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The proliferation of 5G networks has revolutionized wireless communication by delivering enhanced speeds, ultra-low latency, and widespread connectivity. However, in heterogeneous cloud radio access networks (H-CRAN), efficiently managing inter-cell interference while ensuring energy conservation remains a critical challenge. This paper presents a novel energy-efficient, dynamic enhanced inter-cell interference coordination (eICIC) scheme based on deep reinforcement learning (DRL). Unlike conventional approaches that focus primarily on optimizing parameters such as almost blank subframe (ABS) ratios and bias offsets (BOs), our work introduces the transmission power during ABS subframes (TPA) and the channel quality indicator (CQI) threshold of victim user equipments (CTV) into the optimization process. Additionally, this approach uniquely integrates energy consumption into the scheme, addressing both performance and sustainability concerns. By modeling key factors such as signal-to-interference-plus-noise ratio (SINR) and service rates, we introduce the concept of energy-utility efficiency to balance energy savings with quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves up to 70 % energy savings while enhancing QoS satisfaction, showcasing its potential to significantly improve the efficiency and sustainability of future 5G H-CRAN deployments. [ABSTRACT FROM AUTHOR]

In recent years, reconfigurable intelligent surfaces (RIS) have made significant progress in engineering application research and industrialization, as well as in academic research. However, the engineering application research field of RIS still faces several challenges. This article analyses and discusses the two deployment modes of RIS‐assisted wireless networks, namely network controlled mode and standalone mode. It also presents three typical collaboration scenarios of RIS networks, including multi‐RIS collaboration, multi‐user access, and multi‐cell coordination, which reflect the differences between the two deployment modes of RIS. The article proposes collaborative regulation mechanisms for RIS and analyses their applications in the two network deployment modes in‐depth. Furthermore, the article establishes simulation models of three scenarios and provides rich numerical simulation results. An actual field test environment is also built, where a specially designed and processed RIS prototype was used for preliminary field test and verification. Finally, this article puts forward future trends and challenges. [ABSTRACT FROM AUTHOR]

MEC (Multi-access Edge Computing) is vital in 5G and beyond (B5G) for reducing latency and enhancing network efficiency through local processing, crucial for real-time applications and improved security. This drives the adoption of advanced architectures like Fog Radio Access Network (F-RAN), which uses distributed resources from Radio Resource Heads (RRHs) or fog nodes to enable parallel computation. Each user equipment (UE) task can be processed by RRHs, fog access points, cloud servers, or the UE itself, depending on resource capacities. We propose MoNoR, a centralized approach for optimal task processing in F-RAN. MoNoR optimizes the selection of offloading modes, assignment of tasks to computation nodes, and allocation of radio resources using global network information. Given the computational complexity of this endeavor, we employ an evolutionary optimization technique rooted in Genetic Algorithms to address the problem efficiently. Simulations show MoNoR's superiority in minimizing latency over previous F-RAN offloading strategies. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has experienced rapid growth in various applications, resulting in significant advancements that exhibit considerable variations in characteristics and requirements. Cognitive radio networks (CRNs) present a promising solution for ultra-reliable communication and dynamic spectrum sharing among IoT devices in 6G environment. The most critical task in CRNs is to identify unused spectrum opportunities, known as holes, across different times and locations. Addressing this challenge requires an effective spectrum sensing strategy at the medium access control layer to optimize spectrum use while minimizing interference with licensed user signals. In this paper, we have proposed a novel dynamic spectrum access scheme, which aims to address both spectrum availability and network reliability for various secondary user flows in IoT-centric CRNs. Our study examines the effect of random channel failure and their recovery on the performance of CRN. Moreover, we develop a continuous-time Markov chain model to examine the network performance across various key performance indicators (KPIs) in the presence of multiple channel failures and sensing errors. This analysis helps identify valuable trade-offs among the KPIs. [ABSTRACT FROM AUTHOR]

Machine learning is taking on a significant role in materializing a new vision of 6G. 6G aspires to provide more use cases, handle high-complexity tasks, and improvise the current 5G and beyond 5G infrastructure. Artificial Intelligence (AI) and machine learning (ML) are the optimal candidates to support and deliver these aspirations. Traffic steering functions encompass many opportunities to help enable new use cases and improve overall performance. The emergence and advancement of the non-terrestrial network is another driving factor for creating an intelligence selection scheme to have a dynamic traffic steering function. With service-based architecture, 5G and 6G are data-driven architectures that use massive transactional data to emerge a new approach to handling highly complex processes. A highly complex process, a massive volume of data, and a short timeframe require a scheme using machine learning techniques to resolve the challenges. In this paper, the study creates a scheme to use the massive historical data and provide a decision scheme that enables dynamic traffic steering functions addressing the future emergence of the heterogeneous transport network and aligns with the Open Radio Access Network (O-RAN). The proposed scheme in this paper gives an inference to be programmed in the telecommunication nodes. It provides a novel scheme to enable dynamic traffic steering functions for the 6G transport network. The study shows an appropriate data size to create a high-performance multi-output classification model that produces more than 90% accuracy for traffic steering functions. [ABSTRACT FROM AUTHOR]

