Energy Efficiency and Spectrum Efficiency in Underlay Device-to-Device Communications Enabled Cellular Networks

In this paper, we investigate the energy efficiency and spectrum efficiency, including one-hop device-to-device(D2D) communications mode and two-way amplify-and-forward(AF) relaying D2D communications mode in underlay D2D communications enabled cellular networks. An analysis of average energy efficiency and spectrum efficiency are developed and closed-form expressions are obtained for two types of D2D communications modes under the effect of Rayleigh fading channel, path loss, and co-channel interference. Analytical results are validated through numerical simulations. Based on the simulation, the effects of the interference, the distance between D2D pair and the position of relay node on the energy efficiency and spectrum efficiency of D2D communications are investigated. The optimal D2D transmission powers of these two modes to maximize the energy efficiency are also investigated.

A Green Clustering Protocol for Mobile Sensor Network Using Particle Swarm Optimization

Energy consumption of sensor nodes is one of the crucial issues in prolonging the lifetime of wireless sensor networks. One of the methods that can improve the utilization of sensor nodes batteries is the clustering method. In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization （PSO） algorithm. We define a new fitness function that can optimize the energy consumption of the whole network and minimize the relative distance between cluster heads and their respective member nodes. We also take into account the mobility factor when defining the cluster membership, so that the sensor nodes can join the cluster that has the similar mobility pattern. The performance of the proposed protocol is compared with well-known clustering protocols developed for wireless sensor networks such as LEACH （low-energy adaptive clustering hierarchy） and protocols designed for sensor networks with mobile nodes called CM-IR （clustering mobility-invalid round）. In addition, we also modify the improved version of LEACH called MLEACH-C, so that it is applicable to the mobile sensor nodes environment. Simulation results demonstrate that the proposed protocol using PSO algorithm can improve the energy consumption of the network, achieve better network lifetime, and increase the data delivered at the base station.

Design of Power Switching and Distribution Circuits for Solar Power Generation

Abstract--With the development of clean energy, switching and distribution issues in a photovoltaic system are getting much attention in recent years. This paper designs a DC to AC inverter and power switching and distribution system between a solar power system and the municipal system by using the Darlington amplifier structure with the photosensitive resistor and accompanying relays, and details the system circuits. The proposed system can achieve a stable output of IIOV AC, as well as self-generating driving voltage and switching between the municipal electrical system and the solar power system. The mathematic analysis and actually test results demonstrate that the proposed method is an easy, inexpensive, and low cost way to build a solar power switching and distribution system.

Sparse Optimization for Green Edge AI Inference

With the rapid upsurge of deep learning tasks at the network edge,effective edge artificial intelligence(AI)inference becomes critical to provide lowlatency intelligent services for mobile users via leveraging the edge computing capability.In such scenarios,energy efficiency becomes a primary concern.In this paper,we present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference through minimizing the overall power consumption consisting of both computation and transmission power consumption,yielding a mixed combinatorial optimization problem.By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector,we reformulate the optimization as a group sparse beamforming problem.To solve this challenging problem,we propose a logsum function based three-stage approach.By adopting the log-sum function to enhance the group sparsity,a proximal iteratively reweighted algorithm is developed.Furthermore,we establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm.Simulation results will demonstrate the effectiveness of the proposed approach for improving energy efficiency in edge AI inference systems.