Mobile Broadband Communications Research Articles

A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper

Due to the fact that the quality of the received signal is adversely affected by the beam squint phenomenon, which is especially pertinent to the millimeter wave (mmwave) bands, many studies have been utilised by other researchers to provide light on some of the challenges that come with analysing this type of occurrence. Squint causes several issues, the most important of which are its detrimental effects on gain, line of sight, angle of arrival, progressive phase, usable bandwidth, and fading effect. As a result of these obstacles, the advantages of adopting a high-frequency band such as mmwave in modern wireless communication systems are severely limited. Squint-phenomena-related difficulties, such as decreased channel capacity, increased bit error rate (BER), and lowered quality of services, may have a substantial detrimental impact on channel performance. The squint phenomenon and associated issues become more pressing with the expansion of the frequency spectrum and the deviation of the arrival angle from boresight. The purpose of this article is to provide a comprehensive overview of the relevant literature and to compare and contrast various methods in order to identify the most fruitful lines of inquiry for future research.

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective.

Power Minimization of Downlink Spectrum Slicing for eMBB and URLLC Users

5G technology allows heterogeneous services to share the wireless spectrum within the same radio access network. In this context, spectrum slicing of the shared radio resources is a critical task to guarantee the performance of each service. We analyze a downlink communication serving two types of traffic: enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). Due to the nature of low-latency traffic, the base station knows the channel state information (CSI) of the eMBB users while having statistical CSI for the URLLC users. We study the power minimization problem employing orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. Based on this analysis, we propose a lookup table-based approach and a block coordinated descent (BCD) algorithm. We show that the BCD is optimal for the URLLC power allocation. The numerical results show that NOMA leads to lower power consumption than OMA, except when the average channel gain of the URLLC user is very high. For the latter case, the optimal approach depends on the channel condition of the eMBB user. Even when OMA attains the best performance, the gap with NOMA is negligible, showing the capability of NOMA to reduce power consumption in practically every condition.

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.

Guest Editorial: AI for Open Programmable Virtualized Networks in 6G

Programmable virtualized networks decouple data and control planes and have the power of supporting the demands of diverse and varied use cases, allowing the services to scale and adapt dynamically. The diverse demands of Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low-latency Communications (uRLLC), and massive Machine Type Communications (mMTC) users inevitably increase the network complexity, making programmability alone not sufficient. To fulfill these demands, next generation mobile networks need to respond with the capacity of supporting a greater number of heterogeneous devices, the availability of more spectrum and the introduction of complex protocol stacks, making automated and intelligent tools for management, reconfiguration, adaptation, and coordination of network resources an impelling necessity.

Toward Smaller and Lower-Cost 5G Devices with Longer Battery Life: An Overview of 3GPP Release 17 RedCap

The fifth generation (5G) wireless technology is primarily developed to support three classes of use cases, namely, enhanced mobile broadband (eMBB), ultra-reliable and low-latency communication (URLLC), and massive machine-type communication (mMTC), with significantly different requirements in terms of data rate, latency, connection density, and power consumption. Meanwhile, there are several key use cases, such as industrial wireless sensor networks, video surveillance, and wearables, whose requirements fall in-between those of eMBB, URLLC, and mMTC. In this regard, 5G can be further optimized to efficiently support such mid-range use cases. Therefore, in Release 17, the 3rd generation partnership project (3GPP) developed the essential features to support a new device type enabling reduced capability (RedCap) new radio (NR) devices aiming at lower cost/complexity, smaller physical size, and longer battery life compared to regular 5G NR devices. In this article, we provide a comprehensive overview of 3GPP Release 17 Red-Cap while describing newly introduced features, cost reduction and power saving gains, and performance and coexistence impacts. Moreover, we present key design guidelines, fundamental tradeoffs, and future outlook for RedCap evolution.

Superposition-Based URLLC Traffic Scheduling in 5G and Beyond Wireless Networks

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method.

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.

