Joint Optimization Of Resource Allocation Research Articles

An adaptive MLP-based joint optimization of resource allocation and relay selection in device-to-device communication using hybrid meta-heuristic algorithm

Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network’s sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach.

A Lyapunov-Based Approach to Joint Optimization of Resource Allocation and 3-D Trajectory for Solar-Powered UAV MEC Systems

A Lyapunov-Based Approach to Joint Optimization of Resource Allocation and 3-D Trajectory for Solar-Powered UAV MEC Systems

Joint Optimization of Resource Allocation and Trajectory in UAV-assisted Wireless Semantic communication System

In this paper, we investigate the resource allocation design of an unmanned aerial vehicle (UAV)-enabled semantic communication system, in which UAVs are assigned to transmit semantic information to multiple user nodes. Our goal is to maximize the semantic information sum rate of the communication system by jointly optimizing the subcarrier allocation strategy and the trajectory of the UAVs, taking into account the minimum required semantic data rate for each user node, the minimum semantic similarity that can be received, the maximum cruise speed of the UAVs, and the initial/final position of the UAVs. The design is formulated as a generally tricky mixed-integer nonconvex optimization problem. Subsequently, a computationally efficient iterative algorithm is proposed to obtain a locally optimal solution, and further proofs demonstrate that the proposed algorithm is guaranteed to converge to a solution that satisfies at least the KKT condition of the original optimization problem.

Joint Optimization of Resource Allocation and SIC Ordering in Energy-Harvesting Relay-Aided NOMA NB-IoT Networks

Joint Optimization of Resource Allocation and SIC Ordering in Energy-Harvesting Relay-Aided NOMA NB-IoT Networks

Joint Optimization of Resource Allocation and Trajectory Control for Mobile Group Users in Fixed-Wing UAV-Enabled Wireless Network

Joint Optimization of Resource Allocation and Trajectory Control for Mobile Group Users in Fixed-Wing UAV-Enabled Wireless Network

Joint optimization of resource allocation and trajectory based on user trajectory for UAV-assisted backscatter communication system

Joint optimization of resource allocation and trajectory based on user trajectory for UAV-assisted backscatter communication system

Joint Optimization of Resource Allocation and Data Selection for Fast and Cost-Efficient Federated Edge Learning

Joint Optimization of Resource Allocation and Data Selection for Fast and Cost-Efficient Federated Edge Learning

Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

Joint Optimization of Resource Allocation and Flight Trajectory for UAV-IoT Underwater Detecting Systems

Joint Optimization of Resource Allocation and Flight Trajectory for UAV-IoT Underwater Detecting Systems

Joint Optimization of Resource Allocation, Phase Shift, and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment and further enhance the service quality of the UAV-enabled MEC. Motivated by this vision, in this paper, we consider both the amount of completed task bits and the energy consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled MEC systems with non-orthogonal multiple access (NOMA) protocol, where the bit allocation, transmit power, phase shift, and UAV trajectory are jointly optimized by an iterative algorithm with a double-loop structure based on the Dinkelbach’s method and block coordinate decent (BCD) technique. Simulation results demonstrate that: 1) our proposed algorithm can achieve higher energy efficiency than baseline schemes while satisfying the task tolerance latency; 2) the energy efficiency first increases and then decreases with the increase of the mission period and the total amount of task-input bits of IoT devices; 3) the energy efficiencies of schemes with imperfect channel state information (CSI) are lower than corresponding schemes with perfect CSI, and the performance gain of NOMA over OMA diminishes under the imperfect CSI.

Joint optimization of resource allocation and computation offloading based on game coalition in C-V2X

In order to solve the problem of limited resources in the vehicle, mobile edge computing (MEC) is integrated into the cellular-vehicle-to-everything (C-V2X) system to provide users with low-latency services. Considering the resource limitation of computing, storage and communication of MEC servers, it is necessary to improve the system performance through the collaborative optimization of multi-domain resources. Therefore, for applications with double dependence on time and data in C-V2X scenarios, we design a MEC hierarchical resource management framework to jointly optimize system offloading decision, scheduling decision, and caching decision to minimize the system delay. The optimization problem is decomposed into a resource allocation problem within a single MEC server and a load balancing problem among multiple MEC servers. For the former, we present a task scheduling algorithm based on the latest start time and a caching decision algorithm based on dynamic programming to minimize the average task completion delay; for the latter, we present a load balancing algorithm based on the coalition game to minimize the global system delay. Simulation results show that the proposed scheme can significantly improve the performance in terms of application completion delay, application failure rate and resource utilization compared with the benchmark schemes.

