Joint Optimization Of Resources Research Articles

A joint resource optimization allocation algorithm for NOMA‐D2D communication

AbstractThe AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved.

Joint resource optimization and flexible QoS provision using hybrid optical core node architecture

All-optical networks (AON) provide large bandwidth, high-speed switching, and quality of service (QoS). However, the emergence of recent applications generating flexible traffic demands affects AON resource utilization and QoS management. These concerns are handled by AON using core nodes with large wavelength converters (WC) and fiber delay line (FDL) spaces, resulting in a high cost of implementation and operation. This paper proposes a hybrid core node architecture, upgrading existing nodes with limited effective modification to minimize physical topology costs. This hybrid architecture increases WC and FDL availability and reduces wavelength utilization according to traffic behavior. These new node functions are enabled using an advanced algorithm integrating a novel cost formulation merging WC and FDL with lightpath deflection (LD). This algorithm activates resource availabilities using a flexible QoS scheme considering the required and real-time traffic loss and blocking delay. The experimental results indicate that the proposed approach optimizes AON resources while guaranteeing required traffic demands compared to the existing techniques.

Joint Resource and Trajectory Optimization in Active IRS-Aided UAV Relaying Networks

Joint Resource and Trajectory Optimization in Active IRS-Aided UAV Relaying Networks

Multitask and Multiobjective Joint Resource Optimization for UAV-Assisted Air-Ground Integrated Networks Under Emergency Scenarios

To face the challenges in emergency scenarios, a multi-task and multi-objective optimization algorithm for computation offloading and relay communication is investigated for the air-ground integrated networks, composed of unmanned aerial vehicles (UAVs), emergency vehicle users (EVUs) and ground sensor nodes (GSNs). We propose an HFL-DDQN algorithm, which combines horizontal federated learning (HFL) with double deep Q-network (DDQN). First, UAVs are separated into two clusters according to the services they provide, i.e., edge computing or relay communication. Next, the optimization problems are formulated for two types of services respectively. For the computation offloading tasks of EVUs, the optimization objective is to minimize the weighted sum of delay and energy consumption. For the sensor data transmission of GSNs, the optimization objective is to maximize the minimum rate of relay links. We define the total cost of the system as the sum of two types of services. Then, federated aggregation is used to joint training the global neural networks model without sharing raw data. Furthermore, the DDQN is improved by adopting prioritized experience replay to achieve better convergence. The simulation results show that the proposed HFL-DDQN algorithm not only outperforms the state-of-art baselines in terms of the system cost, but also promotes the generalization in execution process, which is especially applicable to the rescue scene under accidents.

Distributed Joint Resource Optimization for Federated Learning Task Distribution

The effective utilization of computation and communication resources is one of the primary challenges for Artificial Intelligence (AI)-based applications. The complexity of the edge computation environment and the diversity of communication interference in the wireless environment make it particularly difficult to quickly and accurately complete multiple federated learning tasks. This paper examines energy cost, communication capacity, computation capacity, and other factors in a network-slicing environment. The virtual queue vector and the associated Lyapunov drift are presented to analyze queue backlog performance and optimize each slice’s Key Performance Indicator (KPI) performance, including energy and other Quality of Experience (QoE) factors. In response, we design a Federated Learning (FL) Service-Oriented Allocation Policy Calculation Algorithm to address the allocation policy calculation problem in a time-varying environment. Finally, our simulation experiments demonstrate that our proposed algorithm outperforms other benchmarks.

Joint resource and trajectory optimization for secure UAV-based relay NOMA system

In this paper, we propose and analyze the maximal secrecy capability of an unmanned aerial vehicle (UAV)-based relay non-orthogonal multiple access system. In this system, a UAV acts as an aerial relay station to assist transmission from a source to two users in the presence of a terrestrial eavesdropper and a friendly jamming UAV. The minimum achievable secrecy rate (MASR) of two users is derived as a metric indicator to quantify the security of the system under the assumption that the location of the eavesdropper is imprecise. In order to maximize MASR, an efficient iterative algorithm is proposed to optimize system parameters, i.e., the trajectory of UAVs, transmit and jamming powers, as well as power allocation factors. Since the objective function and constraints of the total problem are non-convex, the successive convex approximation and block coordinate descent techniques are applied to find efficient approximate solutions. Simulation results demonstrate that the proposed algorithm significantly enhances the MASR of the system compared to benchmark schemes.

