Proposed Resource Allocation Scheme Research Articles

A study on resource allocation scheme for self-powered visible light communication and positioning systems

In this work, a novel resource allocation scheme is proposed for the self-powered visible light communication and positioning (SPVLCP) system based on orthogonal frequency division multiplexing (OFDM). This scheme can balance the performance of communication, positioning, and energy harvesting by optimizing the power splitting (PS) at the receiver and power allocating on each subcarrier using the multi-verse optimization (MVO) algorithm. The performance of the SPVLCP system using the proposed resource allocation scheme is investigated through simulation and experiment for different constraint conditions and transmission rates. The results indicate that the SPVLCP system using the proposed resource allocation scheme has better performance compared with that using the uniform power allocation scheme.

A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication

In communication technologies, device-to-device (D2D) communication is essential for resource management and power control, which are major research concerns nowadays. D2D resource allocation involves dividing vital resources, such as time, power, and spectrum, among several devices. Each device can connect to other devices via one or more frequency channels. D2D communication shares the cellular user resources, while signal power transmission causes interference to the users who share the same channel. So, there is a need to control the power of the D2D device to prevent interference. For proper power control and optimization of multi-channel D2D communication, which is a challenging task, we proposed a deep learning approach incorporating a hybrid resource allocation framework. This framework aims to increase the sum rate of D2D user equipment (DUE) while considering quality of service (QoS) factors like limiting interference to cellular user equipment (CUE) and guaranteeing individual DUE rates above a certain threshold. The proposed resource allocation scheme combines two methods, namely a metaheuristic hybrid particle swarm Cauchy approach to African vulture optimization (HPSCAV) and a modified long short-term memory (MLSTM) based approach. The HPSCAV scheme helps to ensure that the QoS constraints are met, while the MLSTM-based approach is utilized for efficient resource allocation by optimizing the power and improving it with HPSCAV. Simulation results validate that the proposed model achieved better performance in various metrics such as system capacity, power consumption, spectral efficiency (SE), and energy efficiency (EE).

Multi-dimensional resource allocation strategy for LEO satellite communication uplinks based on deep reinforcement learning

In the LEO satellite communication system, the resource utilization rate is very low due to the constrained resources on satellites and the non-uniform distribution of traffics. In addition, the rapid movement of LEO satellites leads to complicated and changeable networks, which makes it difficult for traditional resource allocation strategies to improve the resource utilization rate. To solve the above problem, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy takes the weighted sum of spectral efficiency, energy efficiency and blocking rate as the optimization objective, and constructs a joint power and channel allocation model. The strategy allocates channels and power according to the number of channels, the number of users and the type of business. In the reward decision mechanism, the maximum reward is obtained by maximizing the increment of the optimization target. However, during the optimization process, the decision always focuses on the optimal allocation for current users, and ignores QoS for new users. To avoid the situation, current service beams are integrated with high- traffic beams, and states of beams are refactored to maximize long-term benefits to improve system performance.Simulation experiments show that in scenarios with a high number of users, the proposed resource allocation strategy reduces the blocking rate by at least 5% compared to reinforcement learning methods, effectively enhancing resource utilization.

Joint power and subchannel allocation regarding energy harvesting in MC-NOMA based wireless networks

We propose resource allocation schemes for a multi-carrier non-orthogonal multiple access (MC-NOMA) system with the consideration of energy harvesting. The resource allocation schemes includes an optimization scheme for maximizing the total system capacity, and an optimization scheme for maximizing the fairness among users, such that both schemes guarantee minimum data rate and harvested energy to each user. Because NOMA is one of the key technologies for enhancing system capacity, we apply a NOMA scheme to an Energy Harvesting Wireless Network (EHWN) to increase the capacity whilst at the same time supplying sufficient harvested energy and data rate for each user. The proposed resource allocation schemes are based on a block coordinate descent (BCD) algorithm that can effectively solve problems in which the objective function is not jointly concave. The performance of each proposed scheme is evaluated using simulations, and we show that the capacity maximization and fairness schemes outperform orthogonal multiple access (OMA) schemes in terms of capacity and fairness, respectively.

