Frontiers in Communications and Networks

The integration of terahertz (THz) communication with cell-free massive multiple-input multiple-output (CFMM) systems presents a promising strategy to enhance energy efficiency and reduce system complexity in future wireless networks. However, this integration faces significant challenges, such as dynamic and unpredictable channel behavior. Traditional channel estimation techniques are inadequate for handling these dynamic conditions. To address these issues, a convolutional neural network (CNN)-based hybrid precoding scheme is proposed for CFMM systems operating at THz frequencies. This method leverages CNN to predict optimal precoding weights, significantly improving the adaptability of hybrid precoding. The CNN-based model not only mitigates pilot contamination (PC) but also enhances channel estimation by capturing temporal and spatial dynamics. Simulation results indicate that the CNN-based approach achieves superior energy efficiency and lower system complexity compared to conventional techniques. At a signal-to-noise ratio (SNR) of 30 dB, it achieves 1.2 bits per joule and reduces system complexity to 1,400 FLOPs, demonstrating better scalability and resource optimization. These findings highlight the potential of CNN-based hybrid precoding to revolutionize THz communication in next-generation wireless networks by optimizing energy efficiency and system complexity.

Tadele A. Abose

Yitbarek A. Mekonen

Binyam G. Assefa

Naol W. Gudeta

Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication

基于CNN的混合预编码在太赫兹通信下的大规模MIMO无蜂窝网络中的能效与系统复杂度分析

Introduction This paper introduces a novel approach for optimizing service caching in smart buildings through the integration of Internet of Things (IoT) and edge computing technologies. Traditional cloud-based solutions suffer from high latency and resource consumption, which limits the performance of smart city applications. Methods The proposed solution involves a dynamic crowdsourcing and caching algorithm that leverages IoT gateways and edge servers. This algorithm reduces latency and enhances responsiveness by prioritizing services for caching based on a newly developed efficiency metric. The metric takes into account cloud and edge-computed response times, memory usage, and service popularity. Results Experimental results show a reduction in average response time (ART) by up to 25% and a 15% improvement in resource utilization compared to traditional cloud-based methods. Discussion These findings underscore the potential of the proposed approach for resource-constrained environments and its suitability for smart city infrastructures. The results provide a foundation for further advancements in edge-based service optimization in smart cities.

Mokhtar Harrabi

Abdelaziz Hamdi

Jamel Bel Hadj Tahar

Optimizing service caching in smart buildings: a dynamic approach for responsive IoT and edge computing integration in smart cities

智能建筑中的服务缓存优化：智能城市中响应式物联网与边缘计算集成的动态方法

The non-orthogonal multiple access (NOMA) scheme has been recognized as a promising candidate for future generation wireless communication networks that require a high data rate and spectral efficiency. In order to achieve a superior spectral efficiency, the orthogonal frequency division multiplexing (OFDM) technique with the robust channel estimation algorithm can be combined with the NOMA scheme. However, due to the inherent problem of inter-carrier interference in the OFDM-based systems, channel estimation using training symbols are not usually perfect. Furthermore, the successive interference cancellation (SIC) process commonly carried out in NOMA systems to address the interference among users is also imperfect under practical scenarios. Thus, a theoretical framework of the OFDM-based NOMA system to analyze the effect of both the imperfect SIC and channel estimation errors is presented in this paper. In particular, the effect of both the channel estimation errors and imperfect SIC, introduced at the receiver, on the bit error rate (BER) performance metric is analyzed under Rayleigh fading channel conditions. Our analytical results are compared to the corresponding simulation results for different design parameters. It is shown that the analytical and simulation results are in good agreement. Moreover, while revealing the importance of CSI and SIC, the results equally show that the assumption of perfect CSI and SIC is not practical, hence the need for accurate schemes for improved BER performance.

