Radio Access Technologies Research Articles

A NOVEL ARCHITECTURE OF INTELLIGENT DECISION MODEL FOR EFFICIENT RESOURCE ALLOCATION IN 5G BROADBAND COMMUNICATION NETWORKS

Intelligent Decision Model for efficient resource allocation in 5G broadband communication networks is essential for ensuring the most efficient use of available resources. This model considers several factors, such as traffic demand, network topology, and radio access technology, to make the most efficient decisions about resource allocation. It is based on intelligent algorithms and advanced analytics, which allow the network to quickly and accurately identify the optimal resource allocation for a given situation. This model can reduce costs, improve network performance, and increase customer satisfaction. In addition, the Intelligent Decision Model can help operators reduce the complexity and cost of managing a 5G network. The intelligent decision model for efficient resource allocation in 5G broadband communication networks is based on a combination of artificial intelligence (AI) and optimization techniques. The proposed decision models can use AI to identify patterns in traffic and user behavior. In contrast, the proposed can use optimization techniques to maximize resource utilization and reduce latency in the network. This model can also leverage predictive analytics and machine learning algorithms to determine the most efficient allocation of resources. Additionally, the proposed model can use AI to detect and mitigate potential security threats and malicious activities in the network. the proposed IDM has reached 91.85% of accuracy, 90.05% of precision, 90.96% of recall and 91.33% of F1-score.

Bandit Approach for Fair and Efficient Coexistence of NR-U in Unlicensed Bands

New radio in unlicensed spectrum (NR-U) is an evolutionary extension of the existing unlicensed spectrum technologies, which allows New radio (NR) to operate in the shared and unlicensed frequency bands. However, in such bands, NR-U should coexist with other radio access technologies (RATs) in a commonly shared spectrum. As various RATs possess dissimilar physical and link-layer configurations, NR-U should comply with the requirements for harmonious coexistence with them. For this reason, the majority of the existing studies on NR-U are focused on fair coexistence. In contrast, the efforts on attaining efficiency of the spectrum and fairness concurrently have gained comparatively few interests as they exhibit an adverse feature. Motivated by this limitation, we propose an algorithm called Thompson's sampling-based online gradient ascent (TS-OGA), which jointly considers the fairness between NR-U and incumbents and, at the same time, the efficiency via pertinent idle period adjustment of the incumbents in the operating channel. Because NR-U deals with the two conflicting and competing objectives (i.e., fairness and efficiency), we model it as a multi-objective multi-armed bandit problem using the Generalized Gini Index aggregation function (GGAF). In the proposed scheme, TS-OGA, a Thompson's sampling (TS) policy is employed together with the online gradient ascent to address the multi-objective optimization problem. Through simulation results, we show that TS-OGA can significantly enhance overall channel throughput, while maintaining fairness. Further, TS-OGA provides the best performance compared to three different baseline algorithms such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy, upper confidence bound, and pure TS.

Distributed Offloading for Multi-UAV Swarms in MEC-Assisted 5G Heterogeneous Networks

Mobile edge computing (MEC) is a novel paradigm that offers numerous possibilities for Internet of Things (IoT) applications. In typical use cases, unmanned aerial vehicles (UAVs) that can be applied to monitoring and logistics have received wide attention. However, subject to their own flexible maneuverability, limited computational capability, and battery energy, UAVs need to offload computation-intensive tasks to ensure the quality of service. In this paper, we solve this problem for UAV systems in a 5G heterogeneous network environment by proposing an innovative distributed framework that jointly considers transmission assessment and task offloading. Specifically, we devised a fuzzy logic-based offloading assessment mechanism at the UAV side, which can adaptively avoid risky wireless links based on the motion state of an UAV and performance transmission metrics. We introduce a multi-agent advantage actor–critic deep reinforcement learning (DRL) framework to enable the UAVs to optimize the system utility by learning the best policies from the environment. This requires decisions on computing modes as well as the choices of radio access technologies (RATs) and MEC servers in the case of offloading. The results validate the convergence and applicability of our scheme. Compared with the benchmarks, the proposed scheme is superior in many aspects, such as reducing task completion delay and energy consumption.

