Communication Resource Allocation Research Articles

Optimization of gamified Learning Education Model based on XR + 5G technology

The new intelligent technology represented by 5G and XR has become the core driving force of the fourth industrial revolution, and the integrated application of the two in the field of education is also promoting the continuous transformation of teaching to gamification. Based on XR + 5G fusion technology, this paper proposes the optimization design of a gamified education model. Firstly, the problem of how to improve the network performance through D2D communication in a full-load 5G network is studied, and a D2D communication resource allocation framework based on cross-layer optimization design is proposed. In D2D user access control, a D2D user access criterion based on the distance between the CU user and D2D user receiver is designed according to the QoS requirement, maximum transmit power limit, and user channel information. XR simulation platform was designed based on a 5G network with optimized communication resource allocation. UMa and InH simulation scenarios were set according to XR business model working scenarios, and Wrap Around algorithm was used to model two circles of interference for users, ensuring the reliability of results while improving simulation efficiency. Finally, the platform channel was modeled strictly according to the channel modeling method in technical document 38.901, and the channel was calibrated with the channel provided by 3GPP. Based on the simulation platform, gamified education design is optimized. Including class platform and class platform gamification design, system demand analysis, and database design. In the gamification design, a relatively novel classroom interaction mode is introduced to increase students’ enthusiasm in class. In the demand analysis of the system, the main problems in class are listed, based on which the business and functional requirements of the gamification platform are determined. Experiments show that in the IID scenario, the proposed XR + 5G fusion technology can achieve 1.09 times the training speed and obtain 0.83 % learning accuracy gain compared with the individual learning algorithm. In the Non-IID scenario, the training speed of the proposed fusion technique is 1.03 times that of the individual learning algorithm, and there is a learning accuracy gain of 2.27 %.

EFraS: Emulated framework to develop and analyze dynamic Virtual Network Embedding strategies over SDN infrastructure

The integration of Software-Defined Networking (SDN) into Network Virtualization (NV) significantly enhances network management, isolation, and troubleshooting capabilities. However, it brings forth the intricate challenge of allocating Substrate Network (SN) resources for various Virtual Network Requests (VNRs), a process known as Virtual Network Embedding (VNE). It encompasses solving two intractable sub-problems: embedding Virtual Machines (VMs) and embedding Virtual Links (VLs). While the research community has focused on formulating embedding strategies, there has been less emphasis on practical implementation at a laboratory scale, which is crucial for comprehensive design, development, testing, and validation policies for large-scale systems. However, conducting tests using commercial providers presents challenges due to the scale of the problem and associated costs. Moreover, current simulators lack accuracy in representing the complexities of communication patterns, resource allocation, and support for SDN-specific features. These limitations result in inefficient implementations and reduced adaptability, hindering seamless integration with commercial cloud providers. To address this gap, this work introduces EFraS (Emulated Framework for Dynamic VNE Strategies over SDN). The goal is to aid developers and researchers in iterating, testing, and evaluating VNE solutions seamlessly, leveraging a modular design and customized reconfigurability. EFraS offers various functionalities, including generating real-world SN topologies and VNRs. Additionally, it integrates with a diverse set of evaluation metrics to streamline the testing and validation process. EFraS leverages Mininet, Ryu controller, and OpenFlow switches to closely emulate real-time setups. Moreover, we integrate EFraS with various state-of-the-art VNE schemes, ensuring the effective validation of embedding algorithms.

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.

Implementation of the Land and Building Tax Amnesty Program: A Case Study Analysis at the Regional Tax Service Agency