Risk-Resistant Resource Allocation for eMBB and URLLC Coexistence Under M/G/1 Queueing Model

In this paper, we study the joint resource scheduling problem for multiplexing two distinct 5 G services: enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC). While eMBB services require high data rates, URLLC services lay more emphasis on extremely low latency and high reliability. To satisfy these heterogeneous requirements for eMBB and URLLC coexistence, we propose a risk-resistant scheme for resource allocation to both eMBB and URLLC traffic that aims to maximize the eMBB data rate, while considering the URLLC delay threshold violation to maintain URLLC delay requirements. Based on the M/G/1 queueing model, the URLLC packet latency target is realized by minimizing the average packet delay in violation cases, where &#x201C;violation&#x201D; is judged by introducing the &#x201C;conditional value at risk (CVaR)&#x201D; as a risk metric (i.e., &#x201C;risk&#x201D; here is defined as URLLC delay threshold violation). To solve the formulated problem, we transform it into two convex optimization problems to obtain a near-optimal resource allocation solution. Simulation results show that the proposed scheme improves the fairness among the eMBB users and eMBB throughput by <inline-formula><tex-math notation="LaTeX">$30\%\sim 34\%$</tex-math></inline-formula> compared to existing works, while satisfying the stringent URLLC delay requirements.

Delay Optimization in Mobile Edge Computing: Cognitive UAV-Assisted eMBB and mMTC Services

Mobile edge computing (MEC) in cognitive radio networks is an optimistic technique for improving the computational capability and spectrum utilization efficiency. In this study, we developed an MEC system assisted by a cognitive unmanned aerial vehicle (CUAV), where a CUAV equipped with an MEC server can serve as a relay node and computing node. In such networks, a non-orthogonal multiple access scheme is considered to serve enhanced mobile broadband communication (eMBB) and massive machine-type communication (mMTC) users, in which the transmission delay for both users is derived. To optimize the delay in this system, we formulated an optimization problem aimed at minimizing the processing delay of eMBB and mMTC users by jointly optimizing the transmit power of the users’ information, considering the constraints of the transmit power of the secondary network. The numerical results demonstrate that the proposed Rosen’s gradient projection algorithm can considerably minimize the processing delay for a CUAV with a fixed position compared with a CUAV with a predetermined trajectory.

Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

The heterogeneous novelty applications present in the 5th generation (5G) era, including machine-type communication (mMTC), enhanced mobile broadband (eMBB) communication, and ultra-reliable low latency communication (URLLC), which required mobile edge computing (MEC) for local computation and services. The next-generation radio networking (NGRN) will rely on new radio (NR) with the millimeter-wavelength (mmWave) technologies that enable ultra-dense connectivities of the deployed heterogeneous mobile terminal gateways (MTG). However, the mission-critical mMTC applications will suffer from inadequate radio resource management and orchestration (MANO), which will diminish end-to-end (E2E) communication reliability in edge areas. This paper proposed optimal MTG selections and resource allocation (RA) based on the complementary between MTG loading prediction based on recurrent neural network-based long short-term memory (RNN-LSTM) and MTG loading adjustment based on the applied deep reinforcement learning (DRL) approaches, respectively. Furthermore, the RNN-LSTM enhances offloading and handover decisions with discrete-time predictions, while the DRL plays an essential role in adjusting the determined MTG during congestion situations. The proposed method contributed to remarkable outcomes in crucial performance metrics over reference approaches regarding computation and communication quality of service (QoS).

Designing and Calculating Bandwidth of the LTE Network for Rural Areas

The standard of broadband mobile communication LTE can be used to build a mobile communication network in remote populations and provide the population with modern mobile communication services, such as video calls, data transmission, high-speed mobile Internet, Internet TV, etc. In this work, a LTE network has been designed, and the network capacity has been calculated for a certain area. Muynak of the Republic of Karakalpakstan, located in the northwest of the Republic of Uzbekistan, was taken as the territory. The paper presents the characteristics of the selected equipment required for the construction of the mobile network, calculations of network bandwidth, number of potential and active network subscribers, the number of base stations, the average bandwidth of the projected network, the average traffic per subscriber in the busy hour, the radio frequency plan, the geographical location of the base stations, and the LTE network design scheme has been developed. In addition, the characteristics of the number of potential subscribers are described in tabular form, as well as graphs of the number of base stations, the average bandwidth of the designed network, the total traffic of the designed network depending on the number of potential subscribers. The LTE network frequency plan has been developed for the city of Muynak. The designed network will increase the number of subscribers to 12,000 without reducing bandwidth.