A multi variable resource allocation problem is investigated in network environments, specifically focusing on the consideration of quality of service in open radio access network. The main objective is to minimise the combined latency of various servers while complying with network limitations. The delay of each server is represented by a non‐linear function that has exponentially based. This characteristic inherently brings non‐convexity into the objective function. In contrast, the constraints comprise various linear combinations of network variables, including resource block allocations, power consumption, and number of virtual machines. The purpose of these constraints is to guarantee that the allocation of resources adheres to practical limitations and upholds fairness among servers. Nevertheless, the inclusion of a non‐convex objective function significantly adds complexity to the optimisation problem and non‐convex behaviour, requiring specialised algorithms and techniques to identify solutions. Subsequently, the Lagrange multiplier method has been used to solve this problem mathematically. Numerically, three algorithms have been utilised and compared to solve the problem, these are active‐set, interior point and sequential quadratic programming. Note that the total delay as an objective function is based on the total power consumption of the servers. Previous to optimising the total delay, a delay model is proposed and compared with two research works that are based on experimental and real time data. The proposed model shows data matching with the other works and permits for more adaptation/integration with any other works that uses different servers' characteristics and network parameters. [ABSTRACT FROM AUTHOR]

The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio. [ABSTRACT FROM AUTHOR]

The use of Reinforcement Learning (RL) techniques has been widely addressed in the literature to cope with capacity sharing in 5G Radio Access Network (RAN) slicing. These algorithms consider a training process to learn an optimal capacity sharing decision-making policy, which is later applied to the RAN environment during the inference stage. When relevant changes occur in the RAN, such as the deployment of new cells in the network, RL-based capacity sharing solutions require a re-training process to update the optimal decision-making policy, which may require long training times. To accelerate this process, this paper proposes a novel Transfer Learning (TL) approach for RL-based capacity sharing solutions in multi-cell scenarios that is implementable following the Open-RAN (O-RAN) architecture and exploits the availability of computing resources at the edge for conducting the training/inference processes. The proposed approach allows transferring the weights of the previously learned policy to learn the new policy to be used after the addition of new cells. The performance assessment of the TL solution highlights its capability to reduce the training process duration of the policies when adding new cells. Considering that the roll-out of 5G networks will continue for several years, TL can contribute to enhancing the practicality and feasibility of applying RL-based solutions for capacity sharing. [ABSTRACT FROM AUTHOR]

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN's dynamic slicing and LLAMA_V2's optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries. [ABSTRACT FROM AUTHOR]

In the evolving landscape of Open Radio Access Networks (Open RAN), the dynamic and unpredictable nature of network conditions presents significant challenges for traditional spectrum allocation strategies. This paper introduces an innovative framework that leverages Federated Learning (FL) to refine and enhance spectrum allocation within Open RAN. Utilizing the decentralized architecture of FL, our model introduces a system that is not only more adaptive to real-time changes but also offers enhanced robustness for spectrum management. We delve into the advantages of this approach, such as significant improvements in data traffic management, latency reduction, and overall network capacity enhancement. Additionally, we address potential implementation challenges, providing strategic countermeasures to ensure the successful deployment of our FL-based framework. Through this exploration, our paper underscores the transformative potential of integrating FL with Open RAN, marking a significant step forward in the application of AI technologies for optimizing wireless communication networks. This contribution opens new avenues for research in AI-driven spectrum allocation, setting a foundation for future empirical validations and the development of more efficient, intelligent telecommunication infrastructures. [ABSTRACT FROM AUTHOR]

G networks can offer network slices customized according to the demands of the services to enhance the quality of their users' experience. The time for selecting an appropriate network slice to facilitate traffic flow between users and services by the core network functions in 5G networks is crucial for services such as emergency service and ultra-reliable low latency services. Therefore, we propose a distributed slice selection architecture for 5G and beyond networks to reduce the waiting time for starting services for users. The proposed architecture distributes slice selection function (SSF) to the edge of the network. The networks have to ensure stealthy slice selection attack (S3 attack) free operation, as moving the SSF to the edge increases attack surface. Attackers can launch S3 attack by manipulating the slice selection decisions of SSFs distributed in the network edge. The S3 attacker intentionally maps service requests from users to inappropriate network slices to damage cloud radio access network utilization and the quality of experience of users. In this article, we also present a countermeasure to tackle the S3 attack using a novel protocol called VACCINE (verifiable privacy-preserving protocol for slice selection in 5G and beyond networks). VACCINE also ensures privacy-preserving slice selection by the SSFs to prevent traffic analysis attacks. We prove the chosen-ciphertext attack security strength of VACCINE and also compare the computational cost of VACCINE with other related protocols. [ABSTRACT FROM AUTHOR]

