LiSteer

Haider, Muhammad Kumail; Ghasempour, Yasaman; Koutsonikolas, Dimitrios; Knightly, Edward W.

doi:10.1145/3241539.3241542

Cited by 40 publications

(5 citation statements)

References 25 publications

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“…It exploits spatial correlation in channel profiles for prediction. In [11], an algorithm called Listeer is proposed, which steers the beam at the AP towards a mobile UE. It acquires direction estimates for beams at APs using indicator Light Emitting Diodes (LEDs) on APs and off-the-shelf light sensors at UEs.…”

Section: Related Workmentioning

confidence: 99%

“…The scheme maps pose information of the mobile UE with measured link quality and selects a beam based on pose information. In contrast to the work in [9], [10], [11], [12], where beam selection is done for a network consisting of a single UE, our work considers the problem of UE and beam selection in a mmWave network having multiple APs and multiple UEs.…”

Section: Related Workmentioning

confidence: 99%

“…To overcome the above mentioned challenges and to provide robust connectivity to user equipment (UE), several algorithms have been proposed to select beams and UEs for each AP in mmWave networks [9], [10], [11], [12], [13], [14], [15], [16], [17]. In [9], [10], [11], mmWave networks with a single AP and a single UE are considered and algorithms for beam selection at the AP for the UE are designed such that the connection remains robust to blockage and UE mobility. In [12], mmWave networks with multiple cooperating APs and a single UE are considered and an algorithm is designed to select a beam for each AP to make a robust connection with the mobile UE.…”

Section: Introductionmentioning

confidence: 99%

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NGUB: Novel Greedy Algorithms for User and Beam Selection in mmWave Networks

Singh,

Sahu,

Thawait

et al. 2023

2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)

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We study the problem of selecting a user equipment (UE) and a beam for each access point (AP) for concurrent transmissions in a millimeter wave (mmWave) network, such that the sum of weighted rates of UEs is maximized. We prove that this problem is NP-complete. We propose two algorithms-Markov Chain Monte Carlo (MCMC) based and local interaction game (LIG) based UE and beam selection-and prove that both of them asymptotically achieve the optimal solution. Also, we propose two fast greedy algorithms-NGUB1 and NGUB2-for UE and beam selection. Through extensive simulations, we show that our proposed greedy algorithms outperform the most relevant algorithms proposed in prior work and perform close to the asymptotically optimal algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

NGUB: Novel Greedy Algorithms for User and Beam Selection in mmWave Networks

Singh,

Sahu,

Thawait

et al. 2023

2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)

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“…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. UbiG relies on perfect knowledge of all beam patterns to solve for the angle of arrival (AoA)s with a non-convex optimization problem.…”

Section: Related Workmentioning

confidence: 99%

“…Work in [8] reports experimental work that eectively reduces overhead when more than one spatial stream is used in a hybrid array. Finally, some prototypes also rely on the side information, e.g., sub-6 GHz [14,18] and visible light [10] measurements, for mmW beam alignment.…”

Section: Related Workmentioning

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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