Research on Resource Allocation Strategy of Indoor Visible Light Communication and Radio Frequency Systems Integrating Orthogonal Frequency-Division Multiple Access Technology

Aiming at the problem of resource allocation strategies limiting the system transmission rate in indoor visible light communication and radio frequency (VLC/RF) systems, a new resource allocation method is proposed, and orthogonal frequency-division multiple access (OFDMA) technology is introduced. The capacity of the communication system is effectively increased. In the VLC/RF system model based on OFDMA, the Lyapunov optimization method is used to transform the time averaging problem into a series of single-slot online problems to reduce its computational complexity, the optimization problem is decomposed into three independent subproblems, and the Lagrange relaxation method and convex optimization theory are used to solve the subproblems to maximize the average transmission rate of the system, and the Lyapunov drift is used to ensure the stability of the system. Simulation verifies that the Lyapunov optimization algorithm does not require iteration, which improves the optimization speed to a high extent. The simulation results show that the proposed resource allocation strategy effectively balances the system queue length and transmission rate, improves the average transmission rate of the system to the greatest extent under the premise of ensuring the stability of the system, and compares with other algorithms from the aspects of system stability and transmission rate, which proves the effectiveness of the Lyapunov optimization algorithm.

Resource Allocation of NOMA Communication Systems for Federated Learning

Federated learning is a new communication and computing concept that allows naturally distributed data sets (e.g., as in data acquisition sensors) to be used to train global models, and therefore successfully addresses privacy, power and bandwidth limitations in wireless networks. In this paper, we study the communications problem of latency minimization of a multi-user wireless network used to train a decentralized machine learning model. To facilitate low latency, the wireless stations (WSs) employ non-orthogonal multiple access (NOMA) for simultaneous transmission of local model parameters to the base station, subject to the users’ maximum CPU frequency, maximum transmit power, and maximum available energy. The proposed resource allocation scheme guarantees fair resource sharing among WSs by enforcing only a single WS to spend the maximum allowable energy or transmit at maximum power, whereas the rest of the WSs transmit at lower power and spend less energy. The closed-form analytical solution for the optimal values of resource allocation parameters is used for efficient online implementation of the proposed scheme with low computational complexity.

Enhancing equitable resilience of urban energy systems via strategic planning of EV charging infrastructure

This paper seeks to address the profound power resilience inequity in New York City by means of strategic allocation of electric vehicle (EV) charging infrastructure to support the power grid operation in challenging scenarios, such as when facing high demand or during natural disasters. First, we uncover the most disproportionately affected communities in New York by developing a metric of power resilience inequity to measure the combined impact of power failure-related factors on these areas. We employ data-driven approaches to infer the statistical relationships between communities’ power resilience index, their available EV charging infrastructure, and several other prominent socio-demographical variables. This inference yields the development of a machine learning model that can predict the reduction of power resilience inequity after deployment of the proposed resource allocation strategy. We further develop an optimization framework that jointly considers equity and efficiency to guide the optimized distribution of EV charging infrastructure across the city. A number of case studies are leveraged to demonstrate the capability of the devised approach in enhancing urban power resilience equity, yielding favorable results in marginalized communities.

Cell-Free Massive MIMO With OTFS Modulation: Statistical CSI-Based Detection

We study the downlink spectral efficiency (SE) of a cell-free massive multiple-input multiple-output (CF-mMIMO) system with orthogonal time frequency space (OTFS) modulation. Each user equipment (UE) uses the minimum mean-squared error-based successive interference cancellation (MMSE-SIC) scheme to detect the received signal. Given the statistical channel information at the UEs, an analytical closed-form expression for the downlink SE is derived. To improve the per-user SE, a max-min fairness resource allocation is proposed and developed. Numerical results show that the proposed resource allocation strategies for superimposed pilot-based (SP-CHE) and embedded pilot-based channel estimation (EP-CHE) achieve up to 43% and 65% performance gain over the baseline system without power allocation.