Using drone cells to optimize Radio Access Networks is an exemplary way to enhance the capabilities of terrestrial Radio Access Networks. Drones fitted with communication and relay modules can act as drone cells to provide an unobtrusive network connection. The multi-drone-cell placement problem is solved using adapted Dispersive Flies Optimization alongside other meta-heuristic algorithms such as Particle Swarm Optimization and differential evolution. A home-brewed simulator has been used to test the effectiveness of the different implemented algorithms. Specific environment respective parameter tuning has been explored to better highlight the possible advantages of one algorithm over the other in any particular environment. Algorithmic diversity has been explored, leading to several modifications and improvements in the implemented models. The results show that by using tuned parameters, there is a performance uplift in coverage probability when compared to the default meta-heuristic parameters while still remaining within the constraints implied by the problem's requirements and resource limitation. This paper concludes by offering a study and comparison between multiple meta-heuristic approaches, investigating the impact of parameter tuning as well as analyzing the impact of intermittent restarts for the algorithms' persistent diversity. [ABSTRACT FROM AUTHOR]

Abstract In recent years, reconfigurable intelligent surfaces (RIS) have made significant progress in engineering application research and industrialization, as well as in academic research. However, the engineering application research field of RIS still faces several challenges. This article analyses and discusses the two deployment modes of RIS‐assisted wireless networks, namely network controlled mode and standalone mode. It also presents three typical collaboration scenarios of RIS networks, including multi‐RIS collaboration, multi‐user access, and multi‐cell coordination, which reflect the differences between the two deployment modes of RIS. The article proposes collaborative regulation mechanisms for RIS and analyses their applications in the two network deployment modes in‐depth. Furthermore, the article establishes simulation models of three scenarios and provides rich numerical simulation results. An actual field test environment is also built, where a specially designed and processed RIS prototype was used for preliminary field test and verification. Finally, this article puts forward future trends and challenges.

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The emergence of various technologies such as terahertz communications, Reconfigurable Intelligent Surfaces (RIS), and AI-powered communication services will burden network operators with rising infrastructure costs. Recently, the Open Radio Access Network (O-RAN) has been introduced as a solution for growing financial and operational burdens in Beyond 5G (B5G) and 6G networks. O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs. By disaggregating conventional Base Band Units (BBUs) into O-RAN Distributed Units (O-DU) and O-RAN Centralized Units (O-CU), O-RAN offers greater flexibility for upgrades and network automation. However, this openness introduces new security challenges compared to traditional RANs. Many existing studies overlook these security requirements of the O-RAN networks. To gain deeper insights into the O-RAN system and security, this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G applications. We then delve into specifications of O-RAN security threats and requirements, aiming to mitigate security vulnerabilities effectively. By providing a comprehensive understanding of O-RAN architecture, use cases, and security considerations, this work serves as a valuable resource for future research in O-RAN and its security. [ABSTRACT FROM AUTHOR]

This work addresses the optimized planning of survivable optical 5G Xhaul access networks employing passive Wavelength Division Multiplexing (WDM) technologies. Specifically, it focuses on the reliability of optical transmission paths connecting remote radio sites to a central hub ensured by using a novel, cost-effective, flexible, and dedicated path protection (DPP-F) scheme, protecting against single-link failures. The proposed DPP-F network protection approach allows for switching of individual wavelengths or the complete multiplexed WDM signal, flexibly applying the best switching option according to given traffic demands. Concurrently, it enables traffic aggregation on the transmission paths from the end and intermediate nodes to minimize the overall network deployment cost. The problem of selecting primary (working) and backup (protection) paths, together with the selection of the best switching and traffic aggregation options, is modeled and solved as a mixed-integer linear programming (MILP) optimization problem. To evaluate the cost savings achieved with DPP-F, we compare it with two reference DPP schemes based on switching the entire multiplexed WDM signal (DPP-M) and individual wavelengths (DPP-W). Numerical experiments conducted across a wide range of network scenarios reveal, among other things, that DPP-F's performance is at least as good as that of the reference methods, bringing significant cost savings (from several to tens of percent) in most of the analyzed network scenarios. [ABSTRACT FROM AUTHOR]

