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5G technology is intended to support three promising services with heterogeneous requirements: Ultra-Reliable and Low Latency Communication (uRLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communication (mMTC). 6G is required to support even more challenging scenarios, including the presence of a large number of uRLLC devices, under the massive uRLLC (mURLLC) use case scenario. The presence of these services on the same network creates a challenging task of resource allocation to meet their diverse requirements. Given the critical nature of uRLLC applications, uRLLC traffic will always have the highest priority which causes a negative impact on the performance of other services. In this paper, the problem of uRLLC/eMBB resource allocation is investigated. An optimal resource allocation scheme is proposed with two scenarios including a guaranteed fairness level and minimum data rate among eMBB users. In addition, a knapsack-inspired punctured resource allocation algorithm is proposed where the users' channel qualities of both services are considered at each time slot leading to the most suitable Resource Block (RB) selection for puncturing in a way that minimizes the negative impact on eMBB performance. The proposed solution was compared with three puncturing baseline reference algorithms and the performance was evaluated in terms of eMBB Sum throughput and Fairness level. The simulation results show that the proposed algorithm outperforms the above-mentioned reference algorithms in all evaluation metrics and is proved to be comparable to the optimal solution given its low complexity. [ABSTRACT FROM AUTHOR]

In the paper, we study the problem of multiplexing Ultra-Reliable Low-Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) traffic in the downlink channel of 5G systems. The Multi-User Multiple Input Multiple Output (MU-MIMO) technology is used for multiplexing, which allows a base station to transmit data simultaneously to several users in the same time–frequency resources. We propose a new algorithm (scheduler) that allocates time–frequency resources and transmission parameters for eMBB and URLLC users and dynamically distributes the base station transmission power between users. Simulation shows that the proposed scheduler increases the coverage for URLLC users and the throughput for eMBB users compared with the existing schedulers. [ABSTRACT FROM AUTHOR]

The vision of the 5G network is to provide wireless connectivity to different market verticals with a diverse quality of service requirements. To meet the requirements of these verticals, network resources at each layer (core, transmission, and radio access) of 5G architecture need efficient resource management. Network slicing is one of the key features of 5G networks where network resources form virtual sub-networks to handle diverse resource requirements from verticals. In this paper, we propose a framework using multi-objective Markov decision process that models radio resource management (RRM) for 5G radio access network slices. In particular, we present a multi-objective scheduler for 5G radio that allocates inter-slice radio resources efficiently for enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (uRLLC) slices. Probabilistic model checking is used to analyze the performance of the scheduler and to perform quantitative verification. The proposed scheduler takes into account key design parameters such as mmWave radio channel condition and network load condition to optimize the performance of bandwidth greedy eMBB and latency sensitive uRLLC slices through appropriate joint resource allocation. Results show that the proposed scheduler provides optimal strategy synthesis for joint resource management of shared radio bandwidth in eMBB and uRLLC slices. [ABSTRACT FROM AUTHOR]

Ultra-Reliable Low Latency Communication (URLLC) is mostly used for core services in the fifth generation (5G) and beyond cellular networks. Due to the conflicting demands of low latency and extremely high reliability, the ongoing research on this subject remains in its nascent phase. The utilization of Device-to-Device (D2D) communication shows great potential in facilitating URLLC. The implementation of URLLC and its Quality of Service (QoS) requirements may encounter high power consumption in dynamic wireless channels, potentially conflicting with the system's power limitations. In this paper, a packet delivery mechanism is introduced, aiming to achieve energy efficiency by incorporating a Power-Aware QoS-centric Preemptive Dropping mechanism to minimize latency while ensuring the successful transmission of URLLC data. The method aims to optimize bandwidth and channel assignment to effectively reduce overall power consumption. The optimization problem is formulated which is non-convex, posing challenges in selecting an optimal solution. To address this problem, we have undertaken the task of transforming the original problem into two subproblems: bandwidth allocation and subchannel assignment. To achieve optimal resource allocation for the formulated problem, we propose the Optimal Bandwidth Allocation (OBA) and Optimal Sub-channel Assignment (OSA) algorithms. The simulation results validate the proposed method outperforms the benchmark schemes, demonstrating higher energy efficiency ranging from 6.96 to 31.6%, as well as a reduction in latency ranging from 5.7 to 25.67%. The proposed method exhibits broad applicability in various domains, including remote healthcare and Industrial Internet of Things (IIoT) applications. [ABSTRACT FROM AUTHOR]

Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra's algorithm for link mapping. During the slice creation phase, the proposed DNN training method's classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature. [ABSTRACT FROM AUTHOR]

