Learning-Based Beamforming for Multi-User Vehicular Communications: A Combinatorial Multi-Armed Bandit Approach

Nasim, Imtiaz; Ibrahim, Ahmed S.; Kim, Seungmo

doi:10.1109/access.2020.3043301

Cited by 13 publications

(10 citation statements)

References 32 publications

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“…There is a huge interest in enabling high-rate for highlymobile vehicular communication, given its various applications such as safety, online route mapping, along with the infotainment services [110]. With the availability of a large amount of bandwidth, mmWave communications can support massive sensor data sharing in vehicular networks [111], [112], [107].…”

Section: Codebook-based Beamforming For Vehicular Communicationsmentioning

confidence: 99%

“…To handle the beam selection problem in a vehicular network, the authors in [110] propose a Reinforcement Learning (RL) approach called Combinatorial Multi-Armed Bandit (CMAB) framework. Suppose the codebook is F = [f 1 , ..., f m ] where f i is the i-th codeword for i = 1, ..., m and m is the maximum number of beams available in the codebook.…”

Section: ) Combinatorial Multi-armed Bandit Framework For Multi-user ...mentioning

confidence: 99%

“…At each time t, the best beam for the vehicle u can be designed by f u,t where u = 1, ..., U and U is the total number of vehicles. According to [110], first, the problem formulation of maximizing the average network capacity should transformed into a CMAB one as follows:…”

Section: ) Combinatorial Multi-armed Bandit Framework For Multi-user ...mentioning

confidence: 99%

“…As shown in [110], this scheme embodies for appropriate selection of simultaneous beams in a high-mobility vehicular environment. However, it produces a higher complexity over the search space which increases exponentially with the number of users.…”

Section: Adaptive Cts Algorithmmentioning

confidence: 99%

“…To reduce the computational complexity, this method selects the best beams one-by-one for each UE. According to [110], the proposed sequential approach performs almost similar to the simultaneous adaptive CTS scheme for tracking optimal beams in a multi-vehicular network with much reduced complexity.…”

Section: Ts Algorithmmentioning

confidence: 99%

See 4 more Smart Citations

Codebook Designs for Millimeter-Wave Communication Systems in Both Low- and High-Mobility: Achievements and Challenges

Mabrouki

Dayoub

et al. 2022

IEEE Access

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More connectivity, higher data rates, more reliability, massive network capacity, higher performance and fewer delays are required in the fifth generation (5G) of cellular networks. The last ten years have contained explosive growth in mobile data traffic due to the rapid proliferation of Internetconnected smart devices. For 5G mobile and wireless networks, one of the challenges is to discover how to solve the dilemma between capacity requirements and spectrum shortage. Millimeter-wave communication is therefore a key enabler for 5G technologies. Due to the high path and penetration losses at millimeter wavelengths, antenna beamforming assumes a pivotal role in establishing and maintaining a robust communication link. Recently, codebook-based beamforming has been proposed to achieve a fair balance between complexity and performance and to eliminate the overheads. In this paper, we track the techniques of codebook-based beamforming for millimeter-wave communications in the context of the distinct requirements for low-mobility channel and high-mobility channel scenarios. Subsequently, we will provide a comparison of existing codebook-based beamforming techniques in terms of their respective benefits and shortcomings. Finally, some open directions of research are discussed, and challenges that need to be met are pointed out.

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Section: Codebook-based Beamforming For Vehicular Communicationsmentioning

confidence: 99%

Section: ) Combinatorial Multi-armed Bandit Framework For Multi-user ...mentioning

confidence: 99%

Section: ) Combinatorial Multi-armed Bandit Framework For Multi-user ...mentioning

confidence: 99%

Section: Adaptive Cts Algorithmmentioning

confidence: 99%

Section: Ts Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Codebook Designs for Millimeter-Wave Communication Systems in Both Low- and High-Mobility: Achievements and Challenges

Mabrouki

Dayoub

et al. 2022

IEEE Access

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Resource Allocation for Multi-source Multi-relay Wireless Networks: A Multi-Armed Bandit Approach

Khansa

Visoz

Hayel

et al. 2021

Lecture Notes in Computer Science

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Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance

Kaur,

Bhatti,

Tan

et al. 2024

APL Machine Learning

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Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users’ location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks.

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Learning-Based Beamforming for Multi-User Vehicular Communications: A Combinatorial Multi-Armed Bandit Approach

Cited by 13 publications

References 32 publications

Codebook Designs for Millimeter-Wave Communication Systems in Both Low- and High-Mobility: Achievements and Challenges

Codebook Designs for Millimeter-Wave Communication Systems in Both Low- and High-Mobility: Achievements and Challenges

Resource Allocation for Multi-source Multi-relay Wireless Networks: A Multi-Armed Bandit Approach

Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance

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