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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 8495

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Dr. Gang Xie

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Guest Editor

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: cognitive radio technologies; cooperative communication; wireless resource allocation

Special Issue Information

Dear Colleagues,

Cooperative communication technology has become a research hotspot in wireless sensor networks in recent years. The concept of cooperative communication has evolved from the original cooperative diversity to the broad collaboration category, including device cooperative transmission and reception, base station cooperation, UAV cooperation, network cooperation and other forms of cooperation. It improves communication capability and efficiency through cooperation among multiple communication entities, and completes communication tasks together. In addition, it can overcome the influence of wireless communication network dynamics (channels, locations, services) and shortage of wireless resources (spectrum, energy, etc.), effectively support diversified broadband services with different service quality requirement (e.g., low latency, high reliability, high spectral efficiency). Suggested areas include, but are not limited to, the following subject categories:

Resource allocation and management in cooperative communication.
Cooperative technology in cognitive radio.
Research on cellular cooperation and related technologies.
Research on cell-free cooperation and related technologies.
Research on UAV cooperation and related technologies.
Interference cancellation and multiuser detection in cooperative networks.
D2D cooperative communication.
Communication security in cooperative communication.
Architecture and strategy design for cooperative system.

Dr. Gang Xie
Guest Editor

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Published Papers (5 papers)

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Research

15 pages, 3542 KiB

Open AccessArticle

Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks

by Reem Alkanhel, Ahsan Rafiq, Evgeny Mokrov, Abdukodir Khakimov, Mohammed Saleh Ali Muthanna and Ammar Muthanna

Sensors 2023, 23(16), 7083; https://doi.org/10.3390/s23167083 - 10 Aug 2023

Cited by 1 | Viewed by 977

Abstract

Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models. Full article

(This article belongs to the Special Issue Resource Allocation for Cooperative Communications)

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18 pages, 11120 KiB

Open AccessArticle

Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network

by Zhiguo Liu, Weijie Li, Jianxin Feng and Jiaojiao Zhang

Sensors 2022, 22(22), 8678; https://doi.org/10.3390/s22228678 - 10 Nov 2022

Cited by 10 | Viewed by 1917

Abstract

The current satellite network traffic forecasting methods cannot fully exploit the long correlation between satellite traffic sequences, which leads to large network traffic forecasting errors and low forecasting accuracy. To solve these problems, we propose a satellite network traffic forecasting method with an improved gate recurrent unit (GRU). This method combines the attention mechanism with GRU neural network, fully mines the characteristics of self-similarity and long correlation among traffic data sequences, pays attention to the importance of traffic data and hidden state, learns the time-dependent characteristics of input sequences, and mines the interdependent characteristics of data sequences to improve the prediction accuracy. Particle Swarm Optimization (PSO) algorithm is used to obtain the best network model Hyperparameter and improve the prediction efficiency. Simulation results show that the proposed method has the best fitting effect with real traffic data, and the errors are reduced by 26.9%, 37.2%, and 57.8% compared with the GRU, Support Vector Machine (SVM), and Fractional Autoregressive Integration Moving Average (FARIMA) models, respectively. Full article

(This article belongs to the Special Issue Resource Allocation for Cooperative Communications)

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19 pages, 907 KiB

Open AccessArticle

Optimal Achievable Transmission Capacity Scheme for Full-Duplex Multihop Wireless Networks

by Aung Thura Phyo Khun, Yuto Lim and Yasuo Tan

Sensors 2022, 22(20), 7849; https://doi.org/10.3390/s22207849 - 16 Oct 2022

Cited by 1 | Viewed by 1010

Abstract

Full-duplex (FD) communication has been attractive as the breakthrough technology for improving attainable spectral efficiency since the 5G mobile communication system. Previous research focused on self-interference cancellation and medium access control (MAC) protocol to realize the FD system in wireless networks. This paper proposes an optimal achievable transmission capacity (OATC) scheme for capacity optimization in the FD multihop wireless networks. In this paper, the proposed OATC scheme considers the temporal reuse for spectral efficiency and the spatial reuse with transmit power control scheme for interference mitigation and capacity optimization. OATC scheme controls the transmit power to mitigate interference and optimizes the transmission capacity, which leads to the optimal achievable network capacity. We conduct the performance evaluation through numerical simulations and compare it with the existing FD MAC protocols. The numerical simulations reveal that considering only the concurrent transmissions in the FD system does not guarantee optimal transmission capacity. Moreover, the hybrid mechanism, including the sequential transmissions, is also crucial because of the interference problem. Besides, numerical simulation validates that the proposed OATC scheme accomplishes the optimal achievable network capacity with lower interference power and higher achievable throughput than the existing MAC protocols. Full article