C-RAN (Cloud Radio Access Network) is a 5G architecture that consists of sites and three-layer Data Centers (DCs), which include the central office DC, local DC, and regional DC. Network slicing, which enables infrastructure providers (InP) to create independent logical networks, is essential in this architecture. By utilizing this technology, InPs can maximize the utility of the network by providing slices to service providers in response to their slice requests. However, almost all of the recent research on slice admission control (SAC) schemes has only considered one or two layers of DCs, which limits the efficiency of the slicing process and decreases network utility. To address these issues, this paper proposes an intelligent SAC scheme called ISAC that considers all three-layer DCs. Instead of relying on reinforcement learning algorithms like Q-learning, which are effective in discrete environments with limited state space but give poor performance in continuous environments, ISAC employs the Approximate Reinforcement Learning (ARL) algorithm. ARL is better suited for 5G network modeling because it can adapt to continuous environments, allowing for a more accurate representation of the underlying physical processes. Extensive simulation studies demonstrate that ISAC significantly improves performance in terms of slice request rejection rates, InP revenue, accepting more slices, and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

5G makes industrial Ultra-Reliable Low Latency Communications (URLLC) possible by virtue of orthogonal frequency division multiple access and data link layer retransmission technologies. In this paper, we propose a Downlink Highly Reliable Retransmission method (DHRR) for industrial URLLC over 5G networks. First, we jointly consider the packets and retransmission packets of the downlink data queue, and propose an on-demand retransmission strategy based on the hybrid data scheduling. Second, we devise a dynamic switching strategy based on on-demand/cyclic retransmission reliability analysis. The switching strategy enables 5G networks to select the more reliable retransmission strategy online, according to the data queue and available communication resources. Different from state-of-the-art methods, DHRR for the first time synergizes the above strategies to improve the utilization efficiency of communications resources. Simulation results show that DHRR outperforms existing works in terms of URLLC performance. [ABSTRACT FROM AUTHOR]

Due to the dense deployment and the diversity of user service types in the 5G HUDN environment, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user's quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning. Firstly, the state, action space and reward function of the user-selected network are designed. Then, by calculating the network selection benefits for different types of services initiated by users, the analytic hierarchy process is used to establish the weight relationship between the different user services and the network attributes. Finally, a deep Q neural network is used to solve and optimize the proposed target and obtain the user's best network selection strategy and long-term network selection benefits. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times. [ABSTRACT FROM AUTHOR]

One of the biggest innovations on 5G and beyond is the support of three different services with particular delay and bandwidth requirements, such as Massive Machine Type Communications (MMTC), enhanced Mobile Broad Band (eMBB) and Ultra-Reliable Low Latency Communications (URLLC). In order to achieve these multiple service requirements, all users have to share resources over the 5G Orthogonal Frequency-Division Multiple Access (OFDMA) frame. One of the strategies proposed by the 5G standard is puncturing, which allows the scheduler to assign eMBB services on a timescale, and on a shorter timescale to preemptively overwrite part of the eMBB assignment when a URLLC user arrives. The optimization of puncturing poses a challenging problem: the optimal allocation depends on traffic arriving over different timescales, which forces the scheduler to make allocation decisions without knowledge of future users' demands, all while having to satisfy several strong constraints. This kind of multiple timescales optimization with restrictions is also to be found in many interesting problems, such as energy management. We propose a learning mechanism where the system learns offline the optimal allocation according to the network state. This learned estimation is then used online to determine the optimal allocation. Through simulations, we verify that the proposed learning strategy provides results close to the optimal policy, improving state of the art proposals for puncturing schemes. [ABSTRACT FROM AUTHOR]

Network Slicing in 5G networks enables allocation and sharing of the underlying network resources of an infrastructure provider (INP) among multiple tenants of the INP. Each tenant generates slice requests specifying the resources required and the INP collects revenue from the tenants for hosting the admitted slices. In this paper, we have used the Prioritised Experience Replay-based Deep Q-Network with N step return (N-PERDQN) Reinforcement learning (RL) approach to solve the slice admission control and associated resource allocation problem, in a dynamic environment. The slices are classified as elastic and inelastic, with flexible and rigid service guarantee requirements respectively. We have also used Long Short-Term Memory (LSTM) to predict the admitted elastic slices’ future resource requirements to share resources among elastic slices more efficiently. This enables the INP to accept more slice requests to increase its revenue while maximizing system utilization. The system has been modeled using the SimPy discrete-event simulator and TensorFlow machine learning. Performance metrics studied include INP revenue generated, class-specific slice acceptance rate and resource utilization were studied on Materna data center trace. The proposed N-PERDQN approach performs better than other RL and heuristic methods by up to 10% on average, in terms of generating INP revenue. Also, N-PERDQN+LSTM accepts around 6% more slice requests than N-PERDQN without LSTM. We have also tested the performance of the proposed algorithm with an offline algorithm, that has complete set of incoming slice request information available beforehand. The proposed algorithm is able to achieve close to 82% to 86% in terms of revenue gain of the offline algorithm. The proposed mechanism was also studied using the Alibaba data center trace and similar improvements were observed. [ABSTRACT FROM AUTHOR]