Akinbode A. Olawole

Muyiwa B. Balogun

Funmilayo B. Offiong

Enoruwa Obayiuwana

Abimbola Fisusi

Average BER evaluation of the uplink OFDM-based NOMA system under SIC and channel estimation error

上行OFDM-NOMA系统在SIC和信道估计误差下的平均误码率评估

In this paper, a fast temporal multiple sparse Bayesian learning (FTMSBL)-based channel estimation method for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems is proposed, which is optimized using the fast marginalized likelihood maximization method. The algorithm fully uses the consistent sparse structure and time-domain correlation properties of channels to improve the reconstruction performance and computational efficiency, offering better performance and higher computational efficiency than the traditional Bayesian learning algorithms. At the same time, the FTMSBL algorithm does not require computing the inverse of large matrices and consumes very little storage resources in the operation, making it suitable for hardware implementation. Simulation and sea trial results show that the FTMSBL-based underwater channel estimation algorithm achieves higher channel estimation accuracy than the orthogonal matching tracking algorithm, and the system bit error rate (BER) is significantly reduced; specifically, the FTMSBL algorithm can achieve optimal performance in strong time-dependent channels.

Sichen Zou

Shuyang Jia

Xiaochuan Zhang

Baoheng Liu

A fast temporal multiple sparse Bayesian learning-based channel estimation method for time-varying underwater acoustic OFDM systems

一种基于快速时变多稀疏贝叶斯学习的时变水声OFDM系统信道估计方法

Background In response to the widespread transmission of COVID-19 in Australia, healthcare facilities implemented stringent infection control measures, and mandatory and manual screening procedures were introduced to ensure the safety of patients and healthcare staff. However, these necessary measures resulted in imbalances within the healthcare system, a shortage of front-line workers and impacts on patient experience and wait times. The prioritization of infection control measures shifted resources away from routine care, causing delays in accessing necessary healthcare services. Methods and Findings To address these challenges, we developed and implemented an Internet of Things (IoT) Smart Screening eGate solution in partnership with a large metropolitan children’s hospital in Australia. This solution integrated a contactless health self-service web app, thermal camera, and physical barrier to automate the COVID-19 health screening and data recording process. During the 3-month pilot period, we deployed the eGate at multiple entrances to the hospital, and monitored the number of users of the system in different periods. We also used a framework of formative evaluation to classify user design challenges within limited resources and improved the design of the eGate to enhance its effectiveness. Our findings show that the IoT eGate solution improved the efficiency of the screening process and reduced the workload and exposure risks of front-line staff and anyone who required access to the hospital. By automating the screening process, we reduced the need for manual screening and minimized contact between individuals, thus reducing the risk of potentially infected. Conclusion In conclusion, our pilot study demonstrated the potential of IoT technologies in improving the efficiency and safety of medical facilities during pandemics and provided a series of recommendations for the translation of IoT technologies for medical facilities, including the importance of co-design and collaboration with stakeholders, user-centered design, and ongoing monitoring and evaluation.

Abbreviation	Front. Comms. Net.
Journal Impact	1.94
Quartiles(Global)	TELECOMMUNICATIONS(Q3)

ISSN	2673-530X
h-index	13

Publication Information	Publisher: Frontiers Media SA，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2020，Proportion of original research papers: 0.00%，Self Citation Rate:0.00%， Gold OA Rate: 100.00%
Journal Fees	$2125Details
Average review cycle	网友分享经验：13 Weeks

Frontiers in Communications and Networks

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication

Optimizing service caching in smart buildings: a dynamic approach for responsive IoT and edge computing integration in smart cities

Average BER evaluation of the uplink OFDM-based NOMA system under SIC and channel estimation error

A fast temporal multiple sparse Bayesian learning-based channel estimation method for time-varying underwater acoustic OFDM systems

Healthcare Internet of Things system implementations for COVID-19 prevention

Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication

Optimizing service caching in smart buildings: a dynamic approach for responsive IoT and edge computing integration in smart cities

Average BER evaluation of the uplink OFDM-based NOMA system under SIC and channel estimation error

A fast temporal multiple sparse Bayesian learning-based channel estimation method for time-varying underwater acoustic OFDM systems

Healthcare Internet of Things system implementations for COVID-19 prevention