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Future Wireless Communication Networks (Volume II)

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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 (10 July 2024) | Viewed by 16391

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Dr. Hazer Inaltekin

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The School of Engineering, Macquarie University, Macquarie Park, Sydney, NSW 2109, Australia
Interests: airborne networks; next generation broadband satellite systems; wireless edge computing; AI/ML based protocol design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the successor of 5G technology, 6G wireless networks will continue to revolutionize the industry and society by enabling many game-changing applications, including unmanned vehicle coordination and connectivity, smart factory operation, holographic telepresence, multisensory communications, telehealth, remote farming equipment operation and remote IoT communications. Realizing the full potential of these applications will require order-of-magnitude improvements in data rates, latency, reliability and coverage in 6G network deployments when compared to their 5G counterparts. Some key technologies that are expected to enable these applications include the utilization of terahertz bands, airborne network deployments, 3D networking integrating terrestrial, airborne and satellite networks, next generation satellite systems (e.g., regenerative LEO, MEO and GEO satellites), and edge intelligence for joint communications, computing and control.

This special issue will focus on potential technologies that will underpin the beyond 5G and 6G wireless networks. We solicit high-quality research papers on topics including, but not limited to:

Terahertz communications
Airborne network deployment and optimization
3D networking integrating terrestrial, airborne and satellite networks
AR/VR communications over wireless links
New generation satellite network architectures
AI/ML based protocol design for emerging wireless systems
Quantum communications
Wireless edge computing and control
Airborne edge computing
Satellite IoT systems
Blockchain based wireless networks

Dr. Hazer Inaltekin
Guest Editor

Manuscript Submission Information

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Published Papers (9 papers)

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Research

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23 pages, 5651 KiB

Open AccessArticle

Enhancing Wireless Network Efficiency with the Techniques of Dynamic Distributed Load Balancing: A Distance-Based Approach

by Mustafa Mohammed Hasan Alkalsh and Adrian Kliks

Sensors 2024, 24(16), 5406; https://doi.org/10.3390/s24165406 - 21 Aug 2024

Viewed by 1088

Abstract

The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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21 pages, 808 KiB

Open AccessArticle

Deep Reinforcement Learning-Based Adaptive Scheduling for Wireless Time-Sensitive Networking

by Hanjin Kim, Young-Jin Kim and Won-Tae Kim

Sensors 2024, 24(16), 5281; https://doi.org/10.3390/s24165281 - 15 Aug 2024

Viewed by 1205

Abstract

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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18 pages, 987 KiB

Open AccessArticle

TransNeural: An Enhanced-Transformer-Based Performance Pre-Validation Model for Split Learning Tasks

by Guangyi Liu, Mancong Kang, Yanhong Zhu, Qingbi Zheng, Maosheng Zhu and Na Li

Sensors 2024, 24(16), 5148; https://doi.org/10.3390/s24165148 - 9 Aug 2024

Viewed by 892

Abstract

While digital twin networks (DTNs) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for split learning (SL) tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreported resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by

9.3 %

and convergence estimation accuracy by

22.4 %

compared to traditional equation-based methods. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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16 pages, 2328 KiB

Open AccessArticle

The Latency Performance Analysis and Effective Relay Selection for Visible Light Networks

by Baozhu Yu and Xiangyu Liu

Sensors 2024, 24(9), 2748; https://doi.org/10.3390/s24092748 - 25 Apr 2024

Viewed by 790

Abstract

In visible light communication (VLC), the precise latency evaluation of wireless access networks and the efficient forwarding strategy of core networks are the crux for end-to-end reliability provisioning. Leveraging martingale theory, an elegant latency-bounded reliability analysis framework is studied for the VLC network. Considering the characteristic that VLC links are easy to block, the martingale of the service process is constructed. Based on the time shift features, the martingale process related to latency is proposed for the VLC system, which models the influence of entanglement between aggregate arrivals and random service on latency. A stopping time event about latency is defined. Renting the stopping time theory, a tight upper bound of the unreliability with regard to latency is derived. In the core network, a dynamic forward backhaul framework is proposed, which uses the relay selection algorithm based on back-pressure theory to improve data transmission quality. The theoretical latency-bounded reliability matches the simulation results well, which verifies the effectiveness of the proposed analysis framework, and the proposed relay selection algorithm can also improve network performance under data-intensive transmission. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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20 pages, 2363 KiB

Open AccessArticle

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network

by Xiaomin Gong, Shuangyin Ren, Chunjiang Wang and Jingchao Wang

Sensors 2024, 24(4), 1086; https://doi.org/10.3390/s24041086 - 7 Feb 2024

Cited by 1 | Viewed by 1391

Abstract

Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GÉANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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23 pages, 999 KiB

