Browsing by Subject "mmWave"

Now showing 1 - 4 of 4

Capacity and coverage of mmWave ad hoc networks
(2014-05) Thornburg, Andrew Scott; Heath, Robert W., Jr, 1973-
Ad hoc networks provide a flexible, infrastructure-free means to communicate between soldiers in war zones, aid workers in disaster areas, or consumers in device-to-device (D2D) applications. Ad hoc networks, however, are stilled plagued by interference. Communication with millimeter-wave (mmWave) devices offers hope to ad hoc networks through higher bandwidth, reduced interference due to directional antennas, and a lighter interference field due to blockage. This report uses a stochastic geometry approach to characterize the one-way and two-way coverage probability of a mmWave ad hoc network with directional antennas and random blockages. The coverage probability in the presence of noise and both line-of-sight and non-line-of-sight interference is analyzed and used to derive the transmission capacity. Several reasonable simplifications are used to derive the transmission capacity. Performance of mmWave is then analyzed in terms of area spectral efficiency and rate coverage. The results show that mmWave networks support larger densities, higher area spectral efficiencies, and better rate coverage compared to microwave ad hoc networks.
Machine learning-assisted mmWave beam management
(2022-12-02) Heng, Yuqiang; Andrews, Jeffrey G.; Evans, Brian L.; Dimakis, Alex; Kim, Hyeji; Chandrasekhar, Vikram
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.
Millimeter wave and massive MIMO communications for next-generation wireless systems
(2016-12) Alkhateeb, Ahmed Abulkareem Nageeb Youssef; Heath, Robert W., Jr, 1973-; Andrews, Jeffrey G.; Leus, Geert; Nikolova, Evdokia; Shakkottai, Sanjay
Multiple-input multiple-output (MIMO) communication is expected to play a central role in future wireless systems through the deployment of a large number of antennas at the transmitters and receivers. In low-frequency systems, massive MIMO offers high multiplexing gains that boost system spectral efficiency. In millimeter wave (mmWave) systems, the deployment of large antenna arrays at both the base station and mobile users is necessary to guarantee sufficient received signal power. Realizing these systems in practice, however, requires addressing several key challenges: (i) fully-digital solutions are costly and power hungry, (ii) channel training and estimation process has high overhead, and (iii) precoders design optimization problems are non-trivial. In this dissertation, precoding and channel estimation strategies that address these challenges are proposed for both mmWave and massive MIMO systems. The proposed solutions adopt hybrid analog/digital architectures that divide precoding/combining processing between RF and baseband domains and lead to savings in cost and power consumption. Further, the developed techniques leverage the structure and characteristics of mmWave and massive MIMO channels to reduce the training overhead and precoders design complexity. The main contributions of this dissertation are (i) developing a channel estimation solution for hybrid architecture based mmWave systems, exploiting the sparse nature of the mmWave channels, (ii) designing hybrid precoding algorithm for multi-user mmWave and massive MIMO systems, (iii) proposing a multi-layer precoding framework for massive MIMO cellular systems, and (iv) developing hybrid precoding and codebook solutions for frequency selective mmWave systems. Mathematical analysis as well as numerical simulations illustrate the promising performance of the proposed solutions, marking them as enabling technologies for mmWave and massive MIMO systems.
Modeling and analyzing the evolution of cellular networks using stochastic geometry
(2017-05) Li, Yingzhe, Ph. D.; Andrews, Jeffrey G.; Baccelli, F. (François), 1954-; de Veciana, Gustavo; Heath, Robert W; Novlan, Thomas D
The increasing complexity of cellular network due to its continuous evolution has made the conventional system level simulations time consuming and cost prohibitive. By modeling base station (BS) and user locations as spatial point processes, stochastic geometry has recently been recognized as a tractable and efficient analytical tool to quantify key performance metrics. The goal of this dissertation is to leverage stochastic geometry to develop an accurate spatial point process model for the conventional homogeneous macro cellular network, and to address the design and analysis challenges for the emerging cellular networks that will explore new spectrum for cellular communications. First, this dissertation proposes to use the repulsive determinantal point processes (DPPs) as an accurate model for macro BS locations in a cellular network. Based on three unique computational properties of the DPPs, the exact expressions of several fundamental performance metrics for cellular networks with DPP configured BSs are analytically derived and numerically evaluated. Using hypothesis testing for various performance metrics of interest, the DPPs are validated to be more accurate than the Poisson point process (PPP) or the deterministic grid model. Then the focus of this dissertation shifts to emerging networks that exploit new spectrum for cellular communications. One promising option is to allow the centrally scheduled cellular system to also access the unlicensed spectrum, wherein a carrier sensing multiple access with collision avoidance (CSMA/CA) protocol is usually used, as in Wi-Fi. A stochastic geometry-based analytical framework is developed to characterize the performance metrics for neighboring Wi-Fi and cellular networks under various coexistence mechanisms. In order to guarantee fair coexistence with Wi-Fi, it is shown that the cellular network needs to adopt either a discontinuous transmission pattern or its own CSMA/CA like mechanisms. Next, this dissertation considers cellular networks operating in the millimeter wave (mmWave) band, where directional beamforming is required to establish viable connections. Therefore, a major design challenge is to learn the necessary beamforming directions through the procedures that establish the initial connection between the mobile user and the network. These procedures are referred to as initial access, wherein cell search on the downlink and random access on the uplink are the two major steps. Stochastic geometry is again utilized to develop a unified analytical framework for three directional initial access protocols under a high mobility scenario where users and random blockers are moving with high speed. The expected delay for a user to succeed in initial access, and the average user-perceived downlink throughput that accounts for the initial access overhead, are derived for all three protocols. In particular, the protocol that has low beam-sweeping overhead during cell search is found to achieve a good trade-off between the initial access delay and user-perceived throughput performance. Finally, in contrast to the high mobility scenario for initial access, the directional cell search delay in a slow mobile network is analyzed. Specifically, the BS and user locations are fixed for long period of time, and therefore a strong temporal correlation for SINR is experienced. A closed-form expression for the expected cell search delay is derived, indicating that the expected cell search delay is infinite for noise-limited networks (e.g., mmWave) whenever the non-line-of-sight path loss exponent is larger than 2. By contrast, the expected cell search delay for interference-limited networks is proved to be infinite when the number of beams to search at the BS is smaller than a certain threshold, and finite otherwise.