Joint Resource and Trajectory Optimization for Heterogeneous-UAVs Enabled Aerial-Ground Cooperative Computing Networks

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) is a promising paradigm for providing extensive coverage and additional computation services to the Internet of Things (IoT) devices with smart applications, which are normally computation-intensive and delay-critical, especially in some remote areas or emerging scenarios without available infrastructure. However, considering the restricted computation capabilities of UAVs, even multiple UAVs may not satisfy the quality of service (QoS) requirement of IoT users with heavy-computation applications. In this paper, a heterogenous-multiple-UAVs enabled collaborative aerial-ground MEC framework is proposed. The total computation bits maximization problem under the time-division multiple access (TDMA) scheme and partial offloading mode is formulated by jointly optimizing the user association, the CPU-cycle frequencies of users and computing UAVs, the transmit power of users and UAVs, the offloading time of users and UAVs, as well as the UAVs' trajectories. Due to the non-convexity of the original problem with the nonlinear coupling of different variables, a two-layered alternative optimization (TLAO) algorithm is proposed, where the outer layer optimizes the user association and resource allocation, while the inner layer optimizes the UAV trajectory scheduling. Simulation results demonstrate that our proposed TLAO scheme can improve the total computation bits compared with other benchmark schemes, and achieve fast convergence. Moreover, comparative results also verify that our proposed strategy is flexible to apply to homogeneous multi-UAVs and single-UAV scenarios.

Joint Optimization of Resources in Fog-Radio Access Network with Binary Computation Offloading

With the dramatic increase in the number of emerging Internet services, the Fog-Radio Access Network (F-RAN) has recently emerged as a promising paradigm to enhance high-load task processing capabilities for mobile devices, such as the Internet of things (IoT) and mobile terminals. Hence, it becomes a challenge for the F-RAN to reduce the offloading cost by designing an effective offloading strategy and rational planning of limited network resources to improve the quality of experience (QoE). This article investigates the F-RAN with a binary offload policy. It proposes an intelligent algorithm capable of optimally adapting to task offload policy, fog computing resource allocation, and offload channel resource allocation. To evaluate the offloading strategy intuitively, we design a system utility metric defined as a delay-energy weighted sum. The joint optimization problem is converted into a convex problem based on this metric, i.e., a mixed integer nonlinear programming (MINLP) problem. A novel algorithm based on improved double-deep Q neural networks is DDQN, which is proposed to address this problem. Furthermore, an action space mapping method in the DDQN framework is presented to obtain offloading decisions. Extensive experimental data indicate that the proposed DDQN algorithm can effectively reduce the offloading cost and is adaptable to different offloading scenarios.

Joint resource optimization and trajectory design for energy minimization in UAV-assisted mobile-edge computing systems

This paper studies an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, where a rotary-wing UAV equipped with computing platforms is used to assist ground users (GUs) with insufficient computing resource. Each GU offloads part of the task to the UAV for computation through the uplink, and the remaining computation task is computed by itself. Then, UAV will return the results to the corresponding GU via downlink. Considering the results for each GU is not only used by itself, but may also be sent to other GUs for data fusion, we thus design two specific scenarios in this paper: (1) homologous UAV-assisted MEC scenario, i.e. UAV receives the computation task of GUs and then returns the results to the identical GUs, and (2) non-homologous UAV-assisted MEC scenario, i.e., UAV receives the computation task of source GUs and returns the results to the corresponding destination GUs. For these two scenarios, we propose an energy minimization problem by jointly optimizing task partition, the time slot length and UAV trajectory. In particular, we use the path discretization approach to convert the problem as a problem form which is finite variables. Since the problem is non-convex, we use successive convex approximation (SCA) techniques to tackle the non-convexity. Considering the initial trajectory has an important affect on the experimental result, then a specific trajectory initialization design via combining with the Pickup-and-Delivery Problem (PDP) is proposed. Numerical results proof our proposed design is superior compared with the benchmark cases.