Adaptive analog beamforming is a key technology to enable spatial control of millimeter-wave wireless signals radiated from phased array antennas (PAAs) which is essential to maximize the capacity of future mobile networks and to ensure efficient usage of scarce spectrum. Intermediate frequency-over-fiber (IFoF), on the other hand, is a promising technology for the millimeter-wave (mm-wave) mobile fronthaul due to its low complexity, high optical spectral efficiency, and low latency. The combination of IFoF and PAA is key to implement mm-wave mobile communications in a scalable, centralized, efficient, and reliable manner. This work presents, for the first time to the best of the authors' knowledge, an extensive outdoor measurement campaign where an experimental IFoF mm-wave wireless setup is evaluated by using PAAs with adaptive beamforming on the transmitter and receiver sides. The configuration of the experimental setup is according to 5G standards, transmitting signals wirelessly at 27 GHz central frequency in the n258 band. The employed PAAs are composed of 8-by-8 patch antenna arrays, allowing beam steering in the azimuth and elevation angles. Furthermore, different end-user locations, antenna configurations, and wireless scenarios are tested in the outdoor experiment, showing excellent EVM performance and achieving 64-QAM transmission over up to 165.5 m at up to 1.88 Gbit/s. The experimental results enable optimization of the experimental setup for different scenarios and prove the system's reliability in different wireless conditions. In addition, the results of this work prove the viability and potential of IFoF combined with PAA to be part of the future 5G/6G structure. [ABSTRACT FROM AUTHOR]

In design and analysis problems of wireless communication systems, a set of signal-to-noise ratio random variables may have to be dealt with. For example, this applies to transmit and/or receive diversity systems, and to scheduling and resource allocation problems. In such cases, it can be important to know how a given signal-to-noise ratio random variable ranks among the whole set of random variables. For example, in opportunistic multiuser systems, allocating resources to a user depends strongly on the probability of that user's signal-to-noise ratio being larger than those of all (or most) other users. Considering a set of signal-to-noise ratio random variables, ranking probability is defined in this paper as the probability of a signal-to-noise ratio random variable being smaller than a given number of signal-to-noise ratio random variables in the set. Candidacy probability is defined as the probability of a signal-to-noise ratio random variable belonging to a subset of signal-to-noise ratio random variables with highest values. Closed form expressions for ranking and candidacy probabilities of signal-to-noise ratio random variables are derived, assuming Rayleigh fading. The derived probabilities are compared to those found by simulation. Comparisons confirm the correctness of the derived expressions. Simulations have been performed assuming a cellular base station that is serving a number of users. In several experiments, different levels of variation in the average signal-to-noise ratios of the users have been assumed. Results confirm the need for applying some fairness constraints in signal-to-noise ratio order-based scheduling algorithms to prevent situations where some users may not get enough access to system resources. [ABSTRACT FROM AUTHOR]

Sparse code multiple access (SCMA) is a technology that allows for extremely low latency and high reliability in modern wireless communication networks. Moreover, due to the sparse layout of its codebooks, SCMA competence for multiple access techniques leads the way for futuristic ultra reliable low latency communications (URLLC), which aims for high reliability with low latency. Therefore, a deep Q learning-based SCMA, called Q-SCMA, is proposed, in which codebooks are adaptively constructed to minimize bit error rate (BER), maximizing throughput within the constraints of reliability and latency, i.e., supporting URLLC. Therefore, the rewards for Q-SCMA are formulated such that, while imposing a latency constraint, they also provide excellent reliability. The performance of the proposed approach is evaluated in terms of BER, loading factor, level of interference, bit error probability, throughput, latency, and computational complexity. It is also analyzed and compared with contemporary approaches, conventional SCMA (C-SCMA) and deep neural network SCMA (D-SCMA), for average white Gaussian noise as well Rayleigh fading channel, and for industrial wireless network environment modified Rician fading channel is considered. Simulation outcomes show considerable performance disparities between the proposed approach and D-SCMA along with C-SCMA. This underlines the requirement for using a deep Q learning model as a performance indicator for developing URLLC supporting SCMA for industrial wireless networks. [ABSTRACT FROM AUTHOR]

