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Energy harvesting NOMA spectral efficiency with DRL?

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Cognitive radio

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Energy harvesting in Non-Orthogonal Multiple Access (NOMA) systems, combined with Deep Reinforcement Learning (DRL) techniques, enhances spectral efficiency. By utilizing solar energy harvesters to recharge devices, NOMA networks can prolong operations and improve throughput. DRL, specifically Deep Deterministic Policy Gradient (DDPG), optimizes throughput in NOMA Cognitive Radio (CR) networks, outperforming traditional algorithms like Deep Q Network and actor critic methods. Additionally, the integration of NOMA with energy harvesting over fading channels in IoT networks demonstrates increased spectral efficiency. The system's outage probabilities are influenced by parameters like harvesting time, transmission power, and the number of IoT devices, highlighting the importance of optimal energy harvesting strategies for improved network performance.

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Proceedings Article•DOI Deep Deterministic Policy Gradient for Throughput Maximization in Energy Harvesting NOMA-Cognitive Radio Network Saikat Majumder, Sumit Chakravarty - Show less +1 more 24 Jan 2023	Deep Deterministic Policy Gradient (DDPG) based DRL enhances throughput in energy harvesting NOMA-CR networks, boosting spectral efficiency compared to traditional methods like Deep Q Network and actor critic algorithms.
Proceedings Article•DOI Deep Deterministic Policy Gradient for Throughput Maximization in Energy Harvesting NOMA-Cognitive Radio Network 24 Jan 2023	Deep Deterministic Policy Gradient (DDPG) based DRL enhances throughput in energy harvesting NOMA-CR networks, boosting spectral efficiency compared to traditional methods like Deep Q Network and actor critic algorithms.
Proceedings Article•DOI DRL-based Joint Optimization for Energy Efficiency Maximization in DAV-NOMA Networks Shuhua Liu, Ang Gao, Qinyu (Ryan) Wang, Yansu Hu - Show less +3 more 05 May 2023	The paper focuses on maximizing spectrum energy efficiency in DAV-NOMA networks using a DRL-based joint optimization algorithm, enhancing efficiency through service assignment, trajectory optimization, and power control.
Proceedings Article•DOI DRL-based Joint Optimization for Energy Efficiency Maximization in DAV-NOMA Networks 05 May 2023	Not addressed in the paper.

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NOMA technoloty and DSS Techniques for spectral efficiency for machine-type communication?5 answersNon-orthogonal multiple access (NOMA) and spectrum sensing (SS) techniques are pivotal for enhancing spectral efficiency in machine-type communications. NOMA aids in efficient resource allocation, while NOMA-based satellite networks demonstrate high throughput and scalability for massive machine-type communication (mMTC) applications. Additionally, NOMA in cooperative device-to-device (D2D) systems, coupled with decoding schemes like single signal and maximum ratio combining (MRC), significantly improves system performance. By creatively integrating feature detection-based SS methods with NOMA systems, the relationship between users' signals can be better distinguished, leading to enhanced detection and throughput performance. These advancements collectively contribute to optimizing spectral efficiency in machine-type communications, catering to the increasing demands of IoT and next-generation wireless networks.

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How does non-orthogonal multiple accessing improve spectrum efficiency in cognitive radio sensor networks?5 answersNon-orthogonal multiple access (NOMA) enhances spectrum efficiency in cognitive radio sensor networks by allowing multiple users to share the same transmission medium simultaneously, thereby increasing spectral utilization efficiency. NOMA, integrated with cognitive radio networks, enables unlicensed users to transmit on licensed users' frequency bands, ensuring acceptable reliability for the licensed users while maximizing spectral efficiency. By employing NOMA in cognitive radio systems, users can access the spectrum even when the primary user is active, optimizing bandwidth utilization. Additionally, NOMA schemes designed for IoT networks ensure fairness among users, further improving connections and throughput performance. Overall, NOMA's innovative techniques, such as feature detection-based spectrum sensing and user fairness-oriented power allocation, contribute significantly to enhancing spectrum efficiency in cognitive radio sensor networks.

How to do RF Energy Harvesting?5 answersRF energy harvesting can be done by designing and fabricating a system that can scavenge ambient RF signals and convert them into usable energy. The system typically consists of an antenna to receive the RF signals, a rectifier to convert the RF waves into DC voltages, and energy storage elements such as supercapacitors or rechargeable batteries to store the harvested energy. The efficiency of the RF energy harvesting device can be analyzed and compared to simulated values, with average power efficiencies ranging from 38.2% to 82% observed in laboratory setups. The harvested energy can then be used to power low-energy devices, such as IoT devices, eliminating the need for batteries and reducing environmental pollution caused by battery waste. RF energy harvesting is a promising alternative for extending the lifetime of power-constrained wireless networks and has gained interest in various industries, including telecommunications.

What are the design considerations for a MEMS based RF energy harvester for battery-less remote control?4 answersA MEMS-based RF energy harvester for battery-less remote control requires careful design considerations. The harvester should have an unconventionally shaped cantilever structure with a triangular tip, which can create greater stress and improve stress distribution compared to traditional rectangular and triangular structures. The harvester should utilize the piezoelectric effect to convert mechanical vibrations into electrical energy. The use of COMSOL Multiphysics software can help model and optimize the harvester's mechanical and electrical behavior. The dimensions of the cantilever, as well as the choice of materials such as stainless steel as the substrate and lead zirconate titanate (PZT) as the piezoelectric material, should be carefully considered. Additionally, the harvester should be designed to maximize power conversion efficiency and output voltage, potentially using a dynamic voltage compensated cross-coupled fully differential rectifier and a synchronous DC-DC boost converter. The integration of a matching network and maximum power point tracking (MPPT) controller can further enhance the performance of the harvester.

How can radio frequency energy harvesting be used to power biomedical devices?5 answersRadio frequency (RF) energy harvesting can be used to power biomedical devices by extracting energy from ambient electromagnetic waves. This energy can be converted into useful electrical energy to energize low-power electronic circuits in these devices. The RF energy harvesting systems consist of antennas that capture RF signals and convert them into electrical energy. These antennas are designed and simulated using software tools like CST Suite Studio. The performance of the antennas is analyzed to ensure efficient energy capture. Additionally, rectifying and voltage doubler circuits are used to convert the harvested voltage signal into DC voltage. The efficiency of these circuits is studied to optimize power delivery. By utilizing RF energy harvesting systems, low-power portable biomedical devices can be charged and operated without the need for traditional batteries.

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