A Comprehensive Investigation of Beam Management Through Conventional and Deep Learning Approach

5G spectrum uses cutting-edge technology which delivers high data rates, low latency, increased capacity, and high spectrum utilization. To cater to these requirements various technologies are available such as Multiple Access Technology (MAT), Multiple Input Multiple Output technology (MIMO), Millimetre (mm) wave technology, Non-Orthogonal Multiple Access Technology (NOMA), Simultaneous Wireless Information and Power Transfer (SWIPT). Of all available technologies, mmWave is prominent as it provides favorable opportunities for 5G. Millimeter-wave is capable of providing a high data rate i.e., 10 Gbit/sec. Also, a tremendous amount of raw bandwidth is available i.e., around 250 GHz, which is an attractive characteristic of the mmWave band to relieve mobile data traffic congestion in the low frequency band. It has a high frequency i.e., 30 – 300 GHz, giving very high speed. It has a very short wavelength i.e., 1-10mm, because of this it provides the compact size of the component. It will provide a throughput of up to 20 Gbps. It has narrow beams and will increase security and reduce interference. When the main beam of the transmitter and receiver are not aligned properly there is a problem in ideal communication. To solve this problem beam management is one of the solutions to form a strong communication link between transmitter and receiver. This paper aims to address challenges in beam management and proposes a framework for realization. Towards the same, the paper initially introduces various challenges in beam management. Towards building an effective beam management system when a user is moving, various steps are present like beam selection, beam tracking, beam alignment, and beam forming. Hence the subsequent sections of the paper illustrate various beam management procedures in mmWave using conventional methods as well as using deep learning techniques. The paper also presents a case study on the framework's implementation using the above-mentioned techniques in mmWave communication. Also glimpses on future research directions are detailed in the final sections. Such beam management techniques when used for mmWave technology will enable build fast, efficient, and capable 5G networks

A Survey of Beam Management for mmWave and THz Communications Towards 6G

Communication in millimeter wave (mmWave) and even terahertz (THz) frequency bands is ushering in a new era of wireless communications. Beam management, namely initial access and beam tracking, has been recognized as an essential technique to ensure robust mmWave/THz communications, especially for mobile scenarios. However, narrow beams at higher carrier frequency lead to huge beam measurement overhead, which has a negative impact on beam acquisition and tracking. In addition, the beam management process is further complicated by the fluctuation of mmWave/THz channels, the random movement patterns of users, and the dynamic changes in the environment. For mmWave and THz communications toward 6G, we have witnessed a substantial increase in research and industrial attention on artificial intelligence (AI), reconfigurable intelligent surface (RIS), and integrated sensing and communications (ISAC). The introduction of these enabling technologies presents both open opportunities and unique challenges for beam management. In this paper, we present a comprehensive survey on mmWave and THz beam management. Further, we give some insights on technical challenges and future research directions in this promising area.Comment: accepted by IEEE Communications Surveys & Tutorial