Edge computing allows IoT tasks to be processed by devices with passive processing capacity at the network's edge and near IoT devices instead of being sent to cloud servers. However, 5G‐enabled architectures such as Fog Radio Access Network (F‐RAN) use smart devices to bring the delay down to even a few milliseconds. This is important, especially in latency‐sensitive applications such as online digital games. However, a trade‐off must be made between the delay and energy consumption. If too many tasks are processed locally on edge devices or fog servers, energy consumption increases because mobile devices such as smartphones and tablets have limited energy charges. This paper proposes a Deep Reinforcement Learning (DRL) method for offloading optimization. In designing states, we consider all three critical components of memory consumption, the number of CPU cycles, and network mode. This makes the modeling aware of the workload of the tasks. As a result, the model matches the requirements of the real world. For each mobile device that submits a task to the system, we consider a reward. It includes the total delay of tasks and energy consumption. The output of our DRL model specifies to which edge/fog/cloud device each task should be offloaded. The results show that the DRL technique produces less resource waste than RL when the number of tasks is very high. In addition, DRL consumes 30% less network resources than the FIFO method. As a result, DRL provides a better trade‐off between offloading and local execution than other methods. [ABSTRACT FROM AUTHOR]

Network slicing is an advanced technology that significantly enhances network flexibility and efficiency. Recently, reinforcement learning (RL) has been applied to solve resource management challenges in 6G networks. However, RL-based network slicing solutions have not been widely adopted. One of the primary reasons for this is the slow convergence of agents when the Service Level Agreement (SLA) weight parameters in Radio Access Network (RAN) slices change. Therefore, a solution is needed that can achieve rapid convergence while maintaining high accuracy. To address this, we propose a Teacher and Assistant Distillation method based on cosine similarity (TADocs). This method utilizes cosine similarity to precisely match the most suitable teacher and assistant models, enabling rapid policy transfer through policy distillation to adapt to the changing SLA weight parameters. The cosine similarity matching mechanism ensures that the student model learns from the appropriate teacher and assistant models, thereby maintaining high performance. Thanks to this efficient matching mechanism, the number of models that need to be maintained is greatly reduced, resulting in lower computational resource consumption. TADocs improves convergence speed by 81% while achieving an average accuracy of 98%. [ABSTRACT FROM AUTHOR]

Summary: The emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as "network slices," which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks. [ABSTRACT FROM AUTHOR]

The rapid development of the Fifth Generation (5G) networks encourages researchers to improve the Radio over Fiber (RoF) technique to achieve data rates of 10 Gbps and beyond. That led to a significant increase in bandwidth and range while reducing latency and cost. This paper evaluates an Analog Radio over Fiber (ARoF) technique compatible with Next-Generation (NG) long-haul communication systems, aiming for simplicity and lower cost. Transmitting a 28 GHz, 256 Quadrature Amplitude Modulation (QAM) signal through Single-Mode Fiber (SMF) is possible by modulating it through two parallel Mach-Zehnder Modulators (MZM), allowing signal reception over long distances. The Error Vector Magnitude (EVM) appraises performance of the system. The simulation results indicate that the prototype can transfer data at 10 Gbps through the optical link up to 570 km with an EVM of 3.375% and received optical power of 4.015 dBm. The proposed system supports a high bit rate and maintains the EVM within 3GPP limits, making it superior to peer publications and highly appropriate for NG long-haul communication systems. [ABSTRACT FROM AUTHOR]

Next-generation networks are expected to adapt not only to a wide range of application demands but also to the specific needs of individual users, end device heterogeneity, and changing operational conditions. In this work, we discuss how to enhance the programmability of network devices (e.g., radio end devices, base stations, and routers) to achieve an unprecedented level of network adaptation and customization in 6G systems. We focus on two main innovations: full network programmability and morphable networking. The former breaks down traditional boundaries between protocol layers as well as domains (e.g., network segments, technological realms, and administrative domains); the latter enables the protocol stack of each network node to be dynamically and consistently reconfigured across different domains, enabled by embedded artificial intelligence (AI). [ABSTRACT FROM AUTHOR]

Next Generation Wireless Networks (NGWNs) have two main components: Network Slicing and Open Radio Access Networks (O-RAN). NS is needed to handle various Quality of Services (QoS). O-RAN adopts an open environment for network vendors and Mobile Network Operators (MNOs). In recent years, Deep Reinforcement Learning (DRL) approaches have been proposed to solve some key issues in NGWNs. The primary obstacles preventing the DRL deployment are being slowly converged and unstable. Additionally, these algorithms have enormous carbon emissions that negatively impact climate change. This paper tackles the dynamic allocation problem of O-RAN radio resources for better QoS, faster convergence, stability, lower energy and power consumption, and reduced carbon emissions. Firstly, we develop an agent with a newly designed latency-based reward function and a top-k filtration mechanism for actions. Then, we propose a policy Transfer Learning approach to accelerate agent convergence. We compared our model to another two models. [ABSTRACT FROM AUTHOR]

