[PDF] Location Aided Beam Alignment And Handover In Millimeter Wave Networks eBook

Author : Peng Yang
Publisher : Springer Nature
Page : 169 pages
File Size : 40,13 MB
Release : 2021-10-27
Category : Computers
ISBN : 3030886301

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This book provides a comprehensive review and in-depth study on efficient beamforming design and rigorous performance analysis in mmWave networks, covering beam alignment, beamforming training and beamforming-aided caching. Due to significant beam alignment latency between the transmitter and the receiver in existing mmWave systems, this book proposes a machine learning based beam alignment algorithm for mmWave networks to determine the optimal beam pair with a low latency. Then, to analyze and enhance the performance of beamforming training (BFT) protocol in 802.11ad mmWave networks, an analytical model is presented to evaluate the performance of BFT protocol and an enhancement scheme is proposed to improve its performance in high user density scenarios. Furthermore, it investigates the beamforming-aided caching problem in mmWave networks, and proposes a device-to-device assisted cooperative edge caching to alleviate backhaul congestion and reduce content retrieval delay. This book concludes with future research directions in the related fields of study. The presented beamforming designs and the corresponding research results covered in this book, provides valuable insights for practical mmWave network deployment and motivate new ideas for future mmWave networking. This book targets researchers working in the fields of mmWave networks, beamforming design, and resource management as well as graduate students studying the areas of electrical engineering, computing engineering and computer science. Professionals in industry who work in this field will find this book useful as a reference.

Author : Yuqiang Heng
Publisher :
Page : 0 pages
File Size : 30,44 MB
Release : 2022
Category :
ISBN :

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Millimeter wave (mmWave) devices need to leverage highly directional beamforming (BF) to overcome the higher isotropic path loss. On the other hand, such narrow beams are sensitive to the propagation conditions including blockage and reflections. As a result, beam management - finding and maintaining good analog BF directions - is critical to enabling communication at the mmWave spectrum. This dissertation will focus on designing beam management solutions for mmWave systems that can find near-optimal beams with low overhead and latency. In the first part of this dissertation, a machine learning (ML)-aided beam alignment method is proposed where ML models are trained to predict candidate beams and serving base stations (BSs) using only the location information of user equipments (UEs) as context information. At the cost of only a small overhead in uplink feedback of a UE's coordinates through lower-frequency links, the proposed method can reduce the search space by approximately 4× for the optimal BS and over 10× for the optimal beam, even in a dynamic environment with imperfect UE coordinates. A dataset modeling a realistic, generalizable environment is created using a state-of-the-art commercial ray-tracing software and published to train and validate the ML models. To further enhance the ease of adoption without modifications to the existing cellular network standards, a 5G-compatible beam alignment method that uses a site-specific probing codebook to predict candidate beams is proposed in the second part of this dissertation. The probing codebook and the beam predictor are jointly trained with a novel neural network (NN) architecture. By sweeping a small learned codebook that is adapted to the propagation environment, the proposed NN beam predictor can accurately select the optimal narrow beam while reducing the beam sweeping overhead by as much as 14× in challenging non-line-of-sight scenarios. The third part of this dissertation further explores the idea of site-specific probing, and proposes a grid-free beam alignment approach that uses the measurements of a few probing beams to directly compute arbitrary BF weights for each UE from the continuous search space. The probing beams and the beam synthesizer functions are jointly trained in a novel deep learning pipeline so that UEs can both be discovered with high probability and achieve high BF gain. The proposed method is better than the exhaustive search by orders of magnitude in terms of the trade-off between signal-to-noise ratio (SNR) and beam alignment speed. It also improves upon the approach proposed in the second part by eliminating the per-UE search and achieving higher SNR than standard codebooks of narrow beams

Author : Wen Wu
Publisher :
Page : 133 pages
File Size : 26,74 MB
Release : 2019
Category : Beamforming
ISBN :

