Multiple Radio Access Technologies Research Articles

Investigation on cellular LTE C-V2X network serving vehicular data traffic in realistic urban scenarios

The need for reliable vehicular communication is an essential precondition of future Cooperative Intelligent Transportation Systems (C-ITS). The C-ITS applications and their diverse requirements necessitate a hybrid approach that combines ad hoc and cellular networks to support seamless and robust connectivity. While the research showed tremendous potential, the successful adaptation of the hybrid approach depends on the optimal understanding of its impact on network and protocol performance in an actual reference scenario. This work, which has practical implications for the field, focuses on creating a realistic representation of the dual-link-enabled Hybrid Vehicular Network (HVN) in the simulator. Several enhancements were implemented into a network simulator, including multiple Radio Access Technologies (multi-RAT) support, models for path loss predictions, static subscribers, background interference, and SINR throughput mapping within a multi-cell environment. We then presented a parameterized data traffic steering algorithm that enables switching between Dedicated Short-Range Communication (DSRC) and Long Term Evolution (LTE) RAT based on channel congestion and cell load conditions. The simulation result shows the high potential of a dual-link-enabled hybrid approach and its implications on both networks under realistic vehicular networking conditions. The investigations reveal that an intelligent decision to steer traffic can target balanced connectivity and highly reliable vehicular communication at a reduced network load.

A machine learning access network selection in a heterogeneous wireless environment

AbstractIn the context of Heterogeneous Wireless Networks (HWN), achieving the best connectivity is crucial for users. With the availability of multiple Radio Access Technologies (RATs), users are constantly looking to be “Always Best Connected” (ABC) by connecting to the best possible RAT. This challenge is also important in Next‐Generation Networks (NGN), where researchers are proposing models and algorithms to make the ABC paradigm a reality. One such challenge is the network selection problem, which involves the automatic and transparent selection of the best RAT at a given time. Many papers in this field have used Multi‐Attribute Decision‐Making (MADM) methods, which have been implemented with various objectives such as increasing quality of service (QoS), reducing energy consumption and costs etc. This paper proposes a modified machine learning algorithm, specifically an adapted K‐means algorithm, to address this challenge. The results show that this proposal outperforms legacy MADM methods in terms of latency, jitter, packet loss, and data rate. So, the paper presents a new approach to network selection in HWN and NGN, and the results suggest that it could be a promising solution to this important challenge.

Characterizing optimal LPWAN access delay in massive multi-RAT smart grid deployments

Massive machine-type communications services penetrating the market, such as smart grids, differ from conventional services characterized by stochastic arrival patterns. Requiring permanent end device (ED) connectivity, control centers poll EDs over regular time intervals, leading to batch message arrivals that affect the mean access delay at the air interface. In addition, the smart grid requires high reliability and network availability that can be achieved by utilizing multiple radio access technologies in EDs. Radio access technologies (RATs), i.e., narrowband internet of things (NB-IoT) and long-term evolution machine type communication (LTE-M), have been identified as promising solutions for these use-cases. This work first reports results of an extensive performance evaluation measurement campaign showing that LTE-M can be considered a preferred option and NB-IoT as a possible backup solution for smart grids. We also show that none of the selected technologies can fully meet the requirements of smart grid use cases. We then develop a theoretical model and corresponding association algorithm for balancing traffic load between two low-power wide area network (LPWAN) technologies to minimize the mean access delay. Our results demonstrate that the optimized usage of multi-RAT EDs considerably increases the number of supported EDs operating in polling-based mode. For 500EDs utilizing a single LTE-M technology, the mean access delay is over two seconds — contradicting the minimum requirements of smart grid applications. On the other hand, multi-RAT EDs running the developed algorithm increase the service capacity by up to six times (up to 3000EDs) while still satisfying the two-second latency budget.

Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking.

Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes.

Channel Access Optimization in Unlicensed Spectrum for Downlink URLLC: Centralized and Federated DRL Approaches

The sixth-generation (6G) communication research is currently in the early stage, where ultra-reliable low-latency communication (URLLC) is still an important service as in the fifth-generation (5G). Since 6G networks are expected to provide even higher levels of massive connectivity, high spectrum efficiency, high reliability, and low latency than 5G communication, it would confront much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. However, how to achieve URLLC requirements in NR-U networks is extremely challenging due to interference and collisions among multiple radio access technologies (e.g., WiFi). Therefore, it is urgent to design efficient spectrum-sharing algorithms to support URLLC in emerging 6G networks. In this paper, we develop novel centralized deep reinforcement learning (CDRL) and federated DRL (FDRL) frameworks, respectively, to optimize the downlink URLLC transmission in NR-U and WiFi coexistence systems through dynamically adjusting energy detection (ED) thresholds. Our results show that both CDRL and FDRL approaches have improved the reliability of the NR-U system significantly, but the CDRL framework has sacrificed the reliability of the WiFi system. To guarantee the reliability of the WiFi system while improving the NR-U system, we take fairness into account by redesigning the reward of CDRL.

