Suhanya Jayaprakasam

PSOGSA-Explore

Graphical abstractDisplay Omitted HighlightsWe propose a new algorithm to lower the sidelobes in collaborative beamforming.We achieved up to 100% improvement in sidelobe reduction.Variable parameter tuning is simplified in the proposed algorithm.The proposed algorithm successfully avoids the problem of premature convergence.The proposed method does not increase the computational complexity of the system. A conventional collaborative beamforming (CB) system suffers from high sidelobes due to the random positioning of the nodes. This paper introduces a hybrid metaheuristic optimization algorithm called the Particle Swarm Optimization and Gravitational Search Algorithm-Explore (PSOGSA-E) to suppress the peak sidelobe level (PSL) in CB, by the means of finding the best weight for each node. The proposed algorithm combines the local search ability of the gravitational search algorithm (GSA) with the social thinking skills of the legacy particle swarm optimization (PSO) and allows exploration to avoid premature convergence. The proposed algorithm also simplifies the cost of variable parameter tuning compared to the legacy optimization algorithms. Simulations show that the proposed PSOGSA-E outperforms the conventional, the legacy PSO, GSA and PSOGSA optimized collaborative beamformer by obtaining better results faster, producing up to 100% improvement in PSL reduction when the disk size is small.

Multiobjective Beampattern Optimization in Collaborative Beamforming via NSGA-II With Selective Distance

Collaborative beamforming is usually characterized by high, asymmetrical sidelobe levels due to the randomness of node locations. Previous works have shown that the optimization methods aiming to reduce the peak sidelobe level (PSL) alone do not guarantee the overall sidelobe reduction of the beampattern, especially when the nodes are random and cannot be manipulated. Hence, this paper proposes a multiobjective amplitude and phase optimization technique with two objective functions: PSL minimization and directivity maximization, in order to improve the beampattern. A novel selective Euclidean distance approach in the nondominated sorting genetic algorithm II (NSGA-II) is proposed to steer the candidate solutions toward a better solution. Results obtained by the proposed NSGA with selective distance (NSGA-SD) are compared with the single-objective PSL optimization performed using both GA and particle swarm optimization. The proposed multiobjective NSGA provides up to 40% improvement in PSL reduction and 50% improvement in directivity maximization and up to 10% increased performance compared to the legacy NSGA-II. The analysis of the optimization method when considering mutual coupling between the nodes shows that this improvement is valid when the inter-node Euclidean separations are large.

Sidelobe reduction and capacity improvement of open-loop collaborative beamforming in wireless sensor networks

Collaborative beamforming (CBF) with a finite number of collaborating nodes (CNs) produces sidelobes that are highly dependent on the collaborating nodes’ locations. The sidelobes cause interference and affect the communication rate of unintended receivers located within the transmission range. Nulling is not possible in an open-loop CBF since the collaborating nodes are unable to receive feedback from the receivers. Hence, the overall sidelobe reduction is required to avoid interference in the directions of the unintended receivers. However, the impact of sidelobe reduction on the capacity improvement at the unintended receiver has never been reported in previous works. In this paper, the effect of peak sidelobe (PSL) reduction in CBF on the capacity of an unintended receiver is analyzed. Three meta-heuristic optimization methods are applied to perform PSL minimization, namely genetic algorithm (GA), particle swarm algorithm (PSO) and a simplified version of the PSO called the weightless swarm algorithm (WSA). An average reduction of 20 dB in PSL alongside 162% capacity improvement is achieved in the worst case scenario with the WSA optimization. It is discovered that the PSL minimization in the CBF provides capacity improvement at an unintended receiver only if the CBF cluster is small and dense.

Distributed and Collaborative Beamforming in Wireless Sensor Networks: Classifications, Trends, and Research Directions

Distributed and collaborative beamforming (DCBF) scheme in wireless sensor networks (WSNs) is receiving new-found interest in recent times due to the rapid advancements in wireless technology and embedded systems. Although studies on distributed and collaborative beamforming have been carried out for more than ten years, the DCBF was initially considered impractical due to high complexity and hardly achievable requirements. It gained prominence only in the past few years as small wireless communication electronic sensors with high processing capability became easily available. Recent works showcasing distributed and collaborative beamforming as a suitable solution for 5G communication systems such as mm-wave communication and machine to machine communications has further ignited the interest in this research field. Motivated by these factors, this paper presents a survey on the research trends of distributed and collaborative beamforming in WSNs. We provide classifications of the DCBF research areas and conduct an extensive review of the various proposals which have appeared in the literature for each classification. This survey uncovered that majority of existing research can be broadly divided into four major research trends: beampattern analysis, power and lifetime optimization, synchronization, and finally, prototype design. The inherent features, constraints and challenges of each research category in the distributed and collaborative beamforming are presented and the lessons learned from the shortcomings of previous research are summarized. Finally, this paper has unveiled open research directions in the field of distributed and collaborative beamforming in WSNs.

Euclidean Matchings in Ultra-Dense Networks

In order to investigate the fundamental limits of network densification in and beyond 5G, we study the spatial spectral efficiency gain achieved when communication devices densely embedded in the d-dimensional Euclidean plane are optimally matched in near-neighbor pairs. We then proceed to assign these pairs their own data capacity given by Shannons theorem. The length of the shortest matching on the points then corresponds to the maximum one-hop capacity in the network. Interference is then added as a further constraint, which is modeled using shapes as guard regions, such as a disk, diametral disk, or equilateral triangle, matched to points, in a similar manner to computational geometry. The disk, for example, produces the Delaunay triangulation, while the diametral disk produces a beta skeleton. We also discuss deriving the scaling limit of both models using the replica method from the physics of disordered systems.

Robust Beam-Tracking for mmWave Mobile Communications

We propose a robust beam-tracking algorithm to maintain the communication link between a base station (BS) and a mobile station (MS) in a millimeter wave mobile communications system, with antenna arrays at both the BS and MS. The channel is tracked with the extended Kalman filter (EKF) at the static BS and the beamforming weight is updated with a robust minimum mean squared error beamformer bounded by the array vector error which is fed from the error variance estimated by the EKF. Results show that our proposed method is able to maintain a link with the MS with a smaller mismatch error compared with existing beamtracking method at moderate MS mobility and antenna array size.