Radio Access Network Research Articles

Cost dynamics of converged optical-wireless networks: enabling low-latency xRANs through a reconfigurable hybrid split

Future 6G and beyond wireless networks are anticipated to be highly versatile, accommodating a wide range of services, from ultra-low-latency applications like autonomous vehicles and extended reality to enhanced mobile broadband and massive connectivity for the Internet of Things. In tackling this, xRANs (cloud/virtualized/open radio access networks) encounter significant challenges, including automation, interoperability, scalability, reconfigurability, and standardization, within crosshaul (comprising fronthaul, midhaul, and backhaul) networks. Therefore, the development of programmable converged optical-wireless networks with exceptional flexibility is crucial. This study concentrates on the design of integrated optical and wireless networks to achieve the reconfigurability necessary for automation and to fulfill diverse latency requirements. Initially, we analyze the latency contributions from different network segments and traffic factors in the xRAN, followed by a comprehensive examination of the associated cost dynamics. Subsequently, we investigate the feasibility of integrating high-layer and low-layer splits within the same network to achieve different latency levels. Finally, our study delves into the relationship between latency and cost for converged optical-wireless networks with varying mixed split scenarios and throughput levels. Overall, this article aims to assist network planners in making well-informed decisions that balance throughput performance, cost, and latency requirements in upcoming network deployments.

Enabling cost-effective wireless THz communication through semiconductor optical amplifier’s direct/cross gain modulation for seamless integration of hybrid fiber-coaxial connectivity

We propose a cost-effective THz wireless communication scheme for future femtocell networks that leverages either direct gain modulation (DGM) or cross gain modulation (XGM) of a semiconductor optical amplifier (SOA) to convert coaxially or optically carried data into intensity-modulated THz waves, respectively. This architecture, based on an SOA and a uni-traveling-carrier photodiode (UTC-PD), has advantages over typical THz modulation schemes involving a resonant tunneling diode or an electro-optic modulator combined with a UTC-PD. Consequently, it facilitates the seamless convergence of hybrid fiber-coaxial networks to THz radio access networks with low cost, straightforward monolithic integration, and a more compact footprint. Furthermore, we experimentally verified the possibility of non-return-to-zero on–off keying wireless communication over 0.5 m in the 300 GHz band, achieving real-time transmission of 4 Gbit/s for coaxial access via an SOA’s DGM and 10 Gbit/s for fiber access via the SOA’s XGM. Notably, these data rates were attained by simple non-coherent THz intensity demodulation based on a Fermi-level managed barrier diode while meeting the 7% hard-decision forward error correction (HD-FEC) threshold (3.4×10−3). Finally, we conducted an analysis of the merits and drawbacks of the proposed THz communication scheme based on the combination of an SOA and a UTC-PD, discussed bottlenecks that hinder further rate improvement, and explored possibilities for optimization. In conclusion, this scheme is crucial for realizing future ultra-high-speed wireless networks that seamlessly connect to fiber-coaxial networks.

Proportional-fair uplink resource allocation with statistical QoS provisioning for RAN slicing

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively.

Towards efficient RAN slicing: A deep hierarchical reinforcement learning approach

Radio access network (RAN) slicing can significantly improve network flexibility and resource utilization efficiency. Generally, deep reinforcement learning (DRL) is a prevailing approach to implementing practicable resource management in RAN slicing. However, this demand-aware resource allocation suffers from long training time and slow execution efficiency. This can lead to the inability to quickly achieve the desired spectral efficiency and service level agreement satisfaction rate. To tackle this issue, we propose a novel RAN slicing resource allocation framework based on a deep hierarchical RL framework for efficient resource scheduling. Structurally, our framework consists of a policy selection network and a policy evaluation network. In particular, a newly built action acceleration unit can achieve quick reward accumulation, thus speeding up the optimal policy search. Extensive simulations show that our proposal has higher system utility and faster convergence compared to the state-of-the-art DRL algorithms.

Joint block sparse signal recovery-based active user detection in 5G cloud radio access networks

Joint block sparse signal recovery-based active user detection in 5G cloud radio access networks

K-means non-uniform-quantization digital-analog radio-over-fiber scheme for THz-band photonics-aided wireless fronthaul.

We propose a non-uniform-quantization digital-analog radio-over-fiber (NUQ-DA-RoF) scheme based on an advanced K-means NUQ algorithm and demonstrate it experimentally in a 2-m 300-GHz photonics-aided wireless fronthaul system. Results show that the NUQ-DA-RoF scheme achieves a SNR gain of ∼1.9 dB compared to the uniform-quantization DA-RoF (UQ-DA-RoF) at an equivalent Common Public Radio Interface equivalent data rate (CPRI-EDR). Remarkably, the NUQ-DA-RoF scheme exhibits an ∼1.6-dB power sensitivity enhancement over the UQ-DA-RoF at the 256-QAM SNR threshold. These findings highlight the advantages of the NUQ-DA-RoF scheme over UQ-DA-RoF in terms of power budget and SNR improvement, suggesting promising prospects for future radio access networks and wireless fronthaul.