A reinforcement learning agent for mixed-numerology interference-aware slice spectrum allocation with non-deterministic and deterministic traffic

5G RAN slicing is an essential tool to support the simultaneous coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) network slices on a shared mixed-numerology physical layer. Moreover, due to recent advance of the private network paradigm, RAN slicing assumes a central role to provide dedicated radio coverage to industry 4.0 applications as a standalone RAN. Unlike the stochastic traffic behavior characterizing URLLC slices in classical mobile networks, industrial networks support URLLC services with deterministic and periodic traffic patterns. Based on this alternative network characterization, we design a deep reinforcement learning (DRL) agent that simultaneously provides a spectrum allocation fulfilling the eMBB and URLLC service requirements and mitigates the inter-numerology interference (INI). Furthermore, by exploiting the information about the deterministic traffic patterns, we specialize the agent reward function to improve the spectrum allocation effectiveness for URLLC slices deployed in industrial environments. We assess the agent performance with respect to resource allocation schemes that are INI agnostic. Results reveal that the proposed solution outperforms the benchmark schemes in terms of service provisioning performance in both network scenarios (e.g. mobile and industrial) and showcase the benefit of INI mitigation.

Enabling URLLC Applications Through Reconfigurable Intelligent Surfaces: Challenges and Potential

Next-generation communication systems, including the sixth generation (6G) cellular systems, are expected to support a wide range of new ultra-reliable low-latency communications (URLLC). These emerging applications strictly demand high data rates and/or massive connectivity besides their strict reliability and latency requirements. To enable such URLLC applications alongside the enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC), it is imperative to develop spectrally efficient methods, and non-conventional technologies and networking architectures. To this end, reconfigurable intelligent surfaces (RISs) have recently emerged as a key promising technology to enhance the capabilities of 6G wireless networks. The RIS technology offers a wide range of advantages, including offering a low-cost and energy-efficient solution for controlling the propagation environments. We study the potential of deploying RIS in 6G networks with an emphasis on accommodating the coexistence of URLLC and eMBB traffic. Specifically, we demonstrate through several case studies the potential of integrating RIS into 6G networks with the objective of meeting the requirements of URLLC without compromising the performance of eMBB users. Motivated by this and the fact that the RIS technology has not been fully investigated, we propose a number of relevant research directions and highlight what kind of challenges such directions may entail. We also elaborate on how such challenges, once tackled, may lead to realizing the full potential of integrating RIS into future wireless networks.

Generation and Analysis of 5G Downlink Signal

The evolution of wireless communication technology has provided a viable important concept for the next-generation wireless mechanism. The Technology may provide a variety of services for different terminals, such as voice and multimedia data services through a mobile phone, as well as improved packet delivery across cellular networks. Fifth Generation (5G) New Radio (NR) is the most recent radio air interface designed for next-generation wireless communication networks to fulfill the increasing needs of connected devices. It is built to enable a variety of situations, including increased large machine-to-machine communications, mobile broadband, and ultra-reliable low-latency communications. The 5G NR signal is generated and analyzed using the various parameters in the waveform generator app in MATLAB. The downlink scenario is considered. The modulation used here for the generation of 5G signal is 16 QAM and 64 QAM modulation technique.

Intelligent Resource Allocations for Software-Defined Mission-Critical IoT燬ervices

Heterogeneous Internet of Things (IoT) applications generate a diversity of novelty applications and services in next-generation networks (NGN), which is essential to guarantee end-to-end (E2E) communication resources for both control plane (CP) and data plane (DP). Likewise, the heterogeneous 5th generation (5G) communication applications, including Mobile Broadband Communications (MBBC), massive Machine-Type Commutation (mMTC), and ultra-reliable low latency communications (URLLC), obligate to perform intelligent Quality-of-Service (QoS) Class Identifier (QCI), while the CP entities will be suffered from the complicated massive HIOT applications. Moreover, the existing management and orchestration (MANO) models are inappropriate for resource utilization and allocation in large-scale and complicated network environments. To cope with the issues mentioned above, this paper presents an adopted software-defined mobile edge computing (SDMEC) with a lightweight machine learning (ML) algorithm, namely support vector machine (SVM), to enable intelligent MANO for real-time and resource-constraints IoT applications which require lightweight computation models. Furthermore, the SVM algorithm plays an essential role in performing QCI classification. Moreover, the software-defined networking (SDN) controller allocates and configures priority resources according to the SVM classification outcomes. Thus, the complementary of SVM and SDMEC conducts intelligent resource MANO for massive QCI environments and meets the perspectives of mission-critical communication with resource constraint applications. Based on the E2E experimentation metrics, the proposed scheme shows remarkable outperformance in key performance indicator (KPI) QoS, including communication reliability, latency, and communication throughput over the various powerful reference methods.

