Optimization-based Resource Allocation Research Articles

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

Spider monkey optimization based resource allocation and scheduling in fog computing environment

Spider monkey optimization based resource allocation and scheduling in fog computing environment

Impact of Channel Aging on Dual-Function Radar-Communication Systems: Performance Analysis and Resource Allocation

In conventional dual-function radar-communication (DFRC) systems, the radar and communication channels are routinely estimated at fixed time intervals based on their worst-case operation scenarios. Such situation-agnostic repeated estimations cause significant training overhead and dramatically degrade the system performance, especially for applications with dynamic sensing/communication demands and limited radio resources. In this paper, we leverage the channel aging characteristics to reduce training overhead and to design a situation-dependent channel re-estimation interval optimization-based resource allocation in a multi-target tracking DFRC system. Specifically, we exploit the channel temporal correlation to predict radar and communication channels for reducing the need for training preamble retransmission. Then, we characterize the channel aging effects on the Cramer-Rao lower bounds (CRLBs) for radar tracking performance analysis and achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) transmit beamforming for communication performance analysis. In particular, the aged CRLBs and achievable rates are derived as closed-form expressions with respect to the channel aging time, bandwidth, and power. Based on the analyzed results, we optimize these factors to maximize the average total aged achievable rate subject to individual target tracking precision demand, communication rate requirement, and other practical constraints. Since the formulated problem belongs to a non-convex problem, we develop an efficient one-dimensional search based optimization algorithm to obtain its suboptimal solutions. Finally, simulation results are presented to validate the correctness of the derived theoretical results and the effectiveness of the proposed allocation scheme.

Artificial Neural Network-Based Joint Mobile Relay Selection and Resource Allocation for Cooperative Communication in Heterogeneous Network

Supervised machine learning is a promising technique to achieve joint resource allocation and selection of mobile relays in cooperative heterogeneous networks. Joint resource allocation and mobile relay selection are NP-hard problems in the sense that they cannot be solved in polynomial time. To solve this problem, highly complex heuristic solutions based on channel conditions and physical-layer parameters have been considered. Our research explores an artificial neural network (ANN) approach to the joint allocation of resources and choice of a mobile relay with efficient utilization of resources in terms of energy efficiency (EE) and spectral efficiency (SE). To this, there exists a tradeoff between the two, i.e., EE and SE, and to consider this tradeoff, a new metric resource efficiency (RE) has already been introduced in the literature. This article addresses the problem of learning a regression model for determining optimal transmit power and resource allocation and choosing the appropriate relay for cooperative communication to maximize RE. Specifically, a multilayer fully connected ANN-based resource allocation and relay selection (ANN-RARS) algorithm is proposed, and a comparison of ANN-RARS with particle swarm optimization-based resource allocation and relay selection (PSO-RARS) algorithm is performed. Based on the performance analysis, ANN-RARS performs better than the PSO-RARS algorithm.

Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment

Rapid advancements of sixth-generation (6G) network and Internet of Everything (IoE) supports numerous emerging services and application. Increasing mobile internet traffic and services, on the other hand, presented a number of challenges that could not be addressed with the current network design. The cybertwin is equipped with a variety of capabilities, including communication assistants, network data loggers, and digital asset owners, to address these difficulties. While spectrum resources are limited, effective resource management and sharing are essential in achieving these requirements. With this motivation, this article presents a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment. The incorporation of the blockchain in 6G enables the network to monitor, manage, and share resources effectively. The proposed MWBA-RAT technique designs a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process and this shows the novelty of the work. The QO-SRO algorithm involves the integration of the quasi oppositional based learning concept with the traditional SRO algorithm to improve its convergence rate. A wide range of experiments are performed to highlight the enhanced outcomes of the MWBA-RAT technique.