Outage analysis for multi-radio heterogeneous networks in the presence of aerial jammers

Recently, aerial jammers (AJs) have been actively considered because of their swiftness and adaptability to maximize the impact of jammers. The previous studies mainly focused on the optimization of a single AJ (transmit power, location, etc.) consisting of intended users and eavesdroppers in single-cell environment. In this paper, macroscopic impact of multiple randomly deployed AJs to multiple radio access technologies (multi-RATs) is analyzed, which have been actively studied for future cellular systems. In particular, multi-radio heterogeneous networks consisting of WiFi-enabled small base stations (SBSs) and 5G-enabled macro/micro base stations (MBSs) are considered. An integrated framework is developed and evaluated for multi-RATs in the presence of both co-channel interference and AJ interference based on tools from stochastic geometry. The main challenge is to analyze the optimal control of portion of AJs to interfere two different RATs to maximize the outage probability. Both analytical and simulation results are presented to demonstrate the impacts of various network parameters to the overall outage probability.

An Energy-Saving Scheme With Edge Computing and Energy Harvesting in mmWaves Backhauling HetNets

The exponential growth of data traffic from mobile devices requires the implementation of heterogeneous networks (HetNets), which densely deploy multiple radio access technologies to match such demands. The deployment of many small base stations (SBSs) leads to backhauling problems where wired backhauling is neither available nor efficient. millimeter waves (mmWaves) can potentially mitigate the backhauling problem by providing high throughput and low capital expenditure (CAPEX). However, due to the high attenuation rate in mmWaves, the increase in the distance between SBS and macro base station (MBS) can severely degrade the system’s overall performance. On the other hand, densely deployed SBSs with wireless backhauling can cause high energy consumption in the system. Therefore, in this work, a novel network model is presented in which a combination of SBS, active antenna units (AAUs), and edge computing units (ECUs) are deployed to minimize the overall energy consumption of the network while maintaining the sufficient quality of service (QoS). A mathematical model based on optimization modeling is introduced to solve user equipments (UEs) association, dynamic sleeping, backhauling and fronthauling, and power transmission. Due to the complexity of the formulated problem, a heuristic algorithm is introduced. Namely, integrated access and backhauling (IAB) with Edge Computing and Dynamic Sleeping Algorithm (IEDS) algorithm is introduced to decompose the formulated problem into two parts and solve them iteratively. Finally, computer simulation results that demonstrate the model’s performance are presented for comparison between optimal solution, IEDS, and HBDS, which shows that IEDS outperformed HBDS in performance with negligible computation difference.

Data Freshness and End-to-End Delay in Cross-Layer Two-Tier Linear IoT Networks.

The operational and technological structures of radio access networks have undergone tremendous changes in recent years. A displacement of priority from capacity-coverage optimization (to ensure data freshness) has emerged. Multiple radio access technology (multi-RAT) is a solution that addresses the exponential growth of traffic demands, providing degrees of freedom in meeting various performance goals, including energy efficiencies in IoT networks. The purpose of the present study was to investigate the possibility of leveraging multi-RAT to reduce each user's transmission delay while preserving the requisite quality of service (QoS) and maintaining the freshness of the received information via the age of information (AoI) metric. First, we investigated the coordination between a multi-hop network and a cellular network. Each IoT device served as an information source that generated packets (transmitting them toward the base station) and a relay (for packets generated upstream). We created a queuing system that included the network and MAC layers. We propose a framework comprised of various models and tools for forecasting network performances in terms of the end-to-end delay of ongoing flows and AoI. Finally, to highlight the benefits of our framework, we performed comprehensive simulations. In discussing these numerical results, insights regarding various aspects and metrics (parameter tuning, expected QoS, and performance) are made apparent.

