Communication Resource Allocation Research Articles

Joint optimization for computation offloading and 3C resource allocations over wireless-powered and NOMA-enabled multi-access MEC

Multi-access mobile edge computing (MEC) enables mobile users (MUs) to offload tasks to proximal multiple MEC servers for fast task processing. Since MUs generally have stringent delay requirements and limited energy and MEC servers have finite communication, computation, and caching (3C) resources, the joint co-design and optimization over computation offloading and 3C resource allocation for wireless-powered multi-access MEC systems have been highly demanded, while having not been well studied yet. We consider a wireless-powered multi-access MEC, where multiple energy harvesting (EH) based MUs and multiple base stations (BSs) each equipped with an MEC server coexist. Each MU first harvests energy from nearby MEC servers, and then offloads its sub-tasks to multiple MEC servers concurrently based on non-orthogonal multiple access (NOMA), so as to reduce data offloading delay. We first formulate a delay minimization problem, by jointly optimizing MUs’ computation offloading, MEC servers’ computation and caching resources allocation, and system communication resources allocation. Then we propose an alternating direction multiplier method (ADMM) based distributed scheme to decompose the formulated optimization problem into several sub-problems, and use the block coordinate descending (BCD) method and the successive convex approximation (SCA) method to transform all sub-problems each corresponding to one MEC server to convex subproblems. Finally, we validate and evaluate our proposed scheme through numerical analyses, which show that our proposed distributed scheme can greatly reduce the min–max delay of all MUs by allowing each MU’s concurrent data offloading to multiple MEC servers using NOMA and by jointly optimizing computation offloading and 3C resource allocations. Also, the delay imposed by our proposed scheme is much smaller than that imposed by the Interior Point method, which shows the effectiveness of our proposed scheme.

Energy-Efficient Resource Allocation for IRS-Assisted SWIPT-Enabled D2D Communications With Imperfect CSI

Energy-Efficient Resource Allocation for IRS-Assisted SWIPT-Enabled D2D Communications With Imperfect CSI

Assessment of Communication Resource Allocation by the Transmission Control Protocol for the Target Virtual Connection under Competitive Conditions

The mathematical framework presented in this article focuses on the controlled-transmission protocol’s asynchronous process of bandwidth allocation for the target virtual connection implemented under competition for communication resources. The studied process is formalized as a two-dimensional discrete Markovian chain, taking into account the distributions of queue lengths of TCP data fragments from competing client nodes. Such a chain describes the dynamics of filling the stack of transmitted but unacknowledged data fragments of the investigated end device. Distributions of the chain states were found for various ratios of the target virtual-connection bandwidth, transmission-protocol parameters, and communication-channel characteristics. Analytical dependencies for computing the performance of the target virtual connection for different operating modes were obtained. The results of experiments conducted based on the obtained analytical constructions showed that the performance of the virtual connection with a selective repeat mode is mainly determined by the data-loss intensity, the queue size distribution in transit nodes, and the ratio between the protocol window size and the route length.

$\alpha$-Fair Resource Allocation for Downlink Multiuser Short Packet Communications

$\alpha$-Fair Resource Allocation for Downlink Multiuser Short Packet Communications

Optimizing resource allocation for D2D communications with incomplete CSI

Optimizing resource allocation for D2D communications with incomplete CSI

3-D Trajectory Optimization and Communication Resources Allocation in UAV-Assisted IoT Networks for Sustainable Industry 5.0

3-D Trajectory Optimization and Communication Resources Allocation in UAV-Assisted IoT Networks for Sustainable Industry 5.0

CORA: Cooperative Communication and Optimal Resource Allocation in Multihop Wireless Multimedia Sensor Networks

CORA: Cooperative Communication and Optimal Resource Allocation in Multihop Wireless Multimedia Sensor Networks

Resource allocation in vehicular network based on sparrow search algorithm and hyper-graph in the presence of multiple cellular users

PurposeSince the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).Design/methodology/approachAn optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.FindingsThe performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.Originality/valueThere is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

Joint Resource Allocation for Terahertz Band Drone Communications

Joint Resource Allocation for Terahertz Band Drone Communications

Latency Minimization for Wireless Federated Learning With Heterogeneous Local Model Updates

In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local model updates in each communication round. We formulate a total latency minimization problem with probabilistic device selection, taking into account both the communication and computation latency in the whole FL procedure. However, it is highly challenging to optimally solve this problem due to the coupling issues of model convergence and latency minimization problem caused by the heterogeneity of local model updates. Through convergence analysis, we reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting sub-problems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulation results show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve up to 47.04% single-round latency reduction.