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To support increasing data-intensive wireless applications, millimeter-wave (mmWave) communication emerges as the most promising wireless technology that offers high data rate connections by exploiting a large swath of spectrum. Beamforming (BF) that focuses the radio frequency power in a narrow direction, is adopted in mmWave communication to overcome the hostile path loss. However, the distinct high directionality feature caused by BF poses new challenges: 1) Beam alignment (BA) latency which is a processing delay that both the transmitter and the receiver align their beams to establish a reliable link. Existing BA methods incur significant BA latency on the order of seconds for a large number of beams; 2) Medium access control (MAC) degradation. To coordinate the BF training for multiple users, 802.11ad standard specifies a new MAC protocol in which all the users contend for BF training resources in a distributed manner. Due to the "deafness" problem caused by directional transmission, i.e., a user may not sense the transmission of other users, severe collisions occur in high user density scenarios, which significantly degrades the MAC performance; and 3) Backhaul congestion. All the base stations (BSs) in mmWave dense networks are connected to backbone network via backhaul links, in order to access remote content servers. Although BF technology can increase the data rate of the fronthaul links between users and the BS, the congested backhaul link becomes a new bottleneck, since deploying unconstrained wired backhaul links in mmWave dense networks is infeasible due to high costs. In this dissertation, we address each challenge respectively by 1) proposing an efficient BA algorithm; 2) evaluating and enhancing the 802.11ad MAC performance; and 3) designing an effective backhaul alleviation scheme. Firstly, we propose an efficient BA algorithm to reduce processing latency. The existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which leads to significant latency. Thus, an efficient BA algorithm without search- ing the entire beam space is desired. Accordingly, a learning-based BA algorithm, namely hierarchical BA (HBA) algorithm is proposed which takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to iden- tify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm can effectively identify the optimal beam pair with low latency. Secondly, we analyze and enhance the performance of BF training MAC (BFT-MAC) in 802.11ad. Existing analytical models for traditional omni-directional systems are un- suitable for BFT-MAC due to the distinct directional transmission feature in mmWave networks. Therefore, a thorough theoretical framework on BFT-MAC is necessary and significant. To this end, we develop a simple yet accurate analytical model to evaluate the performance of BFT-MAC. Based on our analytical model, we derive the closed-form expressions of average successful BF training probability, the normalized throughput, and the BF training latency. Asymptotic analysis indicates that the maximum normalized throughput of BFT-MAC is barely 1/e. Then, we propose an enhancement scheme which adaptively adjusts MAC parameters in tune with user density. The proposed scheme can effectively improve MAC performance in high user density scenarios. Thirdly, to alleviate backhaul burden in mmWave dense networks, edge caching that proactively caches popular contents at the edge of mmWave networks, is employed. Since the cache resource of an individual BS can only store limited contents, this significantly throttles the caching performance. We propose a cooperative edge caching policy, namely device-to-device assisted cooperative edge caching (DCEC), to enlarge cached contents by jointly utilizing cache resources of adjacent users and BSs in proximity. In addition, the proposed caching policy brings an extra advantage that the high directional transmission in mmWave communications can naturally tackle the interference issue in the cooperative caching policy. We theoretically analyze the performance of DCEC scheme taking the network density, the practical directional antenna model and the stochastic information of network topology into consideration. Theoretical results demonstrate that the proposed policy can achieve higher performance in offloading the backhaul traffic and reducing the content retrieval delay, compared with the benchmark policy. The research outcomes from the dissertation can provide insightful lights on under- standing the fundamental performance of the mmWave networks from the perspectives of BA, MAC, and backhaul. The schemes developed in the dissertation should offer practical and efficient solutions to build and optimize the mmWave networks.

