Contextual Multi-Armed Bandit based Beam Allocation in mmWave V2X Communication under Blockage

© 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/VTC2023-Spring57618.2023.10200248Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented

Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

Handover Management for Vehicular Communications in Dense and Directional mmWave Networks

In the 5G network, dense deployment of small cells and utilisation of millimetre wave (mmWave) band are some of the key approaches to boost network capacity. In such scenario, however, V2X (Vehicle to Vehicle and Vehicle to Infrastructure) communication systems are required to apply strict mobility management proceedings to reduce delay due to frequent handovers during attachment of different points. Moreover, dense deployment of mmWave small cells using narrow directional beams will escalate the cell and beam related handovers for high mobility of vehicles, which may in turn limits the performance gain promised by 5G-mmWave based vehicle-to infrastructure (V2I) communication. Thus, it is vitally important to suppress the frequent handovers in such networks. Handover reduction mechanisms are proposed in this thesis by identifying long-lasting connections. This thesis has three main contributions.End-to-end system-level simulations are conducted to evaluate the impact of mobility on performance metrics, such as communication latency and packet loss ratio, in densely deployed small cells Heterogeneous Network (HetNet). To analyse the impact, we develop a windowing mechanism to distinguish the packet transmission performance around the handover period from the whole connection duration to capture the actual impact. Thus, the findings state that the impact of mobility becomes more significant in dense networks due to frequent exposure to cell borders and handovers.As one of the key promising aspects of 5G for V2I is directional mmWave networks, frequent handovers even become more dominant due to small cell sizes and directional beam coverage in such networks. In this regard, an analytical model is proposed to find the theoretical upper-bound for vehicle sojourn time in a dense mmWave network. The theoretical upper-bound is developed to be employed as a benchmark for the performance of any practical design. Furthermore, we propose a Fuzzy Logic (FL) based beam-centric distributed algorithm to determine the beam among all visible beams where a vehicle can achieve the longest displacement within it. In this regard, we consider a densely deployed mmWave network and propose a handover decision to minimise the chance of handover events by selecting the beam offering the longest sojourn time when a vehicle travels across the network. State-of-the-art schemes, such as the strongest signal-based handover and Sticky Handover schemes are used to show the effectiveness of the proposed systems.With the emerging popularity of Machine Learning (ML) and Artificial Intelligence (AI) in cellular networks, a Deep Q-Network based beam-centric handover decision (DQN-BD) method is proposed. The DQN-BD adaptively finds optimal cell and beam to maximize effective beam connection time while minimizing handover related cost and beam misalignment time. Different from the previous work, the analytical model is revisited for a specific vehicular environment on the road section of highway with dynamic beam direction and extended to consider blockage probability

Profiling Vehicles for Improved Small Cell Beam-Vehicle Pairing Using Multi-Armed Bandit

The 5G technology has tapped into millimeter wave (mmWave) spectrum to create additional bandwidth for improved network capacity. The use of mmWave for specific applications including vehicular networks has widely discussed. However, applying mmWave to vehicular networks faces challenges of high mobility nodes and narrow coverage along the mmWave beams. In this paper, we focus on a mmWave small cell base station deployed in a city area to support vehicular network application. We propose profiling vehicle mobility for a machine learning agent to learn the performance of serving vehicles with different mobility profiles and utilize the past experiences to select appropriate mmWave beam to service a vehicle. Our machine learning agent is based on multi-armed bandit learning model, where classical multi-armed bandit and contextual multi-armed bandit are used. Particularly for the contextual multi-armed bandit, the contexts are vehicle mobility information. We show that the local street layout has naturally constrained vehicle movement creating distinct mobility information for vehicles, and the vehicle mobility information is highly related to communication performance. By using vehicle mobility information, the machine learning agent is able to identify vehicles that can remain within a beam for longer time period to avoid frequent handovers