Network slicing (NS) has emerged as a promising solution that enables network operators to slice network resources such as spectrum and bandwidth to adapt to different beyond 5G scenarios. This allows new operators to enter the market: the infrastructure provider (InP), who owns the infrastructure, and the mobile virtual network operator (MVNO), who may purchase a resource slice from the InP to provide a specific service to their end-users. To better deal with the resource allocation problem, efficient algorithms and methods have been done such as the auction model, bidding method, and game theory. This paper presents an upper-tier resource allocation based on game theory. This mechanism considers a single base station (BS) and multi MVNOs-users that share aggregated bandwidth radio access networks to maximize utilized BS resources. The proposed method takes both the bandwidth utilization of BS and the service requirements of MVNO users. Accordingly, the Game Theory solution takes two contradictory objectives: the InP aims to maximize its revenue while the MVNOs want to serve their users by paying the minimum amount. We prove that our proposal achieves an optimal solution from both InP and MVNOs' in terms of revenue and quality of service. [ABSTRACT FROM AUTHOR]

5G receiver design at acceptably low cost poses a significant challenge, particularly where the 5G new radio (NR) use cases enhanced mobile broadband (eMBB), massive machine type communication (mMTC) or ultra-reliable low latency communication need to be satisfied. One of the key design parameters that has to be looked at for successful implementation of these use cases is the maintenance of high signal-to-noise ratio (SNR). Consequently for high SNR requirements, implementation of phased array antennas for suitable beamforming in radio access networks has been preferred by 5G systems designers. The authors have proposed an orthogonal coded spread spectrum-based digital beam forming (SSDBF) technique, which can be a better choice for 5G designers. They have introduced orthogonal Fourier codes in the phased array system which drastically reduces the number of circuit elements, thereby minimizing cost, size, weight and power requirements. The proposed scheme has been simulated and tested over a 28 GHz 5G link considering micro- and macro-channel scenarios in urban environments, following established urban micro (UMi) and urban macro (UMa) models, respectively. The results are then compared with a conventional digital beam forming (CDBF) technique. The BER versus SNR of both the SSDBF and CDBF cases are found to be almost equal. Moreover, the scheme is also tested with a 2 × 2 MIMO (multiple input multiple output), and the throughput is observed to be almost doubled in comparison with the corresponding SISO (single input single output) implementation, which suggests that the proposed scheme can also be used for MIMO applications. [ABSTRACT FROM AUTHOR]

Network Slicing is a key technology of 5G mobile systems. This technology enables operators to create customized logical networks on the same physical substrate in order to support different services and vertical industries, such as public safety and entertainment. In this paper we present NS-ENFORCER, a dynamic slicing mechanism for 4G/5G Radio Access Networks. NS-ENFORCER receives slicing rules from an SDN Controller. Based on these rules and the network state, NS-ENFORCER dynamically assigns spectrum resources to slices, and performs slicing in both downlink and uplink communication channels. Such assignment procedure is supported by a novel allocation model proposed in this work. The performance evaluation of NS-ENFORCER was carried out using a scenario having different types of applications and mobility of devices. The results show the benefits that can be achieved with NS-ENFORCER, such as the improvement of the QoS experienced by end users. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of implementing distributed slice allocation algorithms for tactical networks deployed in regions lacking a communication infrastructure. We propose a multi-agent game implementation in which Application Slice Agents (ASAs) compete for shared computational and bandwidth resources in the network. We prove that the game can be implemented as a two stage potential game, with the first stage ensuring the selection of computational servers with load balancing, and the second stage improving connectivity and network throughput by promoting inter-slice cooperation. Our proposed analysis framework also incorporates a theoretical model to capture the impact of mitigation methods for timing channel leaks for the shared resources for different slices, and proposes a theoretical framework that allows for a security-delay tradeoff analysis. [ABSTRACT FROM AUTHOR]

The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device's reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs. [ABSTRACT FROM AUTHOR]

