Broadband Communication Services Research Articles

Performance of advanced-deep learning algorithm for modeling and prediction of rain height over some selected locations in the tropics and sub-tropics zone for radio propagation applications

As demand for high-frequency broadband communication services keeps rising, rain-induced attenuation remains the predominant threat to radiowave propagation. Accurate prediction of attenuation requires continuous measurement and monitoring of rain-induced meteorological parameters, specifically rain rate and rain height, due to their spatio-temporal variations. Rain height is an upper dataset mostly computed from Zero- degree Isotherm Heights (ZDIH) measured by radar. This research proposes a novel approach for predicting rain height from earth surface data such as surface temperature, pressure, total cloud cover, dew point temperature, surface solar radiation, water vapor amount in the air, and humidity. This research investigates the relationship between meteorological surface data and rain height. Subsequently, six machine learning models were employed for predicting rain height using the ten years surface data as input variables. The models were applied to six sub-tropical (Polokwane, Pretoria. and Cape Town) and tropical (Sokoto, Akure, and Port Harcourt (PH)) stations in South Africa and Nigeria, respectively. Analysis of the results shows that the Gradient Boosting Algorithm (GBA) performed best with determination coefficients greater than 0.80 and RMSE less than 350 in all three stations in South Africa. However, all the models failed to produce good result for the Nigeria stations. The Random Forest model has the fairest performance metrics with r2 of 0.40, 0.46 and 0.46 in Sokoto, Akure and PH. respectively. GBA is recommended for predicting rain height in South Africa. The research outcome would assist radio engineers in improving the prediction of rain-induced attenuation and determining appropriate fade mitigation techniques.

Internet of Things (IoT): An Educational Need

Education around the world is experiencing major paradigm shifts in educational practices of teaching and learning under the umbrella of ICT enable learning environment. The world is witnessing spontaneous growth in communication technology computer network and information technology. Development of new broadband communication services and convergence of telecommunication with computers which can be set to be the internet of things have created numerous possibilities to use a variety of new technology tools for teaching and learning systems. The Internet of things is so much into everything these days that it has started to be called as Internet of everything. It is not very surprising that internet of things, as it is now called has made its way into the education industry as well. Keeping in the mind the intervention of computers and smart phones into our lives and its effect on the educational system today, it. Sounds a bit farfetched at first for the internet of things to do anything with the education industry. While some of the effect that internet of things can bring into the education sector are apparent but some will be implicit and are likely to make a silent change from the inside. The internet of things can begin disrupting the education on procedure as early as kindergarten and can continue to do so through 12th standard but perhaps most profound effects occur in higher education. We will discuss the change that the internet of things can bring into the field of education and the challenges and impact of the internet of things in the 21st century education sector.

CoMP-Enhanced Flexible Functional Split for Mixed Services in Beyond 5G Wireless Networks

With explosively escalating service demands, beyond fifth generation (B5G) aims to realize various requirements for multi-service networks, i.e., higher performance of mixed enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services than 5G. To flexibly serve diverse traffic, various functional split options (FSOs) are specified by 5G protocols enabling different network functions. In order to improve signal qualities for edge users, we consider FSO-based coordinated multi-point (CoMP) transmission as a prominent technique capable of supporting high traffic demands. However, due to conventional confined hardware processing capability, a processor sharing (PS) model is introduced to deal with high latency for multi-service FSO-based networks. Therefore, it becomes essential to assign CoMP-enhanced functional split modes under PS model. A more tractable FSO-based network in terms of ergodic rate and reliability is derived by stochastic geometry approach. Moreover, we have proposed CoMP-enhanced functional split mode allocation (CFSMA) scheme to adaptively assign FSOs to provide enhanced mixed throughput and latency-aware services. The simulation results have validated analytical derivation and demonstrated that the proposed CFSMA scheme optimizes system spectrum efficiency while guaranteeing stringent latency requirement. The proposed CFSMA scheme with the designed PS FFS-CoMP system outperforms the benchmarks of conventional FCFS scheduling, non-FSO network, fixed FSOs, and limited available FSO selections in open literature.

