Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G

Yao, Yujie; Zhou, Hao; Erol‐Kantarci, Melike

doi:10.1109/globecom48099.2022.10001317

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Table VII summarizes the mentioned ML approaches in the field of tracking and localization for ISAC. Other localization works discussed in other use cases include [43], [63], [65].…”

Section: E Data-driven Methods In Isac For Tracking and Localizationmentioning

confidence: 99%

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Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

Salem,

Quamar,,

Magad

et al. 2023

Preprint

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<p>This paper aims to explore integrating various ML techniques into ISAC systems, covering various applications. These applications span intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs) and autonomous cars, as well as radar applications, localization and tracking, millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming. The contributions of this paper lie in its comprehensive survey of ML-based works in the ISAC domain and its identification of challenges and future research directions. By synthesizing the existing knowledge and proposing new research avenues, this survey serves as a valuable resource for researchers, practitioners, and stakeholders involved in advancing the capabilities of ISAC systems in the context of 6G networks.</p>

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“…Table VII summarizes the mentioned ML approaches in the field of tracking and localization for ISAC. Other localization works discussed in other use cases include [43], [63], [65].…”

Section: E Data-driven Methods In Isac For Tracking and Localizationmentioning

confidence: 99%

“…Another DRL-based beam management scheme is proposed in [65], where user location uncertainty was considered in mmWave networks in a joint vision-aided sensing and communication system. Features were extracted from satellite images to enhance the localization accuracy.…”

Section: Data-driven Approaches For Beamforming In Isacmentioning

confidence: 99%

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

Salem,

Quamar,,

Magad

et al. 2023

Preprint

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“…ISAC is recently emerging as a key technology to support ubiquitous wireless connectivity and accurate sensing [208]. Target detection and parameter estimation are two primary tasks in radar sensing [209], and RISs can be deployed to provide virtual LoS signal transmission, enabling the radar to sense targets in blocked areas [210]. Radar communication coexistence (RCC) and dual-functional radar communication (DFRC) are two typical ISAC applications.…”

Section: F Ris-aided Isacmentioning

confidence: 99%

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Zhang¹,

Erol‐Kantarci²,

Poor³

2023

IEEE Wireless Commun.

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Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey on optimization techniques for RISaided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sum-rate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining low-complexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Modelbased, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RISaided applications towards 6G networks and identify future challenges.

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“…DL does not require any knowledge about the underlying models as it is optimized based on training data, which inherently captures the potential impairments of the system. DL has been investigated in the context of ISAC for a vast range of applications, such as predictive beamforming in vehicular networks [23]- [25], waveform design [26] and channel estimation [27] in intelligent reflecting surface (IRS)-assisted ISAC scenarios, multitarget sensing and communication in THz transmissions [28], or efficient resource management [29], [30]. However, most previous works on DL for ISAC consider single-component optimization, either at the transmitter or receiver.…”

mentioning

confidence: 99%

End-to-End Learning for Integrated Sensing and Communication

Mateos-Ramos

Song

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments. A monostatic orthogonal frequencydivision multiplexing (OFDM) sensing and multiple-input singleoutput (MISO) communication scenario is considered, incorporating hardware imperfections at the ISAC transceiver antenna array. To enable end-to-end learning of the ISAC transmitter and sensing receiver, we propose a novel differentiable version of the orthogonal matching pursuit (OMP) algorithm that is suitable for multi-target sensing. Based on the differentiable OMP, we devise two model-based parameterization strategies to account for hardware impairments: (i) learning a dictionary of steering vectors for different angles, and (ii) learning the parameterized hardware impairments. For the single-target case, we carry out a comprehensive performance analysis of the proposed model-based learning approaches, a neural-networkbased learning approach and a strong baseline consisting of least-squares beamforming, conventional OMP, and maximumlikelihood symbol detection for communication. Results show that learning the parameterized hardware impairments offers higher detection probability, better angle and range estimation accuracy, lower communication symbol error rate (SER), and exhibits the lowest complexity among all learning methods. Lastly, we demonstrate that learning the parameterized hardware impairments is scalable also to multiple targets, revealing significant improvements in terms of ISAC performance over the baseline.Index Terms-Hardware impairments, integrated sensing and communication (ISAC), joint communication and sensing (JCAS), machine learning, model-based learning, orthogonal matching pursuit (OMP). I. INTRODUCTIONN EXT-generation wireless communication systems are expected to operate at higher carrier frequencies to meet the data rate requirements necessary for emerging use cases such as smart cities, e-health, and digital twins for manufacturing [1]-[4]. Higher carrier frequencies also enable new functionalities, such as integrated sensing and communication (ISAC).

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Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G

Cited by 6 publications

References 15 publications

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

End-to-End Learning for Integrated Sensing and Communication

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