Radio Access Network Research Articles

Alarm Webs: A Framework for Decoding RAN Alarm Dynamics

This paper introduces “Alarm Webs”, a pioneering framework designed to decode the dynamics of Radio Access Network (RAN) alarms, utilizing a novel graph-theoretic approach. Leveraging directed graphs (digraphs), this work uniquely encodes RAN alarm data, with each alarm represented as a node and its severity indicated by distinct colors. The edges of these graphs signify the sequence of alarms, creating a directional flow from one alarm to the next. Utilizing a comprehensive dataset from a major telecommunications service provider in the UK, encompassing alarms from 4G and 5G networks across over 7,000 base stations, the framework ofers an unprecedented analysis of alarm patterns and interactions. The proposed methodology involves the calculation of in-degrees and out-degrees for each node, identifying those with in-degrees higher than four as significant indicators of commonly resulting alarms. This threshold-based selection is instrumental in isolating the most impactful alarms. Additionally, the application of Sankey diagrams brings an intuitive visualization of alarm causality, based on the in-degree centrality of the nodes. This approach not only clarifies the complex interrelationships among alarms but also aids in identifying potential cascading issues within the network. By effectively mapping alarm sequences and identifying key alarms, this framework paves the way for enhanced network reliability and proactive maintenance strategies in the ever-evolving telecommunications landscape.

Coverage and Data Rate Analysis for a Novel Cell-Sweeping-Based RAN Deployment

Adequate and uniform network coverage provision is one of the main objectives of cellular service providers. Additionally, the densification of cells exacerbates coverage and service provision challenges, particularly at the cell-edges. In this paper, we present a new approach of cell-sweeping-based Base Stations (BSs) deployments in cellular Radio Access Networks (RANs) where the coverage is improved by enhancing the cell-edge performance. In essence, the concept of cell-sweeping rotates/sweeps the sectors of a site in azimuth continuously/discretely resulting in near-uniform distribution of the signal-to-interference-plus-noise ratio (SINR) around the sweeping site. This paper investigates the proposed concept analytically by deriving expressions for the PDF/CDF of SINR and achievable rate; and with the help of system-level simulations, it shows that the proposed concept can provide throughput gains of up to 125% at the cell-edge. Then, using a link-budget analysis, it is shown that the maximum allowable path loss (MAPL) increases by 2.1 dB to 4.1 dB corresponding to the gains in wideband SINR and post-equalized SINR, respectively. This increase in MAPL can be translated to cell-radius/area with the help of the Okumura-Hata propagation model and results in cell-coverage area enhancement by 30% to 66% in a Typical Urban cell deployment scenario.

Expected Policy Gradient for Network Aggregative Markov Games in Continuous Space.

In this article, we investigate the Nash-seeking problem of a set of agents, playing an infinite network aggregative Markov game. In particular, we focus on a noncooperative framework where each agent selfishly aims at maximizing its long-term average reward without having explicit information on the model of the environment dynamics and its own reward function. The main contribution of this article is to develop a continuous multiagent reinforcement learning (MARL) algorithm for the Nash-seeking problem in infinite dynamic games with convergence guarantee. To this end, we propose an actor-critic MARL algorithm based on expected policy gradient (EPG) with two general function approximators to estimate the value function and the Nash policy of the agents. We consider continuous state and action spaces and adopt a newly proposed EPG to alleviate the variance of the gradient approximation. Based on such formulation and under some conventional assumptions (e.g., using linear function approximators), we prove that the policies of the agents converge to the unique Nash equilibrium (NE) of the game. Furthermore, an estimation error analysis is conducted to investigate the effects of the error arising from function approximation. As a case study, the framework is applied on a cloud radio access network (C-RAN) by modeling the remote radio heads (RRHs) as the agents and the congestion of baseband units (BBUs) as the dynamics of the environment.

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-1 Approach

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-1 Approach

Recent Trends in Cloud Radio Access Networks

Recent Trends in Cloud Radio Access Networks

Communication-Computing Built-in-Design in Next-Generation Radio Access Networks: Architecture and Key Technologies

Communication-Computing Built-in-Design in Next-Generation Radio Access Networks: Architecture and Key Technologies

Access Points Cooperation Based Secretive Coded Caching in Fog Radio Access Networks

Access Points Cooperation Based Secretive Coded Caching in Fog Radio Access Networks

A Survey on NB-IoT Random Access: Approaches for Uplink Radio Access Network Congestion Management

A Survey on NB-IoT Random Access: Approaches for Uplink Radio Access Network Congestion Management

Low-Complexity Coding Techniques for Cloud Radio Access Networks

Low-Complexity Coding Techniques for Cloud Radio Access Networks

Proactive Mobility Load Balancing through Interior-point Policy Optimization for Open Radio Access Networks