Application of no-light fault prediction of PON based on deep learning method

The increased demand for broadband communication services has led to a wider deployment of fiber in the last mile. However, this expansion comes with the challenge of high maintenance costs for optical networks. To address this issue, there is a need for cost-efficient and reliable monitoring solutions that can detect and locate faults in different fiber access architectures. One specific concern in the operation of Passive Optical Networks (PONs) is the occurrence of no-light faults in the PON ports. To tackle this practical problem, we propose an intelligent scheme based on Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Units (GRUs). These deep learning models are trained on historical data of the Optical Line Terminal (OLT) and PON behavior to predict future fault occurrences in real-time. To evaluate the performance of our proposed scheme, we conducted extensive experiments using real-world data. The results demonstrate the superiority of our approach over traditional methods and state-of-the-art models, as evidenced by improved accuracy, precision, recall, and F1 measure.

A Genetic Algorithm Based Scheduling Method With Carrier Aggregation In 5G Networks

Abstract Recently, 5G has become an important mechanism to serve a growing amount of users in the network, which provides enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services. Meanwhile, Carrier Aggregation (CA) is a promising 5G technology which lets users aggregate fragmented carrier components (CCs) to enhance transmission efficiency. This paper addresses 5G scheduling problem in the CA-enabled eMBB–URLLC coexistence network using genetic algorithm (GA). The key idea is first treating the serving time slot of each user equipment (UE) as a gene and combining all genes to form a chromosome, then generating a set of chromosomes as the initial population and letting the population evolve to iteratively select the fittest one as the resource scheduling decision. Up to now, this is the first work using GA for solving the scheduling problem in the CA-enabled 5G network. Simulation results show that our proposed method can serve the most UEs ($4\%$) with the highest throughput ($7.1\%$) and lower delay.

Multi-connectivity in 5G New Radio: Optimal resource allocation for split bearer and data duplication

Mobile radio networks have been evolving towards the integration of services and devices with a diverse set of throughput, latency, and reliability requirements. To support these requirements, 3GPP has introduced Multi Connectivity (MC) as a more flexible architecture for 5G New Radio (NR), where multiple radio links can be simultaneously activated to split or duplicate data traffic. Multi connectivity improves single user performance at the cost of higher interference due to the increase of radio transmissions, which negatively affects system throughput.This paper analyzes the problem of admission control and resource allocation in multi connectivity scenarios, considering different requirements and 5G NR features. Specifically, we formulate two optimization problems that leverage the features of the Packet Data Convergence Protocol (PDCP) layer, which controls the flow of data packets of the data radio bearer: the PDCP Split-Bearer Decision (PSD) and the PDCP Duplication Decision (PDD) problems, which are tailored for the enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (uRLLC) services, respectively. We further provide heuristic approaches, specifically designed for the PSD and PDD problems, to effectively solve both these problems.Numerical results in realistic network deployments confirm that our solutions can effectively allocate radio resources increasing admission rate and system throughput, while guaranteeing the required reliability level.

Resource Management for Multiplexing eMBB and URLLC Services Over RIS-Aided THz Communication

Integrating the multitude of emerging internet of things (IoT) applications with diverse requirements in beyond fifth generation (B5G) networks necessitates the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. However, bandwidth limited and congested sub-6GHz bands are incapable of fulfilling this coexistence. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided wideband terahertz (THz) communication system to this end. In specific, we formulate a resource management problem, aiming at jointly optimizing the reflection coefficient of the RIS elements and the transmit power of the base station, as well as the wideband THz resource block allocation. To solve this problem, we adopt a supervised learning approach relying on optimization, deep learning and ensemble learning methods. Simulation results show that for an RIS of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$11\times 11$ </tex-math></inline-formula> , up to 49% spectral efficiency gain is achieved for the eMBB service compared to the counterparts, while ensuring the reliability and latency requirements of the URLLC service. Further, the ensemble learning model can perform real-time resource management at the expense of up to 1% performance loss, compared to the optimization approach.