6G Fabric Compiles Microdomains for Sophisticated Service Delivery [From the Guest Editors

While the industry is exploring the key drivers and future needs of 6G cellular communication systems, 6G research is in full swing, globally supported by large programs. As a result, many new technologies are being proposed. This could be noticed in various fora and publications that convey new visions for the verticals that 6G would serve and the key elements that could make this happen. New radio and optical wireless access technologies for 3D coverage, new waveforms, artificial intelligence (AI) and machine learning (ML), edge–cloud supported by AI, joint communication and sensing, optical fiber networks, nonterrestrial network (NTN) integration, new network architectures, trusted computing platforms, quantum communications, and digital twin provisioning systems are some of many technology enablers for 6G. However, 6G will support many more vertical industries. This requires defining a new model for network infrastructures that builds on 5G architectures and designs, especially private networks and open networking. This means that operating minor networks independently at their local domains and allowing separated domains to handle services without centralized management may become more mainstream. AI and ML will become important enablers for future network management and control. This segregation within the mobile network architecture allows AI-driven components to maximally utilize resources at their local domain. In addition, minor domains would be able to decompose service requests to reduce operational cost incurred by the network. This network modeling takes advantage of fully automated elements and services that could be onboarded upon user demand for more efficient networks that fully utilize resources in a cost-efficient manner. Therefore, operators and vendors need to work closely with network planning engineers and academia to bring such designs into reality.

Accurate Phase Angle Measurement of Backscatter Signal under Noisy Environment

Backscatter communication is an emerging wireless access technology to realize ultra-low power terminals exploiting the modulated reflection of incident radio wave. This paper proposes a method to measure the phase angle of backscatter link using principal component analysis (PCA). The phase angle measurement of backscatter link at the receiver is essential to maximize the signal quality for subsequent demodulation and to measure the distance and the angle of arrival. The drawback of popular phase angle measurement with naive phase averaging and linear regression analysis is to produce erroneous phase angle, where the phase angle is close to $\pm\frac{\pi}{2}$ radian and the signal quality is poor. The advantage of the proposal is quantified with a computer simulation, a conducted experiment and radio propagation experiments.

DRL-assisted delay optimized task offloading in automotive-industry 5.0 based VECNs

The rapid growth of Automotive-Industry 5.0 and its emergence with beyond fifth-generation (B5G) communications, is making vehicular edge computing networks (VECNs) increasingly complex. The latency constraints of modern automotive applications make it difficult to run complex applications on vehicle on-board units (OBUs). While multi-access edge computing (MEC) can facilitate task offloading to execute these applications, it is still a challenge to access them promptly and optimally. Traditional algorithms struggle to guarantee accuracy in such dynamic environment, but deep reinforcement learning (DRL) methods offer improved accuracy, robustness, and real-time decision-making capabilities. In this paper, we propose a DRL-based mobility, contact, and load aware cooperative task offloading (DCTO) scheme. DCTO is designed for both cellular and mmWave radio access technologies (RATs), and both binary and partial offloading mechanisms. DCTO targets delay minimization by opportunistically switching RATs and offloading mechanisms. We consider relative efficacy and neutrality factors as key performance indicators and use them to derive the DRL agent’s reward function. Extensive evaluations demonstrate that the DCTO scheme exhibits a substantial enhancement in task success rate, with an increase from 2.61% to 21.34%. It also improves the efficacy factor from 1.38 to 3.52 and reduces the neutrality factor from 4.99 to 0.76. Furthermore, the average task processing time is reduced by a range of 3.77% to 24.15%. Additionally, the DCTO scheme outperforms the other evaluated schemes in terms of reward and TFPS ratio.