Energy-Efficient Resource Allocation for Dual-NOMA-UAV Assisted Internet of Things

Employing unmanned aerial vehicles (UAVs) characterized by low cost, high maneuverability, and on-demand deployment as aerial base stations (BSs) of Internet of Things (IoT) can guarantee communication performance in the absence of terrestrial BSs. However, the limited energy budget of UAV constrains its development. In this paper, a dual-UAV-assisted IoT using non-orthogonal multiple access (NOMA) is proposed to improve IoT capacity. To reduce energy consumption of the UAVs while ensuring a certain throughput, a joint resource optimization problem of communication scheduling, transmit power and motion parameters of UAVs is formulated to maximize energy efficiency of UAVs. To solve the proposed non-convex optimization problem, we present an alternating iterative optimization algorithm to alternately optimize three sub-problems: communication scheduling optimization, UAV transmit power optimization and UAV motion parameters optimization, each of which can be converted into convex optimization and solved using Lagrange multiplier method, subgradient descent method and successive convex approximation (SCA). The numerical results show that optimizing UAV motion parameters can effectively improve energy efficiency of UAVs, and the proposed dual-NOMA-UAV assisted IoT can achieve higher energy efficiency than the orthogonal multiple access (OMA)-UAV assisted IoT.

Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design

Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design

Joint Resource and Detection Threshold Optimization for Maneuvering Targets Tracking in Colocated MIMO Radar Network

Compared with monostatic phased array radar, colocated MIMO radar network (CMRN) that owns richer controllable resources is able to offer superior tracking performance. In this paper, for targets tracking in cluttered environment with CMRN, a joint detection threshold optimization and transmit resource allocation (JDTO-TRA) algorithm is proposed to simultaneously maximize the tracking performance and minimize the resource consumption, whose key mechanism is to adaptively control the working parameters at both transmitters and detectors via optimization technique. To be more specific, we first formulate the JDTO-TRA strategy as a bi-objective optimization problem, where both the tracking performance and the resource consumption are considered in the objective function. The analytical expression for the posterior Cramér-Rao lower bound (PCRLB) incorporated with information reduction factor (IRF), which is caused by the measurement origin uncertainty (MOU), is given and adopted as the tracking performance metric, and the total transmit energy of CMRN is employed as the resource consumption metric. Hereafter, to tackle the formulated mixed-integer and non-convex problem effectively, an iterative and efficient two-step solution technique incorporating the simulated annealing (SA)-based hybrid particle swarm optimization (HPSO) and the cyclic minimization algorithm (CMA) is proposed, where the radar-target assignment, the sub-array number and the transmit energy of each activated colocated MIMO radar, and the false alarm rate (FAR) of the target can be controlled jointly and adaptively. Numerical simulation results are provided to demonstrate the effectiveness as well as the advantages of the JDTO-TRA algorithm compared with other popular existing algorithms.

Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks

Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new model of multiple federated learning services at the multi-access edge computing server. Accordingly, the sharing of CPU resources among learning services at each mobile device for the local training process and allocating communication resources among mobile devices for exchanging learning information must be considered. Furthermore, the convergence performance of different learning services depends on the hyper-learning rate parameter that needs to be precisely decided. Towards this end, we propose a joint resource optimization and hyper-learning rate control problem, namely <inline-formula><tex-math notation="LaTeX">${{\sf MS-FEDL}}$</tex-math></inline-formula> , regarding the energy consumption of mobile devices and overall learning time. We design a centralized algorithm based on the block coordinate descent method and a decentralized JP-miADMM algorithm for solving the <inline-formula><tex-math notation="LaTeX">${{\sf MS-FEDL}}$</tex-math></inline-formula> problem. Different from the centralized approach, the decentralized approach requires many iterations to obtain but it allows each learning service to independently manage the local resource and learning process without revealing the learning service information. Our simulation results demonstrate the convergence performance of our proposed algorithms and the superior performance of our proposed algorithms compared to the heuristic strategy.