Delay Optimization for Cooperative Multi-Tier Computing in Integrated Satellite-Terrestrial Networks

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multi-tier computing in the integrated satellite-terrestrial network, in which the computation tasks of users are processed by leveraging the cooperation of devices, edge nodes, and cloud servers. Based on the proposed three-tier computing framework, we formulate the cooperative edge-cloud offloading problem to minimize the total delay of the network. Considering the computation task is dividable, we propose the optimal task splitting strategy based on the partial offloading model, in which the closed-form solution is derived for each computation task. With the optimal task splitting strategy obtained, the original optimization problem is reformulated as the problem of the time slot allocation strategy and the computation capacity allocation strategy. Then, we further propose the cooperative edge-cloud computing strategy to optimize the delay performance of the network. Finally, numerical results are presented to demonstrate the performance of the proposed three-tier computing architecture and the offloading strategies.

Resource Slicing for eMBB and URLLC Services in Radio Access Network Using Hierarchical Deep Learning

Network slicing is a promising technique for wireless service providers to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services in a shared radio access network (RAN) infrastructure. In this paper, we apply numerology, mini-slot based transmission, and punctured scheduling techniques to support eMBB and URLLC network slices. For efficient allocation of radio resources (e.g., physical resource blocks, transmit power) to the users, we formulate RAN slicing problem as a multi-timescale problem. To solve this problem and address the dynamics of the traffic, we propose a hierarchical deep learning framework. Specifically, in each long time slot, the service provider employs a deep reinforcement learning (DRL) algorithm to determine the slice configuration parameters. The eMBB and URLLC schedulers use their own attention-based deep neural network (DNN) algorithm to allocate radio resources to their corresponding users in each short and mini time slot, respectively. Simulation results show that the proposed framework can achieve a higher aggregate throughput and a higher service level agreement (SLA) satisfaction ratio compared to some other RAN slicing approaches, including the resource proportional placement algorithm, decomposition and relaxation based resource allocation algorithm, and distributed bandwidth optimization algorithm.

Energy-Efficient Optimization via Joint Power and Subcarrier Allocation for eMBB and URLLC Services

This letter considers joint subcarrier and power allocation in the distributed MIMO system for enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. Orthogonal and non-orthogonal scheduling scheme can be selected by subcarrier assignment to improve spectral efficiency and meet the reliability requirement of URLLC. Short packet transmission is adopted for URLLC to meet the low latency requirements. We formulate a mixed-integer optimization problem by maximizing the energy efficiency (EE) of the system subject to quality of service (QoS) constraints. The problem is intractable to solve and then we propose a low computational complexity algorithm to find the suboptimal solution of the problem. Simulation results show that the proposed algorithm can converge fast and significantly improve the EE for eMBB and URLLC services.

Evolution Toward 6G Multi-Band Wireless Networks: A Resource Management Perspective

In this article, we first present the vision, key performance indicators, key enabling techniques (KETs), and services of 6G wireless networks. Then, we highlight a series of general resource management (RM) challenges as well as unique RM challenges corresponding to each KET. The unique RM challenges in 6G necessitate the transformation of existing optimization-based solutions to artificial intelligence/machine learning-empowered solutions. In the sequel, we formulate a joint network selection and subchannel allocation problem for 6G multi-band network that provides both further enhanced mobile broadband (FeMBB) and extreme ultra reliable low latency communication (eURLLC) services to the terrestrial and aerial users. Our solution highlights the efficacy of multi-band network and demonstrates the robustness of dueling deep Q-network in obtaining efficient RM solution with faster convergence rate compared to deep Q-network and double deep Q-network algorithms.

Cooperative Multilayer Edge Caching in Integrated Satellite-Terrestrial Networks

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multilayer edge caching in the integrated satellite-terrestrial network to reduce the communication delay, in which the base station cache, the satellite cache, and the gateway cache cooperatively provide content service for ground users. We first propose the three-layer cooperative caching model of the network, based on which we analyze the content retrieving process and derive the cache hit probability for different caching locations. Considering limited cache sizes, we formulate the content placement problem to minimize the average content retrieving delay of users. Then, two caching strategies, the non-cooperative caching strategy and the cooperative caching strategy, are proposed with exhaustive theoretical analysis. By introducing the concept of delay reduction gains, the optimal caching strategies are obtained based on the proposed iterative algorithms. Finally, numerical results are presented to demonstrate the performance of the proposed cooperative caching architecture and the caching strategies.

