Deep Learning for Location Based Beamforming with Nlos Channels

Magoarou, Luc Le; Yassine, Taha; Paquelet, Stéphane; Crussière, Matthieu

doi:10.1109/icassp43922.2022.9747293

Cited by 9 publications

(7 citation statements)

References 18 publications

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Section: Mlpmentioning

confidence: 99%

“…In [174], the authors propose using a neural network with a structure based on random Fourier features (RFF) to determine the most appropriate precoder matrix based on the user's location only. Their approach is capable of handling both LoS and NLoS channels [174]. They show that, depending on how the users' locations are obtained, it is possible to reduce or even eliminate the need for pilots.…”

Section: Mlpmentioning

confidence: 99%

See 1 more Smart Citation

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

Brilhante¹,

Manjares²,

Moreira³

et al. 2023

Preprint

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The performance of modern wireless communication systems is highly dependent on the adoption of multiple antennas and the associated signal processing. In 5G and 6G networks, beamforming and beam management become challenging tasks due to aspects such as user mobility, increased number of antennas, and the adoption of higher frequencies. Artificial intelligence, and more specifically, machine learning, are efficient tools to reduce the complexity involved in generating beams and the overhead associated with beam management without sacrificing system performance. Therefore, AI-aided beamforming and beam management have received a lot of attention recently. This article presents a complete survey on this topic, emphasizing open problems and promising directions. The discussion includes architectural and signal processing aspects of modern beamforming and beam management. The article presents communication problems and respective solutions using centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

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Section: Mlpmentioning

confidence: 99%

Section: Mlpmentioning

confidence: 99%

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

Brilhante¹,

Manjares²,

Moreira³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…A recurrent neural network (RNN) is proposed to predict b t+1 u as shown in Figure 1 1 . First, b t u is one-hot encoded and stacked for the observation window w. Then, P t u is encoded using the random Fourier features (RFF) which provide a better emphasis for the location information [16]. It has been shown that classical neural networks which use MLPs cannot capture the low-dimensional features such as raw location data.…”

Section: B Proposed Prediction Modelmentioning

confidence: 99%

“…It has been shown that classical neural networks which use MLPs cannot capture the low-dimensional features such as raw location data. The authors in [16] demonstrated the efficiency improvement for location-aided beamforming utilizing an RFF layer. Following a similar approach for the location encoding, trajectory history for u-th user is encoded using an RFF layer.…”

Section: B Proposed Prediction Modelmentioning

confidence: 99%

LiDAR aided Wireless Networks - Beam Prediction for 5G

Marasinghe

Jayaweera

Rajatheva

et al. 2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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5G New Radio (NR) mmWave operates with narrow beams. Beam-based connections require careful management of beams to ensure a reliable connection, specially when the user has mobility. 5G NR beam management achieve this, at the expense of periodic reporting with increased overheads and resource usage. Concurrently, recent interest in sensing for assisting wireless systems provides an opportunity to extract situational awareness information which can aid in proactive decisions for the network. In this work, we utilize an infrastructure-mounted light detection and ranging (LiDAR) sensor system simultaneously operating with the wireless system to predict future beam decisions. A recurrent neural network (RNN) based learning model is proposed for the beam prediction, employing tracking information of users facilitated by the LiDARs and beam sequence information from the wireless system. Furthermore, a method for predictive beam management with increased periodicity of the reporting mechanism and aperiodic reporting is analyzed. The results for the considered scenario reveal 86.8% of the resources can be saved compared to the conventional beam reporting procedure, while achieving an 88.7% accuracy for optimal beam decisions.

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“…Moreover, using machine learning methods in order to achieve channel estimation has attracted a huge interest in the past years [14,15,16]. Previous works about the learning of the location or pseudolocation to beamformer mapping exist [17,18]. However, to the best of the authors' knowledge, there is no previous work specifically focused on learning the location-to-channel mapping.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Learning for Location-to-Channel Mapping

Chatelier,

Magoarou,

Corlay

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Modern communication systems rely on accurate channel estimation to achieve efficient and reliable transmission of information. As the communication channel response is highly related to the user's location, one can use a neural network to map the user's spatial coordinates to the channel coefficients. However, these latter are rapidly varying as a function of the location, on the order of the wavelength. Classical neural architectures being biased towards learning low frequency functions (spectral bias), such mapping is therefore notably difficult to learn. In order to overcome this limitation, this paper presents a frugal, model-based network that separates the low frequency from the high frequency components of the target mapping function. This yields an hypernetwork architecture where the neural network only learns low frequency sparse coefficients in a dictionary of high frequency components. Simulation results show that the proposed neural network outperforms standard approaches on realistic synthetic data.

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Deep Learning for Location Based Beamforming with Nlos Channels

Cited by 9 publications

References 18 publications

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

LiDAR aided Wireless Networks - Beam Prediction for 5G

Model-Based Learning for Location-to-Channel Mapping

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