DNN Based Beam Selection in mmW Heterogeneous Networks

Jagyasi, Deepa; Coupechoux, Marceau

doi:10.1007/978-3-030-87473-5_16

Cited by 5 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the multi-path effect, the sub-6 GHz bands are not often explored in channel probes and massive MIMO systems, but knowledge about the network can be established even in these bands. Jagyasi et al [83] consider a heterogeneous communication network, where small BS operating at mmWave coexist with sub-6 GHz macro-cell BSs. Through basic signals extracted from the sub-6 GHz channel, a deep neural network model is applied in order to divide the problem into two sub-problems, one for BS selection and another for beam selection.…”

Section: Beam Selection In Mimo Systemsmentioning

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

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.

show abstract

Section: Beam Selection In Mimo Systemsmentioning

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

show abstract

“…This is exploited in [14], where a DNN model is proposed to predict the best mmWave beam index using the power delay profile (PDP) of a sub-6 GHz channel for a single cell with both mmWave and sub-6 GHz gNBs. Similarly, sub-6 GHz channel coefficients obtained from a sample ray-tracing environment are used to predict the best mmWave beams in [15]. A self-supervised DL method for channel-beam mapping with sub-6 GHz to predict mmWave beamforming vectors is proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

“…A neural network architecture is designed in [17] to jointly learn site-specific probing beams and the beam predictor. The dataset provided in [18] is used in [13], [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Secure Initial Access and Beam Alignment Using Deep Learning in 5G and Beyond Systems

Ozmat,

Yazici,

Demirkol

2024

IEEE Access

View full text Add to dashboard Cite

5G and beyond networks will require fast, energy efficient, and secure initial access. In this study, a deep learning-based secure initial beam selection method is proposed that ranks the beam pairs between a transmitter and a legitimate user aiming to maximize the signal strength the user receives, while keeping the signal strength that the eavesdropper sees below a threshold. Instead of an exhaustive search, the initial beam selection is performed over a limited number of the top beam pairs, leading to reduced communication overhead and energy consumption. The proposed scheme is evaluated using data obtained from a real-life mobile network topology as well as a synthetic data set based on the same geographical site but with statistical system-level environment variables. Numerical results show that the signalling overhead can be reduced by 75% with 99.66% accuracy in terms of the best beam pair, and 99.89% of the achievable signal strength. In terms of security, the proposed method has been shown to improve secure coverage probability by 68.12% compared to the best-coverage beam selection scenario.INDEX TERMS 5G, physical layer security, beam management, mMIMO, NR SSB beam sweeping, deep learning, DNN.Recently, machine learning (ML), especially deep learning (DL), has become a prominent technique in a wide range of research areas for mobile and wireless networks. Potential areas of DL include mobility analysis, user localization, wireless sensor networks, network control, network security, signal processing, network-level and application-level mobile data analysis [5]. Specifically, DL has been proposed to be used in the beam management processes. It excels at extracting nonlinear features in angular and time domains, ensuring highly accurate beam pair prediction. Additionally, it has been proposed for predictive beam switching to mini-

show abstract

Secure Multi-Point Coordinated Beamforming using Deep Learning in 5G and Beyond Networks

Ozmat,

Yazici,

Demirkol

2023

2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

View full text Add to dashboard Cite

DNN Based Beam Selection in mmW Heterogeneous Networks

Cited by 5 publications

References 14 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

Secure Initial Access and Beam Alignment Using Deep Learning in 5G and Beyond Systems

Secure Multi-Point Coordinated Beamforming using Deep Learning in 5G and Beyond Networks

Contact Info

Product

Resources

About