Cognitive radio is known to be an important technology to overcome the shortage of spectrum resources, and the resource allocation problem of multi-service multi-carrier slice in 5G system has remained a challenge. In this paper, a multi-leader multi-follower Stackelberg game is designed to analyze the interaction between operators and users over licensed and unlicensed bands. Further, we verify the impact of users' dynamic throughput allocation ratio on Nash equilibrium (NE) in the game. The game is divided into two independent subgames: licensed band and unlicensed band games. A simplified Vickrey-Clarke-Groves algorithm is designed in the former sub-game to ensure the fairness of user transmission. In the latter, an interference price is set to protect the transmission performance of primary users in unlicensed band. By predicting the actions of other players, optimal prices or spectrum and power demands are set in a non-cooperative way. We prove the existence of NE solution in the Stackelberg game and design dynamic distributed algorithms for operators and users to achieve NE. Simulation results show the effectiveness of our proposed resource management schemes based on Stackelberg game. Compared to other Stackelberg models, our scheme provides users with high-quality services and provides a guidance on pricing strategies for operators. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the Internet technology such as 5G/6G and artificial intelligence, more and more new network applications appear. Customers using these applications may have different individual demands and such a trend causes great challenges to the traditional integrated service and routing model. In order to satisfy the individual demands of customers, the service customization should be considered, during which the cost of Internet Service Provider (ISP) naturally increases. Hence, how to reach a balance between the customer satisfaction and the ISP profit becomes vitally important. Targeting at addressing this critical problem, this work proposes a service customization oriented reliable routing mechanism, which includes two modules, that is, the service customization module and the routing module. In particular, the former (i.e., the service customization module) is responsible for classifying services by analyzing and processing the customer's demands. After that, the IPv6 protocol is used to implement the service customization, since it naturally supports differentiated services via the extended header fields. The latter is responsible for transforming the customized services into specific routing policies. Specifically, the Nash equilibrium based economic model is firstly introduced to make a perfect balance between the user satisfaction and the ISP profits, which could finally produce a win-win solution. After that, based on the customized service policies, an optimized grey wolf algorithm is designed to establish the routing path, during which the routing reliability is formulated and calculated. Finally, the experiments are carried out and the proposed mechanism is evaluated. The results indicate that the proposed service customization and routing mechanism improves the routing reliability, user satisfaction and ISP satisfaction by about 8.42%, 15.5% and 17.75% respectively compared with the classical open shortest path first algorithm and the function learning based algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a user-centric frequency reuse scheme depending on the user classification in the cell in a homogeneous Poisson point process network. Each cell is partitioned into multiple regions delimited by the signal-to-interference ratio of the user considered. A given resource block (RB) is assigned to one user in a cell and cannot be shared by another user in this cell. The typical user only uses a fraction of the RB, and the remaining part is left unused to avoid interference with the other cells. The base stations (BS) interfering set is hence determined by the coverage probability of the typical user, and this set can be approximated by a thinned version of the original PPP. Using this, coverage probability and spectral efficiency (SE) for each user types are derived, and a BS cooperation technique is proposed to improve the SE of the cell edge user. Finally, the average network spectral efficiency and fairness among users are analyzed under the developed theoretical framework. Our results show that the user-centric frequency reuse improves the coverage probability and the SE compared to the conventional frequency reuse scheme. [ABSTRACT FROM AUTHOR]

Enforcing network slice isolation in 5G Radio Access Networks (RANs) can degrade a network slicing solution’s ability to efficiently support different types of traffic. In addition, competing virtual network operators may share one network infrastructure that forces network slicing solutions to provide fair treatment between them. This work presents Flexible Priority Scheduling (FPS). This RAN network slicing solution provides isolated treatment to slices with different traffic types, further exploring their needs and flexibility to achieve a more efficient solution. It does so by defining a contract interface that allows a flexible representation of different types of traffic, from heavy throughput services to low latency and bursty traffic. Furthermore, the presented contract representation allows a fair treatment of the tenant’s traffic in the Medium Access Control (MAC) scheduler that enforces this slicing solution: Priority Adaptation Slice Scheduler (PASS). [ABSTRACT FROM AUTHOR]

In 5G network slicing environments, if insufficient resources are allocated to the associated resource-intensive service slices, its quality of service (QoS) can be considerably degraded. In this paper, we propose a priority-based dynamic resource allocation scheme (PDRAS), in which a resource management agent maintains slicing information such as priorities, demand profiles, and average resource adjustment time to change allocated resources to slices. To maximize QoS of slices while maintaining the total amount of allocated resources below a certain level, a constrained Markov decision process problem is formulated and the optimal allocation policy is obtained using linear programming. Extensive evaluation results demonstrate that PDRAS with the optimal policy has better performance regarding QoS and the resource usage efficiency compared with other schemes. [ABSTRACT FROM AUTHOR]