B5G 초고밀도 네트워크에서 유연한 이동성 관리 기법

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 박세웅.차세대 모바일 이동통신을 위한 새로운 서비스와 어플리케이션이 등장함에 따라, 사용자들은 기존 시스템 대비 더 높은 전송 속도를 요구한다. 더불어 사용자들은 높은 데이터 전송 속도를 안정적으로 보장 받기를 원한다. 모바일 네트워크 사용자의 데이터 트래픽이 증가할수록 네트워크의 밀집도를 증가시키는 초고밀도 네트워크 (ultra-dense network)에 대한 관심이 높아지고 있다. 그러나 이러한 초고밀도 네트워크 환경에서는 기존 네트워크 대비 핸드오버가 자주 발생하게 되고, 이로 인해 네트워크 성능이 제한되는 문제가 드러나고 있다. 따라서 초고밀도 네트워크의 성능을 극대화하기 위해서는 효율적인 이동성 관리의 중요성이 어느때보다 부각되고 있다. 본 학위논문에서는 초고밀도 네트워크 환경에서 효율적인 이동성 관리를 위하여 다음의 세 가지 전략을 고려한다. 1) MIAB (mobile integrated access and backhaul) 네트워크에서의 이동성 관리, 2) 딥러닝 기반 장애물 예측을 통한 사전적인 핸드오버, 3) 다중 연결 환경에서의 강인한 이동성 관리. 첫째로, MIAB 네트워크에서 사용자의 QoS (quality-of-service)에 심각한 영향을 미치는 핸드오버 지연 시간을 감소시키기 위한 핸드오버 기법을 제안한다. MIAB 네트워크 환경에서 발생할 수 있는 intra-gNB 핸드오버, inter-gNB 핸드오버, 부모 MIAB 노드 핸드오버의 세 가지 핸드오버 케이스를 분류하고, 부모 MIAB 노드와 자녀 MIAB 노드의 속도에 따른 각 핸드오버 케이스 발생 확률 모델을 제시하였다. 또한, 상향링크 컨트롤 플레인 데이터 전송 지연 시간을 분석하였다. 제안하는 핸드오버 기법은 저지연 상향링크 컨트롤 플레인 데이터 전송 기법과 자녀 MIAB 노드의 RACH-less 핸드오버 기법을 포함한다. 시뮬레이션을 통해 제안하는 MIAB 핸드오버 기법이 기존 핸드오버 기법 대비 핸드오버 지연 시간 및 오버헤드 성능보다 매우 뛰어난 것을 확인하였다. 다음으로 안정적인 이동성 관리를 위한 장애물 예측 기반의 사전 핸드오버 (BAPH) 기법을 제시한다. BAPH는 딥러닝 기법을 활용하여 장애물과 차량의 이동성을 예측하고, 특정 차량이 기지국과 LoS (line-of-sight)에 있는지를 예측한다. 예측된 차량의 미래 위치와 장애물의 미래 위치 정보를 이용하여 gNB와 RLF (radio link failure)를 겪기 이전 시점에 다른 gNB로 핸드오버를 수행한다. 제안하는 사전 핸드오버 기법의 성능을 다양한 도로 환경 및 차량 속도를 반영한 시뮬레이션을 통해 평가하였다. 마지막으로 다중 연결 네트워크의 장점을 최대로 끌어내기 위한 안정적인 핸드오버 기법을 제안한다. 앵커 BS와 액티브 BS 집합이 각 모바일 기기마다 설정되는 사용자 중심 초고밀도 네트워크 (UUDN)을 고려한다. 앵커 BS는 주변 BS들의 RACH 오케스트레이션을 통하여 모바일 기기가 핸드오버를 위해 전송하는 RACH 프리앰블을 다수의 BS가 받을 수 있도록 한다. 또한 핸드오버 과정에서 하나의 액티브 BS에 RLF가 발생하는 경우 핸드오버 과정을 지속하여 빠르게 RLF를 복구하는 기법을 제안한다. 시뮬레이션을 통해 제안하는 핸드오버 기법이 RLF 지속 시간을 현재 핸드오버 기법 대비 줄이는 것을 확인하였으며, 연결 수가 증가하는 경우에 RLF 지속 기간을 더 감소시키는 것을 확인하였다. 요약하면, 차세대 모바일 네트워크에서 이동성 관리와 관련된 새로운 네트워크 구조 및 프로토콜에 대한 문제를 제기한다. 현재 이동성 관리 기법이 고이동성 환경이나 초고밀도 네트워크 환경에서 네트워크의 성능을 저하시키는 것을 확인하였다. 따라서 차세대 모바일 네트워크 시스템에서의 새로운 이동성 관리 기법 및 전략을 제안한다. 제안하는 모든 이동성 관리 기법은 시뮬레이션 결과를 통해 성능을 평가되었다.As new services and applications for next-generation mobile communication emerge, users of mobile communication systems require a higher data rate than the existing system. In addition, mobile communication users want a high data rate to be reliably guaranteed anytime, anywhere. As data traffic of mobile network users increases, ultra-dense networks (UDN) that increase network density are in the limelight. However, in such a UDN environment, handovers (HOs) occur more frequently than in the existing mobile network system, which limits network performance. Therefore, to maximize the performance of the UDN, the importance of efficient mobility management is being emphasized more than ever. In this dissertation, the following three strategies are considered for efficient mobility management in the UDN environment: 1) Mobility management in mobile integrated access and backhaul (MIAB) networks, 2) Proactive HO through blockage prediction based on deep learning technology, 3) Reliable HO using the anchor node in the multi-connectivity environment. First, we propose a novel handover (HO) scheme for the MIAB network to reduce handover interruption time (HIT) and radio link failure (RLF) that have a significant impact on users' quality of service (QoS). We investigate HO cases that cover intra-gNB HO, inter-gNB HO, and parent MIAB node HO and develop their probabilistic models according to the velocities of the parent MIAB node and the child MIAB node. In addition, we investigate the latency in uplink (UL) control plane (CP) data transmission and each HO case for the baseline MIAB network. Our proposed HO scheme consists of low-latency UL CP data transmission with semi-persistent resource pre-allocation and RACH-less HO procedure for child MIAB nodes. Through simulation, we verify our proposed MIAB HO scheme outperforms the baseline HO scheme in terms of HO delay and HO overhead. Second, we propose the blockage-aware proactive HO (BAPH) scheme to support reliable mobility management. BAPH leverages a deep neural network (DNN) to predict the mobility of blockages and which BSs will be in the line-of-sight (LoS) with the vehicle device. With the predicted future blockage locations, the network supports proactive HO to the target gNB before radio link failure (RLF) occurs with the current serving gNB. We evaluate the performance of the proposed proactive HO scheme through simulations in various road environments. Finally, a reliable HO scheme that fully leverages the advantage of the multi-connectivity network is proposed. We consider a user-centric UDN (UUDN) architecture composed of an anchor BS and active BSs set