CA Energy Saving Joint Resource Optimization Scheme Based on 5G Channel Information Prediction of Machine Learning

Carrier aggregation (CA) is considered as a key enabling technology to provide higher rates for users in LTE/5G networks. However, the increased transmission rate is accompanied by higher energy consumption. The existing CA energy efficiency resource optimization allocation scheme in 5G does not fully consider the following two issues, namely, the impact of delayed channel state information feedback on the rationality of resource allocation and the increasing in energy consumption caused by frequent switching of component carriers (CCs) by narrowband users; this paper proposed a CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction. The proposed scheme (PEJA) fully considers the above two issues. Firstly, the algorithm of random forest predicting channel state information is designed. On the basis, the CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction is proposed. The simulation results show that the proposed algorithm PEJA has a higher energy efficiency and throughput than the comparison algorithm under different numbers of users and different transmission powers. The PEJA algorithm is more energy efficient than the PEJA-NC algorithm, which does not consider the CC handover of narrowband users. To sum up, the proposed PEJA energy-efficient resource allocation scheme maximizes system energy efficiency and achieves a higher throughput.

Joint Resource and Trajectory Optimization for Energy Efficiency Maximization in UAV-Based Networks

The explosive growth of unmanned aerial vehicles (UAVs)-based networks has accelerated in recent years. One of the crucial tasks of a UAV-based network is managing and allocating resources, including time, power, fly trajectory, and energy resources. We investigate a UAV-based network that gathers information from smart devices, sensor devices, and IoT devices (IDs) with respect to energy efficiency (EE) maximization. The EE of users served by the UAV over the time slots of a cycle is maximized through three categories: UAV trajectory optimization, power allocation, and time slot assignment. However, these are non-convex problems that are very difficult to solve. To solve the problem efficiently, we divide it step by step and convert the non-convex optimization problem into an equivalent convex optimization problem, optimizing each equivalent problem over each variable while other variables are fixed. Firstly, we perform a UAV trajectory optimization with a different number of ground users. Secondly, the Dinkelbach method is used to construct a non-convex fractional power allocation problem. In addition, we develop an algorithm to distribute time slots to all users, which continually raises the EE value. Eventually, a scheme is provided to sequentially update the method to each equivalent problem. The numerical results provide evidence that by solving the proposed sum-rate maximization problem, the performance of ground users has significantly improved with the support of the UAV-based network.

Joint resource optimization for nonorthogonal multiple access‐enhanced scalable video coding multicast in unmanned aerial vehicle‐assisted radio‐access networks

AbstractA joint resource‐optimization scheme is investigated for nonorthogonal multiple access (NOMA)‐enhanced scalable video coding (SVC) multicast in unmanned aerial vehicle (UAV)‐assisted radio‐access networks (RANs). This scheme allows a ground base station and UAVs to simultaneously multicast successive video layers in SVC with successive interference cancellation in NOMA. A video quality‐maximization problem is formulated as a mixed‐integer nonlinear programming problem to determine the UAV deployment and association, RAN spectrum allocation for multicast groups, and UAV transmit power. The optimization problem is decoupled into the UAV deployment–association, spectrum‐partition, and UAV transmit‐power–control subproblems. A heuristic strategy is designed to determine the UAV deployment and association patterns. An upgraded knapsack algorithm is developed to solve spectrum partition, followed by fast UAV power fine‐tuning to further boost the performance. The simulation results confirm that the proposed scheme improves the average peak signal‐to‐noise ratio, aggregate video‐reception rate, and spectrum utilization over various baselines.

