Full-Duplex meets Reconfigurable Surfaces: RIS-assisted SIC for Full-Duplex Radios

Tewes, Simon; Heinrichs, Markus; Staat, Paul; Kronberger, Rainer; Sezgin, Aydin

doi:10.1109/icc45855.2022.9839223

Cited by 21 publications

(8 citation statements)

References 13 publications

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“…It is based on the ray-tracing simulator Remcom Wireless Insite [57]. Tewes et al [58] gathered physical channel measurements of various geometric antenna arrangements and RIS surfaces to generate a dataset. The data are publicly available on the IEEEDataPort platform [59] and on a GitHub repository [60].…”

Section: Resources For Generating Databases To Train ML On Wireless C...mentioning

confidence: 99%

“…The model performance depends directly on data. Although there exist benchmarks that use channel realizations to generate an input to build DL-based solutions for RIS-assisted wireless communications [56], only a few researchers have considered gathering physical measurements to train ML algorithms [58]. Ensuring data integrity, security and scalability are the main concerns when creating a database for the implementation of learning algorithms.…”

Section: Database Shortagementioning

confidence: 99%

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Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Ibarra-Hernández,

Castillo-Soria,

Gutiérrez

et al. 2024

Future Internet

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Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic properties for controlling the propagation of the signals in the wireless channel. The ML-optimized (RIS)-assisted wireless communication systems can be an effective alternative to mitigate the degradation suffered by the signal in the wireless channel, providing significant advantages in the system’s performance. However, the variety of approaches, system configurations, and channel conditions make it difficult to determine the best technique or group of techniques for effectively implementing an optimal solution. This paper presents a comprehensive review of the reported frameworks in the literature that apply ML and RISs to improve the overall performance of the wireless communication system. This paper compares the ML strategies that can be used to address the RIS-assisted system design. The systems are classified according to the ML method, the databases used, the implementation complexity, and the reported performance gains. Finally, we shed light on the challenges and opportunities in designing and implementing future RIS-assisted wireless communication systems based on ML strategies.

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Section: Resources For Generating Databases To Train ML On Wireless C...mentioning

confidence: 99%

Section: Database Shortagementioning

confidence: 99%

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Ibarra-Hernández,

Castillo-Soria,

Gutiérrez

et al. 2024

Future Internet

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“…IRSs have been considered in other contexts as well. For example, in [17], the authors utilize IRS to enhance self-interference cancellation for in-band full-duplex communication systems. Several authors have explored the use of IRSs in vehicular and aerial networks.…”

Section: Related Workmentioning

confidence: 99%

Static Reflective Surfaces for Improved Terahertz Coverage

Le,

Singh

2024

Preprint

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LoS (Line of Sight) MIMO (Multiple Input Multiple Output) is considered the best way to deliver high capacity channels for terahertz communications due to the severe attenuation suffered by reflected components. Unfortunately, terahertz links are easily blocked by any obstruction resulting in link breakage. Therefore, it is necessary to provide alternative paths via reflectors. A problem shared by LoS paths and reflected paths (via polished reflectors) is that the channel matrix is rank 1 in the far-field. As a result, the achieved capacity is lower than what can theoretically be achieved in a rich multi-path environment. In this work, we simultaneously solve the channel rank problem and the coverage problem by using static reflective surfaces which provide limited scattering of the incident signal in a way that minimizes signal loss but provides multiple paths to the receiver with varying phase. We construct such a surface and characterize the received signal using a terahertz testbed. We show that using our surface, we can improve channel capacity for 2x2 LoS MIMO. We also develop a theoretical model for the received signal and show that the reflected capacity matches the measured capacity well.

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“…If the performance is improved by achieving a higher sum-rate and lower power consumption, then the on/off status will be updated [52]. Similarly, this greedy scheme is applied to control RIS phase shifts in [30], [64], [110], indicating that one element is optimized at a time, and all RIS elements are optimized sequentially over the iterations.…”

Section: Greedy Algorithmsmentioning

confidence: 99%

“…Greedy algorithms are used for RIS phase control in [30], [64], [110] and on/off control in [52] as low-complexity solutions.…”

Section: Greedy Algorithmsmentioning

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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Full-Duplex meets Reconfigurable Surfaces: RIS-assisted SIC for Full-Duplex Radios

Cited by 21 publications

References 13 publications

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Static Reflective Surfaces for Improved Terahertz Coverage

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

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