Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard

Roy, Debashri; Salehi, Batool; Banou, Stella; Mohanti, Subhramoy; Reus-Muns, Guillem; Belgiovine, Mauro; Ganesh, Prashant; Dick, Chris; Chowdhury, Kaushik R.

doi:10.1016/j.comnet.2023.109729

Cited by 8 publications

(4 citation statements)

References 69 publications

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“…The authors of [5] claimed that AI/ML techniques have gained substantial attention for beam management frameworks in MMW/THz bands due to their ability to extract and track nonlinear environmental characteristics. A comprehensive survey of AI/ML-enabled beamforming techniques using out-of-band and multimodal data for MMW communication in next-generation networks is presented in [31]. The study demonstrates that Incorporating (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration.…”

Section: A Related Workmentioning

confidence: 99%

“…Also, to prevent overfitting in NNs, 5% dropout for all hidden layers is employed. We use Adam optimizer [31] in the training phase with 15 epochs, while the minibatch size is progressively increased from 16, 32, 64, 128, to 7190 samples. To reduce the effects of initial weights of NNs on SRU performance, we averaged results with 7 random weight initializations for each experiment.…”

Section: A Datasetmentioning

confidence: 99%

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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%

Section: A Datasetmentioning

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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“…To this end, various novel tech-nologies have been introduced in the physical layer, such as millimeter-wave (mmWave) transmission [3], non-orthogonal multiple access (NOMA) [4] as well as massive multiple input multiple output (m-MIMO) configurations [5]. Moreover, as the discussions on the next generation of wireless networks (sixth generation, 6G) have already started taking place [6], network densification is leveraged as an efficient way to provide seamless connectivity to a vast number of mobile devices. In this context, the single link concept (i.e., base station (BS) to mobile station (MS)) is replaced by various potential links from access points (APs) and relay nodes.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Adaptive Beamforming in Massive MIMO Millimeter Wave 5G Multicellular Networks

Lavdas,

Gkonis,

Tsaknaki

et al. 2023

Electronics

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The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network has the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system-level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, the EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required with respect to the non-ML framework.

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“…Reus-Muns et al utilized in [12] spatial information of mobile users such as location, speed, and surrounding scene images and proposed a method called channel covariance matrix to estimate the moving region containing all possible user locations at any given time and later they introduced deep learning based denoising method to reduce the error of the user locations not estimated accurately. In [13], Roy et al presented a survey related to deep learning-based fusion framework leveraging the GPS location information with the combination of the visual data. Further, Salehi et al presented in [14] beam selection for mmWave links in a vehicular scenario by leveraging the data collected from sensors like LiDAR, camera images, and GPS.…”

Section: Introductionmentioning

confidence: 99%

On the Vision-Beam Aided Tracking for Wireless 5G-Beyond Networks Using Long Short-Term Memory with Soft Attention Mechanism

Sinani

Yılmaz

2022

EJRnD

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The growth of 5G technology and the continuous success of deep learning for various computer vision tasks in healthcare, self-driving cars, visual recognition, and many other areas, brought new challenges in the field of wireless communication. Moreover, 5G-Beyond networks primarily rely on how to maintain line-of-sight (LOS) links between base stations and mobile users. As such, one of the main challenges in 5G-Beyond networks is how to proactively maintain the hand-over mechanism for mobile users before blockages prevent mobile users from communicating, so as to avoid the latency of searching the best beamforming for the best performance. Accordingly, vision-aided millimeter-wave (mmWave) beam and blockage prediction has opened the door for new research for proactive hand-off and resource allocation. The purpose of this paper is to study wireless beam tracking on mmWave bands using deep learning approach evaluated on the Vision-Wireless ViWi-BT dataset [1]. We present how to predict future beam sequences from previously observed beam sequences and images using a long short-term memory (LSTM) network as a base predictive method. As such, we utilize the soft attention mechanism to intelligently choose the most important features and thus we suggest replacing the softmax attention function with different periodic attention functions to eliminate the gradient vanishing problem.

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Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard

Cited by 8 publications

References 69 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

A Deep Learning Framework for Adaptive Beamforming in Massive MIMO Millimeter Wave 5G Multicellular Networks

On the Vision-Beam Aided Tracking for Wireless 5G-Beyond Networks Using Long Short-Term Memory with Soft Attention Mechanism

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