Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach

Li, Xingjian; Fang, Jun; Huang, Duan; Chen, Zhi; Li, Hongbin

doi:10.1109/tsp.2019.2929460

Cited by 45 publications

(28 citation statements)

References 42 publications

(39 reference statements)

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“…The analysis reveals that the BA bisection search algorithm achieves better performance than BA iterative and exhaustive search algorithms. In [18] authors formulate the BA problem as a sparse encoding and phaseless decoding problem. The scenario that is here considered is with one BS and one AP, and the proposed algorithm can perfectly recover the support and magnitude of the sparse signal (uniquely associated to the beams' directions) in the noiseless case.…”

Section: A Previous Contributionsmentioning

confidence: 99%

Multi-UE Multi-AP Beam Alignment in User-Centric Cell-Free Massive MIMO Systems Operating at mmWave

Buzzi,

D'Andrea,

Fresia

et al. 2021

Preprint

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This paper considers the problem of beam alignment in a cell-free massive MIMO deployment with multiple access points (APs) and multiple user equipments (UEs) simultaneously operating in the same millimeter wave frequency band. Assuming the availability of a control channel at sub-6 GHz frequencies, a protocol is developed that permits estimating, for each UE, the strongest propagation path from each of the surrounding APs, and to perform user-centric association between the UEs and the APs. Estimation of the strongest paths from nearby APs is realized at the UE in a one-phase procedure, during which all the APs simultaneously transmit on pseudo-randomly selected channels with pseudo-random transmit beamformers. An algorithm for orthogonal channels assignment to the APs is also proposed, with the aim of minimizing the mutual interference between APs that transmit on the same channels. The performance of the proposed strategy is evaluated both in terms of probability of correct detection of the directions of arrival and of departure associated to the strongest beam from nearby APs, and in terms of downlink and uplink signal-to-interference-plus-noise ratio. Numerical results show that the proposed approach is effective and capable of efficiently realizing beam alignment in a multi-UE multi-AP wireless scenario.

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Section: A Previous Contributionsmentioning

confidence: 99%

Multi-UE Multi-AP Beam Alignment in User-Centric Cell-Free Massive MIMO Systems Operating at mmWave

Buzzi,

D'Andrea,

Fresia

et al. 2021

Preprint

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“…The receiver's response vector a R k (φ q ) can be written similarly. 1 For clarity of presentation, the temporal channel dynamics is not made explicit here. The chosen mobility model of the users was introduced in [15] and changes the AoD, AoA and pathloss channel parameters at each coherence time.…”

Section: System Modelmentioning

confidence: 99%

“…The migration of wireless communications to the mmWave band (ranging between 30 GHz and 300 GHz) represents a potential solution to enable gigabits-per-second data rates thanks to the large available bandwidth [1]. Nevertheless, this comes with critical challenges that need to be addressed before being considered for beyond 5G networks.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Channel Sparsity for Beam Alignment in mmWave Systems via Exponential Learning

Chafaa

Belmega

Debbah

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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The large available spectrum in the millimeter wave (mmWave) band represents an attractive alternative for the congested sub-6 GHz spectrum. To overcome the difficult propagation conditions at high frequencies, directional communications via multiple antenna arrays and high-gain beams can be employed. Nevertheless, these beams need to be well aligned to reliably transmit data, which is a challenging task given the user mobility and the unpredictable changes of the wireless environment. In this paper, we propose a new distributed beam-alignment strategy relying on a single bit of feedback, which equals one if the signal-to-interference-plus-noise (SINR) reaches a predefined threshold. The novelty consists in a modified reward function, inspired from the sparse nature of the mmWave channel, coupled with the well-known exponential weights algorithm (EXP3). First, we show that our resulting adaptive policy comes with optimal theoretical guarantees in terms of sub-linear regret. Second, our numerical results demonstrate significant performance gains of our beam-alignment policy compared with the original EXP3 algorithm and other existing policies in a mmWave setting with user mobility.

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“…[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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Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach

Cited by 45 publications

References 42 publications

Multi-UE Multi-AP Beam Alignment in User-Centric Cell-Free Massive MIMO Systems Operating at mmWave

Multi-UE Multi-AP Beam Alignment in User-Centric Cell-Free Massive MIMO Systems Operating at mmWave

Exploiting Channel Sparsity for Beam Alignment in mmWave Systems via Exponential Learning

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

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