The present research focuses on optimizing 5G base station deployment and visualization, addressing the escalating demands for high data rates and low latency. The study compares the effectiveness of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimizer (GWO) in both Urban Macro (UMa) and Remote Macro (RMa) deployment scenarios that overcome the limitations of the current method of 5G deployment, which involves adopting Non-Standalone (NSA) architecture. Emphasizing population density, the optimization process eliminates redundant base stations for enhanced efficiency. Results indicate that PSO and GA strike the optimal balance between coverage and capacity, offering valuable insights for efficient network planning. The study includes a comparison of 28 GHz and 3.6 GHz carrier frequencies for UMa, highlighting their respective efficiencies. Additionally, the research proposes a 2.6 GHz carrier frequency for Remote Macro Antenna (RMa) deployment, enhancing 5G Multi-Tier Radio Access Network (RAN) planning and providing practical solutions for achieving infrastructure reduction and improved network performance in a specific geographical context. [ABSTRACT FROM AUTHOR]

The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for achieving fair resource allocation in such network scenario. Hence, this study formulates the fairness-aware UA problem as a utility-based combinatorial optimization problem, which is computationally hard to solve. Hence, an efficient Binary Particle Swarm Optimization (BPSO)-based UA scheme is proposed to solve the problem. Through simulations of an O-RAN based HetNet with network slicing-enabled, performance of the proposed BPSO-UA scheme is compared against two other baseline UA schemes. Results demonstrate the effectiveness of the proposed BPSO-UA scheme in achieving high fairness through equitable network slicing resource allocation, thereby leading to higher user connectivity rate and comparable average spectral efficiency. This innovative approach sheds light on the potential of metaheuristic algorithms in tackling intricate UA challenges, offering valuable insights for the future design and optimization of mobile networks. [ABSTRACT FROM AUTHOR]

The article discusses the importance of optical and photonic technologies in supporting 5G and future 6G mobile networks. These advanced technologies are necessary for the construction and implementation of these networks, particularly in the core networks, backhaul, midhaul, and fronthaul components. Optical technologies such as DWDM, CWDM, OTN, EON, and PON are used in these networks, and Li-Fi optical wireless connectivity is also expected to be utilized in 6G networks. The article highlights the need for efficient management of optical resources and the potential use of machine learning and artificial intelligence in optimizing future optical networks. The article also provides a summary of five contributions in the field of optics and photonics for telecommunications systems, including research on passive optical networks, advanced optical modulations, optical path analysis, and software management for optical converters. [Extracted from the article]

This paper describes the design of a multistage decimator for the Physical Random Access Channel (PRACH) receiver based on the hybrid time/frequency domain architecture. Flexible structure of the multistage decimator allows to support all combinations of PRACH subcarrier spacings and carrier bandwidths defined in the 3rd Generation Partnership Project (3GPP) Release 15 for Frequency Range (FR) 1. Impulse responses of low pass filters for each decimation stage do not depend on a selected combination allowing to facilitate hardware implementation. The multistage decimator jointly uses the Cascaded Integrator-Comb (CIC) and Nyquist filters to minimize number of required multipliers. Acceptable degradation level due to decimation in the receiver performance is estimated based on requirements defined by the Radio Access Network (RAN) working group 4. Assuming no specific detection algorithm, it is shown that the proposed design introduces negligible degradation, less than 0.1 dB in the operating Signal-to-Noise Ratio (SNR) region. [ABSTRACT FROM AUTHOR]

Current cutting-edge solutions to the spectrum shortage problem are unable to meet the growing demand for a limited spectrum. A key dimension beyond state-of-the-art solutions is to exploit the free spectrum more effectively. Although various schemes have been proposed for trading spectrum, few studies have focused on optimal admission of spectrum requests for maximizing service providers' (SP's) profit. Thus, this timely study presents a novel intelligent admission scheme for spectrum requests from the perspective of a non-cooperative game, in which the information of all participants (customers and providers) is incomplete to others, and each player wishes to maximize its benefit. The proposed control admission policy may evict clients in-service to release spectrum for serving certain, e.g., wealthy clients. Evicted clients are compensated using a dynamic strategy that adopts greedy game theory to capture the conflict of interest between SP and evicted users. Simulation experiment results validate and demonstrate the feasibility and efficiency of the proposed scheme, compared to a benchmark reinforcement learning approach and another widely used scheme for admission and eviction control of cognitive radio users. [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]