URLLC and eMBB in 5G Industrial IoT: A Survey

Fifth generation (5G)-industrial Internet of things (IIoT) is the integration of IIoT and a private 5G network. The IIoT is a concept that involves incorporating smart objects, gadgets, and solutions into cutting-edge industrial operations to increase reliability, efficiency, and over-production costs. Furthermore, the integration of IIoT and 5G/beyond 5G (B5G) provides the potential for ubiquitous and instantaneous connectivity. The 5G architecture can handle the IIoTs stringent ultra-low latency, real-time processing, high data rate, nearby storage, and reliability requirements. A new era of economic growth is predicted for IIoT-assisted 5G/B5G wireless networks. It should be noted that the majority of the work in IIoT is focused on the architecture, with reliability and throughput being largely ignored. This paper provides a comprehensive review of B5G assisted IIoT wireless networks, with a focus on enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) services. Furthermore, it provides insights into various applications and key enabling technologies from the perspective of URLLC, eMBB, and their tradeoff.

Characterisation of Signal Amplitude-Frequency for Indoor Power Line Communication Channel in the 1 — 30 MHz Broadband Frequencies

The transmission of data signals over power lines is a very promising technique for delivering indoor broadband communication services. However, since power grids were originally designed for high-voltage low-frequency signal transmission, there is a frequency mismatch between the power grid and high-frequency data signals. This mismatch poses a challenge to deploying power lines as a communication channel. Although, studies and researches conducted in several countries have made transmission of data over power lines possible, the behaviour and properties of the power grid cannot be generalised. Hence, the need for in-depth experiment and measurement on the suitability and capability of the Nigerian power grid for data transmission is crucial for proper characterising and modelling of the power line communication (PLC) channel. In this paper, we present experimental measurements and results of the effects of frequency variations on the attenuation experienced by broadband high-speed data signals transmitted over the Nigerian indoor power line network.

Guest Editorial: Full Duplex Communications Theory, Standardization, and Practice

Mobile data traffic grew by more than 50 percent between the first quarters of 2019 and 2020 and is projected to grow by close to a factor of 5 to reach 164 exabytes per month by 2025. This insatiable demand for mobile broadband communication services, including video, social networking, web browsing, audio, software updates, and peer-to-peer file sharing, has been driven by both the rising number of smartphone subscriptions and the increasing average data volume per subscription [1].

Вопросы радиочастотного обеспечения космических систем на основемногоспутниковых группировок с учетом требований МСЭ-R и результатов ВКР-19

Deployment of space systems based on multi-satellite constellations to provide broadband Internet access and communication services acutely raises the question of their radio frequency support including issues of electromagnetic compatibility (EMC) between competing space systems using multi-satellite constellations (StarLink, OneWeb, Kuiper, etc.), their EMC with traditional space systems (satellite communication systems that employ spacecraft in geostationary and non-geostationary orbits, space systems of other services), as well as regulatory issues of entering into service and staged deployment of space systems. The article formulates the problematic issues of radio frequency support of modern space systems taking into account the trends of satellite constellations deployment and proposes the solutions with consideration to international and national requirements in the regulation of radio frequency spectrum including the ITU requirements and the requirements of Russian State Commission for Radio Frequencies. The paper shows the ways to share the radio frequency spectrum to meet new requirements of the World Radiocommunication Conference 2019 (WRC-19).

Priority-Based Joint Resource Allocation With Deep Q-Learning for Heterogeneous NOMA Systems

For heterogeneous demands in fifth-generation (5G) new radio (NR), a massive machine type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable and low-latency communication (URLLC) services have been introduced. To ensure these quality-of-service (QoS) requirements, non-orthogonal multiple access (NOMA) has been introduced in which multiple devices can be served from the same frequency by manipulating the power domain and successive interference cancellation (SIC) technique. To maximize the efficiency of NOMA systems, an optimal resource allocation, such as power allocation and channel assignment, is a key issue that needs to be solved. Although many researchers have proposed multiple solutions, there have been no studies addressing the 5G QoS requirements and three services that coexist in the same network. In this paper, we formulate an optimal power allocation scheme under Karush-Kuhn-Tucker (KKT) optimality conditions incorporating different NOMA constraints to maximize the channel sum-rate and system fairness. We then propose a priority-based channel assignment with a deep Q-learning algorithm to maintain the 5G QoS requirements and increase the network performance. Finally, We conduct extensive simulations with respect to different system parameters and can confirm that the proposed scheme performs better than other existing schemes.

