Machine Learning Inspired Codeword Selection For Dual Connectivity in 5G User-Centric Ultra-Dense Networks

Yang, Yang; Deng, Xinyi; He, Dazhong; You, Yanan; Song, Ruoning

doi:10.1109/tvt.2019.2923314

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…Herein, the communication traffic is calculated considering the deep learning models, described earlier, with 14,789 parameters, while assuming that each parameter is represented by 4 bytes, as in the PyTorch tutorial [43]. Moreover, we remark that by leveraging the recent technologies adopted by 5G, two edge nodes (such as macro cell base station and small cell base station) can simultaneously transmit the data to one EU with the help of millimeterwave (mmWave) massive MIMO technology [44]. This can significantly decrease the overhead resulting from the dual connectivity.…”

Section: Simulation Resultsmentioning

confidence: 99%

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

Abdellatif¹,

Mhaisen²,

Mohamed³

et al. 2021

Preprint

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Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hierarchical FL in IoT heterogeneous systems and propose an optimized solution for user assignment and resource allocation on multiple edge nodes. In particular, this work focuses on a generic class of machine learning models that are trained using gradient-descent-based schemes while considering the practical constraints of non-uniformly distributed data across different users. We evaluate the proposed system using two real-world datasets, and we show that it outperforms state-of-the-art FL solutions. In particular, our numerical results highlight the effectiveness of our approach and its ability to provide 4-6% increase in the classification accuracy, with respect to hierarchical FL schemes that consider distance-based user assignment. Furthermore, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75-85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.

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Section: Simulation Resultsmentioning

confidence: 99%

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

Abdellatif¹,

Mhaisen²,

Mohamed³

et al. 2021

Preprint

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“…In dual connection schemes, the UE stays connected to two BSs simultaneously, reducing the overhead when a context transfer is needed and increasing the data rate. However, dual connection also increases overhead and complexity, which is tackled in [110] using a SVM classifier for codeword selection from the available CSI samples.…”

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

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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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“…To simplify the evaluation of multi-connectivity techniques beyond outage probability and include throughput, latency and reliability metrics, authors in [11] present a closed-form expression to derive the symbol error rate as a function of the received SINRs across multiple connections. Machine learning techniques have been proposed to reduce the complexity of scheduling radio resource in ultra-dense scenarios and improve network performance when multi connectivity is enabled [12,13]. Furthermore, early attempts to enhance positioning schemes in mmWave deployments using MC have been discussed in [14,15].…”

Section: Related Workmentioning

confidence: 99%

Multi-Connectivity in 5g New Radio: Optimal Resource Allocation for Split Bearer and Data Duplication

Elias,

Martignon,

Paris

2022

SSRN Journal

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Machine Learning Inspired Codeword Selection For Dual Connectivity in 5G User-Centric Ultra-Dense Networks

Cited by 19 publications

References 13 publications

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

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

Multi-Connectivity in 5g New Radio: Optimal Resource Allocation for Split Bearer and Data Duplication

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