The main contribution of the investigation is the Markov model of the process of resource allocation management between subscribers of eMBB and mMTC services within the 5G cluster. The proposed model, considers the organization of the channel resource in the format of resource blocks. The presented model allows to estimate the average duration of IoT sessions, the average number of active multimedia/IoT sessions, the average number of channel resource units occupied by multimedia/IoT traffic, the average number of resource blocks occupied by multimedia/IoT traffic. The metrics are generalized by three management schemes of the investigated process: balanced, competitive and perspective. The first and third schemes enable static/dynamic distribution of channel resources into reserved and common segments for subscribers of eMBB and mMTC services. The proposed model is illustrated with an example showing how to assess the availability and efficiency of channel resource use of the 5G cluster of the cyber-physical system of the Situation Center of the Department of Information Technology of Vinnytsia City Council (Vinnytsia, Ukraine). The article also shows how to use the proposed model to select the 5G network parameters to keep the probabilities of rejection of multimedia and IoT requests below a set threshold. [ABSTRACT FROM AUTHOR]

The evolution of the fifth-generation (5G) new radio (NR) has progressed swiftly since the third generation partnership project (3GPP) standardized the first NR version (Release 15) in mid-2018. Nowadays, the world’s leading carriers are competing to provide various commercial services over 5G networks. Looking ahead to 2025 and beyond, it is expected that over 6.5 million 5G base stations will be installed to offer services to over 58% of the world’s population via over 100 billion 5G connections. Following the rapid development of 5G, an increasing number of commercialization use cases will drive the 5G network to continuously improve performance and expand capabilities. Hence, it is the right time to consider a well-defined framework and standardization for 5G NR evolution (5G-Advanced) to support commercialization between 2025 and 2030. First, this study addresses the key driving forces, requirements, usage scenarios, and capabilities of 5G-Advanced; then, it highlights the main technological challenges and introduces the top 10 promising technological directions in detail. Finally, other fascinating technological directions in 5G-Advanced are shortly mentioned. [ABSTRACT FROM AUTHOR]

In a recent work, a family of rational maps identified as tangent-Chebyshev maps over finite fields was introduced. Such maps have potential applicability in cryptography and channel coding. In this paper, we present new properties for the referred maps: their symmetry and fixed points are determined, their relationships with other maps are described and their functional graphs are characterized. Illustrative examples related to the developed properties are given; in particular, we show that the use of the referred maps to interleave in a turbo code may provide coding gains, when compared with usual approaches. [ABSTRACT FROM AUTHOR]

In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability. [ABSTRACT FROM AUTHOR]

In order to satisfy the delay requirements of telecommunication systems, in this paper, we present a cooperative network with the short packet transmission in the Rayleigh fading channel. The desired relay can be implemented as a two-way half-duplex (HD) or a two-way full-duplex (FD). Also, for more accurate satisfaction and reduction of communication delays, sending and receiving with short packets is considered. Effective capacity appropriately measures the transmission rate under the delay constraint. Therefore, it is considered as a performance evaluation criterion here. With a two-way relay, two nodes exchange data with each other using a relay simultaneously. The priorities and requirements of the two nodes are not necessarily the same. Therefore, to increase performance, the system is modeled and solved as a multi-objective problem. In this way, the available power in the network is divided between the relay and two nodes, and the effective capacity of the two nodes is maximized. Depending on the different conditions, the optimal amount of allocated power to relay and nodes is calculated. However, due to the complexity and time consuming calculations, an approximate method which speeds up the calculation is presented. The approximated solution has a very close performance to the optimal allocated power. Finally, various comparisons have been made in different conditions between the performance of two-way HD and two-way FD relays. The improvement of multi-objective power allocation has been shown, especially when the relay is not located in the middle of two nodes. [ABSTRACT FROM AUTHOR]

This paper prosed a novel 6G QoS over the future 6G wireless architecture to offer excellent Quality of Service (QoS) for the next generation of digital TV beyond 2030. During the last 20 years, the way society used to watch and consume TV and Cinema has changed radically. The creation of the Over The Top content platforms based on Cloud Services followed by its commercial video consumption model, offering flexibility for subscribers such as n Video on Demand. Besides the new business model created, the network infrastructure and wireless technologies also permitted the streaming of high-quality TV and film formats such as High Definition, followed by the latest widespread TV standardization Ultra-High- Definition TV. Mobile Broadband services onset the possibility for consumers to watch TV or Video content anywhere at any time. However, the network infrastructure needs continuous improvement, primarily when crises, like the coronavirus disease (COVID-19) and the worldwide pandemic, creates immense network traffic congestions. The outcome of that congestion was the decrease of QoS for such multimedia services, impacting the user's experience. More power-hungry video applications are commencing to test the networks' resilience and future roadmap of 5G and Beyond 5G (B5G). For this, 6G architecture planning must be focused on offering the ultimate QoS for prosumers beyond 2030. [ABSTRACT FROM AUTHOR]