Dual-Driven Resource Management for Sustainable Computing in the Blockchain-Supported Digital Twin IoT

Nowadays, emerging sixth-generation (6G) mobile networks, the Internet of Things (IoT), and mobile-edge computing (MEC) technologies have played significant roles in developing a sustainable computing network. In sustainable computing networks, with the increasing scale of data-driven applications, massive privacy-sensitive data are generated. How to effectively process such data on resource-limited IoT devices is challenging. Although edge intelligence (EI) is designed to maintain an appropriate level of ultradelay reliability, low-latency communication (URLLC), real-time data processing, and security and privacy are concerning. In this article, we propose a novel blockchain-supported hierarchical digital twin IoT (HDTIoT) framework, which combines the digital twin to edge network and adopts blockchain technology to achieve secure and reliable real-time computation. We first propose a data and knowledge dual-driven learning solution to ensure real-time interaction and efficient optimization between the physical and the digital worlds. To improve communication and computation efficiency with data and knowledge dual-driven learning, the optimization goal is to minimize the system delay and energy consumption and ensure system reliability and the learning accuracy of IoT devices. Moreover, we propose a proximal policy optimization (PPO)-based multiagent reinforcement learning (MARL) algorithm to solve the resource allocation (RA) problem. Experimental results show that the proposed RA scheme can improve the efficiency of the HDTIoT system, guarantee learning accuracy, reliability, and security, and make a balance between system delay and energy consumption.

Energy-Efficient Multiprocessor-Based Computation and Communication Resource Allocation in Two-Tier Federated Learning Networks

In conventional federated learning (FL), multiple edge devices holding local data jointly train a machine learning model by communicating learning updates with a centralized aggregator without exchanging their data samples. Owing to the communication and computation bottleneck at the centralized aggregator and inaccurate learning model caused by the non-IID data, we here consider a two-tier FL network, in which IoT nodes are the core clients that hold data, the model aggregators at the middle tier are the low altitude aerial platforms (UAVs), and the model aggregator at the top-most layer is the high altitude aerial platform (UAV with relatively high altitude). Under the assumption that each IoT node has parallel computing ability, we study the energy-efficient computation and communication resource allocation in such a network within some time budget. Upon formulating the problem as an optimization problem, we solve the computation and communication resource allocation problems as the separate subproblems within a time frame, and then propose an iterative algorithm to solve the entire problem jointly. More specifically, We solve both the energyefficient computation and communication resource allocation subproblems using the dual decomposition technique, and then apply a bisection search-based recursive technique to solve the entire energy efficiency problem jointly. Moreover, we propose offline and online client scheduling schemes that not only select the optimal edge nodes for association but also assign workload to each client based on the data quality and workload constraint. With real data, extensive simulations are conducted to verify the effectiveness of the proposed resource allocation scheme. The results further reveal that the learning performance not only is dependent on the computation and communication energy consumption of the FL process but also the model divergence weight owing to the non-IID data at client IoT nodes.

Profit maximization in cache-aided intelligent computing networks

This paper focuses on the profit maximization problem in a reconfigurable intelligent surfaces (RIS) aided computing network, where multiple heterogeneous users offload their computational tasks to one computational access point (CAP) for seeking computing acceleration at the cost of profit. In particular, the CAP can also pre-store a part of the computing task to speed up computing, and the system has limited communication and computing resources, where heterogeneous users have different offloading requirements and the CAP can dynamically allocate the system resources to meet the requirements of users to earn profits. To maximize the system profit, we devise the system by proposing a resource allocation scheme which employs a genetic algorithm (GA), based on statistical channel state information (CSI) of wireless links. The proposed algorithm maximizes the long-term profit of the system by optimizing resource allocation among users. Finally, simulation results are provided to verify the proposed scheme. The results show that our proposed resource allocation scheme outperforms the conventional ones.

