DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset

Alkhateeb, Ahmed; Charan, Gouranga; Osman, Tawfik; Hredzak, Andrew; Demirhan, Umut; Srinivas, Nikhil

doi:10.48550/arxiv.2211.09769

Cited by 6 publications

(13 citation statements)

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“…After substituting (10) in (9) and performing some mathematical manipulations, the IF signal can be written as follows:…”

Section: B Radar Modelmentioning

confidence: 99%

“…The T R parameter entails several sub-processes that the framework executes to prepare for the next stage of generating the data samples. Table II presents the typical radar system parameters considered in this study [10]. Initially, we measure the duration of each measurement, denoted as T m , which involves transmitting 128 chirps, each lasting for 60 µs, requiring a total of 8.3 ms. Next, we calculate the maximum time a radar signal can remain in the air, given that the maximum radar range is set to 100 meters.…”

Section: B Radar Measurements Processingmentioning

confidence: 99%

“…This consequently reduces the overhead on the network's transmission resources. Benefiting from the real-world DeepSense dataset [10], a set of non-independent and identically distributed (non-IID) features is extracted to collaboratively train a dual-output NN model offline using a group of SBSs. The trained model is then used online at each SBS to predict the occurrence of a blockage event and the remaining time before the obstacle obstructs the link.…”

mentioning

confidence: 99%

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Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

Al-Quraan

Khan

Mohjazi

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users' location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

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“…After substituting (10) in (9) and performing some mathematical manipulations, the IF signal can be written as follows:…”

Section: B Radar Modelmentioning

confidence: 99%

Section: B Radar Measurements Processingmentioning

confidence: 99%

mentioning

confidence: 99%

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Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

Al-Quraan

Khan

Mohjazi

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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“…For more detail on scene creation and rendering, we refer to Sionna's documentation 1 and the video tutorial. 2 Sionna RT allows for the definition of arbitrary radio materials which are characterized by their relative permittivity ε r and conductivity σ. Currently, only non-magnetic materials are supported, i.e., µ r = 1.…”

Section: Sionna Rtmentioning

confidence: 99%

“…Many 6G research topics require the simulation of specific radio environments by ray tracing. Examples are integrated sensing and communications (ISAC) [1], multi-modal sensing [2], reconfigurable intelligent surfaces (RIS) [3], radio-based localization [4], machine learning (ML)-based transceiver algorithms [5], as well as most of the use-cases of the recently started 3GPP study-item on AI/ML for the air interface [6]. The reason for this is that a spatially consistent correspondence between a physical location in a scene and the channel impulse response (CIR) is required which the widely used stochastic channel models such as [7] cannot provide.…”

Section: Introductionmentioning

confidence: 99%

Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling

Hoydis¹,

Aoudia²,

Cammerer³

et al. 2023

Preprint

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Sionna™ is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. Its latest release (v0.14) integrates a differentiable ray tracer (RT) for the simulation of radio wave propagation. This unique feature allows for the computation of gradients of the channel impulse response and other related quantities with respect to many system and environment parameters, such as material properties, antenna patterns, array geometries, as well as transmitter and receiver orientations and positions. In this paper, we outline the key components of Sionna RT and showcase example applications such as learning radio materials and optimizing transmitter orientations by gradient descent. While classic ray tracing is a crucial tool for 6G research topics like reconfigurable intelligent surfaces, integrated sensing and communications, as well as user localization, differentiable ray tracing is a key enabler for many novel and exciting research directions, for example, digital twins.

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Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

Hafi,

Brik,

Frangoudis

et al. 2024

IEEE Access

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Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. The revolution of 6G networks is driven by massive data availability, moving from centralized and big data towards small and distributed data. This trend has motivated the adoption of distributed and collaborative ML/DL techniques. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique that recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.INDEX TERMS 6G networks, wireless communication, federated deep learning, split deep learning, split federated learning.

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DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset

Cited by 6 publications

References 0 publications

Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

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