WOS2 – Radio access networks in 5G and beyond

Thursday, 8 June 2023, 11:00-12:30, Room J2

Session Chair: Lena Wosinska (Chalmers University of Technology, Sweden)

Phase Modulation-Based Fronthaul Network for 5G mmWave FR-2 Signal Transmission over Hybrid Links
Marta Botella-Campos (Universitat Politècnica de València, Spain); Jan Bohata (Czech Technical University in Prague, Czech Republic); Luis Vallejo (Universitat Politecnica de Valencia, Spain); Jose Mora (Universidad Politécnica de Valencia, Spain); Stanislav Zvanovec (Czech Technical University in Prague, Czech Republic); Beatriz Ortega (ITEAM Research Institute, Spain)
In this paper, we experimentally demonstrate the feasibility of a phase modulation (PM)-based analog optical mobile fronthaul network for seamless millimeter wave (mmWave) signal transmission over hybrid standard single-mode fiber (SSMF), free space optics (FSO) and radio links. As a proof of concept, a 64- QAM LTE signal has been transmitted over 25 GHz in a 10 km SSMF, 2 m FSO and 1 m radio link without the need for optical amplifiers. After fiber propagation, non-significant signal degradation has been obtained over the wireless links, with an estimated sensitivity level of received electrical power -45.8 dBm. Despite the advantages of PM-based approaches, the use of optical filtering in our system provides easy scalability by including dense wavelength division multiplexing (DWDM) schemes. Also, the comparison with direct intensity modulation (IM)- based approach allows to assess PM-approach as a promising solution for seamless mmWave signal transmission over future networks.

Power Allocation for Multi-Cell Non-Orthogonal Multiple Access Networks: Energy Efficiency vs. Throughput vs. Power Consumption
Syllas R. C. Magalhães, Suzan Bayhan and Geert Heijenk (University of Twente, The Netherlands)
The pressing need for more energy-efficient networks requires understanding the trade-offs maintained by emerging technologies that are expected to help serve an increasing number of connected devices and meet their rate requirements. While spectral efficiency is typically a key performance indicator, hence used for optimal resource allocation, energy efficiency and power consumption of a wireless network should also be considered while deciding on the potential adoption of a new technology. In this paper, we focus on non-orthogonal multiple access (NOMA) as it is considered as a candidate radio access scheme due to its promise to improve spectral efficiency. With a goal of understanding whether joint transmission offers benefits over conventional NOMA, we investigate the performance of joint-transmission NOMA and NOMA considering three objectives: throughput maximization (SumRate), energy efficiency maximization (EE), and power minimization (minP). Different from the literature, we incorporate a power consumption model that accounts for the overhead introduced by successive interference cancellation that is necessary to distinguish the intended signal of a NOMA receiver from the interfering signals aimed for other users in the same cluster. After formulating the optimal power allocation problems, we present our solution steps to make the original problems convex for solving them optimally. Our numerical analysis shows that, for the studied two-cell scenario, joint-transmission offers a benefit only in terms of finding a feasible power allocation while NOMA fails in more cases irrespective of the considered objective. Additionally, our investigation of trade-offs between the investigated problems shows orders of magnitude difference in energy efficiency and throughput for small variations in power consumption.

Dynamic Resource Allocation for URLLC in UAV-Enabled Multi-Access Edge Computing
Marcos Falcão (Federal University of Pernambuco, Brazil); Caio Bruno Souza (Universidade Federal de Pernambuco, Brazil); Andson M Balieiro and Kelvin Lopes Dias (Federal University of Pernambuco, Brazil)
In the context of Ultra-reliable Low Latency Communications (URLLC), the concepts of Multi-access Edge Computing (MEC), Network Function Virtualization (NFV), and Unmanned Aerial Vehicle (UAV) emerge as complementary paradigms that shall offer fine-grained on-demand distributed resources closer to the User Equipment (UE) and strong Line-ofSight (LoS) paths between UAV and ground transmission nodes. However, compromise between onboard computation resource allocation and the URLLC requirements becomes challenging since UAVs are limited due to their size, weight, and power, and the virtualization adds extra overhead, which imposes a burden on the conventional Network Functions (NFs). This work proposes a NFV-MEC over UAV model based on Continuoustime Markov Chain (CTMC), with an embedded virtual resource scaling scheme for dynamic resource allocation (DRA). It also extensively analyzes the NFV-MEC architecture’s virtualization layer, including node availability and power consumption, besides the URLLC conflicting reliability and latency metrics. The designed model allows analyzing how the main underlying virtualization parameters impact the critical services in a single NFV-MEC over a UAV node, assisting the network operator in proper node dimensioning and configuration.

On Employing Deep Learning to Enhance the Performance of 5G NR Two Step RACH Procedure
Siba Narayan Swain and Ashit Subudhi (Indian Institute of Technology Dharwad, India)
To meet the latency requirements of various business usecases and applications in 5G New Radio (NR), two step grantfree RACH procedure has been proposed in Third Generation Partnership Project (3GPP) release 16 for granting access to subscribers. However, due to the limited number of preambles, there is a non-zero probability that two mobile User Equipments (UEs) selecting same preamble signatures leading to collisions. Consequently, the base stations (gNBs) in 5G Radio Access Network (RAN) are unable to send a response to the UEs. Furthermore, with the increase in the number of cellular UEs and Machine Type Communication (MTC) devices, the probability of such preamble collisions further increases, thereby leading to reattempts by UEs. This in turn, results in increased latency and reduced channel utilization. In order to reduce contention during preamble access, we propose to use deep learning based models to design a RACH procedure that predicts the incoming connection requests in advance and proactively allocates uplink resources to UEs. We have used Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) models to predict UEs which are going to participate in two step RACH procedure. On doing extensive simulations, it is observed that both RNN and LSTM models perform equally good in reducing the number of collisions in a dense user scenario thereby enabling massive user access to 5G network.

User-Centric Network Architecture Design for 6G Mobile Communication Systems
Xueqiang Yan (Huawei Technologies, China); Xueli An (Huawei Technologies, Germany); Wenxuan Ye (Technical University of Munich, Germany); Mingyu Zhao, Yan Xi and Jianjun Wu (Huawei Technologies Co., Ltd., China)
In conventional mobile communications systems, network services are designed to serve a huge amount of subscribers simultaneously, which is normally called a networkcentric design approach. In comparison, this paper aims to investigate the user-centric design approach, which refers to systems that are designed to be user-defined, user-configurable and user-controllable. The user-centric approach allows for dedicated network services to be provided at the granularity of the user. A novel User-Centric Network (UCN) architecture is proposed in this work, which includes key design principles, corresponding network elements as well as procedures. It is envisioned that UCN is distributed in nature by leveraging enabling technologies like Distributed Ledger Technology (DLT) and Distributed Hash Table (DHT). In this way, UCN not only provides extreme customization by offering fine-grained services, but also enables autonomous and trusted data control and privacy protection. A simulation platform is developed to verify the feasibility of the architecture, and to preliminarily evaluate its performance by numerical results in terms of hop count, bandwidth consumption, latency, success ratio and scalability.

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