Dual Mode Localization Assisted Beamforming for mmWave V2V Communication

Mahabal, Chinmay; Wang, Honggang; Fang, Hua

doi:10.1109/tvt.2022.3175165

Cited by 11 publications

(4 citation statements)

References 37 publications

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“…Beam steering is commonly used in 5G and Wi-Fi to direct the radiation or reception beam to a specific station, maximizing gain for that station and minimizing interference with others. These concepts are fundamental for vehicle communications [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

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Evaluating the Performance of Proposed Switched Beam Antenna Systems in Dynamic V2V Communication Networks

Ahmed,

Tiang,

Mahmud

et al. 2023

Sensors

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This paper develops a novel approach for reliable vehicle-to-vehicle (V2V) communication in various environments. A switched beam antenna is deployed at the transmitting and receiving points, with a beam management system that concentrates the power in each beam using a low-computation algorithm and a potential mathematical model. The algorithm is designed to be flexible for various environments faced by vehicles. Additionally, an anti-failure system is proposed in case the intelligent transportation system (ITS) system fails to retrieve real-time Packet Delivery Ratio (PDR) values related to traffic density. Performance metrics include the time to collision in seconds, the bit error rate (BER), the packet error rate (PER), the average throughput (Mbps), the beam selection probability, and computational complexity factors. The proposed system is compared with traditional systems. Extensive experiments, simulations, and comparisons show that the proposed approach is excellent and reliable for vehicular systems. The proposed study demonstrates an average throughput of 1.7 Mbps, surpassing conventional methods’ typical throughput of 1.35 Mbps. Moreover, the bit error rate (BER) of the proposed study is reduced by a factor of 0.1. Additionally, the proposed framework achieves a beam power efficiency of touching to 100% at computational factor of 34. These metrics indicate that the proposed method is both efficient and sufficiently robust.

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Section: Related Workmentioning

confidence: 99%

“…Localization is a critical challenge in V2V communication, and the relationship between beamforming and beam alignment is examined in study [ 19 ]. This study incorporates realistic vehicle dynamics and highlights the limitations of the Kalman filter for non-linear parameters.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Performance of Proposed Switched Beam Antenna Systems in Dynamic V2V Communication Networks

Ahmed,

Tiang,

Mahmud

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, Zhang et al [14] evaluated the performance of beamforming techniques in mmWave vehicular networks and proposed an adaptive beamforming scheme for message dissemination in mmWave vehicle-to-vehicle (V2V) communications. Mahabal et al [15] assessed the impact of vehicle dynamics and beamforming on direct V2V communication in a highway environment, analyzing the joint effect of antenna elements and the number of bits in the digital phase shifter on link coverage probability. In [16], the authors investigated the beam management problem in urban vehicular networks and devised a graph-based model to capture system characteristics resulting from rapid user mobility.…”

Section: Mmwave Vehicular Networkmentioning

confidence: 99%

Beamsteering-Aware Power Allocation for Cache-Assisted NOMA mmWave Vehicular Networks

Cao

et al. 2023

Electronics

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Cache-enabled networks with multiple access (NOMA) integration have been shown to decrease wireless network traffic congestion and content delivery latency. This work investigates optimal power control in cache-assisted NOMA millimeter-wave (mmWave) vehicular networks, where mmWave channels experience double-Nakagami fading and the mmWave beamforming is subjected to beamsteering errors. We aim to optimize vehicular quality of service while maintaining fairness among vehicles, through the maximization of successful signal decoding probability for paired vehicles. A comprehensive analysis is carried out to understand the decoding success probabilities under various caching scenarios, leading to the development of optimal power allocation strategies for diverse caching conditions. Moreover, an optimal power allocation is proposed for the single-antenna case, for exploiting the cached data as side information to cancel interference. The robustness of our proposed scheme against variations in beamforming orientation is assessed by studying the influence of beamsteering errors. Numerical results demonstrate the effectiveness of the proposed cache-assisted NOMA scheme in enhancing cache utility and NOMA efficiency, while underscoring the performance gains achievable with larger cache sizes.

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“…Beamforming techniques have been extensively studied in wireless communication systems to improve the signal-to-interference-plus-noise ratio (SINR), increase capacity, and enhance communication reliability [4]. In vehicular communication systems, adaptive beamforming and beam tracking techniques have been proposed to maintain optimal beam alignment in the rapidly changing environment [5]. Various beam tracking algorithms, such as the Kalman filter [6] and particle filter [7], have been used to predict the direction of arrival (DoA) of the received signal and adjust the beamforming weights accordingly.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Adaptive Beam Tracking and Resource Allocation in 6G Vehicular Networks with Switched Beam Antennas

et al. 2023

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In this paper, we propose a novel switched beam antenna system model integrated with deep reinforcement learning (DRL) for 6G vehicle-to-vehicle (V2V) communications. The proposed system model aims to address the challenges of highly dynamic V2V environments, including rapid changes in channel conditions, interference, and Doppler effects. By leveraging the beam-switching capabilities of switched beam antennas and the intelligent decision making of DRL, the proposed approach enhances the performance of 6G V2V communications in terms of throughput, latency, reliability, and spectral efficiency. The proposed work develops a comprehensive mathematical model that accounts for 6G channel modeling, beam-switching, and beam-alignment errors. The Proposed DRL framework is designed to learn optimal beam-switching decisions in real time, adapting to the complex and varying V2V communication scenarios. The integration of the proposed antenna system and DRL model results in a robust solution that is capable of maintaining reliable communication links in a highly dynamic environment. To validate the proposed approach, extensive simulations were conducted and performance analysis using various performance metrics, such as throughput, latency, reliability, energy efficiency, resource utilization, and network scalability, was analyzed. Results demonstrate that the proposed system model significantly outperforms conventional V2V communication systems and other state-of-the-art techniques. Furthermore, the proposed approach shows that the beam-switching capabilities of the switched beam antenna system and the intelligent decision making of the DRL model are essential in addressing the challenges of 6G V2V communications.

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Dual Mode Localization Assisted Beamforming for mmWave V2V Communication

Cited by 11 publications

References 37 publications

Evaluating the Performance of Proposed Switched Beam Antenna Systems in Dynamic V2V Communication Networks

Evaluating the Performance of Proposed Switched Beam Antenna Systems in Dynamic V2V Communication Networks

Beamsteering-Aware Power Allocation for Cache-Assisted NOMA mmWave Vehicular Networks

Deep Reinforcement Learning-Based Adaptive Beam Tracking and Resource Allocation in 6G Vehicular Networks with Switched Beam Antennas

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