Accurate Base Station Placement in 4G LTE Networks Using Multiobjective Genetic Algorithm Optimization

Cellular mobile communication network planning and optimization involve a complex engineering process that deals with network fundamentals, radio resource elements, and critical decision variables. The continuous evolution of radio access technologies provides new challenges that necessitate efficient radio planning and optimization. Therefore, the planning and optimization algorithms should be highly efficient, advanced, and robust. An important component of 4G LTE network planning is the proper placement of evolved node base stations (eNodeBs) and the configuration of their antenna elements. This contribution proposes a multiobjective genetic algorithm that integrates network coverage, capacity, and power consumption for optimal eNodeB placement in an operational 4G LTE network. The multi-objective-based genetic algorithm optimization has been achieved using the optimization toolbox in MATLAB. By leveraging the proposed method, the effect of different population sizes on the cost of placing the eNodeBs and the percentage coverage of the eNodeBs in a given cell is determined. As a result, the optimal selection technique that minimizes the total network cost without compromising the desired coverage and capacity benchmarks is achieved. The proposed automatic eNodeB antenna placement method can be explored to optimize 4G LTE cellular network planning in related wireless propagation environments.

Optimizing Service Areas in 6G mmWave/THz Systems with Dual Blockage and Micromobility

The modern 5G millimeter wave (mmWave) New Radio (NR) systems as well as future terahertz (THz) radio access technologies (RAT) will heavily rely on beamforming to combat the excessive path losses. Additionally, both RATs target similar bandwidth-greedy non-elastic traffic and are affected by the blockage phenomena. To improve service reliability in these systems multiconnectivity can be utilized to dynamically hand over the ongoing sessions between two technologies. In this article, we investigate the association strategies in collocated deployments of mmWave/THz systems and evaluate the impact of the utilized antenna arrays. Our results show that accepting sessions to THz BS that may experience outage in blocked conditions is preferable when multiconnecitvity is utilized as compared to accepting them to mmWave BS. However, extending the coverage of THz base station (BS) by increasing the number of antenna elements slightly affects performance metrics. Nevertheless, there is still non-negligible probability of dropping sessions accepted for service, implying that in 6G deployments the support of fully reliable microwave technology such as sub-6 GHz NR is vital.

Designing a Vertical Handover Algorithm for Security-Constrained Applications

In heterogeneous networks (HetNets), the vertical handover (VHO) is an essential process for mobile users (MUs) aiming to secure ubiquitous connectivity and maintain the highest quality of service (QoS) across various types of radio access technology (RAT), such as wireless fidelity (Wi-Fi), the global system for mobile communication (GSM), and the universal mobile telecommunications system (UMTS). In the literature, many recent VHO research works have been proposed in which a number of critical issues still arise when performing seamless vertical handover, including VHO packet loss, VHO delay, and VHO throughput. Moreover, the security aspect of triggering VHO has not been carefully considered, particularly when an MU intends to use a payment application. In this paper, we present a comprehensive performance evaluation for a new secure VHO algorithm, taking into account all the above issues, for co-located Wi-Fi and UMTS networks. The simulation results are compared with the media independent handover (MIH) IEEE 802.21 standard, which facilitates the seamless transfer of connection among heterogeneous networks. We show that the proposed algorithm outperforms the MIH standard in terms of VHO delay, VHO packet loss, and VHO throughput in providing uninterrupted connection to security-constrained applications.

Online Radio Access Technology Selection Algorithms in a 5G Multi-RAT Network

In today's wireless networks, a variety of Radio Access Technologies (RATs) are present. However, each RAT being controlled individually leads to suboptimal utilization of network resources. Due to the remarkable growth of data traffic, interworking among different RATs is becoming necessary to overcome the problem of suboptimal resource utilization. Users can be offloaded from one RAT to another based on loads of different networks, channel conditions and priority of users. We consider the optimal RAT selection problem in a Fifth Generation (5G) New Radio (NR)-Wireless Fidelity (WiFi) network where we aim to maximize the total system throughput subject to constraints on the blocking probability of high priority users and the offloading probability of low priority users. The problem is formulated as a Constrained Markov Decision Process (CMDP). We reduce the effective dimensionality of the action space by eliminating the provably suboptimal actions. We propose low-complexity online heuristics for RAT selection which can operate without the knowledge regarding the statistics of system dynamics. Network Simulator-3 (ns-3) simulations reveal that the proposed algorithms outperform traditional RAT selection algorithms under realistic network scenarios including user mobility.