Pricing-based edge caching resource allocation in fog radio access networks

The edge caching resource allocation problem in Fog Radio Access Networks (F-RANs) is investigated. An incentive mechanism is introduced to motivate Content Providers (CPs) to participate in the resource allocation procedure. We formulate the interaction between the cloud server and the CPs as a Stackelberg game, where the cloud server sets nonuniform prices for the Fog Access Points (F-APs) while the CPs lease the F-APs for caching their most popular contents. Then, by exploiting the multiplier penalty function method, we transform the constrained optimization problem of the cloud server into an equivalent non-constrained one, which is further solved by using the simplex search method. Moreover, the existence and uniqueness of the Nash Equilibrium (NE) of the Stackelberg game are analyzed theoretically. Furthermore, we propose a uniform pricing based resource allocation strategy by eliminating the competition among the CPs, and we also theoretically analyze the factors that affect the uniform pricing strategy of the cloud server. We also propose a global optimization-based resource allocation strategy by further eliminating the competition between the cloud server and the CPs. Simulation results are provided for quantifying the proposed strategies by showing their efficiency in pricing and resource allocation.

Energy efficiency optimization-based resource allocation for underlay RF-CRN with residual energy and QoS guarantee

How to achieve energy-efficient transmission in radio frequency energy harvesting cognitive radio network (RF-CRN) is of great importance when nodes in CRN are self-maintained. This paper presents a radio frequency (RF) energy harvesting hardware-based underlay cognitive radio network (RH-CRN) structure, where a secondary transmitter (ST) first harvests energy from RF signals source originating from the primary network, and then communicates with a secondary receiver (SR) in underlay mode by using the harvested energy. The total consumed energy by the secondary user (SU) must be equal to or less than the total harvested energy referred to as energy causality constraint, In addition, the ST possesses some initial energy which may be the residual energy from the former transmission blocks, and we consider the energy loss of energy harvesting circuit as a systematic factor as well. Our goal is to achieve the maximum energy efficiency (EE) of the secondary network by jointly optimizing transmitting time and power. To guarantee the quality of service (QoS) of secondary transceiver, a minimum requirement of throughput constraint is imposed on the ST in the process of EE maximization. As the EE maximization is a nonlinear fractional programming problem, a quick iterative algorithm based on Dinkelbach’s method is proposed to achieve the optimal resource allocation. Simulation results show that the proposed strategy has fast convergence and can improve the system EE greatly while ensuring the QoS.

Cost Optimization based Resource Allocation Scheme for Vehicular Cloud Networks

Cost Optimization based Resource Allocation Scheme for Vehicular Cloud Networks

Resource Intensification for Mobile Devices Using the Approximate Computing Entities

The mobile devices are termed to highly potential due to their capability of rendering services without being plugged to the electric grid. These device are becoming highly prominent due to their constant progress in computing as well as storing capacities and as they are very much closer to the users. Despites its advantages it still faces many problems due to the load balancing and energy consumption due to its limited battery limited and storage availability as some applications or the video downloading requires high storage facilities consuming majority of the energy in turn reducing the performance of the mobile devices. So as to improve the performance and the capability of the mobile devices the mobile cloud computing that integrates the mobile devices with the cloud paradigm has emerged as a promising paradigm. This enables the augmentation of the local resources for the mobile devices to enhance its capabilities in order to improve its functioning. This is basically done by proper offloading and resource allocation. The proposed method in the paper utilizes the optimal offloading strategy (Single and double strand offloading) and follows an Ant colony optimization based resource allocation for improving the functioning the mobile devices in terms of energy consumption and storage.

Planning Framework for Optimal Resource Utilization Strategy in Microgrid

The incorporation of distributed energy resources and technical innovations has restructured the electrical power system network from conventional grid to microgrid. Consequently, significant impacts on both distribution and utilization of power have been observed triggered by diverse operating condition and enhanced power demand. In planning of microgrid, energy management is a critical issue which is related to efficient utilization of available resources at minimum operating cost. Distributed and intermittent nature of generations in microgrid demands efficient and optimal allocation of existing resources. An allocation technique is necessary to attain optimal utilization of assets and to accomplish decision making for optimal power flow route through shortest path. This paper proposes a multi-objective optimization-based resource allocation technique to efficiently control available generation at any load condition to achieve minimum operating cost while maintaining all system constraints. In proposed framework, line flows are maximized to its load-carrying capability and by controlling power flow through shortest possible path. Proposed methodology suggests proper controller position to sustain desired power flow through the shortest path between each generator and load pair. An interconnected microgrid network topology is considered to demonstrate the proposed methodology.

