference leakage function is reformulated to a DC function including difference of two convex terms. Then, an additive function is defined that includes the DC objective function and a penalty function.Results: We propose a novel DC-based IA algorithm that uses solutions of an upper bound of the additive function in each iteration; as the initial state for the next iteration. Through an iterative manner and for the large values of the penalty factor, the solutions of upper bound function converge to the solutions of the original DC objective function (i.e., interference leakage function).Conclusion: In contrast to the frequent IA methods, the proposed DC-based IA algorithm updates transmit- and receive-beamformers in each iteration jointly (not alternately). Simulation results indicate that the proposed method outperforms some competitive IA algorithms by providing more throughputs and less interference leakage.
Communications Networks
M. Ghaderi; V. Tabataba Vakili; M. Sheikhan
Abstract
Background and Objectives: Routing and data aggregation are two important techniques for reducing communication cost of wireless sensor networks (WSNs). To minimize communication cost, routing methods can be merged with data aggregation techniques. Compressive sensing (CS) is one of the effective techniques ...
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Background and Objectives: Routing and data aggregation are two important techniques for reducing communication cost of wireless sensor networks (WSNs). To minimize communication cost, routing methods can be merged with data aggregation techniques. Compressive sensing (CS) is one of the effective techniques for aggregating network data, which can reduce the cost of communication by reducing the amount of routed data to the sink. Spatiotemporal CS (STCS), with the use of spatial and temporal correlation of sensor readings, can increase the compression rate in WSNs, thereby reducing the cost of communication. Methods: In this paper, a new method of STCS technique based on the geographic adaptive fidelity (GAF) protocol is proposed which can effectively reduce the communication cost and energy consumption in WSNs. In the proposed method, temporal data is obtained from random selection of temporal readings of cluster head (CH) sensors located in virtual cells in the clustered sensors' area and spatial data will be formed from the data readings of CHs located on the routes. Accordingly, a new structure of sensing matrix will be created. Results: The results of proposed method show that the proposed method as compared to the method proposed in [29], which is the most similar method in the literature, reduces energy consumption in the range of 22% to 43% in various scenarios which were implemented based on the number of required measurements at the sink (M) and the number of measurements in the routes (mr). Conclusion: In the proposed method, based on spatio-temporal CS (STCS), a new structure of sensing matrix is created that can increase the compression rate, thereby reducing the communication cost in the WSNs.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