In fact, there will have many challenges for 5G networks, towards 2030 for example, that will be progressed in B5G networks. The results show that the OTFS modulation over two-ray channels and the combination of multiple-input multiple-output systems with OTFS modulation can provide a reliable airborne communication network with low BER. In this paper, the authors consider the physical layer security (PLS) in a non-orthogonal multiple access (NOMA) relaying network with radio frequency energy harvesting (RF EH). [Extracted from the article]

Vehicle traffic in large cities is increasing every day. Despite the significant development of telecommunications, communication between such vehicles must still evolve to make mobility smarter. Many studies have been carried out to enable a network between vehicles and computational infrastructures, called Vehicular Ad-Hoc Networks (VANETs). Within the context of VANETs, traffic lights can play an important role in exchanging information with vehicles to reduce congestion. Intelligent traffic lights aim to manage intersections in urban traffic and avoid idle roads. Several research topics have already been explored related to smart traffic control such as network latency, people safety, driver assistance, among others. However, given the variety of problems and solutions, it is complex to identify research trends and gaps related to smart traffic field without a systematic review. This paper presents a systematic review of smart traffic in the context of VANETs. The paper selection was started with 1117 works followed with three processes of refinement wherein 115 papers were selected to thorough revision. The remaining 115 papers were cataloged and classified according to three criteria general focus, evaluation method, and communication type. The review revealed research gaps and trends in the area of smart traffic. This work provides a list of references to find the mapped papers, thus being able to guide more reliable research studies. Besides, we identify research challenges and directions based on the set of papers that were studied. The research challenges are presented according to the main topics explored in this systematic review: security, physical safety of people and protocols. [ABSTRACT FROM AUTHOR]

The wireless networks do not consider ultra-high reliability and low latency (URLL) as the primary factors. There is a delay-tolerant, non-critical content and services that are based on human-centric communication in mobile networks. The focus of the wireless networks is to provide boosted rate in data speed and increasing network coverage by using the best networking approach. The wireless communication device is primarily concerned with determining the entity's status, by offering enhanced wireless connection assistance, and thereby raising the commodity's status. Additionally, there will be occurrence of traffic in an infrequent manner. This adds pressure for the delivery of packets that are large or medium-sized at low latency. This is not provided by the current wireless technology system's state of the art. This is a vital factor that challenges the future development of the wireless system for communication. Thus, to have high reliability with the irregular traffic patterns, there is a need for the development of novel and innovative technologies from physical to the network layer in the wireless communication systems. In this paper, technologies like multiple-access scheme, synchronization, multiplexing, full-duplex transmission, and channel code design are introduced for spectrally systematic ultra-high reliability and low latency communication (URLLC) in the physical layer. The techniques developed for the purpose of monitoring and controlling plant growth are called precision agricultural techniques. These techniques use actuators and sensor networks to monitor and control plant growth. Precision agriculture involves smart irrigation and fertilizer monitoring. This paper also develops a new precision agriculture technique using wireless sensor network (WSN). The WSN uses the Message Queuing Telemetry Transport (MQTT) protocol. This WSN uses IoT technology for connecting devices with the sensors for the purpose of monitoring, collecting, and distributing the data. The IoT integrates with cloud computing to subdue the limitations of IoT in monitoring and controlling systems. Thus, this paper involves the development of the UHLL communication system that can be used in precision agriculture using WSNs. [ABSTRACT FROM AUTHOR]

The high data traffic requirements of the new generation 5G networks will be satisfied with effective and efficient mobility and handover management. However, dense or ultra-dense small cell (eNB) placements in 5G networks may lead to some problems, such as latency, handover failures, frequent handover, ping-pong effect, etc. In this study, we proposed an Entropy-based simple additive weighting decision-making method for multi-criteria handover in software-defined networking (SDN) based 5G small cells for the solution of the aforementioned problems. This method provides the connection of the mobile node to the most suitable eNB using bandwidth, user density and SINR parameters. The proposed handover method is compared with conventional LTE handover and distributed approach in terms of delay, block ratio, handover failure and throughput according to the varying number of mobile users. The scalability of handovers for both approaches according to the user number are also analysed. According to the simulation results, the proposed approach achieved 15%, 48% and 22% improvement in handover delay, blocking probability and throughput, respectively, compared to the conventional LTE handover. [ABSTRACT FROM AUTHOR]