Qore-DL: A QoS-aware joint optimization framework for distributed deep learning training

Resource management of deep learning training (DLT) jobs is critical for cluster resource efficiency and client QoS assurance. Most existing scheduling frameworks require clients to specify job resource configuration, which can lead to over-provision or under-provision issues. Additionally, the performance of some static scheduling frameworks degrades in highly dynamic clusters. In this paper, we propose a QoS-aware joint resource optimization framework called Qore-DL for distributed DLT jobs. We divide the lifecycle of a DLT job into submission, queuing and running stages. Qore-DL automatically configures reasonable resources for submitted jobs and greedily assigns scheduled jobs to hosts. For running jobs, Qore-DL employs a heuristic scheme to adjust their resources. Qore-DL jointly considers the optimization of QoS satisfaction and resource efficiency at the three stages of DLT jobs. We implemented the prototype of Qore-DL in TensorFlow based on Kubernetes and conducted extensive experiments in CPU and GPU clusters to evaluate its performance. The experiment results show that, compared with its counterparts, Qore-DL can improve the job completion rate by up to 42.4% and the cluster resource efficiency by up to 21.8%.

Joint resource optimization for a distributed MIMO radar when tracking multiple targets in the presence of deception jamming

How to utilize the limited resource to achieve better tracking accuracy in a distributed multiple-input multiple-output (MIMO) radar plays a critical role under the electronic countermeasure (ECM) background. In this paper, a joint resource optimization scheme (JROS) is presented for transmit and receive station selection and power allocation in anti-deception jamming scenario. A quality-of-service (QoS) model of minimizing the gap between rejection probability of false target (RPFT) and the threshold is established, and the optimal discriminator is designed on the basis of predicted posterior Cramér–Rao lower bound (PCRLB) on the deception parameters. The binary selection variables lead to the nonconvexity of objective function. To overcome this difficulty, a three-step optimization cycle (TSOC)-based algorithm is proposed. Simulation results confirm the superiority of the proposed scheme, compared with state-of-the-art resource allocation strategies. The results also show that the stations closer to the jammer with lower normalized deception parameters are often activated and allocated to more power resource.

Socially Aware V2X Networks With RIS: Joint Resource Optimization

Inspired by the promising potentialof reconfigurable intelligent surface (RIS)-based transmission, this paper investigates the RIS-aided vehicle-to-everything (V2X) communication system with the social trust, where multiple vehicle-to-infrastructure (V2I) links share the spectrum with vehicle-to-vehicle (V2V) links. Subject to the reliability constraint of V2V links, the sum V2I link capacity is maximized, through jointly optimizing the power allocation, RIS reflection coefficients and spectrum allocation. The formulated mix-integer and non-convex problem is effectively converted into three subproblems, which can be iteratively solved by our proposed three-stage joint resource allocation and RIS optimization (JRARO) algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm, and that the RIS can significantly improve the quality of vehicular communications in terms of the sum V2I capacity.

Effective Throughput Maximization of NOMA With Practical Modulations

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless communications. In most of the existing NOMA schemes, the ideal information rate based on Shannon capacity is used as the performance metric, assuming perfect successive interference cancellation (SIC) and Gaussian transmit signals without considering practical modulations. The implicit assumptions and the resulting schemes may lead to suboptimal performance in practical NOMA systems. In this paper, we consider multi-user multi-channel NOMA systems using practical quadrature amplitude modulation (QAM) with imperfect SIC. We aim to maximize a more practical performance metric, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">effective throughput</i> , which takes into account the data rate and error performance. To achieve this goal, we derive both the exact and approximate expressions of the effective throughput. We also formulate a joint resource optimization problem of the power allocation, channel assignment, and modulation selection to maximize the effective throughput. We develop an efficient power allocation solution by proposing a closed-form power allocation within channels and a waterfilling-form power budget allocation among channels. We also develop efficient channel assignment and modulation selection methods with the aid of matching theory and machine learning, respectively. Consequently, we provide an efficient joint resource allocation algorithm via iterative optimization to maximize the effective throughput. Numerical results are presented to verify the superiority of the proposed NOMA scheme over orthogonal multiple access (OMA) and other NOMA schemes.