Towards intent-based management for Open Radio Access Networks: an agile framework for detecting service-level agreement conflicts

Towards intent-based management for Open Radio Access Networks: an agile framework for detecting service-level agreement conflicts

CRAMP: Clustering-based RANs association and MEC placement for delay-sensitive applications

With advancements in networking technology and ubiquitous computing, there has been a significant increase in the number of edge devices and delay-sensitive applications. To facilitate efficient processing, mobile edge computing (MEC) technology provides resources through MEC servers, which are deployed at the radio access networks (RANs) of 5G networks. However, these MEC servers possess a limited amount of resources, making their effective management of these resources a critical challenge. This is due to the uneven distribution of resource utilization, where some resources become overutilized while others remain underutilized. Addressing the issue above while simultaneously satisfying user requirements for delay-sensitive applications poses a significant challenge at the edge. In this paper, we propose a clustering-based efficient RANs association and MEC server placement model to tackle this challenge. Our primary objective is to minimize MEC server deployment costs while ensuring that the delays of these applications are effectively managed. We propose a greedy algorithm called the clustering-based radio access networks association and mobile edge computing placement (CRAMP) algorithm, which determines the optimal location of MEC servers to associate with RANs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches regarding cost efficiency and delay management.

Optical fiber fronthaul segment in open radio access 5G networks: enhanced performance utilizing AFBG

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.

Latency optimized C-RAN in optical backhaul and RF fronthaul architecture for beyond 5G network: A comprehensive survey

The need for high-speed data connection has significantly strained Mobile Network Operators (MNOs) due to an exponential increase in traffic. Numerous innovative technologies have been used to fulfill consumer service needs in capacity, high-speed data, network coverage, and Quality of Service (QoS), but they still require more thought. Because of several advantageous features in terms of resource utilization, service deployment, Capital Expenditure (CAPEX), Operating Expense (OPEX), and network management, the Cloud-based Radio Access Network (C-RAN) has played a significant role in mitigating the issues faced by mobile network operators. However, the C-RAN technology places high demands on the mobile fronthaul network due to the risk of connection interruptions. This article thoroughly analyzes the essential technologies, problems, design, needs, and solutions for effective fronthaul and backhaul communication a fifth-generation (5G) and beyond systems. There have been reviews of optical-based fronthaul and backhaul technologies. This article makes suggestions for ways to make fronthaul and backhaul communication systems more effective by lowering the costs, bandwidth, latency, and power requirements.

The Latency Performance Analysis and Effective Relay Selection for Visible Light Networks.

In visible light communication (VLC), the precise latency evaluation of wireless access networks and the efficient forwarding strategy of core networks are the crux for end-to-end reliability provisioning. Leveraging martingale theory, an elegant latency-bounded reliability analysis framework is studied for the VLC network. Considering the characteristic that VLC links are easy to block, the martingale of the service process is constructed. Based on the time shift features, the martingale process related to latency is proposed for the VLC system, which models the influence of entanglement between aggregate arrivals and random service on latency. A stopping time event about latency is defined. Renting the stopping time theory, a tight upper bound of the unreliability with regard to latency is derived. In the core network, a dynamic forward backhaul framework is proposed, which uses the relay selection algorithm based on back-pressure theory to improve data transmission quality. The theoretical latency-bounded reliability matches the simulation results well, which verifies the effectiveness of the proposed analysis framework, and the proposed relay selection algorithm can also improve network performance under data-intensive transmission.

Frequency migration challenges and strategic decisions-making in broadband wireless access networks

