Enhance the edge with beamforming: Performance analysis of beamforming-enabled WLAN

Wu, Wen; Shen, Qinghua; Aldubaikhy, Khalid; Cheng, Nan; Zhang, Ning; Shen, Xuemin

doi:10.23919/wiopt.2018.8362872

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…skyrocketing data traffic through harnessing multi-GHz bandwidths. Multiple standardization efforts, such as IEEE 802.11ad [1], [2] and ongoing IEEE 802.11ay [3], [4], and large-scale field-trials have paved the road for the commercialization of mmwave communications.…”

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confidence: 99%

Fast mmwave Beam Alignment via Correlated Bandit Learning

Cheng

Zhang

et al. 2019

IEEE Trans. Wireless Commun.

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128

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Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process.Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.

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confidence: 99%

Fast mmwave Beam Alignment via Correlated Bandit Learning

Cheng

Zhang

et al. 2019

IEEE Trans. Wireless Commun.

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128

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“…Fig. 7: Access delay performance of the A-BFT protocol as calculated by our model, the model in [16] and via simulations. Fig.…”

Section: ) A-bft Access Delaymentioning

confidence: 99%

“…3) Comparison with state of the art: We compare the results of our model to the one introduced in [16], the closest work to ours to the best of our knowledge. The authors modeled a simplified version of the A-BFT protocol in which stations are allowed to attempt an RSS only once per period.…”

Section: ) A-bft Access Delaymentioning

confidence: 99%

“…Evaluation of IEEE 802.11ad A-BFT: A few works have focused on the evaluation of the A-BFT part of 802.11ad, however, it has been mostly using simulations [14], [15]. To the best of our knowledge, only [16], [17] introduce analytical models to study A-BFT. [16] analyzes the success probability of an RSS.…”

Section: B Assessing Ieee 80211ad Beamforming Trainingmentioning

confidence: 99%

“…A few works have considered A-BFT but it has been mostly using simulations [14], [15]. The analytical models introduced in [16], [17] cannot address A-BFT's basic question.…”

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On Assessing the Performance of IEEE 802.11ad Beamforming Training

Dahhani

Beylot

Jakllari

2021

IEEE Trans. Netw. Serv. Manage.

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In the race for Gigabit wireless links, IEEE introduced 802.11ad, an amendment aimed at delivering Gbps capacities in a WLAN setting by leveraging the 60 GHz band. The key innovation of the standard is its beamforming training protocol. Executed periodically at the beginning of every beacon interval, it enables the formation of directional links. To address contention during the uplink part of beamforming training, 802.11ad introduced A-BFT (Association BeamForming Training), an Aloha-inspired, two-level backoff race. While central to the functionality of 802.11ad networks, the performance of A-BFT, however, remains poorly understood.In this paper, we propose an analytical finite-population model for evaluating the performance of IEEE 802.11ad A-BFT under two channel models: loss-free, and a channel introducing a constant bit error rate. After using an open-source simulator to demonstrate its accuracy, we use our model to assess the performance of A-BFT. We find that a counter-intuitive, quiteasily/be-lazy approach by the stations leads to the best overall beamforming training performance.

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