RAS1 – Innovations in Resource Management, Beamforming Strategies, and Architectural Enhancements for Next-Generation Mobile Networks
Tuesday, 4 June 2023, 11:00-13:00, room Pelican
Session Chair: Daniele Croce (University of Palermo, IT)
Beamforming and Functional Split Selection for Scalable Cell-Free mMIMO Networks
Adam Girycki and Md Arifur Rahman (IS-Wireless, Poland); Andrea Guevarra and Sofie Pollin (KU Leuven, Belgium)
The cell-free massive multiple-input multiple-output (CF mMIMO) spectral efficiency (SE) enhancement achieved through beamforming puts new requirements on the fronthaul interface load. At the same time, O-RAN is gaining traction thanks to standardized interfaces allowing for seamless integra- tion of radio access network (RAN) components from different vendors. We propose fronthaul load estimation formulas for CF mMIMO with 7-1 and O-RAN-defined 7-2 functional splits that differ in the location of the beamforming function, which is essential in the CF mMIMO networks. Additionally, we propose a methodology of dynamic beamforming and functional split selection to minimize the fronthaul load while maintaining high sum SE utilization.
Development and Assessment of Resource Management Solutions for Throughput Enhancement in a RIS-Aided Mobile Network
Sakshi Agarwal, Kallol Das and Remco Litjens (TNO, The Netherlands)
Reconfigurable Intelligent Surfaces (RIS) stand out among the key technologies driving 6G mobile network development. In this paper, we develop and assess radio resource management solutions aimed to exploit the potential of RIS deployments for coverage and throughput enhancement for indoor users in 6G mobile networks. We introduce two heuristic algorithms that jointly control the cell-RIS-user association, user scheduling, transmit beamforming and the RIS’s reflective configuration, and compare these algorithms against a RIS-free benchmark. Simulation results are presented to (i) demonstrate the promising potential of RIS deployments in multi-cell/multi-user scenarios; (ii) reveal the inherent trade-off between coverage and throughput enhancement; and (iii) show the performance impact of distinct RIS deployment locations. Our study provides valuable insights for efficiently leveraging RIS in evolving mobile network architectures.
BeGREEN Intelligent Plane for AI-Driven Energy Efficient O-RAN Management
Miguel Catalan-Cid and Jorge Pueyo Morillo (i2CAT Foundation, Spain); Juan Sanchez-Gonzalez (Universitat Politecnica de Catalunya (UPC), Spain); Jesús Gutiérrez (IHP – Leibniz-Institut für Innovative Mikroelektronik, Germany); Mir Ghoraishi (Gigasys Solutions, United Kingdom (Great Britain))
Cellular networks are undergoing a revolutionary transform with the advent of O-RAN architectures and AI/ML solutions. O-RAN’s Non-Real-Time and Near-Real Time RAN Intelligent Controllers open the door to the implementation of automated control-loops that can provide RAN optimisations in numerous scenarios and use cases, and which can be further empowered by AI-driven approaches. Energetic sustainability has raised as one of the main optimisations targets due to the impact of mobile networks on global energy consumption. To this end, the BeGREEN project aims at enhancing the energy efficiency of beyond 5G networks by defining novel AI/ML-based methods at RAN and edge infrastructure. This paper presents BeGREEN Intelligent Plane, a novel framework which implements and exposes AI/ML workflows to O-RAN-based optimisations targeting energy efficiency. We also describe an exemplary application of the Intelligent Plane and its AI Engine, which aims at providing AI-driven cell on/off control.
Crystal Oscillator Error Compensation in Software Defined Radios for 5G Network Testbeds
Stefano Mangione (Università di Palermo, Italy); Alessandra Dino (University of Palermo, Italy); Giovanni Garbo (Università di Palermo, Italy); Daniele Croce (University of Palermo, Italy)
While Software Defined Radios (SDR) feature superior flexibility and ease of use, their need for open-loop asynchronous sampling is also their main limitation, since compensating sampling rate errors incurs a significant complexity increase. In particular, a wireless cellular network Base Station is required a clock stability better than +/-0.05ppm, while the TCXO found in most SDRs feature a +/-2ppm stability. This paper proposes a simple error measurement and compensation technique for SDRs without external reference sources. First, in the measurement phase, a GPSDO-locked device is used as reference. Then, the error correction method is implemented by modification of the Universal Hardware Driver (UHD) for Ettus USRP devices. In order to validate our calibration technique, we tested it on several low cost B210 SDRs, and we were able to setup a stable 5G SA network without any external synchronization reference.