As the global deployment of Fifth Generation (5G) is being well consolidated, the exploration of Sixth Generation (6G) wireless networks has intensified, focusing on novel Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) that extend beyond traditional metrics like throughput and latency. As 5G begins transitioning to vertical-oriented applications, 6G aims go beyond, providing a ubiquitous communication experience by integrating diverse Radio Access Networks (RANs) and fixed-access networks to form a hyper-converged edge. This unified platform will enable seamless network federation, thus realizing the so-called network of networks vision. Emphasizing energy efficiency, the present paper discusses the importance of reducing telecommunications' environmental impact, aligning with global sustainability goals. Central to this vision is the proposal of a novel user plane network protocol architecture, called 6G Recursive User Plane Architecture (6G-RUPA), designed to be scalable, flexible, and energy-efficient. Briefly, 6G-RUPA offers superior flexibility in network adaptation, federation, scalability, and mobility management, aiming to enhance overall network performance and sustainability. This study provides a comprehensive analysis of 6G's potential, from its conceptual framework to the high-level design of 6G-RUPA, addressing current challenges and proposing actionable solutions for next-generation mobile networks. [ABSTRACT FROM AUTHOR]

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications. [ABSTRACT FROM AUTHOR]

In C-RAN architecture, all computational processing is performed in the central baseband unit (BBU) pool, while radio operations are carried out in the remote radio heads (RRHs). The central BBU pool is connected to the RRHs by fronthaul links. Therefore, by separating the processing unit and the radio units, the clustering structure of RRHs can be designed to adapt to network changes. This paper deals with the problem of radio and computation resource allocation to maximize the weighted sum rate. To reduce complexity, we use user-centric clustering and appropriate training resource allocation. Additionally, to lessen channel training overhead, an incomplete model of channel state information is considered, in which only intra-cluster channel state information is estimated. By replacing a sticky lower bound of user data rate in the main problem, the beamforming vectors under the constraints of computational and radio resources are designed in three steps. In the first step, the weighted sum rate maximization problem is solved under maximum radio transmitted power constraints by the weighted minimum mean square error method. Then, in the second step, we propose a greedy algorithm to allocate computational resources to users. In the third step, fronthaul capacity constraints are applied by another greedy algorithm [ABSTRACT FROM AUTHOR]

In this work the design and development of a power processing unit for a vacuum arc thruster is presented. The thruster is part of the Seamless Radio Access Networks for Internet of Space (SeRANIS) mission of the University of the Bundeswehr Munich, which will work as first multifunctional laboratory in orbit with public access. In addition to the basic functionality of generating a voltage peak for igniting the thruster, the power processing unit is equipped with techniques for controlling the ignition sequence and monitoring desired key values. The ignition procedure starts with generating the first trigger signal up to the point where a full-blown plasma is established. The PPU guarantees reliable performance by blocking every additional incoming signal while the ignition sequence is under way and the separation of the satellite's power bus before the thruster discharges. The status of the power processing unit is constantly controlled and information is provided whether ignition was successful or not. The functionality of this circuit is based on simulation before assembly and testing. In addition, the presented system was designed to pass a test cycle of mechanical, thermal and electrical tests before being declared ready for the space mission. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) of mobile networks is emerging as a key enabler of modern factory automation (FA) applications that ensure timely and reliable data exchange between network components. Network slicing (NS), which shares an underlying infrastructure with different applications and ensures application isolation, is the key 5G technology to support the diverse quality of service requirements of modern FA applications. In this article, an end-to-end (E2E) NS solution is proposed for FA applications in a 5G network. Regression approaches are used to construct a performance model for each slice to map the service level agreement to the network attributes. Interference coordination approaches for switched beam systems are proposed to optimize radio access network (RAN) performance models. A case study of a non-public network is used to show the proposed NS solution. Simulation result shows that for services with different QoS requirements, different IC approaches should be used as optimization methods. Design prediction using regression approach has been evaluated and shows that the prediction successful rate increases when more existing data are used. [ABSTRACT FROM AUTHOR]

In this paper, we present our work on developing a Smart Pricing Policies module specifically designed for individual users and Mobile Network Operators (MNOs). Our framework will operate in a multi-MNO blockchain radio access network (B-RAN) and is tasked with determining prices for resource sharing among users and MNOs. Our sophisticated adaptive pricing system can adjust to situations where User Equipment (UE) shifts out of the coverage area of their MNO by immediately sealing a contract with a different MNO to cover the users' needs. This way, we aim to provide financial incentives to MNOs while ensuring continuous network optimization for all parties involved. Our system accomplishes that by utilizing deep reinforcement learning (DLR) to implement a reverse auction model. In our reinforcement learning scenario, the MNOs, acting as agents, enter a competition and try to bid the most appealing price based on the user's request, and based on the reward system, agents that do not win in the current round will adjust their strategies in an attempt to secure a win in subsequent rounds. The findings indicated that combining DRL with reverse auction theory offers a more appropriate method for addressing the pricing and bid challenges, and additionally, administrators can utilize this strategy to gain a notable edge by dynamically selecting and adjusting their methods according to the individual network conditions and requirements. [ABSTRACT FROM AUTHOR]

