Energy-Efficient Optimal Power Allocation for SWIPT Based IoT-Enabled Smart Meter

This paper presents an internet of things (IoTs) enabled smart meter with energy-efficient simultaneous wireless information and power transfer (SWIPT) for the wireless powered smart grid communication network. The SWIPT technique with energy harvesting (EH) is an attractive solution for prolonging the battery life of ultra-low power devices. The motivation for energy efficiency (EE) maximization is to increase the efficient use of energy and improve the battery life of the IoT devices embedded in smart meter. In the system model, the smart meter is equipped with an IoT device, which implements the SWIPT technique in power splitting (PS) mode. This paper aims at the EE maximization and considers the orthogonal frequency division multiplexing distributed antenna system (OFDM-DAS) for the smart meters in the downlink with IoT enabled PS-SWIPT system. The EE maximization is a nonlinear and non-convex optimization problem. We propose an optimal power allocation algorithm for the non-convex EE maximization problem by the Lagrange method and proportional fairness to optimal power allocation among smart meters. The proposed algorithm shows a clear advantage, where total power consumption is considered in the EE maximization with energy constraints. Furthermore, EE vs. spectral efficiency (SE) tradeoff is investigated. The results of our algorithm reveal that EE improves with EH requirements.

Achieving proportional fairness in WiFi networks via bandit convex optimization

In this paper, we revisit proportional fair channel allocation in IEEE 802.11 networks. Traditional approaches are either based on the explicit solution of the optimization problem or use iterative solvers to converge to the optimum. Instead, we propose an algorithm able to learn the optimal slot transmission probability only by monitoring the throughput of the network. We have evaluated this algorithm both (i) using the true value of the function to optimize and (ii) considering estimation errors. We provide a comprehensive performance evaluation that includes assessing the sensitivity of the algorithm to different learning and network parameters as well as its reaction to network dynamics. We also evaluate the effect of noisy estimates on the convergence rate and propose a method to alleviate them. We believe our approach is a practical solution to improve the performance of wireless networks as it does not require knowing the network parameters in advance. Yet, we conclude that the setting of the parameters of the algorithm is crucial to guarantee fast convergence.

Modelling and Development of a Radio Resource Control and Scheduling Algorithm for Long-Term Evolution (LTE) Uplink

Modern radio communication services transmit signals from an earth station to a high-altitude station, space station or a space radio system via a feeder link while in Global Systems for Mobile Communication (GSM) and computer networks, the radio uplink transmit from cell phones to base station linking the network core to the communication interphase via an upstream facility. Hitherto, the Single-Carrier Frequency Division Multiple Access (SC-FDMA) has been adopted for uplink access in the Long-Term Evolution (LTE) scheme by the 3GPP. In this journal, the LTE uplink radio resource allocation is addressed as an optimization problem, where the desired solution is the mapping of the schedulable UE to schedulable Resource Blocks (RBs) that maximizes the proportional fairness metric. The particle swarm optimization (PSO) has been employed for this research. PSO is an algorithm that is very easy to implement to solve real time optimization problems and has fewer parameters to adjust when compared to other evolutionary algorithms. The proposed scheme was found to outperform the First Maximum Expansion (FME) and Recursive Maximum Expansion (RME) in terms of simulation time and fairness while maintaining the throughput.