Dynamic RAT Selection and Transceiver Optimization for Mobile-Edge Computing Over Multi-RAT Heterogeneous Networks

Mobile-edge computing (MEC) integrated with multiple radio access technologies (multi-RATs) is a promising technique for satisfying the growing low-latency computation demand of intelligent Internet of Things (IoTs) applications. Under the wireless MapReduce framework for executing nomographic functions, this article investigates the joint RAT selection and transceiver design for over-the-air (OTA) aggregation of intermediate values (IVAs) in multi-RAT MEC systems, while taking into account the energy budget constraint for the local computing and IVA transmission per wireless device (WD), so as to adapt to the instantaneous communication opportunities in multiple RATs and the dynamic computational task loads. To provide a complete Pareto optimal solution, we minimize the weighted sum of the computational mean squared error (MSE) of the aggregated IVA at the RAT receivers, the IVA transmission cost of the WDs, and the associated transmission time delay. The joint RAT selection and transceiver design problem for OTA aggregation of the IVAs is a nonconvex mixed-integer problem, which is NP hard. We develop a low-complexity algorithm to solve the challenge by continuous relaxation and alternating optimization. Specifically, the optimal receive beamforming vectors at the gNB/access points (APs) are shown to be the minimum MSE (MMSE) filters. Exploiting the hidden convexity of the remaining subproblem, we obtain an efficient iterative algorithm by alternating between the RAT selection and the transmit coefficient variables for OTA aggregation of IVAs. Extensive numerical results verify the effectiveness of our proposed design as compared to other existing schemes.

5G Multi-RAT URLLC and eMBB Dynamic Task Offloading With MEC Resource Allocation Using Distributed Deep Reinforcement Learning

In this article, a deep reinforcement learning (DRL) control scheme is proposed to satisfy the strict Quality-of-Service (QoS) requirements of ultrareliability low-latency communication (URLLC) and enhanced mobile broadband (eMBB) using 5G multiple radio access technology (RAT)-based partial offloading and multiaccess edge-computing (MEC) resource allocation. In the proposed scheme, the user equipment (UE) makes optimal offloading decisions while the MEC server dynamically adjusts the server resources based on offloading requests from multiple UEs using DRL technology. The aim of the proposed scheme is to minimize the energy consumption of the UEs while maximizing the system utility (SU) performance, which is composed of the spectral efficiency (SE) and offloading success rate (OSR) of the MEC server. In addition, multiagent distributed learning technology and best experience push (BEP) techniques are used to enhance the learning efficiency of the DRL framework. The simulation result shows that the proposed scheme provides an improved SU and energy consumption performance compared to the benchmark offloading schemes.

A Sustainable Business Model for a Neutral Host Supporting 5G and beyond (5GB) Ultra-Dense Networks: Challenges, Directions, and Architecture.

With the deployment of the fifth generation (5G) mobile network systems and the envisioned heterogeneous ultra-dense networks (UDNs), both small cell (SmC) and distributed antenna system (DAS) technologies are required by mobile network operators (MNOs) and venue owners to support multiple spectrum bands, multiple radio access technologies (RATs), multiple optical central offices (COs), and multiple MNOs. As a result, the neutral host business model representing a third party responsible for managing the network enterprise on behalf of multiple MNOs has emerged as a potential solution, mainly influenced by the desire to provide a high user experience without significantly increasing the total cost of ownership (TCO). However, designing a sustainable business model for a neutral host is a nontrivial task, especially when considered in the context of 5G and beyond (5GB) UDNs. In this paper, under an integrated optical wireless network infrastructure, we review how SmC and DAS technologies are evolving towards the adoption of the neutral host business model and identify key challenges and requirements for 5GB support. Thus, we explore recent candidate advancements in heterogeneous network integration technologies for the realization of an efficient 5GB neutral host business model design capable of accommodating both SmC and DAS. Furthermore, we propose a novel design architecture that relies on virtual radio access network (vRAN) to enable real-time dynamic resource allocation and radio over Ethernet (RoE) for flexible and reconfigurable fronthaul. The results from our simulations using MATLAB over two real-life deployment scenarios validate the feasibility of utilizing switched RoE considering end-to-end delay requirements of 5GB under different switching schemes, as long as the queuing delay is kept to a minimum. Finally, the results show that incorporating RoE and vRAN technologies into the neutral host design results in substantial TCO reduction by about 81% in an indoor scenario and 73% in an outdoor scenario.

Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks

The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple Radio Access Technologies (multi-RAT) and developing innovative network selection techniques to cope with such intensive demand while improving Quality of Service (QoS). Thus, we propose a distributed framework for dynamic network selection at the edge level, and resource allocation at the Radio Access Network (RAN) level, while taking into consideration diverse applications’ characteristics. In particular, our framework employs a deep Multi-Agent Reinforcement Learning (DMARL) algorithm, that aims to maximize the edge nodes’ quality of experience while extending the battery lifetime of the nodes and leveraging adaptive compression schemes. Indeed, our framework enables data transfer from the network’s edge nodes, with multi-RAT capabilities, to the cloud in a cost and energy-efficient manner, while maintaining QoS requirements of different supported applications. Our results depict that our solution outperforms state-of-the-art techniques of network selection in terms of energy consumption, latency, and cost.

Adaptive Access Selection Algorithm for Multi-Service in 5G Heterogeneous Internet of Things

5G heterogeneous network system incorporates multiple radio access technologies (RATs), which enables the massive connection of Internet of things (IoT) devices and popularity of diverse IoT services. However, with the tremendous growth of IoT connections, personalization of service requirements and deepening of network heterogeneity, how to adaptively optimize the quality of experience (QoE) of IoT users in any motion state while balancing network load is still a major challenge in 5G system. Therefore, this paper proposes an innovative access selection mechanism named REMNS that allows users requesting IoT services to obtain optimal QoE in 5G heterogeneous networks. In particular, a network pre-assessment mechanism based on fuzzy logic is exploited to filter available networks by user devices. Furthermore, REMNS devises a comprehensive preference evaluation framework based on the subjective-oriented Analytic Hierarchy Process (AHP) and objective-oriented Entropy Weight Method (EWM) to measure the preference degrees of each IoT service for network attributes. Subsequently, we put forward a relative entropy based multi-service access selection algorithm to make servers rapidly sort optimal network from the filtered available networks, so as to enhance QoE for users under the constraint of limited network capacities. The evaluation results demonstrate that the REMNS can effectively maintain stable service connections and significantly improve user QoE.

Overview of Prospects for Service-Aware Radio Access towards 6G Networks

The integration of space–air–ground–sea networking in 6G, which is expected to not only achieve seamless coverage but also offer service-aware access and transmission, has introduced many new challenges for current mobile communications systems. Service awareness requires the 6G network to be aware of the demands of a diverse range of services as well as the occupation, utilization, and variation of network resources, which will enable the capability of deriving more intelligent and effective solutions for complicated heterogeneous resource configuration. Following this trend, this article investigates potential techniques that may improve service-aware radio access using the heterogeneous 6G network. We start with a discussion on the evolution of cloud-based RAN architectures from 5G to 6G, and then we present an intelligent radio access network (RAN) architecture for the integrated 6G network, which targets balancing the computation loads and fronthaul burden and achieving service-awareness for heterogeneous and distributed requests from users. In order for the service-aware access and transmissions to be equipped for future heterogeneous 6G networks, we analyze the challenges and potential solutions for the heterogeneous resource configuration, including a tightly coupled cross-layer design, resource service-aware sensing and allocation, transmission over multiple radio access technologies (RAT), and user socialization for cloud extension. Finally, we briefly explore some promising and crucial research topics on service-aware radio access for 6G networks.

Characterizing throughput and convergence time in dynamic multi-connectivity 5G deployments

Fifth-generation (5G) mobile communications are expected to integrate multiple radio access technologies (RATs) within a unified access network by allowing the user equipment (UE) to utilize them concurrently. As a consequence, mobile users face even more heterogeneous connectivity options, which creates challenges for efficient decision-making when selecting a network dynamically. In this work, with the tools of queuing theory, integral geometry, and optimization theory, we develop a novel mobility-centric analytical methodology for multi-RAT deployments. Particularly, we first contribute a framework for optimal data rate allocation in the network-assisted regime. Then, we characterize the convergence time of the distributed optimization algorithms based on reinforcement learning to reduce the signaling overheads. Our findings suggest that network-assisted strategies may improve the UE throughput by up to 60% depending on the considered deployment, where the gains increase with a higher density of millimeter-wave New Radio (NR) base stations. A user-centric solution based on reinforcement learning mechanisms is capable of approaching the performance of the network-assisted scheme. However, the associated convergence time may be prohibitive, on the order of several minutes. To improve the latter, we further propose and evaluate a transfer learning-based algorithm that allows to decrease the convergence time by up to 10 times, thus becoming a simple solution for rate-optimized operation in future 5G NR deployments.