This qualitative descriptive study investigates the implementation of the Land and Building Tax (PBB) exemption program in Sidoarjo Regency, focusing on communication, resources, disposition, and bureaucratic structure. Employing purposive sampling, primary data was gathered through observation, interviews, and documentation. Utilizing Miles and Huberman's data analysis, the study reveals that all variables were fulfilled, with challenges arising from insufficient information dissemination by some villages and a shortage of billing staff. The Sidoarjo Regional Tax Service Agency (BPPD) is urged to innovate communication channels for direct community engagement and enhance staffing levels. This research provides valuable insights for policymakers and tax authorities globally, emphasizing the need for efficient communication strategies and resource allocation to ensure successful tax exemption programs. Highlight : Effective Communication: The study underscores the crucial role of communication in the successful implementation of the Land and Building Tax (PBB) exemption program, emphasizing the need for streamlined information flow between villages and residents. Resource Allocation: The research highlights challenges stemming from a shortage of billing staff in Sidoarjo Regency, emphasizing the significance of adequate resource allocation to ensure the smooth operation of tax exemption policies. Policy Implications: The findings provide actionable insights for tax authorities and policymakers globally, emphasizing the importance of efficient communication strategies and resource management in optimizing the effectiveness of land and building tax exemption programs. Keywords: Land and Building Tax, Exemption Program, Implementation, Sidoarjo Regency, Communication

Optimal Resource Allocation for Integrated Sensing and Communications in Internet of Vehicles: A Deep Reinforcement Learning Approach

Optimal Resource Allocation for Integrated Sensing and Communications in Internet of Vehicles: A Deep Reinforcement Learning Approach

Anti-Jamming Resource Allocation for Integrated Sensing and Communications Based on Game-Guided Reinforcement Learning

Anti-Jamming Resource Allocation for Integrated Sensing and Communications Based on Game-Guided Reinforcement Learning

Analytical Optimal Joint Resource Allocation and Continuous Trajectory Design for UAV-Assisted Covert Communications

Analytical Optimal Joint Resource Allocation and Continuous Trajectory Design for UAV-Assisted Covert Communications

Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications

Event-Triggered Reinforcement Learning Based Joint Resource Allocation for Ultra-Reliable Low-Latency V2X Communications

Source Value-Based Resource Allocation in Task-Oriented Communications

Source Value-Based Resource Allocation in Task-Oriented Communications

Reliability-Oriented Resource Allocation in Short-Packet Backscatter Communications

Reliability-Oriented Resource Allocation in Short-Packet Backscatter Communications

Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

Deep-Learning-Based Resource Allocation for 6G NOMA-Assisted Backscatter Communications

Deep-Learning-Based Resource Allocation for 6G NOMA-Assisted Backscatter Communications

Effectiveness of Police Intra-Departmental Collaboration in Combating Human Trafficking in Nairobi City County, Kenya

The primary aim of this study was to investigate the effectiveness of police intra-departmental collaboration in combating human trafficking in Nairobi County, Kenya. Human trafficking remains a critical issue requiring effective intra-departmental collaboration among police units. This study investigates how various strategies and practices impact the efficiency of police efforts in combating human trafficking; with a focus on Nairobi County. The study employs Network Theory which emphasizes the importance of communication networks in coordinating and enhancing collaborative efforts across different units within an organization. Data was collected through structured interviews and focus groups discussions with key stakeholders, including police officers, community leaders, and National Government Officers. The study utilized a purposive sampling method to ensure diverse perspectives on the effectiveness of intra-departmental collaboration. The research engaged 20 respondents, including local community leaders, senior police officers, and national-level officials. Data was analyzed using thematic analysis for qualitative responses and descriptive statistics for quantitative data. This approach facilitated a comprehensive understanding of the communication practices, command structure, resource allocation, and community involvement in combating human trafficking. The study found that centralized communication systems and routine interdepartmental meetings significantly enhance cooperation and efficiency in responding to human trafficking. Decentralization of command authority improves local responsiveness, while fair resource distribution fosters collaboration. Community leaders and organizations provide crucial intelligence and support, emphasizing the need for strengthened partnerships between police and community stakeholders. Effective anti-trafficking efforts depend on integrated communication systems, improved command structures, equitable resource allocation, and strong community partnerships. While centralized systems and strategic planning enhance inter-departmental collaboration, issues such as inadequate command guidance and resource imbalances hinder cooperation. To improve intra-departmental collaboration, it is recommended to decentralize decision-making authority, implement a unified communication platform, conduct regular interdisciplinary meetings, and ensure equitable distribution of resources. Additionally, fostering stronger relationships with local community leaders and organizations will enhance information sharing and overall effectiveness in combating human trafficking.