Energy Efficient MIMO-OFDM Spectrum Sensing Using Deep Stacked Spiking Delayed Feedback Reservoir Computing

Hamedani, Kian; Liu, Lingjia; Yi, Yang

doi:10.1109/tgcn.2020.3046725

Cited by 14 publications

(5 citation statements)

References 38 publications

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“…Another interesting study developed an energy efficient spectrum-sensing method for multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) systems to take advantage of the spatial and temporal correlations of the environment. The aim of this approach was to address the problematic lack of data when applying ML techniques to MIMO-OFDM systems [57]. A highly relevant study and the most cited in Scopus (175) was authored by C. Liu and colleagues, who reported a deep reinforcement learning (DRL) method for developing smart mobile terminals for industrial IoT applications to facilitate data collection and sharing with minimal energy consumption [58].…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Applications in Energy Efficiency: A Bibliometric Approach and Research Agenda

Valencia-Arias,

García-Pineda,

González-Ruiz

et al. 2023

Designs

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The high demand for energy resources due to the increasing number of electronic devices has prompted the constant search for different or alternative energy sources to reduce energy consumption, aiming to meet the high demand for energy without exceeding the consumption of natural sources. In this context, the objective of this study was to examine research trends in the machine-learning-based design of electrical and electronic devices. The methodological approach was based on the analysis of 152 academic documents on this topic selected from Scopus and Web of Science in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Quantity, quality, and structural indicators were calculated to contextualize its thematic evolution. The results showed a growing interest in the subject since 2019, mainly in the United States and China, which stand out as world powers in the information and communication technology industry. Moreover, most studies focused on developing devices for controlling, monitoring and reducing energy consumption, mainly in 5G and thermal comfort devices, primarily using deep-learning techniques.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning Applications in Energy Efficiency: A Bibliometric Approach and Research Agenda

Valencia-Arias,

García-Pineda,

González-Ruiz

et al. 2023

Designs

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“…Letaief Furthermore, the authors in [23][24][25][26][27][28][29][30][31][32][33][34][35][36] emphasized the importance of EE in 6G wireless communication, demonstrating the reduced energy cost and carbon footprint. Moreover, the authors in [23][24][25][26][27][28][29][30][31][32][33][34][35][36] illustrated green energy efficiency and Energy Harvesting (EH) frameworks, optimization methodologies and RA, routing protocols, and a Game Theory (GT) approach. In particular, P. Annamalai et al introduced a RA algorithm accomplishing increased SE in 5G and beyond cellular networks [24].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Energy, Spectrum, and SINR-Margin Tradeoff Enabled Resource Allocation Strategies for 6G

Pathak,

Chethan,

Pandya

et al. 2024

IEEE Access

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In the rapidly evolving landscape of wireless communication systems, the forthcoming sixthgeneration technology aims to achieve remarkable milestones, including ultra-high data rates and improved Spectrum Efficiency (SE), Energy Efficiency (EE), and quality of service. However, a key challenge lies in the transmission at Terahertz frequencies, which entails significant signal loss, resulting in reduced signalto-interference and noise ratio margins (Γ). Increased transmit power can ameliorate Γ and SE, thereby sacrificing EE. Consequently, it necessitates strategic Resource Allocation (RA) to uphold an optimal tradeoff amid SE, EE and Γ. In this paper, we propose a series of RA strategic algorithms harnessing the Transfer Learning, Growth-Share (GS) matrix, Game Theory (GT), and service priorities to tailor the aforementioned trade-off. This endeavour renders the network more intelligent, self-sufficient, and resilient. Furthermore, we have seamlessly integrated Device-to-Device communication scenarios into our proposed algorithms, enhancing SE and network capacity. The proposed integration aims to strengthen overall system performance and accommodate the evolving demands of future wireless networks. Our primary contribution lies in the development of the GS-GT-based Optimal PathFinder (GS-GTOPF) algorithm to identify optimal paths based on SE using Deep Neural Networks. Thereafter, we formulate an enhanced version of GS-GTOPF by integrating service priorities (GS-GTOPF-SP). This refinement has been further advanced by reducing the Computational Time (CT), resulting in GS-GTOPF-SP-rCT. Further improvement is achieved by introducing the angle criterion (GS-GTOPF-SP-rCT-θ). Extensive simulations demonstrate that GS-GTOPF-SP-rCT-θ, showcases a remarkable 76.12% reduction in CT while maintaining an accuracy surpassing 95% compared to GS-GTOPF. Moreover, prioritizing high-priority services leads to a significant enhancement of 12.97% and 62.95% in SE, 16.14% and 81.97% in EE, and 12.27% and 25.95% in Γ when compared to medium and low-priority services. INDEX TERMS Terahertz (THz) communication, Transferred Learning (TL), Energy Efficiency (EE), Spectrum Efficiency (SE), Signal to Interference and Noise Ratio-margin (Γ), Residual Battery Indicator (RBI). I. INTRODUCTIONThe continuous progression of wireless communication technology's advanced applications and use cases has led to a re-markable surge in the user demand for high Bandwidth (BW) and capacity. This trend has catalyzed a significant evolution in wireless technology, transitioning from the established

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“…The future of wireless communication networks will be defined by the capacity to effectively employ resources to fulfill the demand for expanding diversity in services and user behavior 52,53 . In Reference 54, in DSS contexts, this study introduces a new energy‐efficient spectrum sensing method for MIMO‐OFDM systems.…”

Section: Literature Surveymentioning

confidence: 99%

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

Mazumdar

Gohel

2023

Engineering Reports

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The growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies.

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Energy Efficient MIMO-OFDM Spectrum Sensing Using Deep Stacked Spiking Delayed Feedback Reservoir Computing

Cited by 14 publications

References 38 publications

Machine-Learning Applications in Energy Efficiency: A Bibliometric Approach and Research Agenda

Machine-Learning Applications in Energy Efficiency: A Bibliometric Approach and Research Agenda

Deep Learning Based Energy, Spectrum, and SINR-Margin Tradeoff Enabled Resource Allocation Strategies for 6G

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

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