A surrogate-assisted heuristic approach for the joint optimization of resource allocation and scheduling of an aircraft final assembly line

In an aircraft final assembly line, there exists flexibility in resource allocation in the sense that processing time of a task can be reduced by allocating more workers to the task. As task processing times change, assembly task scheduling needs to be adjusted accordingly, which in turn has an impact on resource allocation. This paper studies the joint optimization problem of resource allocation and task scheduling. Both resource requirement and starting time of each task need to be determined such that the total resource investment cost is minimized. We propose a surrogate-assisted heuristic approach to solve the problem by decomposing it into two phases. In the first phase, a multi-start iterative search algorithm is developed to perform a dedicated exploration on resource allocation. A surrogate model trained on numerical data is applied to evaluate the resource allocation strategy. In the second phase, a hybrid genetic algorithm embedded with a novel local search procedure is designed for task scheduling. Meanwhile, a fine search for resource allocation can also be achieved. Computational experiments demonstrate the superiority of our proposed approach and that the surrogate plays a significant role in terms of improving solution quality. A realistic case study provides valuable managerial insights for real production.

The Design of Urban Sculpture Space with User Behavior Based on Internet of Things and Edge Computing

To solve the application of Virtual Reality (VR) technology in the design of urban sculpture space, a VR network model is implemented based on Internet of Things (IoT) and Edge Computing. First, this work expounds and analyzes the theory of urban sculpture space and the interaction of urban sculpture space. Second, aiming at the problem that the development of IoT cannot meet the large amount of data and low delay of VR, a resource allocation algorithm, Joint Optimization of Resource Allocation and FOV (J-RAF) strategy is proposed based on the user interest and the resource allocation problem of (FOV), and simulation experiments are used to verify the effectiveness of the J-RAF algorithm. Finally, a VR network system is established based on cache cooperation and user interest, and a resource allocation algorithm Joint Cooperative Content Caching and FOV (J-CCF) is proposed, and its effectiveness is verified. Experiments show that compared with random strategy and random viewport strategy, algorithm J-CCF has 3% and 20% system gain. Compared with noncooperative cache scheme and random viewport strategy, algorithm J-CCF can improve the system gain by 15% and 25%, respectively. It is verified that the convergence of J-RAF algorithm and J-CCF algorithm and the effectiveness of improving the system gain. This work provides a technical basis for optimizing the design method of urban sculpture and contributes to the optimal design of urban space.

Joint Optimization of Resource Allocation and UAVs’ Trajectories in Multi-UAV-Aided MEC Systems

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has become an up-and-coming technique to provide additional computing support for smart mobile devices with limited battery capacity in the wireless communication network systems. In this paper, a multi-UAV-aided MEC system is modelled where UAVs are equipped with MEC servers and users can execute local computing and computation offloading to UAVs simultaneously. The total amount of processing bits of all users is maximized by the joint optimization of the user-UAV association, local processing frequency, transmit power, bandwidth allocation and UAVs’ trajectories, meanwhile, the energy consumption constraint and the UAVs’ speed constraint are satisfied. Because the formulated problem is not convex, it is challenging to get the global optimal solution directly. To iron out the non-convex problem, it is divided into three subproblems and solved by an alternating optimization algorithm. Simulations show that the algorithm introduced in our paper performs better than the local computing, full offloading and fixed trajectory patterns.

QoS-Oriented joint optimization of resource allocation and concurrent scheduling in 5G millimeter-wave network

With the mature application of massive MIMO, challenges for massive backhaul data and ubiquitous user access in future mobile communication are effectively coped with the potential technology of millimeter wave integrated backhaul and access network (mm-wave IBAN). In order to fully exploit mm-wave directional transmission gain, how to improve the concurrent transmission performance of mm-wave IBAN through effective scheduling scheme under the consideration of resource allocation between the backhaul and access link is an intractable issue. In this paper, an enhanced reverse-TDD frame is designed for mm-wave IBAN, and the QoS-oriented joint optimization problem of resource allocation and concurrent scheduling between backhaul and access link is deeply investigated, where the number of the QoS-satisfied transmission flows is maximized. To obtain a pragmatic solution, a joint resource allocation and concurrent scheduling algorithm is proposed. In the proposed algorithm, the whole transmission duration in each scheduling period is divided into several stages and the transmission flows are partitioned into groups according to the designed QoS-aware priority, then bandwidth resources between backhaul and access in each stage are allocated. The transmission duration of each scheduled flow is determined finally. Performance evaluation of proposed algorithm is verified with extensive simulations in 60 GHz mm-wave IBAN under various system parameters. It is demonstrated that significant enhancements in terms of the network throughput and the number of the successful transmission flows are achieved in the proposed algorithm when compared with other state-of-the-art schemes.

