Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach

Ma, Ke; Du, Shouliang; Zou, Haoming; Tian, Wenqiang; Wang, Zhaocheng; Chen, Sheng

doi:10.1109/tcomm.2022.3222345

Cited by 23 publications

(12 citation statements)

References 49 publications

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“…Researchers have confirmed the increasing use of application of AI/ML-based optimization in network slicing, scheduling, and service provisioning, adapting the network to different slices and user needs. Moving forward, deep learning (DL) is adopted to extract the complex dependence in heterogeneous networks between sub-6 GHz and MMW channels for achieving high prediction accuracy for optimal MMW beams in [35]. On the other hand, a location-and orientation-based single and multi-task DNN architecture for the beam selection method to enable context information (CI)-based beam alignment has been proposed in [36].…”

Section: A Related Workmentioning

confidence: 99%

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-1 Approach

Abd,

Findley,

Soon Kim

2024

IEEE Access

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Next-generation sensor and radio access networks (NG-SRANs) namely, Hydra radio access networks (H-RANs) represent a significant evolution in the telecommunications and sensor ecosystem landscape in anticipation of 6G deployment and beyond. H-RAN's vision derives its strength from integrating various technologies and networks into a single central network with the widespread incorporation of artificial intelligence (AI) technologies throughout the network. As a result, H-RAN's unique features and characteristics can serve as a baseline for innovating new applications and significantly enhance the overall functions of conventional open radio access networks (O-RANs). However, among the many improvements and innovations that the H-RAN architecture promises in its functionality, this paper focuses on the initial access implementation "task 1 " approach. Our solution contains several novelties that enhance both overhead and model accuracy. To this end, we define a novel intelligent perception network inspired by the knowledge distribution idea for collaborative H-RAN networks. We develop sparse multi-task learning (SMTL) as part of the AI/ML D-engine for federated learning to perform multiple tasks simultaneously. The SMTL is designed to select the optimal solution from a list of recommended solutions, namely "tasks". In the simulation, figures of merit include metrics such as top-k validation accuracy, beam selection accuracy, throughput ratios, beam sweep time, latency, and initial access times, which are used to evaluate the performance and efficiency of the proposed technologies. Simulation results demonstrate that by exploiting contextual information from distributed collaborative SRUs, and UE sends its own sensing information via a physical random-access channel in addition to using SMTL, our H-RAN-based initial access scheme can achieve 82.9% throughput of an exhaustive beam search (EBS) based-O-RAN network without any beam search overhead and 96.7% by searching among as few as 5 beams. Compared to the conventional MMW 5G-NR solution, our proposed method significantly minimizes the beam search time needed to reach the desired throughput. INDEX TERMSHydra Radio Access Network (H-RAN), Multi-functional networks, Perceptive networks, heterogeneous data, AI/ML engines, Collaborative-based approach, Sparse multi-task learning (SMTL), Initial access.

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Section: A Related Workmentioning

confidence: 99%

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-1 Approach

Abd,

Findley,

Soon Kim

2024

IEEE Access

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“…Furthermore, the authors of [17] proposed to employ efficient convolutional neural networks (CNN) for determining the optimal narrow-angle beam, while testing only the wide-angle beam codewords. To further reduce the pilot overhead, the authors of [18] proposed to utilize the pilot signals received at the sub-6G BS to narrow down the range of the optimal beam. In [19], a beam training scheme utilising the user's 3D coordinates was proposed, where a FCNN was trained to map the coordinates into optimal beam codewords, but the acquisition of the user's coordinates is difficult in practice.…”

Section: A State-of-the-artmentioning

confidence: 99%

Deep Learning Based Beam Training for Extremely Large-Scale Massive MIMO in Near-Field Domain

et al. 2023

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Extremely large-scale multiple-input multipleoutput (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. Firstly, new near-field channel models and near-field XL-MIMO transmit beamforming (TBF) codebooks have to be adopted due to the dramatic increase in the number of antennas, which results in an excessive pilot overhead for beam training. Secondly, when the user density is high, the wireless propagation environments of the adjacent users are similar and hence the pilot signals received by the BS from different users appear to be interrelated, which is potentially beneficial but difficult to exploit. Thirdly, different users might share the same beam-direction, which causes excessive inter-user interference. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is utilized to map the beamforming gain information of the far-field wide beams to the optimal near-field beam for each user. In addition, the proposed GNN-based scheme can exploit the position-correlation between adjacent users for further improvement of the accuracy of beam training. In the second phase, a beam allocation scheme based on the probability vectors produced at the outputs of GNNs is proposed to address the above beam-direction conflicts between users. In the third phase, the hybrid TBF is designed for further reducing the inter-user interference. Our simulation results show that the proposed scheme improves the beam training performance of the benchmarks based on traditional neural networks. Hence it is more suitable for multiuser XL-MIMO systems. Moreover, the performance of the proposed beam training scheme approaches that of an exhaustive search, despite requiring only about 7% of the pilot overhead.

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“…Due to the same physical environment, channels in low and high carrier frequencies are correlated. To reap this benefit, many studies learn to predict the mmWave channel with full sub-6 GHz channel and limited low-overhead measurement of the mmWave channel [29]- [31]. In [30], a dual-input neural network (NN) architecture is designed to merge the sub-6 GHz channel and the mmWave channel of a few active antennas.…”

Section: A Related Workmentioning

confidence: 99%

Evaluation of Private Enterprises Based on Deep Learning

Meng

Zhang

2022

Mobile Information Systems

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In this paper, we conduct an in-depth research on the corresponding enterprises, combined with some problems existing in the process of data processing and use. We establish a deep learning model on the extensive collection and comprehensive investigation of the research results of domestic and foreign enterprises in all aspects of the process of data processing and use, and determine the research directions. Firstly, in view of the increasing complexity and dimension of enterprise data, and the difficulties of enterprise data application, this paper studies the related data preprocessing methods. Secondly, aiming at the problems of enterprise cost control and customer relationship management, this paper studies the prediction based on enterprise data through the analysis of practical problems and the processing of corresponding data. Finally, in order to progress and advance the efficiency and scientific usefulness of enterprise management, we in this paper study the evaluation based on enterprise data. The model is verified through simulations and compared with several models i.e. cross hybrid and sequential hybrid models. Using certain assumptions, the attained outcomes confirm that the accuracy of the deep learning structure of the single model is sophisticated and greater than that of the cross hybrid model, but lower than that of the sequential hybrid model.

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Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach

Cited by 23 publications

References 49 publications

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-1 Approach

Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, Task-1 Approach

Deep Learning Based Beam Training for Extremely Large-Scale Massive MIMO in Near-Field Domain

Evaluation of Private Enterprises Based on Deep Learning

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