Nonconvex resource allocation for inelastic enterprise applications deployment into the cloud via particle swarm optimization

The advantages of cloud computing attract a large number of enterprises to deploy their applications into the cloud, thereby reducing their own operating costs. This paper considers deploying inelastic applications into the cloud and proposes an optimal resource allocation model. The deployment functions for inelastic applications are nonconvex (e.g., sigmoidal), then the resource allocation model becomes a hard nonconvex optimization problem. The traditional gradient-based resource allocation algorithm cannot effectively achieve the global optimum. Therefore, this paper applies particle swarm optimization (PSO) method to design a resource allocation scheme. This scheme can not only effectively solve the resource allocation problem of deploying inelastic enterprise applications into the cloud, but also solve the hard problem of deploying multi-class applications into the cloud when the enterprise can support both elastic and inelastic applications. We also compare the performance of the proposed PSO-based resource allocation scheme with some other methods and illustrate some numerical examples to verify the effectiveness and superiority of the proposed resource allocation scheme.

Tasks-oriented joint resource allocation scheme for the Internet of vehicles with sensing, communication and computing integration

With the development of artificial intelligence (AI) and 5G technology, the integration of sensing, communication and computing in the Internet of Vehicles (IoV) is becoming a trend. However, the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management problems. In view of the above challenges, this paper proposes a tasks-oriented joint resource allocation scheme (TOJRAS) in the scenario of IoV. First, this paper proposes a system model with sensing, communication, and computing integration for multiple intelligent tasks with different requirements in the IoV. Secondly, joint resource allocation problems for real-time tasks and delay-tolerant tasks in the IoV are constructed respectively, including communication, computing and caching resources. Thirdly, a distributed deep Q-network (DDQN) based algorithm is proposed to solve the optimization problems, and the convergence and complexity of the algorithm are discussed. Finally, the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme, compared to the existing ones. The exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%, and our proposed resource allocation scheme improves the mAP performance by about 0.15 under resource constraints.

Levy Flight Firefly Based Efficient Resource Allocation for Fog Environment

Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things (IoT) applications in the proximity of the network. Fog computing offers computational and storage services between cloud and terminal devices. However, an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services. A large number of heterogeneous shareable resources makes fog computing a complex environment. In the sight of these issues, this paper has proposed an efficient levy flight firefly-based resource allocation technique. The levy flight algorithm is a metaheuristic algorithm. It offers high efficiency and success rate because of its longer step length and fast convergence rate. Thus, it treats global optimization problems more efficiently and naturally. A system framework for fog computing is presented, followed by the proposed resource allocation scheme in the fog computing environment. Experimental evaluation and comparison with the firefly algorithm (FA), particle swarm optimization (PSO), genetic algorithm (GA) and hybrid algorithm using GA and PSO (GAPSO) have been conducted to validate the effectiveness and efficiency of the proposed algorithm. Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service (QoS). The proposed algorithm reduces average waiting time, average execution time, average turnaround time, processing cost and energy consumption and increases resource utilization and task success rate compared to FA, GAPSO, PSO and GA.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter (CSIT). A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CSI feedback stage. In fact, the downlink communication generally includes three stages, i.e., pilot training, CSI feedback, and data transmission. These three stages are mutually related and jointly determine the overall system performance. Unfortunately, there exist few studies on the reduction of CSIT acquisition overhead from the global point of view. In this paper, we integrate the Minimum Mean Square Error (MMSE) channel estimation, Random Vector Quantization (RVQ) based limited feedback and Maximal Ratio Combining (MRC) precoding into a unified framework for investigating the resource allocation problem. In particular, we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio (SNR) based on the deterministic equivalence theory. Then the three performance metrics (the spectral efficiency, energy efficiency, and total energy consumption) oriented problems are formulated analytically. With practical system requirements, these three metrics can be collaboratively optimized. Finally, we propose an optimization solver to derive the optimal partition of channel coherence time. Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

Resource Allocation for D2D Communication in Multiservice Cellular Network

Allowing different services with different requirements to coexist is a hot topic for future communication. In order to meet the different communication requirements of users, this paper proposes a cellular communication system based on filtered orthogonal frequency division multiplexing (filtered-OFDM) supporting device-to-device (D2D) communication. Further, in order to improve the system performance, we study the user resource allocation problem in the system and propose a dynamic iterative resource allocation algorithm based on Hungarian algorithm (DRABH). The algorithm allocates resource blocks for D2D user pairs and all cellular users in the cell under the constraint of the lowest threshold of signal to interference plus noise ratio (SINR), and cellular users have high priority. Under the condition of ensuring that all cellular users can communicate, resource blocks are allocated to as many D2D user pairs as possible to maximize the number of user links in the network. Simulation results show that considering the interference introduced by filtered-OFDM and D2D communication, the proposed resource allocation scheme can significantly improve the number of user links.

