International Journal of Communication Systems最新文献

The rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end-users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL-based approach outperforms other state-of-the-art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively.

Information technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay-based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group-based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.

This paper proposes a new approach for optimizing traffic management in multiple access networks (MANETs) by utilizing the stream-enabled routing (SER) algorithm. The SER algorithm is used to determine which routing path is the most time- and resource-efficient. The proposed approach makes use of multipath routing in a manner that is consistent with the SER method. By combining the states of flows, queues, and links, a graph neural network (GNN)-based model attempts to break the circular dependencies that are described by these functions. The simulation is setup with joint parameters consisting of residual energy, packet delivery rate (PDR), and end-to-end delay. The results of the experiments show that the proposed protocol provides a significant improvement in terms of network efficiency when compared to using some baseline protocols designed for MANETs.

Unmanned aerial vehicle operations are quickly gaining ground due to rapid global market penetration. While on one hand, novel technologies that bridge communication networks to aviation industry are yet to be explored, on the other hand, their development requires highly scalable systems to enable beyond visual line-of-sight missions. This requirement imposes a big bottleneck in terms of computation complexity. This paper presents a method for fast computation of multiple diffraction of radio waves over knife-edge obstacles based on the Deygout technique and some offline computation steps, including a ground profile analysis. We prove that this algorithm is equivalent to the original Deygout algorithm for all non-line-of-sight points, show heuristics confirming that it is mostly applicable in the line-of-sight case. The computational and memory complexity of our algorithm is approximately $�O�\left(�\sqrt{��N��}�\right)$, compared to $�O�\left(�N�\right)$ for the original Deygout algorithm. Finally we discuss how to apply the approach to the Epstein-Peterson technique and the Giovanelli technique, and how to use it to compute clutter-loss.

Relay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal-to-interference-plus-noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision-makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach.

A compact, flexible, low-profile end-fire broadband wearable antenna operating in Ku-band /X-band is proposed in this manuscript for defense and satellite communications (Satcom) applications. The main objective of this work is cross-polarization reduction by the defected ground structure (DGS), which offers a wider bandwidth. Due to its flexibility and ability to absolutely conform to the curved-shaped human body, denim fabric is used as a substrate, whereas copper tape is used as a conductor, which allows for the integration of the antenna into garments and makes it appropriate for a wide range of wearable applications in various bands. The prototype has been developed with a size of $�20�\times �20�\times �0�.�5$ mm³ for experimental validation. The measured results from a fabricated prototype are well matched with the simulated ones of the proposed design, which indicate a wide bandwidth of 57.35% (7.76–14 GHz) appropriate for use in applications such as defense operating from 8 to 12 GHz, satellite TV (11.7–12.2 GHz), Ku-band downlink (10.95–11.7 GHz), Ku-band uplink (11.7–14.5 GHz), and a high gain of 5.1 dBi. The specific absorption rate (SAR) is much below the permissible limit of 1.6 W/kg, with better radiation characteristics. Thus, the proposed antenna is more compact, and it clearly achieves a smaller footprint, larger impedance bandwidths, and a low SAR with potential prospect for Satcom and defense purposes.

Millimeter Wave (mmWave) communication has emerged as a transformative technology at the forefront of wireless communication. One of the key challenges in harnessing the potential of mmWave technology is overcoming the increased susceptibility to propagation losses and environmental obstacles. To address these challenges, Three-Dimensional Massive Multiple-Input Multiple-Output (3D Massive MIMO) systems have gained traction. The 3D aspect extends this concept by considering the elevation dimension, allowing for enhanced spatial resolution and coverage. Accurate estimation of the channel in 3D Massive MIMO scenarios is particularly challenging because of the complex propagation characteristics of mmWave signals. This paper introduces an efficient-Aided Graph Neural Network Combining with Hierarchical Residual Learning (DPrGNN-HrResNetL), designed specifically for beamspace Channel Estimation (CE)in mmWave-Massive MIMO environments. The proposed model leverages deep priors and GNN mechanisms to enhance the extraction of spatial features, while hierarchical residual connections facilitate effective information flow through the network. DPrGNN enables the model to capture and understand complex spatial relationships among different antenna elements. The incorporation of deep priors provides a mechanism for leveraging prior knowledge about channel characteristics. This enhances the efficiency of the learning process, allowing the model to learn and adapt more effectively. The integration of hierarchical residual connections facilitates effective information flow through the network. This is particularly important for modeling complex dependencies within the beamspace channel data, enhancing the learning capacity of the network. The performance of the DPrGNN-HrResNetL model is evaluated across a range of Signal-to-Noise Ratios (SNRs), utilizing metrics such as Normalized Mean Squared Error (NMSE) to measure the accuracy of the estimation. The outcomes underscore the resilience and efficacy of the DPrGNN-HrResNetL approach in achieving precise CE within demanding mmWave scenarios.

In this paper, a novel strategy of video communication over a 5G platform of new radio (NR) is developed, and it is named ViNR. With the support of optimization on the ViNR quality of service (QoS) system design, we stretched the outdated R-D optimization to a novel delay-distortion-rate optimization (DDRO) control method. The entire model is partitioned with two types of coding: source coding and channel coding. For source coding, we affianced the inter-frame prediction method of independent predicted frames (IPPPP) with Lagrange multiplier optimization. Channel coding is intricate with the minimization of delay-distortion and getting the most out of the rate using resource allocation optimization in terms of sub-slice allocation. To perform this sub-slice resource assignment with optimization of DDR, the isolation resource allocation is premeditated in this work to ensure the service level profile of various groups of resource slots or grids dealing with the subject of middling delay and rate. The widespread simulation results divulge that the proposed algorithm NR-DDRO achieves better QoS parameters of delay, distortion, and rate with the metrics of end-to-end distortion, encoding Y-PSNR, encoding bit rate, encoding time, end-to-end PSNR, optimization time, and complete computation time.

Location-based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range-based and range-free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP-TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up-to-date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes.