User-Centric Cooperative Transmissions-Enabled Handover for Ultra-Dense Networks

Kibinda, Nyaura; Ge, Xiaohu

doi:10.1109/tvt.2022.3141455

Cited by 20 publications

(4 citation statements)

References 57 publications

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“…Improved performance thus can be achieved with the UE-based approach we propose, enabled by the O-RAN architecture. User-centric handover schemes have been proposed for non-conventional radio access architectures, such as non-terrestrial networks [31,32], ultra-dense networks with cooperative transmissions [33][34][35], cell-free massive MIMO [35,36], and vehicular networks [37]. These solutions, however, on non-standard 3GPP information and/or parameters that are inaccessible at the RAN side (e.g., the user position and speed).…”

Section: Dual Connectivity and Traffic Steeringmentioning

confidence: 99%

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Lacava

Polese

Sivaraj³

et al. 2024

IEEE Trans. on Mobile Comput.

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5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an "open" architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the userlevel. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducing ns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps and automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative Q-learning (CQL) algorithm, combined with a state-ofthe-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.

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Section: Dual Connectivity and Traffic Steeringmentioning

confidence: 99%

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Lacava

Polese

Sivaraj³

et al. 2024

IEEE Trans. on Mobile Comput.

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“…Nonetheless, increasing the number of UEs increases the interference level, and this reduces the capacity of the system. Generally, to alleviate interference, current coordination systems are established concerning partitioning the resources depending on frequency-domain, time-domain, power domain, and spatial domain either on the UE side or network side, or a control combination between them [131]. On the other hand, overhead signaling is required for cell coordination.…”

Section: Related Work In Udnsmentioning

confidence: 99%

Interference Challenges and Management in B5G Network Design: A Comprehensive Review

et al. 2022

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Beyond Fifth Generation (B5G) networks are expected to be the most efficient cellular wireless networks with greater capacity, lower latency, and higher speed than the current networks. Key enabling technologies, such as millimeter-wave (mm-wave), beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), Device-to-Device (D2D), Relay Node (RN), and Heterogeneous Networks (HetNets) are essential to enable the new network to keep growing. In the forthcoming wireless networks with massive random deployment, frequency re-use strategies and multiple low power nodes, severe interference issues will impact the system. Consequently, interference management represents the main challenge for future wireless networks, commonly referred to as B5G. This paper provides an overview of the interference issues relating to the B5G networks from the perspective of HetNets, D2D, Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs). Furthermore, the existing interference mitigation techniques are discussed by reviewing the latest relevant studies with a focus on their methods, advantages, limitations, and future directions. Moreover, the open issues and future directions to reduce the effects of interference are also presented. The findings of this work can act as a guide to better understand the current and developing methodologies to mitigate the interference issues in B5G networks.

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“…Background R ECENTLY, there has been a proliferation of user equipment (UE) for communication purposes [1]- [3]. The gains in high-throughput, low latency, high QoS, massive connections, and high reliability in 4G and 5G mobile communications [4], [5] have occasioned wireless entry points (or simply put access points (APs)) and a fixed transceiver/base stations (BSs) being installed at an increased rate [6]. With the advent of 5G networks, it is expected that the operating frequency could extend into the millimeter wave band range (i.e., 28GHz − 39GHz) to meet the extreme bandwidth requirements of uses such as online games, high-quality streaming media, and interactive gaming.…”

Section: Introductionmentioning

confidence: 99%

Radiofrequency electromagnetic radiation exposure assessment, analysis, computation, and minimization technique in 5G networks: A perspective on QoS trade-offs

Ajibare,

Oladejo,

Ekwe

et al. 2023

SAIEE Afr. Res. J.

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In this study, the specific absorption rate (SAR) and exposure index (EI) of access points (APs) and user equipment (UEs) in fourth-generation (4G) and fifth-generation (5G) wireless technologies are examined with regard to the effects of exposure to radiofrequency (RF) electromagnetic fields (EMF) radiation and the implications of their reduction. We characterize the EI using a classical mathematical method while considering the power density, the SAR, the electric field strength, and the tissue's density and conductivity. As such, a novel exposure-index open-loop power control algorithm is proposed to evaluate the realistic RF-EMF radiation exposure on human users from both the downlink (DL) and uplink (UL) communication devices. To solve an EI minimization problem using the open-loop power control algorithm, we formulate it in the form of a mixedinteger nonlinear programming (MINLP) problem. As the energy capacity (i.e., power density) in wireless networks determines the radiation exposure (SAR and EI), it minimizes the EI by controlling and managing the transmitted and received powers under the restrictions of Quality of Service (QoS), interference, and power, while ensuring the users' QoS requirements are met. Our proposed scheme is numerically compared to other heuristic algorithms and exposure limits established by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and other similar organizations. Lastly, we compare the emissions from 4G and 5G networks to the emissions from UL and DL transmissions. Our simulation findings indicate that our proposed technique is a good alternative. Our assessment, in terms of numerical results and evaluation, also verifies that the exposures are bearable, fall within the recommended limits, and are minimized without impairing the users' QoS.

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User-Centric Cooperative Transmissions-Enabled Handover for Ultra-Dense Networks

Cited by 20 publications

References 57 publications

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Interference Challenges and Management in B5G Network Design: A Comprehensive Review

Radiofrequency electromagnetic radiation exposure assessment, analysis, computation, and minimization technique in 5G networks: A perspective on QoS trade-offs

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