Multi-Access Edge Computing (MEC) reduces latency, provides high-bandwidth applications with real-time performance and reliability, supporting new applications and services for the present and future Beyond the Fifth Generation (B5G). Radio Network Information Service (RNIS) plays a crucial role in obtaining information from the Radio Access Network (RAN). With the advent of 5G, RNIS requires improvements to handle information from the new generations of RAN. In this scenario, improving the RNIS is essential to boost new applications according to the strict requirements imposed. Hence, this work proposes a new RNIS as a service to the MEC framework in B5G networks to improve MEC applications. The service is validated and evaluated, and demonstrates the ability to adequately serve a large number of MEC apps (two, four, six and eight) and from 100 to 2000 types of User Equipment (UE). [ABSTRACT FROM AUTHOR]

In open Radio access network (oRAN) 5G networks, a fronthaul segment between virtual Distribution unit (vDU) edge cloud and other external Remote radio units (RRUs) sites is very important to transmit high data rate to achieve 5G requirements. Performance parameters such as Quality factor (Q-factor) and Bit error rate (BER) of an optical fiber fronthaul segment are the main important parameters to enhance latency and bit rate. An Apodized fiber bragg grating (AFBG) is utilized with different apodization profiles to optimize linewidth of an optical signal leading to enhance performance. The designed AFBG is used with a data rate of 25 Gb/s optical Ethernet module at the vDU edge cloud to optimize the linewidth of the optical signal between the vDU and the RRU in the fronthaul optical fiber segment. The AFBG is investigated at different connections pre, post, and symmetrical connections, where different apodization functions are used in the AFBG design. It is found that the best results for the maximum Q-factor and minimum BER are 9.2 and 2.72e− 21, respectively, obtained from uniform function in the symmetrical connection. Finally, the proposed model linewidth is 0.408 nm, which is better than 1.56 nm that is evaluated by related work. [ABSTRACT FROM AUTHOR]

Multiple communication applications such as high-quality video streaming, IoT, cellular vehicle to anything, augmented reality, virtual reality, and low-latency web browsing make the cellular network heterogeneous. To seamlessly support heterogeneous functionality in the upcoming 6G mobile network, the open radio access network (O-RAN) Alliance is being formed. The O-RAN Alliance is a step towards defining a standard interface between systems. It aims to reduce complexity and accelerate the deployment of 6G mobile networks. However, in deploying the 6G mobile network, the efficient sharing of resources among heterogeneous users is challenging. We considered a novel ultra-dense heterogeneous 6G O-RAN-based cellular network architecture and proposed a multi-objective particle swarm optimization at both small cell base stations (SBSs) and macrocell base stations (MBSs) for collaborative resource optimization. Analytical expressions are derived for joint energy and spectrum optimization at SBS and MBS. The simulation results show that the proposed scheme has a noticeable effect on the optimization of the energy consumption of the system in different scenarios and prove that this algorithm has a remarkable convergence. [ABSTRACT FROM AUTHOR]

With the ongoing expansion of user numbers and the diversity of business types, wireless networks are progressing toward broader spatial dimensions and increased functionality. With respect to coverage, the space–air–ground integrated network (SAGIN) is considered a promising solution for providing seamless global connectivity to emerging services. In terms of functionality, wireless networks are expected to offer not only communication services but also sensing and computing services, which are crucial for certain businesses. However, within SAGIN there is a significant variance in performance among different types of radio access networks (RANs). This necessitates a reconsideration of system architecture and handover selection schemes to meet the quality of service (QoS) requirements of various business applications. The main focus of this article is to explore the integration of sensing and mobile edge computing (MEC) with SAGIN and also to examine the system architecture, selection schemes, and handover procedures. We first provide an overview of SAGIN’s capabilities and highlight its integrated architecture. Subsequently, we introduce several handover selection schemes, followed by an elaboration on the redesigned handover procedure for SAGIN. Finally, we present some simulation results and discuss challenges that need to be addressed in practical implementation. [ABSTRACT FROM AUTHOR]

The Cloud Radio Access Network (C-RAN) is a state-of-the-art system paradigm that simultaneously improves spectral and energy efficiency. Capacity constraints of the fronthaul links connecting Remote Radio Heads (RRH) to the Cloud Unit are notable limitations of these networks. The multitude of RRHs and users make active user estimation and calculating Channel Side Information (CSI) between active users and RRHs necessary for implementing these networks. Moreover, in C-RAN, user activity detection is essential for energy-efficient resource allocation, calculating CSI, optimal precoder design, interference management, and multi-user detection. This study investigates active user detection in C-RAN as a joint block sparse signal recovery problem and evaluates the impact of fronthaul limitations, sparsity level, and other network parameters for different sparse signal reconstruction methods. We introduce an efficient method that is based on recovering multiple sparse signals sharing the same sparsity pattern or the same support set of non-zero entries. This method is developed using 5G training signals for user activity detection in C-RAN with fronthaul capacity limitations and without prior knowledge of the sparsity of users. In the end, we compare active user detection results for different sparse signal recovery methods, namely joint block sparse signal and block sparse signal algorithms, with different network specifications. [ABSTRACT FROM AUTHOR]