Purpose: The impetus for this study lies in the pressing need for telecommunication companies to adapt to the rapidly evolving technological landscape and regulatory environment to maintain competitiveness and meet increasing consumer demands. Method: This study conducted a comprehensive analysis using SWOT analysis, Qualitative Data Analysis, and the Analytical Hierarchy Process (AHP) to determine the best course of action. Results: This study navigates through the initial stages of identifying the strategic, operational, and financial implications of PT. XYZ Frequency Migration. It delves into the background of Broadband Wireless Access (BWA) technology, the significance of the 3.3 GHz and 10 GHz bands in telecommunications, and the broader context of industry trends necessitating this shift. Methodologically, this research integrates SWOT, stakeholder, qualitative data analysis (QDA), and the Analytical Hierarchy Process (AHP) to provide a holistic understanding of the complexities of migration. This multi-faceted approach facilitates a comprehensive evaluation of the technological, operational, and strategic aspects of migration and offers nuanced insights into the challenges and opportunities inherent in the transition to the 10 GHz band. The study proposes that the convergence of various factors necessitates a strategic realignment that encompasses operational efficiency, technical excellence, financial prudence, regulatory compliance, and customer focus to effectively adapt to the 10 GHz spectrum. Contributions: This study contributes to the academic and practical understanding of network migration within the telecommunications sector, offering nuanced insights into the complexities of transitioning to higher frequency bands. It provides a framework for other telecommunications entities considering similar migrations, emphasizing the importance of a multifaceted strategic approach to navigating challenges and leveraging opportunities presented by such technological advancements.

Service-aware real-time slicing for virtualized beyond 5G networks

Edge Intelligence is expected to play a vital role in the evolution of 5G networks, empowering them with the capability to make real-time decisions regarding various allocations related to their management and service provisioning to end-users. This shift facilitates the transition from a network-aware approach, where applications are developed to manage network quality fluctuations, to a service-aware network that self-adjusts based on the hosted applications. In this paper, we design and implement a service-aware network managed from the network edge. We utilize and assess various Machine Learning models to classify cellular network traffic flows in the backhaul, aiming to predict their future impact on network load. Leveraging these predictions, the network can proactively and autonomously reallocate slices in the Radio Access Network via programmable APIs, ensuring the demands of the traffic-generating applications are met. The approach integrates innovative MLOps methodologies for distributed and online training, enabling continuous model refinement and adaptation to evolving network dynamics. Our framework was tested in a real-world environment with realistic traffic scenarios, and the results were evaluated in real-time, down to a granularity of 10ms. Our findings indicate that the network can swiftly adjust to traffic, providing users with slices tailored to their application needs. Notably, our experiments show that under the studied settings, the users experienced up to 4 times lower latency (jitter) and nearly 4 times higher throughput when interacting with various applications, compared to the standard non-AI/ML unit. Furthermore, our dynamic scheme significantly optimizes resource allocation, ensuring energy efficiency by avoiding over- and under-provisioning of resources.

Computation Offloading Based on a Distributed Overlay Network Cache-Sharing Mechanism in Multi-Access Edge Computing

Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and Offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms.

Component analysis for energy-efficient multimedia networks utilizing 5G radio access technologies

The object of the research is the energy-efficient multimedia networks based on 5G wireless access technologies. The research is aimed at developing energy-efficient multimedia networks based on 5G wireless access technologies, considering the increasing importance of information and communication technologies (ICT) in modern society. With the proliferation of wireless access networks and the advancement of fifth-generation mobile networks (5G), there is a need to assess and reduce the environmental impact of ICT. The article specifically focuses on the challenges related to energy consumption and CO2 emissions in radio access networks, highlighting the responsibility to balance technological advancements with environmental concerns. The paper examines various components and technologies necessary for enhancing energy efficiency in multimedia networks. It discusses the concept of multimedia, including digital storage, data processing, and interactive elements. Statistical data is provided to underscore the significant energy consumption and carbon footprint of the ICT industry, with an emphasis on radio access networks. Heterogeneous networks, non-orthogonal multiple access (NOMA) technologies, and multiple-input multiple-output (MIMO) technologies are identified as key components for achieving energy efficiency. The importance of reducing the distance between transmitters and receivers in heterogeneous networks is emphasized, as well as the use of energy-saving strategies such as putting small base stations into sleep mode during low network loads. Special attention is given to the role of green data centers in reducing CO2 emissions and optimizing the use of green energy in high-performance networks. Proposed methods include leveraging renewable energy sources, improving hardware energy efficiency, and implementing energy-efficient routing. The findings offer valuable insights for the development and implementation of energy-efficient multimedia networks, particularly in the context of 5G networks. The interdisciplinary approach advocated in the conclusion emphasizes the collective efforts needed to address environmental challenges in the field of information and communication technologies. The combination of these technologies ensures efficient resource utilization and reduced environmental impact compared to similar known approaches.

Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources.

Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to resource contention. In this paper, we conduct an empirical analysis of vBBU performance on a Linux RT-Kernel, highlighting the impact of resource sharing with user-space tasks and Kernel threads. Furthermore, we evaluate CPU management strategies such as CPU affinity and CPU isolation as potential solutions to these performance challenges. Our results highlight that the implementation of CPU affinity can significantly reduce throughput variability by up to 40%, decrease vBBU's NACK ratios, and reduce vBBU scheduling latency within the Linux RT-Kernel. Collectively, these findings underscore the potential of CPU management strategies to enhance vBBU performance in Cloud-RAN environments, enabling more efficient and stable network operations. The paper concludes with a discussion on the efficient realization of Cloud-RAN, elucidating the benefits of implementing proposed CPU affinity allocations. The demonstrated enhancements, including reduced scheduling latency and improved end-to-end throughput, affirm the practicality and efficacy of the proposed strategies for optimizing Cloud-RAN deployments.