Green HSR Reliable Communication With LTE-R Using MIMO-DPD

Reliable and efficient High-speed Railways (HSR) is the key component to the concept of Green Smart Cities to combat the prevailing traffic congestion on roads and increasing greenhouse gases in the atmosphere. It is also a fast, safe, and economical transport medium. Long Term Evolution - Railways (LTE-R), though in the developing stage, is a new wireless communications standard for providing intelligent broadband communication services to passengers and train operators. Reliable HSR communication requires more base stations (BSs). The radio frequency power amplifier (RFPA), an integral part of the BS, dominates the overall energy consumption and is inherently nonlinear. Digital Predistortion (DPD), the ultimate Green communication technique, is used to linearize RFPAs and provide less power waste. Multiple-input-multiple-output (MIMO) schemes have been used to achieve enhanced data throughputs and superior transmission reliability. This paper analyzes the effect of DPD arrangement corresponding to these schemes for reliable HSR communication in infrastructure-to-train and inside-station scenarios. The authors consider downlink transmission for the LTE-R specifications at different train speeds. Furthermore, the paper proposes an analytical framework to evaluate the performance of the complete HSR MIMO communications system. This analytical framework characterizes the antenna effects on the Block Error Rate (BLER) and throughput by adopting various transmission modes, viz. SISO, MISO, SIMO and MIMO. Numerical analyses and simulations for the proposed system illustrate better reliability and high throughput performances with higher-order MIMO configurations. Moreover, significant energy savings can be realized by universally deploying DPD across the HSR network in Smart Cities.

INVESTIGATIVE STUDY OF VOICE AND DATA COMMUNICATION OVER POWER LINE COMMUNICATION SYSTEM

Communication is the transfer of information from one point to another over a channel. However, as technology is growing, more information is needed to be passed over large distances for the realization of the world being a global village. There is the need to place a reliable communication system that will transmit effortlessly both data and voice over a channel. Power Line Communication (PLC) also known as Broadband over Power Line (BPL) technology offers high speed and broadband communication services to homes connected to the power lines. This makes use of the electrical lines for transmission of data up to the last mile and there is no need of separating copper cables, short haul satellite systems, optical fibre cable and Wi-Fi. This work presented an overview of a voice and data communication over PLC in terms of the various types, equipment use, method of communication, application, regulatory activities on PLC and the challenges facing the implementation of power line for transmitting voice and data. Therefore, PLC is a viable alternative to all other methods of transmission as it is readily available and can be easily implemented in rural areas where other communication systems are not implemented for the transmission of voice and data communication. Keywords: Communication, Power Line Communication, Broadband over Power Line, Voice and Data Communication, Broadband over Power Line, Transmission, Electrical Line. DOI : 10.7176/MTM/9-8-03 Publication date : August 31st 2019

Towards secure 5G networks: A Survey

To support various new use cases from vertical industries besides enhanced mobile broadband communication services, the 5G system aims to provide higher speed, lower latency, and massive connectivity to various devices by leveraging the evolution of 4G with the addition of new radio technology, service-based architecture, and cloud infrastructure. The introduction of new technologies, new use cases and people’s growing concerns regarding privacy issues brings new challenges to providing security and privacy protection for 5G. This paper makes an extensive review of the state of the art towards ensuring 5G security and privacy. By analyzing the lessons from the 4G security system, the requirements from new scenarios and models, the challenges resulting from new technology and paradigm, we identify typical security and privacy issues to be solved in 5G. Then, we discuss potential solutions from academia and industry to secure 5G networks from several perspectives, including the overall 5G security framework, core network, radio access network, cloud infrastructure, and the Internet of things. Finally, several key open issues and potential research directions are identified and discussed.