Open AccessArticle

Sink Node Placement and Partial Connectivity in Wireless Sensor Networks

by Yun Wang

Sensors 2023, 23(22), 9058; https://doi.org/10.3390/s23229058 - 9 Nov 2023

Viewed by 1387

Abstract

This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources. Through mathematical modeling, theoretical analysis, and simulation evaluations, we derive, compare, and contrast the probabilities of partial and full connectivity within a random WSN, factoring in network parameters and the maximum allowable hop distance/count

h_{m a x}

h_{m a x}

captures the diverse range of delay-sensitive requirements encountered in practical scenarios. Our research underscores the significant impact of the sink node and its placement on network connectivity and the sensor connection rate. The results exemplify a noteworthy decline in the sensor connection rate, dropping from

98.8 %

72.5 %

, upon relocating the sink node from the network center to the periphery. Moreover, as compared with full connectivity, partial connectivity and the sensor connection rate are more suitable metrics for assessing the communication capability of random WSNs. The results illustrate that

1.367

times more energy is required to connect less than

4 %

of the remote sensors, based on the examined network settings. Additionally, to increase the sensor connection rate slightly from

96 %

100 %

, an additional

538 %

more energy is required in multipath fading based on the widely adopted energy consumption model. This research and its outcomes contribute to establishing appropriate performance metrics and determining critical network parameters for the practical design and implementation of real-world wireless sensor networks. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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14 pages, 398 KiB

Open AccessArticle

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications

by Divya Gupta, Shivani Wadhwa, Shalli Rani, Zahid Khan and Wadii Boulila

Sensors 2023, 23(21), 8839; https://doi.org/10.3390/s23218839 - 30 Oct 2023

Cited by 13 | Viewed by 1713

Abstract

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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22 pages, 746 KiB

Open AccessArticle

Grant-Free NOMA: A Low-Complexity Power Control through User Clustering

by Abdulkadir Celik

Sensors 2023, 23(19), 8245; https://doi.org/10.3390/s23198245 - 4 Oct 2023

Cited by 3 | Viewed by 1345

Abstract

Non-orthogonal multiple access (NOMA) has emerged as a promising solution to support multiple devices on the same network resources, improving spectral efficiency and enabling massive connectivity required by ever-increasing Internet of Things devices. However, traditional NOMA schemes operate in a grant-based fashion and require channel-state information and power control, which hinders its implementation for massive machine-type communications. Accordingly, this paper proposes synchronous grant-free NOMA (GF-NOMA) frameworks that effectively integrate user equipment (UE) clustering and low-complexity power control to facilitate the power-reception disparity required by the power-domain NOMA. Although single-level GF-NOMA (SGF-NOMA) designates an identical transmit power for all UEs, multi-level GF-NOMA (MGF-NOMA) groups UEs into partitions based on the sounding reference signals strength and assigns partitions with different identical power levels. Based on the objective of interest (e.g., max–sum or max–min rate), the proposed UE clustering scheme iteratively admits UEs to form clusters whose size is dynamically determined based on the number of UEs and available resource blocks (RBs). Once the UEs are acknowledged with power levels and allocated RBs through random-access response (RAR) messages, UEs can transmit anytime without grant acquisition. Numerical results show that the proposed GF-NOMA frameworks can compute clusters in the order of milliseconds for hundreds of UEs. The MGF-NOMA can reach up to 96–99% of the optimal benchmark max–sum rate, and the SGF-NOMA reaches 87% of the optimal benchmark max–sum rate at the same power consumption. Since the MGF-NOMA and optimal benchmark enforce the strongest and weakest channel UEs to transmit at maximum and minimum transmit powers, respectively, the SGF-NOMA also offers a significantly higher energy consumption fairness and network lifetime as all UEs consume equal transmit powers. Although the MGF-NOMA delivers an inferior max–min rate performance, the SGF-NOMA is shown to reach 3e6 MbpJ energy efficiency compared to the 1e7 MbpJ benchmark. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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Review

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49 pages, 1329 KiB

Open AccessReview

A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches

by Wilfrid Azariah, Fransiscus Asisi Bimo, Chih-Wei Lin, Ray-Guang Cheng, Navid Nikaein and Rittwik Jana

Sensors 2024, 24(3), 1038; https://doi.org/10.3390/s24031038 - 5 Feb 2024

Cited by 16 | Viewed by 5869

Abstract

The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN’s deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks (Volume II))

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