Impact of Mobility on Communication Latency and Reliability in Dense HetNets

One of the cutting edge requirements envisioned for next-generation mobile networks is to support ultra-reliable and low latency communication (URLLC), as well as to meet massive traffic demand in the next few years. Although network densification has been considered as one of the promising solutions to boost capacity and high throughput, the impact of mobility on latency and reliability in dense networks has not been well investigated. Moreover, handovers, especially in dense networks, can cause extra delay to the communication and degrade reliability performance. In this paper, we aim to analyse the impact of different handover hysteresis parameters on the performance metrics, such as end-to-end delay and packet loss ratio (PLR). In this regard, we compare latency and PLR performance around cell borders including the handover process with the overall period of simulation. Simulation results show that the impact of mobility becomes more significant in dense networks due to frequent exposure to cell borders and handovers

Beam-based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

It is envisaged that 5G can enable many vehicular use cases that require high capacity, ultra-low latency and high reliability. To support this, 5G proposes the use of dense small cells technology as well as and highly directional mmWave systems deployment, among many other new advanced communication technologies, to boost the network capacity, reduce latency and provide high reliability. In such systems, enabling vehicular communication, where the nodes are highly mobile, requires robust mobility management techniques to minimise signalling cost and interruptions during frequent handovers. This presents a major challenge that communication system engineers need to address to realise the promise of 5G systems for V2X and similar applications. In this paper, we provide an overview of recent progresses in the development of handover and beam management techniques in 5G communication systems. We conduct a critical appraisal of current research on beam level and cell level mobility management in 5G mmWave networks considering the ultra-reliable and low-latency communication requirements within the context of V2X applications. We also provide an insight into the open challenges and the emerging trends as well as the possible evolution beyond the horizon of 5G

Beam-centric Handover Decision in Dense 5G-mmWave Networks

In 5G network, dense deployment and millimetre wave (mmWave) are some of the key approaches to boost network capacity. Dense deployment of mmWave small cells using narrow directional beams will escalate the cell and beam related handovers for high mobility of vehicles, which may in turn limits the performance gain promised by 5G. One of the research issues in mmWave handover is to minimise the handover needs by identifying long lasting connections. In this paper, we first develop an analytical model to derive the vehicle sojourn time within a beam coverage. When multiple connections offered by nearby all mmWave small cells are available when upon a handover event, we further derive the longest sojourn time among all potential connections which represents the theoretical upperbound limit of the sojourn time performance. We then design a Fuzzy Logic (FL) based distributed beam-centric handover decision algorithm to maximise vehicle sojourn time. Simulation experiments are conducted to validate our analytical model and show the performance advantage of our proposed FLbased solution when compared with commonly used approach of connecting to the strongest connection

The Accuracy of New and Aged Mechanical Torque Devices Employed in Five Dental Implant Systems

Purpose. Friction-style and spring-style torque wrenches are used to tighten implant abutments and prosthetic screws. The mechanical stability of these torque wrenches is crucial for the implant-abutment connection. The purposes of this study were to assess the performance of five brands (Straumann, Zimmer, Implant KA, Bredent, and Biohorizons) of wrench and to evaluate possible changes in applied torque values of aged wrenches. Materials and Methods. Five new and aged wrenches that had been used approximately 250 times in a 1-year period were tested. The torque applied by friction- and spring-style wrenches was measured with a specially designed strain gauge indicator. Descriptive statistics, the one-sample t-test, and the independent-samples t-test were used to analyze values obtained from all torque wrenches. Results. The accuracy of new and aged torque devices of all brands except Bredent differed significantly from the target values, but the mean values for aged and new wrenches did not differ significantly from each other (p > 0.05). Values for the spring- and friction-type torque wrenches deviated from the target values by 11.6% and 10.2%, respectively. Conclusion. The accuracy of aged torque wrenches is adequate for prosthetic screw tightening, but that of new torque wrenches is unsatisfactory and must be examined carefully before delivery