Multi-Task Communication Resource Allocation for MIMO-Based Vehicular Fog Computing

Multi-Task Communication Resource Allocation for MIMO-Based Vehicular Fog Computing

Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

Implementation of Public Information Disclosure Policy in Realizing Good Corporate Governance

The Indonesian Aviation Navigation Service Organizing Agency, operating as a State-Owned Enterprise, is obligated to uphold the tenets of Good Corporate Governance per the Minister of State-Owned Enterprises Regulation Number 01 of 2011. This commitment extends to the implementation of policies outlined in Law Number 14 of 2008, specifically addressing the Openness of Public Information. A nuanced analysis, rooted in George Edward's implementation theory, delves into the complexities of how the Information and Documentation Management Officer at AirNav Indonesia navigates the communication dynamics, resource allocation, organizational disposition, and bureaucratic structure inherent in executing public information disclosure policies. Employing qualitative methods and triangulation techniques, this study unravels the intricate interplay of these components, illustrating that effective implementation transcends procedural adherence to embody a comprehensive orchestration of transparency, accountability, responsibility, independence, and fairness within the context of public information disclosure. The Information and Documentation Management Officer emerges as a pivotal figure, strategically aligning these elements to achieve a robust realization of the principles of good corporate governance.

Reliability-Oriented Resource Allocation for Wireless Powered Short Packet Communications With Multiple WPT Sources

We study a multi-source wireless power transfer (WPT) enabled network supporting multi-sensor transmissions. Activated by the energy harvesting from multiple WPT sources, the sensors transmit short packets to a destination with finite blocklength (FBL) codes. This work <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">for the first time</i> characterizes the FBL reliability for such multi-source WPT enabled network and accordingly provides reliability-oriented resource allocation designs, while a practical nonlinear EH model (including the effects of mutual interference among multiple RF signals) is considered. For the scenario with a fixed frame structure, we aim to maximize the FBL reliability via optimally allocating the transmit power among the multiple WPT sources. In particular, we investigate the relationship between the overall error probability and the transmit power of multiple WPT sources, based on which a power allocation problem is formulated. To solve the formulated non-convex problem, we first introduce auxiliary variables to make the problem analytically tractable, based on which an iterative algorithm is proposed while applying successive convex approximation (SCA) technique to the non-convex components of the problem. Then, we extend our design into a dynamic frame structure scenario, i.e., the blocklength allocated for WPT phase and short-packet transmission phase are adjustable, which introduces more flexibility and new challenges. In particular, we provide a joint power and blocklength allocation design maximizing the overall reliability under total power and blocklength constraints. A problem with high-dimension variables is formulated, which suffers from the complex and non-convex relationship among system reliability, multiple source power and blocklength. To tackle the difficulties, auxiliary variables introduction, multiple variable substitutions along with SCA technique utilization are exploited to reformulate and efficiently solve the problem. Finally, through numerical results, we validate our analytical model and evaluate the system performance, where a set of guidelines for practical system design are concluded.

Event‐triggered security control for fuzzy‐model‐based cyber‐physical systems under Denial‐of‐Service attacks and actuator faults

AbstractThis article addresses the issue of event‐triggered security control for nonlinear cyber‐physical systems, characterized by Denial‐of‐Service (DoS) attacks, time‐varying delays, and actuator faults. These complexities are captured using the Takagi–Sugeno fuzzy model. Significantly, this study focuses on DoS attacks targeting the controller‐to‐actuator channel, and subsequently introduces a model for actuator faults that considers the prolonged utilization or degradation of component parts. To optimize communication resource allocation, a model‐independent event‐triggered mechanism is proposed. Utilizing the Lyapunov–Krasovskii function approach, the objective of achieving stochastic stability is transformed into a linear matrix inequalities problem, thus deriving a set of sufficient conditions. The efficiency and validity of the proposed algorithms is demonstrated through simulation results.