Proactive Mobility Load Balancing through Interior-point Policy Optimization for Open Radio Access Networks

A Survey on AI-Driven Energy Optimization in Terrestrial Next Generation Radio Access Networks

A Survey on AI-Driven Energy Optimization in Terrestrial Next Generation Radio Access Networks

Optimizing the Energy Efficiency Using Quantum Based Load Balancing in Open Radio Access Networks

Optimizing the Energy Efficiency Using Quantum Based Load Balancing in Open Radio Access Networks

Waste Factor and Waste Figure: A Unified Theory for Modeling and Analyzing Wasted Power in Radio Access Networks for Improved Sustainability

Waste Factor and Waste Figure: A Unified Theory for Modeling and Analyzing Wasted Power in Radio Access Networks for Improved Sustainability

Mixed Numerology-Based Intelligent Resource Management in a Sliced 6 G Space–Terrestrial Integrated Radio Access Network

Mixed Numerology-Based Intelligent Resource Management in a Sliced 6 G Space–Terrestrial Integrated Radio Access Network

Leveraging Massive MIMO Over mmWave Fronthaul Link for Throughput Maximization in Cloud Radio Access Networks

Leveraging Massive MIMO Over mmWave Fronthaul Link for Throughput Maximization in Cloud Radio Access Networks

On-Demand Coordinated Spectrum and Resource Provisioning Under an Open C-RAN Architecture for Dense Small Cell Networks

Mobile networks have seen more than a thousand times traffic increases in the past decade. Network operators face an ever increasing demand and cost to deploy denser networks in order to maintain the quality of services. In view of this paradigm shift, an open cloud radio access network (C-RAN) service model is studied in this work. Integrating the cross-layer functions of spectrum resource sharing, channel and power allocation, and interference management in a C-RAN architecture, we explore the feasibility of providing a scalable yet efficient broadband wireless service with dense small cell networks (DSN). The proposed methods and architecture can be applied to DSN that support the functions of (further enhanced) inter-cell interference cancelation (feICIC/ICIC) techniques of 3GPP standard, and have the potential to provide a cost-effective solution for high-quality DSN. Simulation results across an area of 100 km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> show that the proposed scheme can offer an aggregate downlink throughput of 21 Gbps over a maximum channel bandwidth of 20 MHz, which is 5 times the throughput with a typical ICIC method. Moreover, the service satisfaction degree can reach 85% under the proposed C-RAN architecture, making it a promising and cost-effective solution for future broadband wireless services.

Communication-Efficient Federated Deep Reinforcement Learning based Cooperative Edge Caching in Fog Radio Access Networks

Communication-Efficient Federated Deep Reinforcement Learning based Cooperative Edge Caching in Fog Radio Access Networks

5G-CT: Automated Deployment and Over-the-Air Testing of End-to-End Open Radio Access Networks

5G-CT: Automated Deployment and Over-the-Air Testing of End-to-End Open Radio Access Networks

UAV-Assisted Heterogeneous Cloud Radio Access Network With Comprehensive Interference Management

UAV-Assisted Heterogeneous Cloud Radio Access Network With Comprehensive Interference Management

Artificial Intelligence and Energy Efficiency of 5G Radio Access Network

Purpose: This paper is a pioneering study that investigates the integration of Artificial Intelligence (AI) to enhance energy efficiency in 5G Radio Access Networks (RANs). This paper aims to identify AI-driven strategies that can significantly optimize energy consumption in the rapidly evolving 5G network infrastructure, which is essential for meeting the increasing demand for high-speed connectivity.&#x0D; Methodology: The methodology used for this research is a detailed review and analysis of the 5G RAN architecture and its energy dynamics, alongside the exploration of AI applications in optimizing network operations. The study focuses on AI techniques such as resource allocation, traffic prediction, adaptive sleep modes, and fault detection, proposing a holistic approach to energy management in 5G networks. A key contribution of this research is its in-depth examination of AI's role in 5G energy efficiency, highlighting its practical implications and potential for future applications. The paper offers novel insights into the implementation of AI in real-world 5G scenarios and addresses the challenges in transitioning from theoretical models to practical solutions.&#x0D; Findings: The findings reveal that AI integration is a vital step towards reducing the environmental footprint of 5G networks, with AI-based solutions showing promise in enhancing efficiency beyond the inherent capabilities of current 5G technologies. Despite many AI applications being in nascent stages, their potential impact on energy efficiency is significant.&#x0D; Unique contributor to theory, policy and practice: This paper is a valuable guide for researchers, industry professionals, and policymakers in telecommunications and environmental sustainability. It provides a clear roadmap for leveraging AI in 5G networks, emphasizing the synergy between technological innovation and ecological responsibility.