Connectivity Improvement of Hybrid Millimeter Wave and Microwave Vehicular Networks

The network connectivity while traveling in a vehicle is an important issue, which needs to be addressed by mobile vehicular networks. This paper proposes a novel scheme to improve the connectivity of mobile vehicular networks. In particular, the paper proposes a medium access control (MAC) layer hybrid mmWave and microwave scheme for vehicular networks, and leverage their capabilities to improve the vehicle’s connectivity. The novel computational model is derived to evaluate the connectivity for the proposed scheme. The model is used for performance analysis of vehicles moving on a multi-lane highway road and getting connectivity with road-side-units (RSUs) deployed along the roads. Our analysis considers that reference VN has perfect channel state information. It is assumed that the RSU radiates ubiquitously on the road surface using microwave radio access technology (RAT), while it radiates directionally towards reference VN using mmWave RAT. For mathematical analysis, the directionality in mmWave RAT is well approximated by a sectored antenna model. The analysis for the proposed scheme is compared with the existing mmWave network and packet data convergence protocol (PDCP) layer hybrid scheme. The analysis claims that the proposed hybrid scheme significantly improves the connectivity performance in mobile vehicular networks over the existing schemes. The computation results are validated with the simulation results. Also, the paper offers parametric analysis for connectivity probability with vehicle speed and slot duration to enable its practical implementation in 5G/6G technologies.

A Game-Theoretic Framework for Coexistence of WiFi and Cellular Networks in the 6-GHz Unlicensed Spectrum

The recently unlocked 6-GHz spectrum is accessible to cellular and WiFi networks for unlicensed use while they conform to the constraints imposed by the incumbent nodes. We allow only a fraction of the cellular base stations (BSs) and a fraction of WiFi access points (APs) to use the 6-GHz band so that sources of interference are spatially segregated and made sparse, thereby decreasing the overall interference to each other. Through our proposed framework, we control this fraction as we group portions of cellular and WiFi network elements into entities competing with the other entities for the spectrum resources. These entities interact to satisfy their Quality of Service demands by playing a non-cooperative game. The action of an entity corresponds to the fraction of its network elements (WiFi APs and cellular BSs) operating in the 6-GHz band. We use tools from stochastic geometry to derive the theoretical performance metrics for users of each radio access technology, which helps us capture the aggregate behaviour of the network in a snapshot. Due to the decentralized nature of the game, we find the solution using distributed Best Response Algorithm ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D-BRA</monospace> ), which improves the average datarate by 11.37% and 18.59% for cellular and WiFi networks, respectively, with random strategy as the baseline. The results demonstrate how the system parameters affect the performance of a network at equilibrium and highlight the throughput gains of the networks as a result of using the 6-GHz bands, which offer considerably larger bandwidths. We tested our framework using real-world data, which shows that practical implementation of multi-entity spectrum sharing is feasible even when the spatial distribution of the network elements and population are non-homogeneous.

Techno-Economic Analysis of MEC Clustering Models for Seamless CCAM Service Provision

The latency requirements of delay-sensitive applications such as cooperative, connected, and automated mobility (CCAM) services challenge the capabilities of traditional vehicular radio access technologies (i.e., IEEE 802.11P and cellular networks). To this end, the 5G cellular network is adopting the multi-access edge computing (MEC) paradigm. However, the use of this technology comes with several challenges. In this article, MEC placement challenges and their impact on network deployment costs are studied. We propose three MEC clustering models and compare them from a cost perspective. Results show that all the MEC clustering models outperform the non-clustering approach. In addition, the conditions in which specific clustering models yield the most optimal results are analyzed. Results aim to provide insights into cost-effective MEC deployment models.