Resource allocation and OFDM system transmission for 5G cellular communications

Cellular communication technology has been developed for over a hundred years and has reached its fifth generation in mobile. 5G, as a new generation of communication technology, has comprehensively changed the mobile network application model with its ability of high-speed transmission, low network latency, and large-scale connectivity. It has brought a new impetus to the development of various industries. Cellular communication uses cellular wireless networking to connect terminals and network devices through wireless channels so that users can communicate with each other during their activities, with the main feature being the mobility of the terminals and the automatic roaming process across local networks. The use of OFDM in 5G communications can significantly improve the efficiency and capacity of wireless signal transmission. This paper provides a detailed introduction to the use of OFDM in cellular communications through frequency division multiplexing and OFDM.

A game incentive mechanism for energy efficient federated learning in computing power networks

Computing Power Network (CPN) is emerging as one of the important research interests in beyond 5G (B5G) or 6G. This paper constructs a CPN based on Federated Learning (FL), where all Multi-access Edge Computing (MEC) servers are linked to a computing power center via wireless links. Through this FL procedure, each MEC server in CPN can independently train the learning models using localized data, thus preserving data privacy. However, it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers. To address these issues, we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers. Afterwards, we formulate a comprehensive algorithm to jointly optimize the communication resource (wireless bandwidth and transmission power) allocations and the computation resource (computation capacity of MEC servers) allocations while ensuring the local accuracy of the training of each MEC server. The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.

Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications

Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications

Toward Green Metaverse Networking: Technologies, Advancements, and Future Directions

As the Metaverse is iteratively being defined, its potential to unleash the next wave of digital disruption and create real-life value becomes increasingly clear. With distinctive features of immersive experience, simultaneous interactivity, and user agency, the Metaverse has the capability to transform all walks of life. However, the enabling technologies of the Metaverse, i.e., digital twin, artificial intelligence, blockchain, and extended reality, are known to be energy-hungry, therefore raising concerns about the sustainability of its large-scale deployment and development. This article proposes Green Metaverse Networking for the first time to optimize energy efficiencies of all network components for Metaverse sustainable development. We first analyze energy consumption, efficiency, and sustainability of energy-intensive technologies in the Metaverse. Next, focusing on computation and networking, we present major advancements related to energy efficiency and their integration into the Metaverse. A case study of energy conservation by incorporating semantic communication and stochastic resource allocation in the Metaverse is presented. Finally, we outline the critical challenges of Metaverse sustainable development, thereby indicating potential directions of future research towards the green Metaverse.

Deep Reinforcement Learning Based Cooperative Partial Task Offloading and Resource Allocation for IIoT Applications

Compared with traditional cloud computing schemes, edge computing provides an effective solution towards meeting real-time, security and reliability demands of compute-intensive and delay-sensitive Industrial Internet of Things (IIoT) applications. However, many existing studies only optimize end-edge-cloud cooperative task offloading, and neglect the optimization of the communication and computation resource allocation. In this paper, the cooperative partial task offloading and resource allocation (CPTORA) framework is designed, which jointly considers cooperation among various IIoT devices, local edge computing servers (ECSs), non-local ECSs, and cloud computing servers. Then, considering the complex dynamics and unpredictability in IIoT environments, the joint optimization problem is modeled as a constrained Markov decision process. Furthermore, we propose an improved soft actor-critic-based CPTORA (ISAC-CPTORA) algorithm, to make task offloading and resource allocation decisions for each IIoT device. This algorithm innovatively introduces the idea of distributional reinforcement learning to the soft actor-critic, which can effectively reduce Q-value overestimations or underestimations. Meanwhile, this algorithm employs the prioritized experience replay to enhance its learning efficiency. Extensive laboratory experiments indicate that our CPTORA framework and ISAC-CPTORA algorithm efficiently decrease the total system costs, in contrast to various baseline frameworks and algorithms.