The standard development of 5G wireless communication culminated between 2017 and 2019, followed by the worldwide deployment of 5G networks, which is expected to result in very high data rate for enhanced mobile broadband, support ultrareliable and low-latency services and accommodate massive number of connections. Research attention is shifting to future generation of wireless communications, for instance, beyond 5G or 6G. Unlike previous studies, which discussed the use cases, deployment scenarios, or new network architectures of 6G in depth, this paper focuses on a few potential technologies for 6G wireless communications, all of which represent certain fundamental breakthrough at the physical layer — technical hardcore of any new generation of wireless communications. Some of them, such as holographic radio, terahertz communication, large intelligent surface, and orbital angular momentum, are of revolutionary nature and many related studies are still at their scientific exploration stage. Several technical areas, such as advanced channel coding/modulation, visible light communication, and advanced duplex, while having been studied, may find more opportunities in 6G. [ABSTRACT FROM AUTHOR]

Future wireless networks like 5G will carry an increasingly wide variety of data traffic, with different QoS requirements. In addition to conventional data traffic generated from HTTP, FTP and video streaming applications by mobile broadband users [human-type communication (HTC)], traffic from machine-to-machine (M2M) and Internet-of-Things (IoT) applications [machine-type communication (MTC)] has to be supported by 5G networks. Time-of-day variation in arrival rate of connection-level requests and randomness in length of data sessions in HTC result in randomness in aggregate traffic. In MTC, randomness in traffic arises from random number of devices trying to connect to the base station at any given time. Traffic generated by MTC devices may be either periodic or event-triggered. Nevertheless, it is difficult to model aggregate traffic due to non-stationary nature of traffic generated by each type of service. In this paper, special correlation functions of stochastic point processes called Product Densities (PDs) are used for estimating aggregate traffic under non-stationary arrival rates. For HTC, PDs are defined for estimating time-dependent offered load of connection-level service requests and expected number of ON periods in an interval of time (0 , T) . The aggregate traffic is evaluated for light-tail (exponential) and heavy-tail (hyper exponential) servicing times. For MTC, PDs are defined for estimating the random number of devices connected to the base station at any time. Another QoS parameter of interest in high speed networks is the expected number of service requests/devices delayed beyond a critical value of delay. Bi-variate PD is defined to estimate the number of service requests/devices delayed beyond a given critical threshold. The results from PD model show close agreement with simulation results. The proposed PD technique proves effective in performance analysis under time-dependent traffic conditions, and is versatile for application to several studies in wireless networks including power consumption, interference and handover performance. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears. [ABSTRACT FROM AUTHOR]

5G has been developing at high speed since 2012 and has become a global economic driver. In this paper, we offer a survey of 5G covering visions, requirements, roadmap, key technologies, standardization, frequency management, technology trials, industrial ecology, and a list of main 5G contributors. We also point out the contributions to 5G from China, aiming to be ‘globally leading in 5G’ by acting as a main 5G contributor in standardization and promoting/enhancing the Chinese 5G industry. Finally, progress on 5G is reviewed mixed with our rethinking of 5G. [ABSTRACT FROM AUTHOR]

Spectrally contained OFDM-based waveforms are considered key enablers for a flexible air interface design to support a broad range of services and frequencies as envisaged for 5G mobile systems. By allowing for the flexible configuration of physical layer parameters in response to diverse requirements, these waveforms enable the in-band coexistence of different services. One candidate from this category of waveforms is pulse-shaped OFDM, which follows the idea of subcarrier filtering while fully maintaining the compatibility with CP-OFDM. In this paper, we provide an overview of pulse shaping methods in OFDM systems and propose a new pulse-shaped design method with arbitrary length constraint and good time-frequency localization property. Based on the pulse design, we discuss different receiver realizations and present a criterion for pulse shape evaluation. In addition, the parameterizations of OFDM system to address diverse requirements of the services envisaged for the 5G systems are described. Link and system performance results for selected scenarios show that a proper design of the OFDM numerologies and pulse shapes could substantially improve the performance under time and frequency distortions. Furthermore, pulse-shaped OFDM is able to support asynchronous transmissions and reduce the signal sensitivity to Doppler distortions, rendering it beneficial for various applications from the context of vehicular communications and the Internet-of-things. [ABSTRACT FROM AUTHOR]