Energy Efficiency Maximization Algorithm Research Articles

Energy efficient resource allocation for re-configurable intelligent surface-assisted wireless networks

This paper focuses on energy-efficient resource allocation in reconfigurable intelligent surface (RIS)-assisted multiple-input-single-output (MISO) communication systems. Specifically, it revisits the solution to the energy efficiency (EE) problem using the alternating optimization (AO) approach. In each AO iteration, the RIS phase optimization is achieved using the gradient descent method, which unfortunately does not guarantee convergence. To overcome this limitation, we propose two alternatives: the Wolfe-based gradient-descent (GAW) EE maximization Algorithm and the trust region (TR)-based EE maximization algorithm. Additionally, we use Dinkelbach’s algorithm to obtain the optimal transmit power allocation. Our results demonstrate that the proposed methods outperform the existing approach that uses sequential fractional programming (SFP) for phase optimization and the traditional relay-based method.

Energy Efficiency Maximization of Backscatter-Assisted Wireless-Powered MEC With User Cooperation

The integrated backscatter communication (BackCom) and active communication (AC) scheme can improve wireless powered mobile edge computing (WPMEC) system performance in general single-user and multi-user scenarios. However, there is little research in the cooperation-assisted WPMEC scenario. In this paper, we consider a cooperation-assisted WPMEC system consisting of a source node (SN), a helper and a hybrid access point (HAP) integrated with MEC servers. An innovative user cooperation (UC) scheme with integrated BackCom and AC is proposed to enhance the system performance. As a relay, the helper can help the SN to transmit its computing tasks due to the poor communication link between the SN and the HAP. To be specific, we aim at maximizing the user energy efficiency (EE) by jointly optimizing backscatter reflection coefficient for BackCom, transmission power for AC, system time and tasks allocation while considering the minimum computation bits requirement, the channel capacity and energy constraints. Based on a fractional program, the EE maximization problem first is transformed to an equivalent one. Then, we exploit variable substitution and convex optimization to transform this non-convex problem into a convex problem. In addition, semi-closed form expressions of the optimal solution are deduced. An energy efficiency maximization algorithm is proposed to solve this problem. Simulation results demonstrate that the proposed scheme significantly improves the user EE than the existing schemes.

Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems

Recent years have seen a surge in curiosity in machine-to-machine (M2M) collaborations between academics and industry. Machine-to-machine communication devices (MTCDs) are able to communicate automatically and with minimum human intervention in an M2M communications infrastructure. While MTCDs are anticipated to deliver a range of services, resource allocation and clustering approaches in M2M transceivers face issues and limits due to the diverse quality of service (QoS) needs in various network conditions. A major issue in M2M communication systems is how to distribute and cluster resources. This article presents a clustering technique and collaborative resource allocation for MTCD resource management. The clustering and integrated resource allocation challenge is characteristic as a maximization of energy efficiency problem. As a consequence of the original optimization model’s inability to tackle nonlinear fractional utilizations, we separate the issue into two subproblems: power redistribution and cluster. We begin by obtaining the optimal power distribution plan through an iterative energy efficiency maximization algorithm and then offer a modified K -means technique for clustering. The effectiveness of the proposed approach is shown by the numerical solution.

Energy Efficiency Optimization Using Iterative and Metaheuristic Algorithms

The communication technology industry's energy consumption and the resulting energy-related pollution are becoming serious societal and economic challenges. One of the most essential parameters for selecting potential technologies for upcoming wireless communication networks is bandwidth efficiency (BE). However, the main objective is to develop new creative network architecture and technologies that are required to meet the rapid growth in cellular data demand while maintaining same amount of power.Also, Energy Efficiency (EE) has emerged as a key parameter for assessing the performance of wireless communication networks. In this context, the recently suggested massive multiple-input multiple-output (MIMO) technology, which comes under multiuser MIMO with an excess of base station (BS) antennas to support a substantial number of user equipment's (UE) is one of the main technology enablers for 5G.In this paper, new power consumption model is addressed and also novel EE maximization algorithm is proposed which optimizes the resource allocation in massive MIMO network.