English

English

Ant Colony Optimization-based Resource Allocation and Resource Sharing Scheme for V2V Communication

Ant Colony Optimization-based Resource Allocation and Resource Sharing Scheme for V2V Communication

Optimization-based resource allocation for software as a service application in cloud computing

Software as a service (SaaS) is a software that is developed and hosted by the SaaS vendor. SaaS cloud provides software as services to the users through the internet. To provide good quality of service for the user, the SaaS relies on the resources leased from infrastructure as a service cloud providers. As the SaaS services rapidly expand their application scopes, it is important to optimize resource allocation in SaaS cloud. The paper presents optimization-based resource allocation approach for software as a service application in cloud. The paper uses optimization decomposition approach to solve cloud resource allocation for satisfying the cloud user's needs and the profits of the cloud providers. The paper also proposes a SaaS cloud resource allocation algorithm. The experiments are designed to compare the performance of the proposed algorithm with other two related algorithms.

Optimization-based resource allocation in communication networks

The continuously growing number of applications competing for resources in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization Theory can provide the necessary tools to develop such mechanisms that will allocate network resources optimally and fairly among users. The aim of this paper is to provide a starting point for researchers interested in applying optimization techniques in the resource allocation problem for current communication networks. To achieve that we, first, describe the fundamental optimization theory tools necessary to design optimal resource allocation algorithms. Then, we describe the Network Utility Maximization (NUM) framework, a framework that has already found numerous applications in network optimization, along with some recent advancements of the initial NUM framework. Finally, we summarize some of our recent work in the area and discuss some of the remaining research challenges towards the development of a complete optimization-based resource allocation protocol.

CAD-MAC: A Channel-Aggregation Diversity Based MAC Protocol for Spectrum and Energy Efficient Cognitive Ad Hoc Networks

In cognitive Ad Hoc networks (CAHN), because the contentions and mutual interferences among secondary nodes are inevitable as well as secondary nodes usually have limited power budget, spectrum efficiency and energy efficiency are critically important to the CAHN, especially for the medium access control (MAC) protocol design. Aiming at improving both spectrum and energy efficiencies, we in this paper propose a diversity technology called Channel-Aggregation Diversity (CAD), through which each node can utilize multiple channels simultaneously and efficiently allocate the upper-bounded power resource with only one data radio. Based on the proposed CAD technology, we further develop a CAD-based MAC (CAD-MAC) protocol, which enables the secondary nodes to sufficiently use available channel resources under the upper-bounded power and transmit multiple data packets in one transmission process subject to the transmission-time fairness constraint. In order to improve the performance of CAHNs, we propose two joint power-channel allocation schemes. In the first scheme, we aim at maximizing the data transmission rate. By converting the joint power-channel allocation to the Multiple-Choice Knapsack Problem, we derive the optimal allocation policy through dynamic programming. In the second scheme, our objective is to optimize the energy efficiency and we obtain the corresponding allocation policy through fractional programming. Simulation results show that our proposed CAD-MAC protocol can efficiently increase the spectrum and energy efficiencies as well as the throughput of the CAHN compared with existing protocols. Moreover, the energy efficiency of the CAHN can be further improved by adopting the energy efficiency optimization based resource allocation scheme.

