VLSs: A Local Search Algorithm for Distributed Constraint Optimization Problems

Local search algorithms are widely applied in solving large-scale distributed constraint optimization problem (DCOP). Distributed stochastic algorithm (DSA) is a typical local search algorithm to solve DCOP. However, DSA has some drawbacks including easily falling into local optima and the unfairness of assignment choice. This paper presents a novel local search algorithm named VLSs to solve the issues. In VLSs, sampling according to the probability corresponding to assignment is introduced to enable each agent to choose other promising values. Besides, each agent alternately performs a greedy choice among multiple parallel solutions to reduce the chance of falling into local optima and a variance adjustment mechanism to guide the search into a relatively good initial solution in a periodic manner. We give the proof of variance adjustment mechanism rationality and theoretical explanation of impact of greed among multiple parallel solutions. The experimental results show the superiority of VLSs over state-of-the-art DCOP algorithms.

Allied Power Constraint Optimization and Optimal Beam Tracking Scheme for Mobile mmWave Massive MIMO Communications

<div>The fifth generation (5G) networks and internet of things (IoT) promise to transform our lives by enabling various new applications from driver-less cars to smart cities. These applications will introduce enormous amount of data traffic and number of connected devices in addition to the current wireless networks. Thus 5G networks require many researches to develop novel telecommunication technologies to accommodate these increase in data traffic and connected devices. In this paper, novel power constraint optimization and optimal beam tracking schemes are proposed for mobile mmWave massive MIMO communications. A recently published novel channel model that is different from other widely used ones is considered. The channel model considers the number of clusters and number of rays within each cluster as varying due to user mobility. The proposed power constraint optimization scheme harmonizes conventional total power constraint (TPC) and uniform power constraint (UPC) schemes into a new one called allied power constraint (APC) that can significantly improve the system performance in 5G networks while achieving fairness among users. TPC and UPC have major drawbacks with respect to fairness and achieving quality-of-service (QoS) for users in dense networks. Thus APC aims to harmonize TPC and UPC by adjusting each antenna element’s constraint to adapt for some power resilience to a specific antenna element, hence proposing an intermediate solution between the two extreme case power constraint optimization schemes. Three optimal beam tracking schemes: (i) conventional exhaustive search (CES), (ii) multiobjective joint optimization codebook (MJOC), and (iii) linear hybrid combiner (LHS) scheme, have been provided for the mobile mmWave massive MIMO system with the proposed APC scheme. For the proposed APC scheme a comprehensive performance analysis is provided and compared with TPC and UPC. Spectral efficiency (SE), bit-error-rate (BER), Jain’s fairness index, channel occupancy ratio (COR) and instantaneous interfering power metrics are investigated. It has been shown that the proposed scheme can significantly outperform conventional schemes.</div>

Allied Power Constraint Optimization and Optimal Beam Tracking Scheme for Mobile mmWave Massive MIMO Communications

<div>The fifth generation (5G) networks and internet of things (IoT) promise to transform our lives by enabling various new applications from driver-less cars to smart cities. These applications will introduce enormous amount of data traffic and number of connected devices in addition to the current wireless networks. Thus 5G networks require many researches to develop novel telecommunication technologies to accommodate these increase in data traffic and connected devices. In this paper, novel power constraint optimization and optimal beam tracking schemes are proposed for mobile mmWave massive MIMO communications. A recently published novel channel model that is different from other widely used ones is considered. The channel model considers the number of clusters and number of rays within each cluster as varying due to user mobility. The proposed power constraint optimization scheme harmonizes conventional total power constraint (TPC) and uniform power constraint (UPC) schemes into a new one called allied power constraint (APC) that can significantly improve the system performance in 5G networks while achieving fairness among users. TPC and UPC have major drawbacks with respect to fairness and achieving quality-of-service (QoS) for users in dense networks. Thus APC aims to harmonize TPC and UPC by adjusting each antenna element’s constraint to adapt for some power resilience to a specific antenna element, hence proposing an intermediate solution between the two extreme case power constraint optimization schemes. Three optimal beam tracking schemes: (i) conventional exhaustive search (CES), (ii) multiobjective joint optimization codebook (MJOC), and (iii) linear hybrid combiner (LHS) scheme, have been provided for the mobile mmWave massive MIMO system with the proposed APC scheme. For the proposed APC scheme a comprehensive performance analysis is provided and compared with TPC and UPC. Spectral efficiency (SE), bit-error-rate (BER), Jain’s fairness index, channel occupancy ratio (COR) and instantaneous interfering power metrics are investigated. It has been shown that the proposed scheme can significantly outperform conventional schemes.</div>

Optimal Placement of UAV-Assisted Amplify-and-Forward Relays for Mitigation of Pointing Error in Optical Wireless Backhaul

The angular instability of unmanned-aerial-vehicle (UAV) relays as well as the turbulence-induced beam wander may lead to significant pointing error in free-space optical backhaul. The hop distance between each relay in a serial relay scheme is an important parameter that can be optimized in order to enhance the performance of the system. In this study, we have optimized the error and outage probabilities as a function of the hop distance vector for amplify-and-forward relays (AF) under a number of constraints such as the maximum and average relay power and the maximum relay gain. We have discovered that for the unconstrained optimization problem, the optimum hop distances is a strictly increasing sequence for both the error and outage probabilities. For maximum power constraint optimization, the optimum hop distances form a strictly increasing sequence, whereas for maximum gain constraint optimization, the optimum hop distances lead to a nondecreasing sequence. <br>

Optimal Placement of UAV-Assisted Amplify-and-Forward Relays for Mitigation of Pointing Error in Optical Wireless Backhaul

The angular instability of unmanned-aerial-vehicle (UAV) relays as well as the turbulence-induced beam wander may lead to significant pointing error in free-space optical backhaul. The hop distance between each relay in a serial relay scheme is an important parameter that can be optimized in order to enhance the performance of the system. In this study, we have optimized the error and outage probabilities as a function of the hop distance vector for amplify-and-forward relays (AF) under a number of constraints such as the maximum and average relay power and the maximum relay gain. We have discovered that for the unconstrained optimization problem, the optimum hop distances is a strictly increasing sequence for both the error and outage probabilities. For maximum power constraint optimization, the optimum hop distances form a strictly increasing sequence, whereas for maximum gain constraint optimization, the optimum hop distances lead to a nondecreasing sequence. <br>