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

Wireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is a complex and challenging task. On top of including new technologies and wireless standards, one solution is to deploy cross-layer Design (CLD) and multiple Radio Access Technologies (Multi-RAT) to develop scalable QoS-aware IoT networks. However, the complexity of such solutions is high as it involves complex inter-layer interactions and dependencies and inter-application dependencies in multi-RAT networks. Moreover, the wireless environment is very dynamic, so having an optimal constellation of parameters is a challenging task. In this paper, we address the possibilities of using Artificial Intelligence (AI) and Machine Learning (ML) to address these high dimensional and dynamic problems. Based on our findings, we have proposed a distributed network management framework employing AI & ML for studying inter-layer dependencies and developing cross-layer design, traffic classification and traffic prediction at the edge devices for reliable QoS in multi-RAT IoT networks. A thorough discussion on future directions and emerging challenges related to our proposed framework has also been provided for further research in this field.

Ranking of lte cells based on key performance indicators using mcdm methods

The growth in worldwide data traffic and user subscriptions in mobile telecommunication networks makes it increasingly difficult to manage network performance in an environment already containing multiple radio access technologies. Despite the rise of 5G, LTE remains the dominant technology, and new cells are installed daily to support traffic growth and new services such as voice over LTE. Detecting faulty cells in the network is one of the main concerns of operators. Self-organizing networks have been introduced to deal with this problem, and their self-healing function- ality has improved cell fault management. Nonetheless, faulty cell detec- tion remains challenging, and most of the tasks involved are still done manually. This paper introduces a new method of faulty cell detection in an LTE radio access network, applying multiple criteria methods to this problem. The cells are ranked based on selected key performance indica- tors, using the multi-attribute utility theory to construct a utility function. The analytic hierarchy process is used to define weights for the criteria. Keywords: long-term evolution, multiple criteria methods, radio access network perfor- mance management, self-organizing networks.

Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network

ABSTRACT In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user’s minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient meta-heuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-the-art algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources.

On Joint Offloading and Resource Allocation: A Double Deep Q-Network Approach

Multi-access edge computing (MEC) is an important enabling technology for 5G and 6G networks. With MEC, mobile devices can offload their computationally heavy tasks to a nearby server which can be a simple node at a base station, a vehicle or another device. With the increasing number of devices, slices and multiple radio access technologies, the problem of task offloading is becoming an increasingly complex problem. Thus, traditional approaches experience limitations while machine learning algorithms emerge as promising methods. In this paper, we consider binary and partial offloading problems and aim to jointly find optimal decisions for offloading and resource allocation which maximize the number of computed bits while minimizing the energy consumption. This allows improved usage of uplink transmit power and local CPU resources. We propose the Deep Reinforcement Learning for Joint Resource Allocation and Offloading (DJROM) algorithm that uses the double deep Q-network approach and models UEs as agents. We compare the proposed approach with two other machine learning based techniques, namely, multi-agent deep Q-learning (MARL-DQL) and multi-agent deep Q network (MARL-DQN) under fixed and mobile scenarios. Our results show that, DJROM scheme enhances the efficiency better than the other compared algorithms.

5G-Flow: A unified Multi-RAT RAN architecture for beyond 5G networks

Convergence of multiple access technologies is one of the key enablers in providing diverse set of services to the Fifth Generation (5G) users. Though 3rd Generation Partnership Project (3GPP) 5G standard defines a common core supporting multiple Radio Access Technologies (RATs), Radio Access Network (RAN) level decisions are taken separately across individual RATs as the existing 5G architecture lacks a unified control and management framework for a multi-RAT network. A unified access network is likely to utilize RAN resources more efficiently and provide an improved performance. In order to achieve a unified Multi-RAT RAN, we supplement the existing 3GPP 5G RAN architecture with OpenFlow. We refer to the proposed architecture as 5G-Flow. The proposed architecture can be viewed as a step towards further evolution of mobile networks beyond 5G. With minimal changes in the 3GPP 5G RAN and none in the core network, we are able to realize a unified and integrated multi-access 5G-Flow RAN. We simplify the existing 5G RAN by replacing RAN nodes with OpenFlow switches and a software-defined controller. The proposed architecture also allows us to completely decouple User Equipment’s (UE’s) communication with Core Network (CN) from its communication with RAN enabling a UE to use any RAT to connect to any CN (say, use 5G New Radio access to connect to 4G CN, which is not possible in the 3GPP architecture) or to directly connect to Internet from RAN without going via the CN. We have developed an evaluation platform to compare the performance of our proposal with the standard 3GPP architecture. Results demonstrate significant gains in the network performance of 5G-Flow RAN over the existing 3GPP 5G network.