The relentless growth of wireless networks coupled with the burgeoning demand for dynamic resource allocation has spurred research into innovative solutions. This paper presents an evaluation of Cloud-based Dynamic Network Scaling and Slicing (CDNSS) as a promising approach to meet the evolving demands of wireless networks. By leveraging cloud infrastructure and slicing techniques, CDNSS offers the flexibility to dynamically scale resources and allocate network slices tailored to diverse service requirements. The evaluation encompasses the performance of CDNSS in terms of scalability, resource utilisation and Quality of Service (QoS) provisioning. Through extensive simulations and analyses, the efficacy of CDNSS in addressing the challenges of resource management and service differentiation in wireless networks is demonstrated. The findings underscore the potential of CDNSS as a pivotal technology to enhance the efficiency and adaptability of wireless network architectures in the era of dynamic connectivity demands. [ABSTRACT FROM AUTHOR]

In this paper, an eight-port antenna array is presented for 5G handheld terminals to support multiple-input multiple-output (MIMO) operations. The reported design involves three layers: the top contains eight circular microstrip feed elements; the middle is a low-cost FR-4 substrate, and the bottom layer is a ground plane with four etched octagonal slots. Each resonating element is fed by 50-ohm sub-miniature connectors. To mitigate the detrimental effects of mutual coupling of ports and enhance overall isolation between the adjacent microstrip-fed circular patch elements, a neutralization line is strategically implemented between the feed lines of the antenna array. The design configuration involves two elements at each vertex of the printed circuit board (PCB). The overall dimensions of the PCB are 150 × 75 mm2. Each slot and its corresponding radiating elements exhibit linear dual polarization and diverse radiation patterns. The proposed antenna design achieves the required operating bandwidth of more than 1000 MHz spanning from 3 to 4.2 GHz, effectively covering all the upper C-band frequency range of 3.3 GHz to 4.2 GHz allocated for 5G n77 and n78 frequency range 1 (FR1). Required port isolation and lower envelop correlation coefficient (ECC) are achieved for the band of interest. The proposed design gives a peak gain of up to 4 dB for the said band. In addition to these results, degradation in the performance of the antenna array is also investigated during different operating modes of the handheld device. Measured results from the fabricated unit cell and whole array also have a good match with simulated results. On the whole, the proposed antenna possesses the potential to be used in 5G and the open radio access network (ORAN) compliant handheld devices. [ABSTRACT FROM AUTHOR]

Digital Radio over Fiber (DRoF) technologies end up being the most often used option for the backbone of Wide Area Networks (WAN). The main goal of this paper is to evaluate a DRoF scheme called Sigma Delta Radio over Fiber (SDRoF). Studies have been done on the suggested system performance metrics, such as Error Vector Magnitude (EVM). According to the simulation findings, the suggested model can handle 6.144 Gbps data transfer up to 300 km with zero BER and an EVM of about 4.153%. The proposed system model was found to be superior to the most recent peer-reviewed papers. [ABSTRACT FROM AUTHOR]

Continuous deployment has been practiced for many years by companies developing web‐ and cloud‐based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time‐consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software‐intensive embedded systems often provide customer product‐related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in‐depth understanding of the impact of continuous deployment on product‐related services provided by software‐intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software‐intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product‐related services become blurry with continuous deployment. In addition, product‐related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software‐intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement. [ABSTRACT FROM AUTHOR]

A wireless data collection network (DCN) is the key constituent of the IoT. It is used in many applications such as transport, logistics, security and monitoring. Despite the continuous development of DCN, communication between nodes in such network presents several challenges. The major issue is the deployment of connected objects and, more precisely, how numerous nodes are appropriately positioned to attain full coverage. The current work presents a hybrid technique, named DTABC, combining a geometric deployment method, called Delaunay Triangulation diagram DT, and an optimization algorithm named the Artificial Bee Colony (ABC) algorithm. In the centralized approach, this hybrid method is executed on a single node while, in a distributed approach, it is executed in parallel on different nodes deployed in a wireless data collection network. This study aims at enhancing the coverage rate in data collection networks utilizing less sensor nodes. The Delaunay Triangulation diagram is utilized to produce solutions showing the first locations of the IoT objects. Then, the Artificial Bee Colony algorithm is used to improve the node deployment coverage rate. The developed DTABC approach performance is assessed experimentally by prototyping M5StickC nodes on a real testbed. The obtained results reveal that the coverage rate, the number of the objects' neighbors, the RSSI and the lifetime of the distributed approach are better than those of the algorithms introduced in previous research works. [ABSTRACT FROM AUTHOR]