A Self-Supervised Learning-Based Channel Estimation for IRS-Aided Communication Without Ground Truth

Zhang, Zhengming; Ji, Taotao; Shi, Haoqing; Li, Chunguo; Huang, Yongming; Yang, Luxi

doi:10.1109/twc.2023.3233970

Cited by 11 publications

(3 citation statements)

References 42 publications

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“…CE becomes critical in IRS-assisted systems as passive IRS cannot perform signal processing. Therefore, Zhang et al [94] presented a self-supervised learning approach to IRS-CE. During training, a DNN learns to output signals, similar to the original, when provided with the noisy version of the signal.…”

Section: H Data-driven Methods For Channel Estimation and Irsmentioning

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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<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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Section: H Data-driven Methods For Channel Estimation and Irsmentioning

confidence: 99%

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

Salem,

Quamar,,

Magad

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…CSI is manageable in simpler cases for example in the case where two IRSs are considered or in the case where all channels are ignored that are multihop reflected phenomena [19]- [21]. Techniques such as the two-time scale optimization approach [22], and deep learning [23], have been incorporated in IRS systems for the reduction of overheads in the CSI estimation. In addition to its computational complexity, CSI estimation presents a significant practical barrier due to network protocol and chip issues.…”

Section: A Related Workmentioning

confidence: 99%

6G: Technology Evolution in Future Wireless Networks

Shafi,

Jha,

Jain

2024

IEEE Access

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The Sixth Generation (6G) Wireless Communication Network (WCN) is the successive provision to ameliorate the gain with ultra-low latency, and e xtremely high energy efficiency. The 6G WCN enables the specifications of artificial intelligence to optimize the services and capabilities. The vision of the 6G era is expected to address a seamless fusion of communication between the human, physical world, and digital world. The latest 6G WCN standard is a fundamental foundation and requires immense attention in the field of research. This paper presents the framework of 6G WCN with an illustration of its key technologies. The different technologies involved in 6G are well explained with the demonstration of the communication scenario such that the key performance indicators are improved with major differences. The primary contribution of this paper is the explanation of the 6G with the technologies that have a drastic impact on the characteristic aspects of a wireless communication network such as data rate, spectrum efficiency, energy efficiency, connection density, and reliability. All these technologies have the capability to revolutionize the subsequent WCN.

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“…Because the number of channels is exponential in the number of IRSs, channel estimation is a tractable task only in some simple settings, e.g., when there are two IRSs [9]- [12], or when the multi-hop reflected channels are all neglected [13]. Some studies are devoted to the overhead reduction for channel estimation in IRS systems, e.g., the deep learning method [14] and the two-timescale optimization [15]. Aside from the computational difficulty, channel estimation for IRS also imposes a huge practical challenge because of the communication chip issue as well as the network protocol issue [8].…”

Section: Introductionmentioning

confidence: 99%

Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information

Xu¹,

Yao²,

Lai³

et al. 2023

Preprint

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Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a novel strategy called blind beamforming that coordinates multiple IRSs by means of statistics without knowing CSI. Blind beamforming only requires measuring the received signal power at the user terminal for a sequence of randomly generated phase shifts across all IRSs. The main idea is to extract the key statistical quantity for beamforming by exploring only a small portion of the whole solution space of phase shifts. We show that blind beamforming guarantees a signal-to-noise ratio (SNR) boost of Θ(N 2L ) under certain conditions, where L is the number of IRSs and N is the number of reflecting elements per IRS. The above result significantly improves upon the state of the art in the area of multi-IRS assisted communication. Moreover, blind beamforming is justified via field tests and simulations.Index Terms-Intelligent reflecting surface (IRS), reconfigurable intelligent surface (RIS), multi-IRS/RIS systems, blind beamforming without channel state information (CSI).

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A Self-Supervised Learning-Based Channel Estimation for IRS-Aided Communication Without Ground Truth

Cited by 11 publications

References 42 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

6G: Technology Evolution in Future Wireless Networks

Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information

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