Abstract
This paper proposes an optimization algorithm to determine the optimal coherent combination candidates of distributed local beams in a wireless sensor network. The beams are generated from analog uniform linear arrays of nodes and headed toward the random directions due to the irregular surface where the nodes are mounted. Our algorithm is based on one of the meta-heuristic schemes (i.e., the single-objective simulated annealing) and designed to solve the objective of minimizing the average interference-to-noise ratio (INR) under the millimeter wave channel, which leads to the reduction of sidelobes. The simulation results show that synthesizing the beams on the given system can form a deterministic mainlobe with considerable and unpredictable sidelobes in undesired directions, and the proposed algorithm can decrease the average INR (i.e., the average improvement of 12.2 dB and 3.1 dB are observed in the directions of and , respectively) significantly without the severe loss of signal-to-noise ratio (SNR) in the desired direction.
Highlights
Wireless sensor networks (WSNs) have been widely studied and applied for activity-monitoring applications in military [1,2], weather [3,4], and commercial [5,6] areas
These frequency bands suffer from the severe path loss caused by the atmospheric absorption and scattering [11], they are still attractive because their small wavelengths allow to use phased array architectures such as uniform linear arrays (ULAs) [12]
Formulated the combinatorial optimization problem with the node-selection scheme to prune the sidelobes [28], Chen et al utilized the decentralized cross-entropy optimization (CEO) having the significantly reduced complexity compared to [29], Sun et al adjusted the excitation amplitude and phase of the nodes by the firefly algorithm [30], and Jayaprakasam et al proposed the nondominated sorting genetic algorithm II (NSGA-II) to solve the multi-objective amplitude and phase optimization having the goal of minimizing the peak sidelobe level minimization and maximizing the directivity simultaneously [31]
Summary
Wireless sensor networks (WSNs) have been widely studied and applied for activity-monitoring applications in military [1,2], weather [3,4], and commercial [5,6] areas. Formulated the combinatorial optimization problem with the node-selection scheme to prune the sidelobes [28], Chen et al utilized the decentralized cross-entropy optimization (CEO) having the significantly reduced complexity compared to [29], Sun et al adjusted the excitation amplitude and phase of the nodes by the firefly algorithm [30], and Jayaprakasam et al proposed the nondominated sorting genetic algorithm II (NSGA-II) to solve the multi-objective amplitude and phase optimization having the goal of minimizing the peak sidelobe level minimization and maximizing the directivity simultaneously [31] These schemes showed the prominent effects on reducing the sidelobe levels in the undesired directions, they are confined to the case that the nodes are equipped with omni-directional antennas and operate in the conventional frequency bands, which provides the motivation of our paper. The main contribution of our paper is verifying that the CB is useful to increase the transmission range from the nodes being equipped with the ULAs to the desired AP and providing the optimization algorithm to lower the interference in the undesired APs under the mmWave channel
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