Joint Optimization of Resource Allocation for Elastic Optical Intra-Datacenter Network

The ever-increasing demand for cloud services is posing serious challenges to the scalability of the computing, storing, and networking infrastructure deployed inside the datacenters. To address these challenges, we formulate the integer linear program (ILP) model to minimize the spectrum and the switch port usage by exploiting transparent multi-hop connection capability in elastic optical interconnect architecture for datacenters. The proposed ILP model not only takes into account the networking resources but also the other resources (such as computing and storage) in order to achieve a joint resource optimization in intra-datacenter networks. The optimal results show that substantial savings in both spectrum and switching resources can be achieved by properly tuning the objective function of the proposed optimization strategy.

Joint Resource Allocation and User Association for SVC Multicast Over Heterogeneous Cellular Networks

Scalable video coding (SVC) is attractive technology for multicasting video to users with different available transmission capacities. In this paper, we investigate the joint optimization of resource allocation and user association problems for SVC multicast over heterogeous cellular networks (HetNet) employing the schemes of cell range expansion (RE) and almost blank subframe (ABS). We solve the joint optimization problem by decoupling it into two problems, namely, resource allocation (RA) subproblem and user association (UA) master problem. For the RA subproblem, we propose a dynamic programming based algorithm to optimally set the transmission profile. For the UA master problem, we propose a similarity-based negotiation protocol (SBNP) based algorithm to obtain the Pareto-optimal range expansion bias. The simulation results demonstrate the efficiency of these algorithms.

Joint Optimization of Resource Allocation and Relay Selection for Network Coding Aided Device-to-Device Communications

Device-to-device (D2D) communication underlaying cellular networks enhances system capacity through using spectrum resources of idle cellular users, which at the same time can act as relays to form cooperative D2D transmissions. On the other hand, network coding increases the efficiency of relay cooperation. In this letter, we investigate the random linear network coding aided D2D communications by addressing the problem of joint resource allocation and relay selection among multiple idle cellular users and D2D pairs. By formulating it as a binary integer non-linear programming problem, we obtain the optimal solution by introducing the concept of D2D cluster. Extensive simulations demonstrate that our proposed scheme increases the system sum rate by about 50% on average, while guaranteeing the minimum required rate of D2D pair.

Resource Allocation, Transmission Coordination and User Association in Heterogeneous Networks: A Flow-Based Unified Approach

In this paper, we formulate a flow-based framework for the joint optimization of resource allocation, transmission coordination, and user association in a heterogeneous network comprising of a macro base station and a set of pico base stations and/or relay nodes. By incorporating these three important network processes together and by unifying the analysis of pico base stations and relay nodes, our framework can act as an important engineering tool for understanding the performance of different configurations. We use the resulting formulations to characterize the performance of different combinations of resource allocation schemes and transmission coordination mechanisms. We obtained important engineering insights regarding the interplay of these network processes. In particular, under the deployment of pico base stations, we find that partially shared deployment outperforms the co-channel deployment, with or without transmission coordination. In contrast, the results also show that the deployment of relay nodes does not offer meaningful throughput gains for any choice of resource allocation scheme or transmission coordination mechanism.

Joint Optimization of Resource Allocation and User QoS Satisfaction Control for Efficient Grid Resource Management and Scheduling

This paper exploits the interlayer coupling of a cross-layer design concept in Grid computing environment. An algorithm for joint optimization of resource allocation and user quality-of-service (QoS) satisfaction control that combines both application-centric and system-centric scheduling benefits is proposed. The algorithm aims to maximize the global utility as well as the QoS is guaranteed. We formulate the problem into a constrained optimization problem. The simulations demonstrate the advantage of the proposed scheduling algorithm by comparing with application-centric and system-centric scheduling algorithm, respectively.