RAS (Session1) 

Thursday, 9 June 2022, 10:30-12:00, Room A228

Session Chair: Mickael Maman (CEA-Leti Minatec Campus, France)

Throughput Analysis of Network Coding in Grant-Free Transmission with K-Repetition

Jie Ding and Jinho Choi (Deakin University, Australia)
In machine-type communication (MTC), devices may have a message resulting in multiple data packets to deliver in grant-free random access (GFRA). While employing K-repetition is a simple way to improve transmission reliability, its resource utilization is inflexible and the throughput of devices can be significantly compromised. To address this issue, the notion of network coding can be exploited. In this paper, we focus on the throughput analysis and comparison between the proposed network coding based and conventional K-repetition schemes. In particular, we first derive closed-form expressions for the error probabilities and throughput of both the schemes and then provide an analytical comparison to demonstrate the
performance superiority of the network coding based scheme. Simulations validate our analysis under various system setups. Exemplary results show that compared to the conventional K-repetition scheme, the network coding based scheme can improve the throughput by 65% on the basis of saving 33% slot resources.

Toward URLLC with Proactive HARQ Adaptation

Lam Ngoc Dinh (CEA – LETI, France); Labriji Ibtissam (Renault Software Labs, France); Mickael Maman (CEA-Leti Minatec Campus, France); Emilio Calvanese Strinati (CEA-LETI, France)
In this work, we propose a dynamic decision maker algorithm to improve the proactive HARQ protocol for beyond 5G networks.
Based on Lyapunov stochastic optimization, our adaptation control framework dynamically selects the number of proactive retransmissions for intermittent URLLC traffic scenarios under time-varying channel conditions without requiring any prior knowledge associated with this stochastic process. It then better exploits the trade-off between end-to-end latency, reliability and resource efficiency, which is still limited in its realization on current HARQ designs. We then evaluate the performance of several HARQ strategies and show that our proposal further improves end-to-end latency over the reactive regime without affecting the resource efficiency such as fixed proactive retransmission while maintaining target reliability.

Optimal Intra-Frame Sensing Interval in IEEE 802.22 WRAN Multi-Class Systems

Islam S. Abdelfattah (Alexandria University, Egypt); Sherif I. Rabia (Egypt-Japan University of Science and Technology (E-JUST), Egypt); Ahmed Hassan Abd El-Malek (Egypt-Japan University for Science and Technology (E-JUST), Egypt)
In this paper, we consider a cognitive radio network with multi-class customer premises equipment (CPE) devices having different data rate requirements. According to the IEEE 802.22 standard, the base station needs to schedule what is called intra-frame sensing process in order to determine the state of the primary users (PUs) channels, where the interval between two consecutive processes is called “sensing interval”. We propose an optimization model based on the partially observable Markov decision process (POMDP) to determine the optimal sensing interval length. Solving this optimization model gives a common sensing interval length for all CPEs in the case of busy channel and assigns to each CPEs its own sensing interval length according to its data rate requirement in the case of idle channel. The numerical results show that the proposed model provides better balancing between approaching the required data rates and protecting the PU compared to the conventional models that apply fixed sensing interval length.

Towards Closed-Loop Automation in 5G Open RAN: Coupling an Open-Source Simulator with xApps

Theofanis Karamplias and Sotirios Spantideas (National and Kapodistrian University of Athens, Greece); Anastasios E. Giannopoulos (National and Kapodistrian University of Athens, Greece & National Technical University of Athens, Greece); Panagiotis Gkonis and Nikolaos Kapsalis (National and Kapodistrian University of Athens, Greece); Panagiotis Trakadas (University of Athens, Greece)
The goal of this paper is to demonstrate the implementation of technological solutions that will enable the optimization of 5G network resources and services in an automated and self-configured manner. At first, the practical implementation of intelligence loops in the 5G network architecture is presented, according to the O-RAN specifications. Then, the development of an open source, general-purpose simulator, compliant with 3GPP specifications for generating physical-layer measurement reports from the radio access network is presented, while its functional logic and configuration capabilities are fully highlighted. Moreover, this paper illustrates how effectively trained machine learning (ML) models can be incorporated in the architecture for network configuration and optimization. In this context, an indicative use case is presented and evaluated, focusing on closed loop power adjustment of the transmitters in a 5G cellular orientation, via the appropriate deployment of a deep reinforcement learning agent. The simulations results outline the interaction loop between the developed 5G simulator and the deployed ML model, targeting at increasing the network-wide throughput of user equipment.

VNF Lifecycle Evaluation Study for Virtualized FeMBMS

Alvaro Gabilondo and Zaloa Fernandez (Vicomtech, Spain); Angel Martin (Vicomtech-IK4, Spain); Jon Montalban and Pablo Angueira (University of the Basque Country, Spain)
Network Function Virtualization (NFV) is the core technology to boost the digital transformation of 5G networks. However, in order to achieve the promising agility and efficiency of networks to respond in real-time to traffic demands, the performance of virtualization systems to deploy Virtual Network Functions (VNFs) on demand is crucial. One of the most relevant new 5G paradigms is the one that brings multicast and broadcast capabilities to mobile networks, allowing overlaying the network when unicast connections are not efficient in applications that transmit the same content to multiple end-users. This technology coming from 3rd Generation Partnership Project (3GPP) release 14 is Further evolved Multimedia Broadcast Multicast Services (FeMBMS). The capacity of the network to deploy such FeMBMS VNF to satisfy a dense demand of popular contents in a cell means a powerful technology to ensure the Quality of Service (QoS) for a big audience while preserving available resources for concurrent unicast traffic. In this paper, based on OpenAirInterface and the virtualization of its components, we study the performance when deploying FeMBMS VNF instances in different virtualization stacks, such as softwarization of the technology through Docker, the use of Kubernetes or the addition of Open Source Mano (OSM). At the same time, the potential of virtual Radio Access Network (vRAN) is analysed as a tool for the deployment of transmission services based on FeMBMS in 3GPP ecosystems. The results address the low impact of different levels of virtualization on the inner workings of the deployed applications, as well as the potential of vRAN as a tool for the deployment of FeMBMS-based broadcast services.

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