Performance Study of 5G Indoor Small Cells for Industrial MEC
Amy Sokhna Sidibé (University of Helsinki, Finland); Jose Costa-Requena (Aalto University, Finland); Saimanoj Katta (CUMUCORE OY, Finland)
Fifth-generation (5G) and the future 6G are targeted for industrial environments where reliability and low latency processing are required for automation of factories. Multi-Access Edge Computing (MEC) is the emerging paradigm expected to address those requirements. In this study we utilize an Open RAN (O-RAN) base station to conduct extensive performance measurements, primarily focusing on latency and bandwidth metrics. Industrial MEC use cases require the deployment of indoor small cells capable of providing low latency communications for small coverage. In this paper, we are interested in analyzing the effectiveness of a co-deployment of MEC with the Access Network as promised in the 3rd Generation Partnership Project (3GPP) standards [1]. Leveraging the iperf tool, we conduct measurements both within our custom MEC platform, collocated with the gNodeB (gNB), and externally against a public iperf server deployed in the cloud. By comparing these measurements, the paper provides valuable insights into the efficiency of O-RAN technology in industrial MEC environments, shedding light on its potential advantages and limitations. This empirical evaluation serves to inform future deployments and optimizations, contributing to the advancement of efficient and reliable edge computing solutions for industrial use cases.
Early Acceptance Matching Game for User-Centric Clustering in Scalable Cell-Free MIMO Networks
Ala Eddine Nouali (CEA-Leti, Grenoble, France); Mohamed Sana (CEA LETI Grenoble, France); Jean-Paul Jamont (Université Grenoble Alpes, France)
The canonical setup is the primary approach adopted in cell-free multiple-input multiple-output (MIMO) networks, in which all access points (APs) jointly serve every user equipment (UE). This approach is not scalable in terms of computational complexity and fronthaul signaling becoming impractical in large networks. This work adopts a user-centric approach, a scalable alternative in which only a set of preferred APs jointly serve a UE. Forming the optimal cluster of APs for each UE is a challenging task, especially, when it needs to be dynamically adjusted to meet the quality of service (QoS) requirements of the UE. This complexity is even exacerbated when considering the constrained fronthaul capacity of the UE and the AP. We solve this problem with a novel many-to-many matching game. More specifically, we devise an early acceptance matching algorithm, which immediately admits or rejects UEs based on their requests and available radio resources. The proposed solution significantly reduces the fronthaul signaling while satisfying the maximum of UEs in terms of requested QoS compared to state-of-the-art approaches.
Enhancing Cloud-Native Resource Allocation with Probabilistic Forecasting Techniques in O-RAN
Vaishnavi Kasuluru (Universitat Politècnica de Catalunya & Centre Tecnològic Telecomunicacions Catalunya, Spain); Luis Blanco (Centre Tecnològic de les Telecommunicacions de Catalunya (CTTC), Spain); Engin Zeydan (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Spain); Albert Bel (Centre Tecnològic de Telecomunicacions de Catalunya, Spain); Angelos Antonopoulos (Nearby Computing, Spain)
The need for intelligent and efficient resource provisioning for the productive management of resources in real-world scenarios is growing with the evolution of telecommunications towards the 6G era. Technologies such as Open Radio Access Network (O-RAN) can help to build interoperable solutions for the management of complex systems. Probabilistic forecasting, in contrast to deterministic single-point estimators, can offer a different approach to resource allocation by quantifying the uncertainty of the generated predictions. This paper examines the cloud-native aspects of O-RAN together with the radio App (rApp) deployment options. The integration of probabilistic forecasting techniques as a rApp in O-RAN is also emphasized, along with case studies of real-world applications. Through a comparative analysis of forecasting models using the error metric, we show the advantages of Deep Autoregressive Recurrent network (DeepAR) over other deterministic probabilistic estimators. Furthermore, the simplicity of Simple-Feed-Forward (SFF) leads to a fast runtime but does not capture the temporal dependencies of the input data. Finally, we present some aspects related to the practical applicability of cloud-native O-RAN with probabilistic forecasting.
