Beam Discovery Using Linear Block Codes for Millimeter Wave Communication Networks

Shabara, Yahia; Köksal, C. Emre; Ekici, Eylem

doi:10.1109/tnet.2019.2923395

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…However, the existing KM learning method relying on SDRwR to solve (10) suffers from a high computational complexity (O(D 4.5 )) and is thereby difficult to be applied to large-scale antenna-array systems [22], [26]. In particular, the LCQP subproblem in (9), which can be solved by the FW algorithm at the negligible cost of searching for the minimum of an array (O(D)), has been well-studied [24], while resolving the BQP subproblem in (10) introduces a major computational bottleneck. This calls for more efficient KM learning methods, which we will present in Section IV.…”

Section: ) Selectionmentioning

confidence: 99%

“…The hierarchical codebooks, which typically consist of a small number of low-resolution wide beams at the upper layer of the codebook and a large number of high-resolution narrow beams at the lower layer of the codebook, were proposed [1], [6], [7]. Other methods fallen into the same ''structured beam alignment'' paradigm include beam coding [8], [9], overlapped beam patterns [10], [11], and compressed sensing-based algorithms [12]- [17]. Despite a battery of such beam alignment techniques, there still remains a challenge of further reducing the beam training overhead especially when the mobility and link blockage are considered.…”

Section: Introductionmentioning

confidence: 99%

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KM Learning for Millimeter-Wave Beam Alignment and Tracking: Predictability and Interpretability

2021

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A data representation technique dubbed Kolmogorov model (KM), has been applied to the beam alignment problem in large-dimensional antenna systems. The previous learning-based beam alignment solely focused on utilizing the predictive power of KM, i.e., the capability of predicting the outcome of random variables that are outside the training set, to reduce the beam training overhead. However, a distinctive feature of KM, namely, the interpretability which enables the capability of extracting additional information hidden inside the data, has not yet been exploited. Moreover, the prohibitively high computational complexity of the existing KM learning algorithm offsets the benefits brought by KM and hampers its application to large-scale problems. In this paper, we propose a joint beam alignment and tracking framework by incorporating the predictability and interpretability of KM. Especially, our proposed scheme enables a novel interpretable beam tracking that reveals insights on relations among the sounded observations to alleviate the beam sounding overhead after the initial beam alignment. To reduce the computational complexity of KM learning, two enhancement approaches, based on discrete monotonic optimization (DMO) and dual optimization, respectively, are proffered. Numerical results demonstrate that the proposed methods can reduce the computational cost of the existing KM learning algorithm by up to three orders of magnitude. Furthermore, the proposed methods show superior beam alignment and tracking performance over other state-of-the-art techniques, notably in the low signal-to-noise ratio (SNR) regime.

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Section: ) Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

KM Learning for Millimeter-Wave Beam Alignment and Tracking: Predictability and Interpretability

2021

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“…. , D, can be equivalently rewritten as (9), where g and h are increasing on R D + . This completes the proof.…”

Section: A Discrete Monotonic Optimizationmentioning

confidence: 99%

“…The use of directional narrow beams for searching the entire beam space (also called exhaustive beam search) is an extremely time-consuming operation; the exhaustive beam search has been used in existing mmWave WiFi standards including IEEE 802.15.3c [3] and IEEE 802.11ad [4], for example. For reduced overhead beam alignment, hierarchical codebooks [2], [5], compressed sensing-based algorithms [6], [7], overlapped beam pattern [8] and beam coding [9] have been proposed over the years, establishing a "structured beam alignment" paradigm. Despite a plethora of such beam alignment methods, the overhead issue still remains a critical challenge in mmWave communications.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model

Duan

Kim

Ghauch

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based criterion, called Kolmogorov model (KM), previously applied to the beam alignment problem. Unlike the previous KM, whose computational complexity is not scalable with the size of the problem, a new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. We also present a Kolmogorov-Smirnov (KS) criterion for the advanced hypothesis testing, which does not require any subjective threshold setting compared to the frequency estimation (FE) method developed for the conventional KM. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave beam alignment are presented.

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“…Second, it allows more signal power to be propagated from a transmitter (TX) to a receiver (RX). For the latter reason, large MIMO transceivers have emerged as the prominent solution to solve the severe path loss problem in millimeter-wave (mmWave) systems [2], [3].…”

Section: Introductionmentioning

confidence: 99%

How Long to Estimate Sparse MIMO Channels

Shabara¹,

Koksal²,

Ekici³

2021

Preprint

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Large MIMO transceivers are integral components of next-generation wireless networks. However, for such systems to be practical, their channel estimation process needs to be fast and reliable. Although several solutions for fast estimation of sparse channels do exist, there is still a gap in understanding the fundamental limits governing this problem. Specifically, we need to better understand the lower bound on the number of measurements under which accurate channel estimates can be obtained. This work bridges that knowledge gap by deriving a tight asymptotic lower bound on the number of measurements. This not only helps develop a better understanding for the sparse MIMO channel estimation problem, but it also provides a benchmark for evaluating current and future solutions.

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Beam Discovery Using Linear Block Codes for Millimeter Wave Communication Networks

Cited by 9 publications

References 34 publications

KM Learning for Millimeter-Wave Beam Alignment and Tracking: Predictability and Interpretability

KM Learning for Millimeter-Wave Beam Alignment and Tracking: Predictability and Interpretability

Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model

How Long to Estimate Sparse MIMO Channels

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