Author : Miguel Botto-Tobar
Publisher : Springer
Page : 390 pages
File Size : 40,25 MB
Release : 2018-10-17
Category : Technology & Engineering
ISBN : 3030028283

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This book constitutes the proceedings of the 6th Conference on Information Technologies and Communication of Ecuador “TIC-EC”, held in Riobamba City from November 21 to 23, 2018, and organized by Universidad Nacional del Chimborazo (UNACH) and its Engineering School, and the Ecuadorian Corporation for the Development of Research and Academia (CEDIA). Considered as one of the most important ICT conferences in Ecuador, it brought together international scholars and practitioners to discuss the development, issues and projections of the use of information and communication technologies in multiple fields of application. Presenting high-quality, peer-reviewed papers, the book discusses the following topics: • Communication networks • Software engineering • Computer sciences • Architecture • Intelligent territory management • IT management • Web technologies • ICT in education • Engineering, industry, and construction with ICT support • Entrepreneurship and innovation at the Academy: a business perspective The authors would like to express their sincere gratitude to the invited speakers for their inspirational talks, to the authors for submitting their work to this conference, and the reviewers for sharing their experience during the selection process.

Author : Vutha Va
Publisher :
Page : 400 pages
File Size : 34,87 MB
Release : 2018
Category :
ISBN :

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Millimeter wave (mmWave) has the potential to provide vehicles with high data rate communications that will enable a whole new range of applications. Its use, however, is not straightforward due to its challenging propagation characteristics. One approach to overcome the propagation challenge is the use of directional beams, but it requires a proper alignment and presents a challenging engineering problem, especially under the high vehicular mobility. In this dissertation, fast and efficient beam alignment solutions suitable for vehicular applications are developed. To better quantify the problem, first the impact of directional beams on the temporal variation of the channels is investigated theoretically. The proposed model includes both the Doppler effect and the pointing error due to mobility. The channel coherence time is derived, and a new concept called the beam coherence time is proposed for capturing the overhead of mmWave beam alignment. Next, an efficient learning-based beam alignment framework is proposed. The core of this framework is the beam pair selection methods that use side information (position in this case) and past beam measurements to identify promising beam directions and eliminate unnecessary beam training. Three offline learning methods for beam pair selection are proposed: two statistics-based and one machine learning-based methods. The two statistical learning methods consist of a heuristic and an optimal selection that minimizes the misalignment probability. The third one uses a learning-to-rank approach from the recommender system literature. The proposed approach shows an order of magnitude lower overhead than existing standard (IEEE 802.11ad) enabling it to support large arrays at high speed. Finally, an online version of the optimal statistical learning method is developed. The solution is based on the upper confidence bound algorithm with a newly introduced risk-aware feature that helps avoid severe misalignment during the learning. Along with the online beam pair selection, an online beam pair refinement is also proposed for learning to adapt the codebook to the environment to further maximize the beamforming gain. The combined solution shows a fast learning behavior that can quickly achieve positive gain over the exhaustive search on the original (and unrefined) codebook. The results show that side information can help reduce mmWave link configuration overhead.

Author : Michael Rodriguez (M. Eng.)
Publisher :
Page : 50 pages
File Size : 23,13 MB
Release : 2017
Category :
ISBN :

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Millimeter wave (mmWave) technologies promise to revolutionize wireless networks by enabling multi-gigabit data rates. However, they suffer from high attenuation, and hence have to use highly directional antennas to focus their power on the receiver. Existing radios have to scan the space to find the best alignment between the transmitter’s and receiver’s beams, a process that takes up to a few seconds. This delay is problematic in a network setting where the base station needs to quickly switch between users and accommodate mobile clients. This research encompasses the implementation and testing of Agile-link, the first mmWave beam steering system that is implemented and evaluated on phased arrays, and demonstrated to find the correct beam alignment without scanning the space. Instead of scanning, Agile-link hashes the beam directions using a few carefully chosen hash functions. It then identifies the correct alignment by tracking how the energy changes across different hash functions. Two major limitations are addressed in this research. First is the issue of delays in scanning and the second is the accuracy of the beams. Here we propose, implement and examine solutions to these two major issues. Our results show that not only does Agile-link create accurate phase shifted beams, but, it also reduces beam steering delay by orders of magnitude.