Deep-Learning-Assisted IoT-Based RIS for Cooperative Communications

Sagir, Bulent; Aydin, Erdogan; İlhan, Hacı

doi:10.1109/jiot.2023.3239818

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…For example, to optimize the SNR in IRS-aided NOMA, the neural network predicts the phase shifts of the IRS [83]. The architecture of the DNN is illustrated in Figure , comprising an input layer, hidden layers, and a regression layer at the output.…”

Section: Ai/ml For Irs-assisted Cooperative Nomamentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

Sharma,

Mishra,

Kumar

et al. 2024

IEEE Access

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The integration of intelligent reflecting surfaces (IRS) into cooperative non-orthogonal multiple access (NOMA) systems revolutionizes wireless networks by enhancing signal strength, mitigating interference, and optimizing spectral efficiency. The cooperative NOMA (CNOMA) framework, empowered by IRS technology, further promises enhanced performance, robustness, and scalability for next-generation wireless networks as compared to NOMA only systems. This paper explores the synergy between IRS and NOMA to leverage cooperative techniques for superior wireless system design. Fundamental principles, technological advancements, and potential applications of IRS-assisted CNOMA systems are discussed, highlighting existing works. Both underlay and overlay NOMA principles are examined in conjunction with IRS in the paper. Spatial modulation-aided CNOMA is explored for multiple-input multiple-output (MIMO) systems, along with its advantages and practical challenges. Additionally, the paper discusses fundamental principles and technological advancements of IRS-assisted CNOMA systems, emphasizing solutions to potential challenges and the role of machine learning (ML)/deep learning (DL) in resource optimization like transmit power and IRS phase settings. Simulation results are presented to highlight the benefits of IRS-aided CNOMA system design. Finally, the paper outlines future directions and potential research topics in IRSaided CNOMA.

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Section: Ai/ml For Irs-assisted Cooperative Nomamentioning

confidence: 99%

“…Additionally, the DNN minimizes a loss function between the actual phase and the phase shift predicted by the network. DNN phase optimization can significantly enhance system performance, especially in multiple IRS scenarios [83].…”

Section: Ai/ml For Irs-assisted Cooperative Nomamentioning

confidence: 99%

Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

Sharma,

Mishra,

Kumar

et al. 2024

IEEE Access

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“…Extensive simulations demonstrate reduced bit error rates, showcasing the superiority of the proposed cooperative mechanism. In this article, we present and examine a cooperative method called predictive relay selection (PRS), which is supported by deep learning techniques [12] Cooperative RIS (CRIS) models which utilizes two novel DNN ( deep neural network) [13]. Due to the inductive nature of deep learning (DL) principles, which differ from traditional rule-based algorithms, individuals attempting to apply DL techniques for channel estimation may encounter challenges and become overwhelmed by the numerous parameters to manage and intricate nuances to consider.…”

Section: Background Studymentioning

confidence: 99%

Cooperative Diversity with Dual Relay Alamouti Scheme in Wireless Communication

Siddiqui,

Yang,

Xiao

et al. 2023

Preprint

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Wireless communication has continually faced challenges associated with fading propagation channels, with detrimental effects arising from multipath fading. Though effective in addressing these challenges, traditional diversity techniques often require multiple antennas, which a transmitter's size can constrain. This study introduces an innovative approach to overcoming these limitations through cooperative diversity. By leveraging cooperative diversity, we enable single-antenna terminals to create a virtual multi-antenna array, thereby achieving the benefits of spatial transmitting diversity without the need for physical multiple antennas. We built our cooperative system around the Alamouti code, where two relays cooperate: each relay captures, processes, and cross-verifies the transmitted information with its counterpart to ensure accuracy. This cross-verification significantly mitigates the chances of data errors. The processed data is then collectively transmitted to the intended Destination. This cooperative mechanism inherently reduces noise interference, ensuring a higher-quality signal transmission. Through extensive simulations, our results demonstrate a notable reduction in bit error rates, making a solid case for the superiority of our two-relay scheme over conventional single-relay models. Incontrovertibly, our proposed transmission mechanism provides improved signal performance and increased stability in signal propagation, representing a notable advancement in the field of wireless networks.

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“…Since with data-driven machine learning methods many of the drawbacks of model-based approaches can be resolved, currently intensive research is conducted on machine learning approaches for several applications in communications engineering. This includes possible future scenarios like communications assisted by reconfigurable intelligent surfaces (RISs) [2], molecular communications [3], or integrated sensing and communication [4]. However, also in traditional wireless communication systems promising results can be achieved by means of machine learning.…”

mentioning

confidence: 99%

SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

Baumgartner,

Lang,

Huemer

2024

Trans. Mach. Learn. Comm. Netw.

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In recent years data-driven machine learning approaches have been extensively studied to replace or enhance traditionally model-based processing in digital communication systems. In this work, we focus on equalization and propose a novel neural network (NN-)based approach, referred to as SICNN. SICNN is designed by deep unfolding a model-based iterative soft interference cancellation (SIC) method. It eliminates the main disadvantages of its model-based counterpart, which suffers from high computational complexity and performance degradation due to required approximations. We present different variants of SICNN. SICNNv1 is specifically tailored to single carrier frequency domain equalization (SC-FDE) systems, the communication system mainly regarded in this work. SICNNv2 is more universal and is applicable as an equalizer in any communication system with a block-based data transmission scheme. Moreover, for both SICNNv1 and SICNNv2, we present versions with highly reduced numbers of learnable parameters. Another contribution of this work is a novel approach for generating training datasets for NN-based equalizers, which significantly improves their performance at high signal-to-noise ratios. We compare the bit error ratio performance of the proposed NN-based equalizers with state-of-the-art model-based and NN-based approaches, highlighting the superiority of SICNNv1 over all other methods for SC-FDE. Exemplarily, to emphasize its universality, SICNNv2 is additionally applied to a unique word orthogonal frequency division multiplexing (UW-OFDM) system, where it achieves state-of-theart performance. Furthermore, we present a thorough complexity analysis of the proposed NN-based equalization approaches, and we investigate the influence of the training set size on the performance of NN-based equalizers.

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Deep-Learning-Assisted IoT-Based RIS for Cooperative Communications

Cited by 14 publications

References 31 publications

Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

Cooperative Diversity with Dual Relay Alamouti Scheme in Wireless Communication

SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

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