A Non-Convex Distributed Optimization Framework and its Application to Wireless Ad-hoc Networks

The continuously increasing demand for resources in modern, both wired and wireless, communication networks urges for more efficient resource allocation. Such an allocation of resources to network users can be formulated as an optimization problem. Traditional resource allocation protocols, such as TCP, operate inefficiently in cases that there is competition for resources by multimedia applications and some, or possibly all, links in the network are wireless. In this paper, the performance degradation of TCP in modern networks is quantified to highlight the necessity for a novel optimization-based resource allocation protocol. To this direction, a new optimization framework is presented that can provide the theoretical foundations of such a protocol by proving a sufficient, and in some cases also necessary, condition for distributed solution of non-convex problems. The wide applicability of this general framework is illustrated by considering a resource allocation formulation in TDMA/CDMA ad-hoc networks. The convergence properties to the optimal solution are first identified and a distributed algorithm is proposed. Moreover, a novel heuristic is developed to approximate the optimal solution when the condition does not hold and resolve network oscillations. Finally, the performance of the proposed methodology is evaluated and compared against other approaches in literature by simulation.

Chaotic immune optimization based resource allocation in cognitive radio network

In order to optimize the multi-user subcarrier allocation of cognitive wireless network, it is converted into a constraint optimization problem. A chaotic immune optimization algorithm is proposed to solve it. The key techniques and implementation processes are given. The operators, such as coding, clonal, crossover, and mutation, are designed. The experimental results show that in conditions of user rate, the bit error rate and inference constraints, the algorithm minimizes the total transmit power and converges rapidly. It can obtain the better allocation scheme and improve the utilization efficiency of high frequency spectrum.

Feedback-based optimization of a private cloud

The optimization problem addressed by this paper involves the allocation of resources in a private cloud such that cost to the provider is minimized (through the maximization of resource sharing) while attempting to meet all client application requirements (as specified in the SLAs). At the heart of any optimization based resource allocation algorithm, there are two models: one that relates the application level quality of service to the given set of resources and one that maps a given service level and resource consumption to profit metrics. In this paper we investigate the optimization loop in which each application’s performance model is dynamically updated at runtime to adapt to the changes in the system. These changes could be perturbations in the environment that had not been included in the model. Through experimentation we show that using these tracking models in the optimization loop will result in a more accurate optimization and thus result in the generation of greater profit.

Resource allocation and network evolution considering economics and robustness in manufacturing grid

Manufacturing Grid (MG), a complex system, is a kind of collaboration network with scale-free characteristic structure, and system robustness is a critical statistic indicator for its sustainable operation. Resource allocation, a component in MG, plays an important function to satisfy customers’ various requirements, and it is also a key step in MG evolutionary process with the expansion of tasks and factories (resource providers). Firstly, a particle swarm optimization-based resource allocation model is proposed with both task economics and system robustness as the optimization objectives. Then, the collaboration network and evolution models for MG are constructed by integrating the resource allocation model into its evolutionary process. Later on, the influence and impact of the allocation model to MG network structure are analyzed with the evolution of tasks, resources, and factories. Comparison with production–collaboration model for MG (MPC) and single-objective optimization model (SOP-MPC) reveal that the proposed recourse allocation model has much better agreement with our objective. In addition, the MG collaboration network can keep its scale-free structure during the evolution. Finally, a case study is used to illustrate with machine tool products and resources as the empirical analysis data to validate the results and comparisons.

Particle Swarm Optimization Based Approach for Resource Allocation and Scheduling in OFDMA Systems

Orthogonal Frequency-Division Multiple Access (OFDMA) systems have attracted considerable attention through technologies such as 3GPP Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). OFDMA is a flexible multiple-access technique that can accommodate many users with widely varying applications, data rates, and Quality of Service (QoS) requirements. OFDMA has the advantages of handling lower data rates and bursty traffic at a reduced power compared to single-user OFDM or its Time Division Multiple Access (TDMA) or Carrier Sense Multiple Access (CSMA) counterparts. In our work, we propose a Particle Swarm Optimization based resource allocation and scheduling scheme (PSORAS) with improved quality of service for OFDMA Systems. Simulation results indicate a clear reduction in delay compared to the Frequency Division Multiple Access (FDMA) scheme for resource allocation, at almost the same throughput and fairness. This makes our scheme absolutely suitable for handling real time traffic such real time video-on demand.