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Register a new userEfficient Beamforming for Mobile mmWave Networks
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Added by
Sara Khosravi
Publication date
2019
fields
Electronic Engineering
and research's language is
English
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We design a lightweight beam-searching algorithm for mobile millimeter-wave systems. We construct and maintain a set of path skeletons, i.e., potential paths between a user and the serving base station to substantially expedite the beam-searching process. To exploit the spatial correlations of the channels, we propose an efficient algorithm that measures the similarity of the skeletons and re-executes the beam-searching procedure only when the old one becomes obsolete. We identify and optimize several tradeoffs between: i) the beam-searching overhead and the instantaneous rate of the users, and ii) the number of users and the update overhead of the path skeletons. Simulation results in an outdoor environment with real building map data show that the proposed method can significantly improve the performance of beam-searching in terms of latency, energy consumption and achievable throughout.
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Future millimeter-wave (mmWave) systems, 5G cellular or WiFi, must rely on highly directional links to overcome severe pathloss in these frequency bands. Establishing such links requires the mutual discovery of the transmitter and the receiver %in the angular domain potentially leading to a large latency and high energy consumption. In this work, we show that both the discovery latency and energy consumption can be significantly reduced by using fully digital front-ends. In fact, we establish that by reducing the resolution of the fully-digital front-ends we can achieve lower energy consumption compared to both analog and high-resolution digital beamformers. Since beamforming through analog front-ends allows sampling in only one direction at a time, the mobile device is on for a longer time compared to a digital beamformer which can get spatial samples from all directions in one shot. We show that the energy consumed by the analog front-end can be four to six times more than that of the digital front-ends, depending on the size of the employed antenna arrays. We recognize, however, that using fully digital beamforming post beam discovery, i.e., for data transmission, is not viable from a power consumption standpoint. To address this issue, we propose the use of digital beamformers with low-resolution analog to digital converters (4 bits). This reduction in resolution brings the power consumption to the same level as analog beamforming for data transmissions while benefiting from the spatial multiplexing capabilities of fully digital beamforming, thus reducing initial discovery latency and improving energy efficiency.
The use of millimeter wave (mmWave) spectrum for commercial wireless communications is expected to offer data rates in the order of Gigabits-per-second, thus able to support future applications such as Vehicle-to-Vehicle or Vehicle-to-Infrastructure communication. However, especially in urban settings, mmWave signal propagation is sensitive to blockage by surrounding objects, resulting in significant signal attenuation. One approach to mitigate the effect of attenuation is through multi-hop communication with the help of relays. Leveraging the unique characteristics of the mmWave medium, we consider a single-source/destination $2$-hop system, where a cluster of spatially distributed and reconfigurable relays is used to cooperatively amplify-and-forward the source signal to the destination. Our system evolves in time slots, during which not only are optimal beamforming weights centrally determined, but also future relay positions for the subsequent time slot are optimally selected, jointly maximizing the expected signal-to-interference+noise ratio at the destination. Optimal predictive relay positioning is achieved by formulating a 2-stage stochastic programming problem, which is efficiently approximated via a conditional sample-average-approximation surrogate, and solved in a purely distributed fashion across relays. The efficacy of the proposed near-optimal positioning policy is corroborated by comparison against a randomized relay positioning policy, clearly confirming its superiority.
While millimeter wave (mmWave) communications promise high data rates, their sensitivity to blockage and severe signal attenuation presents challenges in their deployment in urban settings. To overcome these effects, we consider a distributed cooperative beamforming system, which relies on static relays deployed in clusters with similar channel characteristics, and where, at every time instance, only one relay from each cluster is selected to participate in beamforming to the destination. To meet the quality-of-service guarantees of the network, a key prerequisite for beamforming is relay selection. However, as the channels change with time, relay selection becomes a resource demanding task. Indeed, estimation of channel state information for all candidate relays, essential for relay selection, is a process that takes up bandwidth, wastes power and introduces latency and interference in the network. We instead propose a unique, predictive scheme for resource efficient relay selection, which exploits the special propagation patterns of the mmWave medium, and can be executed distributively across clusters, and in parallel to optimal beamforming-based communication. The proposed predictive scheme efficiently exploits spatiotemporal channel correlations with current and past networkwide Received Signal Strength (RSS), the latter being invariant to relay cluster size, measured sequentially during the operation of the system. Our numerical results confirm that our proposed relay selection strategy outperforms any randomized selection policy that does not exploit channel correlations, whereas, at the same time, it performs very close to an ideal scheme that uses complete, cluster size dependent RSS, and offers significant savings in terms of channel estimation overhead, providing substantially better network utilization, especially in dense topologies, typical in mmWave networks.
The concept of reconfigurable intelligent surface (RIS) has been proposed to change the propagation of electromagnetic waves, e.g., reflection, diffraction, and refraction. To accomplish this goal, the phase values of the discrete RIS units need to be optimized. In this paper, we consider RIS-aided millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems for both accurate positioning and high data-rate transmission. We propose an adaptive phase shifter design based on hierarchical codebooks and feedback from the mobile station (MS). The benefit of the scheme lies in that the RIS does not require deployment of any active sensors and baseband processing units. During the update process of phase shifters, the combining vector at the MS is also sequentially refined. Simulation results show the performance improvement of the proposed algorithm over the random design scheme, in terms of both positioning accuracy and data rate. Moreover, the performance converges to exhaustive search scheme even in the low signal-to-noise ratio regime.
Switch-based hybrid network is a promising implementation for beamforming in large-scale millimetre wave (mmWave) antenna arrays. By fully exploiting the sparse nature of the mmWave channel, such hybrid beamforming reduces complexity and power consumption when compared with a structure based on phase shifters. However, the difficulty of designing an optimum beamformer in the analog domain is prohibitive due to the binary nature of such a switch-based structure. Thus, here we propose a new method for designing a switch-based hybrid beamformer for massive MIMO communications in mmWave bands. We first propose a method for decoupling the joint optimization of analog and digital beamformers by confining the problem to a rank-constrained subspace. We then approximate the solution through two approaches: norm maximization (SHD-NM), and majorization (SHD-QRQU). In the norm maximization method, we propose a modified sequential convex programming (SCP) procedure that maximizes the mutual information while addressing the mismatch incurred from approximating the log-determinant by a Frobenius norm. In the second method, we employ a lower bound on the mutual information by QR factorization. We also introduce linear constraints in order to include frequently-used partially-connected structures. Finally, we show the feasibility, and effectiveness of the proposed methods through several numerical examples. The results demonstrate ability of the proposed methods to track closely the spectral efficiency provided by unconstrained optimal beamformer and phase shifting hybrid beamformer, and outperform a competitor switch-based hybrid beamformer.
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