Secrecy energy efficiency optimization for DF relaying IoT systems with passive eavesdropping terminal

In this paper, we investigate a cooperative Internet of Things (IoT) communication network with decode-and-forward relay nodes that can be eavesdropped, where the relay node harvests energy from the received signal power to charge its battery, which is used to retransmit the received signal to the destination. The secrecy communication of the energy harvesting Gaussian eavesdropping channel has been studied. The expression of secrecy energy efficiency for the cooperative IoT system with a battery-enabled relay has been derived, which depends on energy absorption rate and energy harvesting time. Then we perform analytical and numerical analysis on the performance on secrecy energy efficiency. Our analytical results reveal that the secrecy energy efficiency has a maximum under constraints. Finally, we demonstrate that our proposed model and methods are feasible from simulation results based on proposed secrecy energy efficiency maximization algorithm.

Energy efficiency maximization algorithm for underwater Mobile sensor networks

Modern Underwater Wireless Sensor Networks (UWSN) would provide big administrations with numerous underwater surveying and technical applications, working in the unstable submerged deep-water conditions. A huge obstacle in these networks is the lifetime limit. The submerged correspondence frameworks mostly employ acoustic communication today. Acoustic interchange communication offers longer ranges that are yet limited by three variables: restricted and subordinate data transmission, time-differing multi-way engendering and low speed of sound. In this paper, an AUV (Autonomous Underwater Vehicle)-assisted acoustic correspondence convention, specifically Energy Efficiency Maximization Algorithm (EEMA) has been proposed to minimize the energy consumption. Underwater sensor networks depend on the hub ceaseless operation, the restricted correspondence transmission capacity and the hub lifetime, which entails difficulties in the operation of USWN. The proposed system will enhance the lifetime by lessening the number of bounces amid sensor transmissions, which fundamentally lessens time utilization and lifetime. Dynamic AUV ways and dynamic gateway assignments will enhance lifetime – proficiency balance proportion in the submerged system. To decrease the system energy utilization with an acceptable conveyance proportion is recommended. The Experimental results show that the proposed methodology has improved the level of energy compared with related techniques.

Offloading Optimization and Time Allocation for Multiuser Wireless Energy Transfer Based Mobile Edge Computing System

This paper considers a wireless energy transfer based mobile edge computing system, where wireless devices can be charged by the radio-frequency signals broadcast by hybrid access point. With Mobile Edge Computing (MEC), wireless devices can execute their computation tasks locally or offload them to the MEC server by time division multiple access protocol. Based on this system, this paper studies the problem of system energy efficiency maximization by joint optimization of computing time allocation, energy consumption, capacity of local computing and task offloading. A Tabu search based system energy efficiency maximization algorithm is proposed for solving the optimization problems. Finally, the performance of the proposed algorithm is valued by extensive simulation experiments. Simulation results verify the effectiveness of the proposed algorithm.

Energy Efficiency Optimization for Massive MIMO Non-Orthogonal Unicast and Multicast Transmission with Statistical CSI

We study the energy efficiency (EE) optimization problem in non-orthogonal unicast and multicast transmission for massive multiple-input multiple-output (MIMO) systems with statistical channel state information of all receivers available at the transmitter. Firstly, we formulate the EE maximization problem. We reduce the number of variables to be solved and simplify this large-dimensional-matrix-valued problem into a real-vector-valued problem. Next, we lower the computational complexity significantly by replacing the objective with its deterministic equivalent to avoid the high-complex expectation operation. With guaranteed convergence, we propose an iterative algorithm on beam domain power allocation using the minorize maximize algorithm and Dinkelbach’s transform and derive the locally optimal power allocation strategy to achieve the optimal EE. Finally, we illustrate the significant EE performance gain of our EE maximization algorithm compared with the conventional approach through conducting numerical simulations.