Load Balanced User Association Scheme in Planned Non-bias Small Cell Deployment

The work presented in this paper focuses on developing a load-balanced user association scheme in a single Radio Access Technology (RAT) Heterogeneous Networks (HetNet). The optimization algorithm maximizes network capacity and optimal user association in a planned small cell deployment framework, considering Quality of Service (QoS) and network load balance. Planned deployment of small cells has many advantages, including controlled interference levels within the HetNet. The proposed Branch and Bound (BAB) based algorithms provide a more realistic interference scenario through the non-use of bias in the small cells. The proposed algorithm achieved higher load balancing than current schemes in literature and high spectral efficiency values.

Base Station Energy Use in Dense Urban and Suburban Areas

Growing energy consumption is a global problem. The information and communications technology (ICT) industry is in a critical role as an enabler of energy savings in other sectors. However, the power consumption of the ICT sector also needs to be addressed, to contribute to the overall reduction of power consumption and carbon emissions. A new era has begun as the fifth generation (5G) mobile data connection rollouts are advancing globally and are expected to reach a 10% share of end-user devices and connections by 2023. The available references on energy consumption in global mobile networks are rather old and highly averaged – only estimates of energy consumption relative to data volumes are available. There is an information gap regarding the energy consumption of emerging 5G and advanced 4G technologies. Therefore, it has been difficult to understand the actual electricity consumption differences between generations and spatially aggregated electricity consumption once these generations are combined to offer capacity and coverage. This article fills this gap by providing a reference on the energy consumption of base transceiver stations for reported mobile data usage for different Radio Access Technologies; 3G, 4G and 5G respectively. To the best of our knowledge, there is no reference to scientific research on the comparison of energy intensity per square kilometer for 3G, 4G and 5G mobile radio technologies, using actual operator data. The objective of this research was to improve the understanding of the actual energy consumption of different Radio Access Technologies (RAT). The results also give insight to decision makers on when to modernize the operator radio access network. The article reports on the results of field measurements on data and visitor volumes and shares of different RATs. The research contains two statistical RAT combination cases, one representing the European average and the other Finnish mobile networks. The analyses were done for dense urban (DU) and suburban (SU) areas.

Six Generation Load Cells Solution Based Congestion Management Control燩urpose

Several assessing efforts are approved making both communication and information technologies available. In addition to being a part of universal access and service, we aim at assessing the impact of radio access technologies on universal access indicators. The proposed work demonstrates the possibility of inter-working of all existing Radio Access Technologies (RATs), in a limited area presented in the Six Generation Radio Resources Allocation (6 G-A) network. We propose a solution for the Vertical hand Over (VHO) in 6 G-A heterogeneous system, that adopts Media Independent Handover (MIH) protocol to benefit between different used technologies in a direct communication firstly and facilitate, in addition, coordination between congestion control mechanisms with the mobility management entity. The mathematical model has been developed to deal with locality coverage and broadband needs, based on a methodological approach consisting in integrating parameters linked to access index through radio technologies. This approach has been applied. In practice, it has contributed to highlighting its relative simplicity of implementation, but it is not enough for six-generation system radio access networks. The congestion control mechanism is integrated within the vertical handover process, this proposition is used to prevent each presented congestion state. The media independent handover IS equipment, as well as six G-A equipment which are both considered as the whole architecture key equipment, perform and guaranty this related process. Efficient communication is established besides information exchange between various technologies. Achieved evaluation results are performant and prove that proposed mechanisms are efficient. Both simulation and tests results are accomplished.

