Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems

Han, Yan; Čabrić, Danijela

doi:10.1109/jstsp.2019.2931206

Cited by 28 publications

(12 citation statements)

References 49 publications

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“…Beam-training is a part of initial access (IA) protocol in mmW networks that achieve alignment of beamforming directions to realize a maximum gain between base station (BS) and user equipment (UE) [54], [55]. Beam-training using PAA requires extensive beam sweeping to estimate angleof-departure (AoD) and AoA (Figure 7(a)), as it can only probe one steering direction at a time.…”

Section: B Beam-squint Array For Beam-training Modementioning

confidence: 99%

“…Beam-training using PAA requires extensive beam sweeping to estimate angleof-departure (AoD) and AoA (Figure 7(a)), as it can only probe one steering direction at a time. As a result, PAAbased IA latency increases with array size and number of users [54]. The majority of existing mmW beam-training algorithms was designed for analog PAA due to their power efficiency.…”

Section: B Beam-squint Array For Beam-training Modementioning

confidence: 99%

See 1 more Smart Citation

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

Lin¹,

Boljanovic²,

Han³

et al. 2021

Preprint

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The decadal research in integrated true-time-delay arrays have seen organic growth enabling realization of wideband beamformers for large arrays with wide aperture widths. This article introduces highly reconfigurable delay elements implementable at analog or digital baseband that enables multiple Spatial Signal Processing (SSP) functions including wideband beamforming, wideband interference cancellation, and fast beam training. Details of the beam-training algorithm, system design considerations, system architecture and circuits with large delay range-to-resolution ratios are presented leveraging integrated delay compensation techniques. The article lays out the framework for true-time-delay based arrays in next-generation network infrastructure supporting 3D beam training in planar arrays, low latency massive multiple access, and emerging wireless communications standards.

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Section: B Beam-squint Array For Beam-training Modementioning

confidence: 99%

Section: B Beam-squint Array For Beam-training Modementioning

confidence: 99%

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

Lin¹,

Boljanovic²,

Han³

et al. 2021

Preprint

Self Cite

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“…The former uses sounding beams that adapt with previous measurement, bisecting the beam width to reduce the search space [7]. The latter is based on either CS, i.e., with coherent complex sample measurements, or compressive sensing phase retrieval (CPR), i.e., with noncoherent received signal strength (RSS) measurements [12,15,21]. Alternatively, UbiG [10] is a model-based algorithm that requires only a constant sounding overhead regardless of array size.…”

Section: Related Workmentioning

confidence: 99%

“…Adaption that uses on-the-y measurements to change either the codebook or the codeword selection order, e.g., a hierarchy search, is not desired. In fact, we focus on pseudo-random sounding codebooks W S , a well adopted design from compressive sensing literature when the Rx does not have prior knowledge of the channel [12,15,16,21]. Specically, the magnitude of each AWV in W S is 1/ p # R and the phase is randomly picked from the set {0, c/2, c, 3c/2}.…”

Section: System Model and Problem Statementmentioning

confidence: 99%

mmRAPID

Han

Domae

Čabrić

2020

Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems

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Millimeter-wave communication has the potential to deliver orders of magnitude increases in mobile data rates. A key design challenge is to enable rapid beam alignment with phased arrays. Traditional millimeter-wave systems require a high beam alignment overhead, typically an exhaustive beam sweep, to nd the beam direction with the highest beamforming gain. Compressive sensing is a promising framework to accelerate beam alignment. However, model mismatch from practical array hardware impairments poses a challenge to its implementation. In this work, we introduce a neural network assisted compressive beam alignment method that uses noncoherent received signal strength measured by a small number of pseudorandom sounding beams to infer the optimal beam steering direction. We experimentally showcase our proposed approach with a 60GHz 36-element phased array in a suburban line-of-sight environment. The results show that our approach achieves post alignment beamforming gain within 1dB margin compared to an exhaustive search with 90.2% overhead reduction. Compared to purely model-based noncoherent compressive beam alignment, our method has 75% overhead reduction. CCS CONCEPTS• Hardware ! Beamforming; • Networks ! Link-layer protocols; Physical links.

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“…Several existing solutions apply CS methods to solve BA. [6] utilizes a matching pursuit (MP) algorithm with channel gain measurements from pseudorandom noise (PN) beams, quasi-omni-directional beams with random phase antenna weight vector (AWV)s. [7] also employs MP with PN sounding beams, but only requires received signal strength (RSS) power measurements by solving phase-less BA as a compressive phase retrieval (CPR) problem. SBG-Code from [8] solves BA for hybrid or digital arrays as a CPR problem.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath Channels

Domae,

Li,

Cabric

2021

Preprint

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Communication systems at millimeter-wave (mmW) and sub-terahertz frequencies are of increasing interest for future high-data rate networks. One critical challenge faced by phased array systems at these high frequencies is the efficiency of the initial beam alignment, typically using only phase-less power measurements due to high frequency oscillator phase noise. Traditional methods for beam alignment require exhaustive sweeps of all possible beam directions, thus scale communications overhead linearly with antenna array size. For better scaling with the large arrays required at high mmW bands, compressive sensing methods have been proposed as their overhead scales logarithmically with the array size. However, algorithms utilizing machine learning have shown more efficient and more accurate alignment when using real hardware due to array impairments. Additionally, few existing phase-less beam alignment algorithms have been tested over varied secondary path strength in multipath channels. In this work, we introduce a novel, machine learning based algorithm for beam alignment in multipath environments using only phase-less received power measurements. We consider the impacts of phased array sounding beam design and machine learning architectures on beam alignment performance and validate our findings experimentally using 60 GHz radios with 36-element phased arrays. Using experimental data in multipath channels, our proposed algorithm demonstrates an 88% reduction in beam alignment overhead compared to an exhaustive search and at least a 62% reduction in overhead compared to existing compressive methods.

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Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems

Cited by 28 publications

References 49 publications

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

mmRAPID

Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath Channels

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