Time-Switching EH-Based Joint Relay Selection and Resource Allocation Algorithms for Multi-User Multi-Carrier AF Relay Networks

In this paper, an energy efficiency maximization (EEM) optimization problem for the multi-user multi-carrier energy-constrained amplify-and-forward (AF) multi-relay network is formulated under the total source transmit power budget and energy-causality constraints. We consider that each relay node is solely powered by the source nodes, employing energy harvesting time-switching (EHTS) protocol to harvest the energy through the ambient radio-frequency (RF) signal transmitted from the source nodes under the simultaneous wireless information and power transfer (SWIPT) paradigm. First, we propose a subcarrier and energy causality-based multi-relay selection policy. Second, we jointly optimize the parameters that control the energy efficiency (EE) of the system namely multi-relay selection, subcarrier pairing, user allocation, power allocation, and RF EHTS time block, that renders the problem to be a mixed integer non-linear programming problem (MINLP) which remains NP-hard to solve. Hence, we remodel the problem to a tractable quasi-concave form by applying a string of convex transformations. Later, we propose an iterative EEM algorithm to optimize the multi-parameter problem. Further, a suboptimal and best relay selection algorithm is studied by trading-off between complexity and performance. The effectiveness of the proposed algorithms is demonstrated by simulation results.

Resource allocation for relay-aided underwater acoustic sensor networks with energy harvesting

Underwater acoustic sensor networks play a significant role in ocean observation, pollution monitoring and underwater remote control. Different from conventional sensor networks, transmission efficiency and lifetime for nodes in underwater acoustic sensor networks is vital since it is difficult to change batteries of buoy relays and underwater sensors. In this paper, two resource allocation algorithms for energy harvesting time and transmit power allocation in underwater acoustic sensor networks with single and multiple wireless energy harvesting buoy nodes are proposed to maximize the sum rate and energy efficiency. Both of the circuit power consumption and transmission power consumption are considered as the power consumption of the system. We first propose an optimal algorithm to allocate the time of energy harvesting slot for sum rate maximization. From the derivation, we show that the sum rate of the network grows as the transmission power increases. However, when the transmission power is extremely large, the energy efficiency of the network starts to decrease. Thus a joint algorithm with energy harvesting time and transmit power allocation is further provided for energy efficiency maximization. Simulation results validate the sum rate improvement under our proposed sum rate maximization algorithm and energy efficiency improvement under the proposed energy efficiency maximization algorithm.

Energy Efficiency Optimization-Based Joint Resource Allocation and Clustering Algorithm for M2M Communication Systems

In recent years, machine-to-machine (M2M) communications have attracted great attentions from both academia and industry. In M2M communication systems, machine type communication devices (MTCDs) are capable of communicating with each other intelligently under highly reduced human interventions. Although diverse types of services are expected to be supported for MTCDs, various quality of service (QoS) requirements and network states pose difficulties and challenges to the resource allocation and clustering schemes of M2M communication systems. In this paper, we address the joint resource allocation and clustering problem in M2M communication systems. To achieve the efficient resource management of the MTCDs, we propose a joint resource management architecture, and design a joint resource allocation and clustering algorithm. More specifically, by defining system energy efficiency as the sum of the energy efficiency of the MTCDs, the joint resource allocation and clustering problem is formulated as an energy efficiency maximization problem. As the original optimization problem is a nonlinear fractional programming problem, which cannot be solved conveniently, we transform the optimization problem into power allocation subproblem and clustering subproblem. Applying iterative method-based energy efficiency maximization algorithm, we first obtain the optimal power allocation strategy based on which, we then propose a modified K-means algorithm to obtain the clustering strategy. Numerical results demonstrate the effectiveness of the proposed algorithm.

Energy Efficient Resource Allocation for Multiuser Relay Networks

In this paper, a novel resource allocation algorithm is investigated to maximize the energy efficiency (EE) in multiuser decode-and-forward (DF) relay interference networks. The EE optimization problem is formulated as the ratio of the spectrum efficiency (SE) over the entire power consumption of the network subject to total transmit power, subcarrier pairing, and allocation constraints. The formulated problem is a nonconvex fractional mixed binary integer programming problem, i.e., NP-hard to solve. Furthermore, we resolve the convexity of the problem by a series of convex transformations and propose an iterative EE maximization algorithm to jointly determine the optimal subcarrier pairing at the relay, subcarrier allocation to each user pair and power allocation to all source and the relay nodes. Additionally, we derive an asymptotically optimal solution by using the dual decomposition method. To gain more insights into the obtained solutions, we further analyze the resource allocation algorithm in a two-user case with interference-dominated and noise-dominated regimes. In addition, a suboptimal algorithm is investigated with reduced complexity at the cost of acceptable performance degradation. Simulation results are used to evaluate the performance of the proposed algorithms and demonstrate the impacts of various network parameters on the attainable EE and SE.