A Survey on Nongeostationary Satellite Systems: The Communication Perspective

The next phase of satellite technology is being characterized by a new evolution in non-geostationary orbit (NGSO) satellites, which conveys exciting new communication capabilities to provide non-terrestrial connectivity solutions and to support a wide range of digital technologies from various industries. NGSO communication systems are known for a number of key features such as lower propagation delay, smaller size, and lower signal losses in comparison to the conventional geostationary orbit (GSO) satellites, which can potentially enable latency-critical applications to be provided through satellites. NGSO promises a substantial boost in communication speed and energy efficiency, and thus, tackling the main inhibiting factors of commercializing GSO satellites for broader utilization. The promised improvements of NGSO systems have motivated this paper to provide a comprehensive survey of the state-of-the-art NGSO research focusing on the communication prospects, including physical layer and radio access technologies along with the networking aspects and the overall system features and architectures. Beyond this, there are still many NGSO deployment challenges to be addressed to ensure seamless integration not only with GSO systems but also with terrestrial networks. These unprecedented challenges are also discussed in this paper, including coexistence with GSO systems in terms of spectrum access and regulatory issues, satellite constellation and architecture designs, resource management problems, and user equipment requirements. Finally, we outline a set of innovative research directions and new opportunities for future NGSO research.

Identification of Cellular Signal Measurements Using Machine Learning

Spectrum awareness has a plethora of civilian and defense applications, such as spectrum resource management, adaptive transmissions, interference detection, and identification of threat signals. This article proposes an identification neural network (INN)-based model that identifies cellular signals from three different radio access technologies, namely global system for mobile (GSM) communications, universal mobile telecommunications service, and long-term evolution. The proposed INN identifies whether or not the measured power spectral density belongs to a certain cellular signal type. Two data collection approaches (DCAs) are considered: in-band and multiple-band. The over-the-air measurements for the two DCAs show that with low computational complexity, the proposed INN model provides an identification accuracy between 93% and 100%, with a false alarm (FA) rate between 0% and 10%.

A Sensing, Communication and Computing Approach for Vulnerable Road Users Safety

Most fatal road accidents in urban areas involve vulnerable road users. New solutions for fighting against these accidents can be considered by leveraging connected, intelligent vehicles and smart cities connecting all parts of an urban environment. This work proposes a multi-sensing and communication approach to prevent potential accidents between vehicles and VRUs, by predicting and notifying both about potential collisions before they happen. This approach leverages and aggregates information from smart city sensors, dispersed in the vehicles (and aggregated by the On-Board Units, OBUs), in the VRUs (e.g., smartphones and smartwatches), and on the road itself (e.g. video cameras, radars, lidars). These elements communicate through several message standards and wireless access technologies (e.g. ITS-G5, C-V2X, LTE, 5G and, in the future, 6G). Using both sensing and communication, fusion and collision detection algorithms, this system predicts and notifies potential hazardous situations involving Vulnerable Road User (VRU)s and vehicles. The results in a real scenario with sensors, VRUs and vehicles on the road show that the system predicts potential collisions with high accuracy and low delay. Results also point to some vital deployment decisions that must be made to ensure proper notification timings, such as the usage of multi-homing, 5G and edge computing.

4 × 4 MIMO Fiber-Wireless System with RoF Transmission Capability

Transport networks, particularly fronthaul systems are facing substantial issues as a result of the expansion of radio access technologies (RATs) in 5G and beyond networks. Due to the difficulties and high expense of installing fiber links, a traditional fiber-optic based mobile transport system will not be adequate for delivering radio signals to new antenna sites. The ease and speed of installation make wireless transport systems an appealing alternative. In this work, a 4×4 MIMO fiber wireless system with wavelength division multiplexing (WDM) and polarisation division multiplexing (PDM) radio over fiber (RoF) transmission capacity is proposed and Simulated. Moreover, a Filter Bank Multicarrier modulation is employed to increase the system capacity. In order to improve the spectral efficiency of RoF and fiber-wireless systems, FBMC is an excellent choice for modulation. Also a 4×4 MIMO OQAM based FBMC signal is simulated and its performance is compared with 2 × 2 MIMO system. Satisfactory performance is confirmed with quality factor of 14.6040 and minimum BER of 1.22×10-45. 4 × 4 MIMO fiber wireless system achieved better quality factor compared to 2×2 MIMO system. Simulation of both systems are performed using Matlab integrated with Optisystem Software. It offers an affordable solution to enable massive amount of MIMO signal transmission and may be a potential approach for upcoming mobile access and radio transport networks in high frequency bands.