(This article belongs to the Special Issue Resource Allocation for Cooperative Communications)

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16 pages, 526 KiB

Open AccessArticle

Sparse Sliding-Window Kernel Recursive Least-Squares Channel Prediction for Fast Time-Varying MIMO Systems

by Xingxing Ai, Jiayi Zhao, Hongtao Zhang and Yong Sun

Sensors 2022, 22(16), 6248; https://doi.org/10.3390/s22166248 - 19 Aug 2022

Cited by 1 | Viewed by 1362

Abstract

Accurate channel state information (CSI) is important for MIMO systems, especially in a high-speed scenario, fast time-varying CSI tends to be out of date, and a change in CSI shows complex nonlinearities. The kernel recursive least-squares (KRLS) algorithm, which offers an attractive framework to deal with nonlinear problems, can be used in predicting nonlinear time-varying CSI. However, the network structure of the traditional KRLS algorithm grows as the training sample size increases, resulting in insufficient storage space and increasing computation when dealing with incoming data, which limits the online prediction of the KRLS algorithm. This paper proposed a new sparse sliding-window KRLS (SSW-KRLS) algorithm where a candidate discard set is selected through correlation analysis between the mapping vectors in the kernel Hilbert spaces of the new input sample and the existing samples in the kernel dictionary; then, the discarded sample is determined in combination with its corresponding output to achieve dynamic sample updates. Specifically, the proposed SSW-KRLS algorithm maintains the size of the kernel dictionary within the sample budget requires a fixed amount of memory and computation per time step, incorporates regularization, and achieves online prediction. Moreover, in order to sufficiently track the strongly changeable dynamic characteristics, a forgetting factor is considered in the proposed algorithm. Numerical simulations demonstrate that, under a realistic channel model of 3GPP in a rich scattering environment, our proposed algorithm achieved superior performance in terms of both predictive accuracy and kernel dictionary size than that of the ALD-KRLS algorithm. Our proposed SSW-KRLS algorithm with

M = 90

achieved 2 dB NMSE less than that of the ALD-KRLS algorithm with

v = 0.001

, while the kernel dictionary was about 17% smaller when the speed of the mobile user was 120 km/h. Full article

(This article belongs to the Special Issue Resource Allocation for Cooperative Communications)

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22 pages, 693 KiB

Open AccessCommunication

Towards Latency Bypass and Scalability Maintain in Digital Substation Communication Domain with IEC 62439-3 Based Network Architecture

by Lilia Tightiz and Joon Yoo

Sensors 2022, 22(13), 4916; https://doi.org/10.3390/s22134916 - 29 Jun 2022

Cited by 5 | Viewed by 2619

Abstract

Parallel redundancy protocol (PRP) and high-availability redundancy protocol (HSR) are widely adopted protocols based on IEC 61850 standard to support zero recovery communication networks for time-critical and reliable interactions in power system substations. However, hiring these protocols comes with technical and economic constraints that impact the size of the substation network arrangement. Therefore, we will undertake a theoretical analysis of HSR, PRP, and their combinations to reach a maximum number of nodes in different substation communication architectures regarding IEC 61850 standard message time constraint requirements and IEC 62439-3 standard regulations. We will validate our findings through a simulation in the OPNET Modeler environment. In addition, we considered bandwidth efficiency by prohibiting the extra circulation of packets in the redundancy Box (RedBox) and QuadBox implementation as interfaces for HSR and PRP connection and HSR rings interconnection, respectively, which represent the main hindrance in utilizing the combination of these protocols. Full article

(This article belongs to the Special Issue Resource Allocation for Cooperative Communications)

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