Black-box optimization for anticipated baseband-function placement in 5G networks

In the context of the ever-evolving 5G landscape, where network management and control are paramount, a new Radio Access Network (RAN) as emerged. This innovative RAN offers a revolutionary approach by enabling the flexible distribution of baseband functions across various nodes, all tailored to meet the ever-shifting demands of both system requirements and user traffic patterns. As users move within the network, the need to anticipate and strategically position these baseband functions becomes crucial for seamless network operation. Traditionally, this challenge has been tackled through a two-step process: first, forecasting traffic patterns, and then optimizing resource allocation accordingly. However, this approach falls short in guaranteeing an efficient placement when actual traffic demands surge onto the network. It often leads to resource overbooking, constraint violations, and excessive power consumption, putting strain on the network’s capabilities. In this paper, we introduce a novel framework based on a black-box optimization approach. This tool empowers prediction algorithms not just with historical traffic data but also with insights from optimization outcomes. The goal is to minimize a loss function related to power consumption and constraint violation: this ensures a predicted placement that is feasible and whose power is close to optimal. This approach ensures that the predicted placement is both feasible and power-efficient, bridging the gap between theoretical prediction and practical implementation. Remarkably, our proposed method, while potentially sacrificing some degree of traffic prediction accuracy, outperforms the conventional two-step approach by delivering a more efficient baseband function placement.

Investigation of the dependence of the multi-linear model parameters on the user mobility pattern in O-RAN in the context of user groups classification

The paper presents the results of a study on the influence of user mobility patterns in the O-RAN (Open Radio Access Net-work) on the parameters of a multi-linear model. The considered multi-linear model is represented as three-dimensional tensors that describe the changes of received signal power or SINR (Signal-to-Interference-plus-Noise Ratio). Factor matrices after the CP (Canonycal Polyadic) decomposition are exploited to separate users by their mobility using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. A model and a set of scenarios are developed for simulating the O-RAN segments and to study the influence of the user mobility on the parameters of the multi-linear model. Simulation results indicate that the number of users in groups with different mobility patterns does not affect the parameters of the multi-linear model and the performance of the DBSCAN algorithm. For the considered scenarios, the best classification results are achieved when the order of the multi-linear model equal to three. Moreover, it is revealed that the topology of a network segment effects on the order of a multi-linear model.

Energy-Efficient 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU) Using a Hybrid OGASCDASA-SAGMDEBROA Scheduling Algorithm

In fifth-generation (5G) networks, the deployment of heterogeneous networks (HetNets) with macrocells layered over tiny cells is considered to be a practical solution to handle the growing demand for mobile traffic. The deployment of a significant count of small cell base stations (BSs) results in a notable rise in energy consumption. In this manuscript, Energy-Efficient (EE) 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU), using Hybrid online green algorithm-based sleep scheduling and cost-efficient deadline-aware Scheduling Algorithm (OGASCDASA), are proposed. Here, the energy efficiency optimization issue is in the downlink of two-tier Heterogeneous Cloud Radio Access Networks (H-CRAN) by lowering micro and pico cells. Hybrid OGASCDASA is proposed for reducing remote radio side energy use when maintaining coverage and Quality of service (QoS) of H-CRAN. At the cloud side Baseband Units (BBU), hybrid Simulated Annealing with the Gaussian Mutation and Distortion Equalization algorithm with Battle Royale optimization is proposed to reduce the energy consumption of BBU by decreasing the count of BBU servers. The proposed EE-HCRAN-Hybrid OGASCDASA-SAGMDEBROA method attains 20.48%, 27.34%, and 32.24% higher throughput and 28.30%, 17.30%, and 32.94% lower delay compared to the existing models, such as heterogeneous computational resource allocation for NOMA (HCRA-NOMA-GMECS), energy-efficient hierarchical resource sharing in uplink-downlink decoupled NOMA heterogeneous networks (EE-HRA-NOMA-HetNet), energy-aware hierarchical resource management with backhaul traffic optimization in heterogeneous cellular networks (EA-HRM-BTO-HCN), respectively.

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the userlevel. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ns-O-RAN</i> , a software framework that integrates a real-world, production-grade near- RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps and automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)- driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative Q-learning (CQL) algorithm, combined with a state-of-the-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.