Access protocol aware controller design for eMBB traffic in SD-CDN

Under a tremendous increase in 5th Generation (5G) traffic, the conventional Domain Name Server(DNS)-based routing in Content Delivery Network (CDN) cannot meet enhanced Mobile BroadBand (eMBB) communication requirements. It routes the traffic flow to the local CDN server which is closest to the end-user geographically using Border Gateway Protocol (BGP). Therefore; the overloading in the local CDN server is inevitable. According to literature, the access types of eMBB content achieve a higher hit-ratio in CDN caching and the anycast-based routing can redirect some of the traffic flows to the original server despite having a geographical disadvantage. Therefore, we consider access protocol types of a user such as TCP-based Hyper-Text Transfer Protocol 2 (HTTP2) and Quick User Datagram Protocol Internet Connections (QUIC) which are commonly used today in iphone Operating System (ioS) and Android-based applications. However, they do not have a central view to select optimal server according to access type in CDN and they are unaware of network dynamics such as protocol-based load and heterogeneity; i.e. the number of total matches packets and the ratio of the packets according to access protocols (HTTP2/QUIC). Therefore, we propose access protocol aware Software Defined Content Delivery Networks (SD-CDN) for eMBB traffic which decouples Data and Control planes and enables easy implementation of anycast routing on this infrastructure. It dynamically takes statistics from OpenFlow switches in the Data plane to calculate a novel load and the heterogeneity metrics per device periodically. It embeds the OpenFlow rules by using a novel access protocol-aware routing algorithm in SD-CDN. According to performance evaluation in a real test-bed environment; the Quality of Server (QoS) of received eMBB content by end-user from either the local or the original CDN servers, the proposed SD-CDN outputs 60% better PSNR than the conventional DNS-based routing with an acceptable rate for 5G requirements.

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.

Optimization of the Spectrum Splitting and Auction for 5th Generation Mobile Networks to Enhance Quality of Services for IoT from the Perspective of Inclusive Sharing Economy

Fifth generation (5G) mobile networks can accomplish enhanced communication capabilities and desired to connect things in addition to people. By means of optimally splitting the spectrum to integrate more efficient segments, mobile operators can deliver better Quality of Services (QoS) for Internet of Things (IoT), even the nowadays so-called metaverse need broadband mobile communication. Drawing on the Theory of Quality Value Transformation, we developed a 5G ecosystem as a sustainable organic coalition constituted of planners, providers, and users. Most importantly, we put forward the altruism as the ethics drive for the organic cooperative evolution to sustain the inclusive sharing economy to solve the problem of the Theory of Games and Economic Behavior. On the top of the collaboration framework for the coalition game for 5G, we adopted Pareto Optimality as the target situation for the optimization via cooperative evolution and further apply ISO 25000 to define the metrics for the value of 5G corresponding to Pareto Frontier. Based on the collaboration framework as above, we conducted a survey to gather the features and costs for the 5G spectrum in relation to IoT and the financial status of the mobile operators as the constraint for the optimization. Taking Simultaneous Multi-Round Auction (SMRA) as the standard rule for spectrum auction, we developed a novel optimization program of two hybrid metaheuristics with the combination of Simulated Annealing (SA), Genetic Algorithm (GA), and Random Optimization (RO) for the multiple objectives of quality, usability, and costs. The results of the simulation show that the coalition game for 5G spectrum auction is a dynamic group decision in which the government authority and mobile operators can achieve a synergy to maximize the profits, quality score, and usability, and minimize the costs. Last but not least, the hybrid metaheuristic with SA and RO is more efficient and effective than that with GA and BO, from the perspective of inclusive sharing economy. It is the first study of its kind as we know.