RAS2: RAN and Network Management

Thursday, 10 June 2021, 16:00-17:30, Zoom Room

Session Chair: Nuno Almeida (Univ. Porto, Portugal)

ML-Based Slice Management in 5G Networks for Emergency Scenarios

Apoorva Arora (KPN BV, The Netherlands); Toni Dimitrovski and Remco Litjens (TNO, The Netherlands); Haibin Zhang (TNO ICT, The Netherlands)
This study proposes a two-step ML-based multi-slice radio resource allocation framework for 5G networks, specifically for emergency scenarios and featuring a good tradeoff between complexity and performance. In the first step, call-level resource demands are predicted using supervised ML, which are then aggregated to predict slice-specific resource demands. An innovative method is included in this step to ensure the collection of representative training data for the supervised ML. In the second step, a contextual multi-armed bandit reinforcement learning model is applied to derive the resource allocation among the slices based on the slice-specific resource demand predictions. The simulation results show that the proposed framework outperforms alternative solutions in the defined utility values for priority emergency traffic at the cost of modest performance sacrifice of the background traffic.

 

Equilibrium Analysis in Wireless Networks Walrasian Markets: A Distributed Approach

Vahid Haghighatdoost (Shahed University, Iran); Siavash Khorsandi (Amirkabir University of Technology, Iran); Zaheer Khan (University of Oulu, Finland); Hamed Ahmadi (University of York, United Kingdom (Great Britain))
A Walrasian Market can be modeled as a distributed system consisting a set of independent buyers and sellers. The Walrasian equilibrium theorem proves the existence of the optimal price that results in the market clean state or Walrasian Equilibrium where the sum of absolute excess demand is zero. It is proved that finding this equilibrium price is an NP-hard problem. In this paper, we present an efficient distributed control-theoretic approach for finding the Walrasian equilibrium in an exchange economy. We have modeled the price adjustment process as a closed-loop control system where the sum of absolute excess demand is measured as the system error that is fed to commodity moderators in a distributed schema simultaneously, and then each commodity moderator adjusts the price of its related commodity. We devised a controller algorithm with low complexity and fast convergence that iteratively moves the error value to zero. The proposed scheme, finds the equilibrium price and Pareto efficient allocation without knowing the shape of user utility functions or their preferences. It is scalable and is usable for exchange economies with multiple goods and many types of users.

 

Flexible Multi-Operator RAN Sharing: Experimentation and Validation Using Open Source 4G/5G Prototype

Maya Kassis (Telecom Sudparis, France); Salvatore Costanzo and Mohamad Yassin (Orange Labs, France)
In this paper, we present an open source Radio Access Network (RAN) sharing prototype, based on Open Air Interface (OAI) platform, which employs slicing features provided by an open source RAN controller, known as FlexRAN. Accordingly, this paper analyzes the benefits of employing network slicing in the RAN to introduce more flexibility in the configuration of RAN sharing architectures. The contribution of this paper is twofold. Firstly, we propose a flexible RAN sharing architecture where a specific radio slice is allocated to each operator that shares the same RAN, while considering specific Service Level Agreement (SLA) constraints. Secondly, we validate the proposed architecture via a simple radio resource allocation algorithm, which enables dynamic creation and configuration of radio slices, making it possible to transform on-the-fly a “Multi-Operator Core Network” (MOCN) sharing scenario, wherein the spectrum is shared by multiple operators, into a “Multi-Operator RAN” (MORAN) scenario, wherein the spectrum is isolated among the operators that share the same RAN. Emulation results show that the employment of the proposed algorithm enables a more flexible allocation of the radio resources among the sharing operators, providing better performance in terms of end-to-end latency and throughput compared to a static RAN sharing approach.

 

SDN-Enabled THz Wireless X-Haul for B5G

Jose Costa-Requena (Aalto University, Finland); Nicola Carapellese (SIAE Microelettronica, Italy); Panteleimon-Konstantinos Chartsias, Eleni Karasoula and Dimitrios S. Kritharidis (Intracom Telecom, Greece); Eduardo. Yusta Padilla (Telefonica, Spain); Abraham Afriyie (Cumucore, Finland)
With the explosive data growth of user traffic in wireless communications, Terahertz (THz) frequency band is envisioned as a promising candidate to support ultra-broadband communications for beyond fifth generation (5G) networks. Software-based networking is being adopted in mobile communications to improve efficiency and reduce operational costs. This paper presents the design of a comprehensive SDN management architecture for joint optimization of radio and network resources. The proposed architecture obtains the most added value out of the use of THz technology integrated with software managed networking for mobile network beyond 5G. In this paper, leveraging optical concepts and photonic integration techniques for an ultra-broadband and ultra-wideband wireless system is presented.

 

A KPI-Based Self-Optimization Algorithm for Inter-Frequency Handover in 4G/5G Networks

Marco Skocaj (University of Bologna & WiLab, CNIT, Italy); Andrea Orsi and Federico Franchini (TIM, Italy); Roberto Verdone (University of Bologna, Italy)
Inter-Frequency Handover (IFHO) is the procedure accounting for the handover of User Equipment (UE) from a serving frequency layer to another. Its proper functioning depends on a plethora of parameters’ settings and it is one of the most important aspects affecting the overall UE-perceived performance. The proposed work tackles an optimization procedure for IFHO in Long Term Evolution (LTE)/5G networks. A two-step algorithm based on network Key Performance Indicators (KPIs) processing is designed and presented in detail. Performance assessment is based on the capability of the algorithm to increase network throughput while avoiding degrading behaviors such as ping-pong effects, augmented inter-Radio Access Technology (RAT) handovers, and outage conditions. The algorithm was tested on TIM’s network infrastructure over the period August 2020 – January 2021 and it turned out to considerably increase performance with respect to an approach based on standard parameters setting.