for each UE. The anchor BS orchestrates the RACH of neighbor BSs that are not included in the active BS set for the target UE so that multiple target BSs can receive the RACH preamble transmitted by the UE. In addition, we propose a fast RLF recovery scheme that allows the existing HO process to continue when RLF occurs in the serving BS included in the active BS set. Through simulation, the performance of the proposed HO scheme is verified that the RLF duration of the UE is reduced compared to the current HO scheme even when the number of connections increases in a multi-connectivity environment. In summary, we claim issues in the new network architecture and protocols for the next-generation mobile networks related to mobility management. We demonstrate that the existing mobility management schemes limit the network performance in high-mobility environments and UDN environments. Therefore, we propose new mobility management schemes and strategies for the future mobile network system. All the proposed mobility management schemes are evaluated with simulation results.1 Introduction 1 1.1 Vision of B5G and Challenges 1 1.1.1 Vision of B5G Networks and Services 1 1.1.2 Research Trends and Challenges 2 1.2 Motivation 3 1.3 Main Contributions 5 1.3.1 Mobile Integrated Access and Backhaul Handover 5 1.3.2 Blockage Prediction-based Proactive Handover 6 1.3.3 Mobility Management in Distributed User-centric Ultra-dense Network 7 1.4 Organization of the Dissertation 7 2 Mobility Management of Multi-hop Mobile Integrated Access and Backhaul Network 9 2.1 Introduction 9 2.1.1 Contributions 11 2.1.2 Organization 12 2.2 Preliminary Study 12 2.2.1 IAB Network and Moving Cells 12 2.2.2 5G NR Handover 13 2.2.3 Motivation 14 2.3 System Model 15 2.3.1 Network Model 15 2.3.2 Communication Model 15 2.3.3 Directional Beamforming Model 16 2.3.4 Handover Model 18 2.4 Analysis of Mobility Management in MIAB Networks 18 2.4.1 UL CP Data Transmission Latency 19 2.4.2 Handover Latency 21 2.4.3 Handover Probability 23 2.5 Proposed Mobile IAB Handover Scheme 27 2.5.1 Low-Latency UL CP Data Transmission Scheme 27 2.5.2 Inter-gNB Handover Scheme 29 2.6 Performance Evaluation 31 2.6.1 HO Probability 33 2.6.2 Handover Latency 34 2.6.3 Effective Spectral Efficiency 37 2.7 Summary 41 3 Blockage-aware Proactive Handover for mmWave V2I Communications 42 3.1 Introduction 42 3.2 Preliminaries and Motivation 44 3.2.1 Effect of Blockages in mmWave Systems 44 3.2.2 DNN based Network Prediction 46 3.2.3 Motivation 46 3.3 Analysis on Blockage Effect 47 3.4 Proposed BAPH System 51 3.4.1 System Architecture 51 3.4.2 Communication Model 51 3.4.3 Deep Learning-based Location Prediction 52 3.4.4 Unobserved Blockage Detection and Estimation 54 3.4.5 Proactive Handover Process 57 3.6 Performance Evaluation 58 3.7 Summary 62 4 Anchor Node Based Reliable Handover in User-centric Ultra-dense Network 64 4.1 Introduction 64 4.2 Motivation 65 4.2.1 HO Process in 5G NR 65 4.2.2 HO Failure in the Baseline HO Scheme 66 4.3 Proposed Distributed User-centric Ultra-Dense Network Architecture 69 4.4 Anchor Node-based Mobility Management 71 4.5 Performance Evaluation 73 4.6 Summary 75 5 Concluding Remarks 76 5.1 Research Contributions 76 5.2 Future Research Directions 77 Abstract (In Korean) 85박

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance

The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mobile scenarios has become a paramount challenge. Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages and implications. Notably, the study presents an impressive 53% improvement in signal-to-noise ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and Zero forcing (ZF) techniques, alongside deep neural networks (DNN) employing Bayesian Optimization (BO), represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino, in enabling location-aware beamforming

MIMO beam selection in 5G using neural networks

In this paper, we consider the cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple-input-multiple-output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user equipment to reduce latency in beam/cell identification. Due to high path-loss in mmWave systems, the beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, a narrow beam must be formed. Thus, the number of possible beam orientations and consequently time needed for the discovery increases significantly when a random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars, and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptableperformance for practical applications. This finding can be useful for user devices with limited processing power