Intelligent Surface Aided D2D-V2X System for Low-Latency and High-Reliability Communications

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS.

Deep transfer reinforcement learning for resource allocation in hybrid multiple access systems

This paper proposes a resource allocation scheme for hybrid multiple access involving both orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The proposed resource allocation scheme employs multi-agent deep reinforcement learning (MA-DRL) to maximize the sum-rate for all users. More specifically, the MA-DRL-based scheme jointly allocates subcarrier and power resources for users by utilizing deep Q networks and multi-agent deep deterministic policy gradient networks. Meanwhile, an adaptive learning determiner mechanism is introduced into our allocation scheme to achieve better sum-rate performance. However, the above deep reinforcement learning adopted by our scheme cannot optimize parameters quickly in the new communication model. In order to better adapt to the new environment and make the resource allocation strategy more robust, we propose a transfer learning scheme based on deep reinforcement learning (T-DRL). The T-DRL-based scheme allows us to transfer the subcarrier allocation network and the power allocation network collectively or independently. Simulation results show that the proposed MA-DRL-based resource allocation scheme can achieve better sum-rate performance. Furthermore, the T-DRL-based scheme can effectively improve the convergence speed of the deep resource allocation network.

Latency-aware computation offloading and DQN-based resource allocation approaches in SDN-enabled MEC

The proposed mobile edge computing can transfer the computing tasks in mobile applications to the nearby edge devices, effectively reducing the processing pressure of local servers and avoiding delays in backhaul and core networks, thus better solving the problem that cloud computing cannot effectively handle resource allocation. However, the complexity of the wireless environment during the communication process leads to the fact that the computing tasks are easily caused by the increase in the number of traffic and packet loss when they are uploaded to the MEC side through the wireless link, which cannot guarantee a lower total system energy consumption and shorter total delay. To address the problem that mobile devices cannot handle many computationally intensive tasks in a timely manner, this paper proposes a task offloading optimization scheme for SDN-enabled MEC environments. Modeling the computation offloading problem based on Lyapunov optimization, and then analyzes the offloading delay with respect to the offloading gain. In order to guarantee application requirements, minimize energy consumption and latency, and better satisfy user QoS requests, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy designs a DQN-based resource allocation algorithm to deploy a joint optimal offloading decision and resource allocation scheme in a mobile edge computing environment under the limited computational resources and the latency constraints of the computational tasks. Based on the experimental results, it is shown that the proposed task offloading strategy can reduce the overall latency; the proposed resource allocation strategy can reduce the total energy consumption and total latency of the system and improve the successful execution rate of tasks.

Secure and reliable computation offloading in blockchain-assisted cyber-physical IoT systems

With the development of the Cyber-Physical Internet of Things System (CPIoTS), the number of Cyber-Physical System (CPS) applications accessed in networks has increased dramatically. Latency-sensitive resource orchestration in CPS applications is extraordinarily essential for maintaining the Quality of Experience (QoE) for users. Although edge-cloud computing performs effectively in achieving latency-aware resource allocation in CPIoTS, existing methods fail to jointly consider the security and reliability requirements, thereby increasing the process latency of tasks and degrading the QoE of users. This paper aims to minimize the system latency of edge-cloud computing coupled with CPS while simultaneously considering the security and reliability requirements. We first consider a time-varying channel model as a Finite-State Markov Channel (FSMC) and propose a distributed blockchain-assisted CPIoTS to realize secure consensus and reliable resource orchestration by offloading computation tasks in edge-cloud computing. Moreover, we propose an efficient resource allocation algorithm, PPO-SRRA, that optimizes computing offloading and multi-dimension resource (e.g., communication, computation, and consensus resource) allocation by using a policy-based Deep Reinforcement Learning (DRL) method. The experimental results show that the proposed resource allocation scheme can reduce the system latency and ensure consensus security.