Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks

Sun, Juan; Zhang, Shubin; Yang, Changsong; Huang, Liang

doi:10.3390/e24050596

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…In [43], the authors studied a prediction-based status update scheme for machine-type communications in the CR IoT, then analyzed the trade-off between the AoI and energy consumption, and designed an optimization algorithm to minimize the AoI. For a WPCRN, the average AoI minimization problem was formulated as partially observable markov decision process and solved by dynamic programming in [44]. In [45], the network throughput maximization problem with AoI constraint was studied for a WPCRN with backscatter communication, and the DDPG algorithm was proposed.…”

Section: Introductionmentioning

confidence: 99%

AoI-Oriented Resource Allocation for NOMA-Based Wireless Powered Cognitive Radio Networks Based on Multi-Agent Deep Reinforcement Learning

He,

Peng,

Liu

et al. 2024

IEEE Access

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In this paper, we study a wireless powered cognitive internet of things (IoT) network, where cognitive radio (CR) and non-orthogonal multiple access (NOMA) technologies are exploited to improve spectral efficiency, and radio frequency based energy harvesting (RF-EH) technology is integrated to achieve the sustainable IoT network. To ensure the freshness of information delivery, we investigate the age of information (AoI) as a performance metric, and formulate a long-term average AoI minimization problem under energy sustainability constraint, in which the working mode and transmit power of the secondary devices (SDs) are jointly optimized. Then, we reformulate it as a decentralized Markov decision process (Dec-MDP) with continuous action space. Accordingly, a deep reinforcement learning (DRL) framework is exploited, and a multi-agent twin delayed deep deterministic policy gradient algorithm with dual action selection mechanism (MATD3-DAS) is proposed, which adopts the centralized training and decentralized execution (CTDE) framework and exploits both actor and critic networks to select actions for improving exploration ability. Simulation results show that the proposed algorithm can significantly reduce the longterm average AoI, where the decrements approach 9.58% and 52.34% compared with the MATD3 algorithm and TD3-DAS algorithm with centralized training and centralized execution (CTCE). INDEX TERMSCognitive radio, non-orthogonal multiple access, radio frequency based energy harvesting, age of information, multi-agent deep reinforcement learning.

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Section: Introductionmentioning

confidence: 99%

AoI-Oriented Resource Allocation for NOMA-Based Wireless Powered Cognitive Radio Networks Based on Multi-Agent Deep Reinforcement Learning

He,

Peng,

Liu

et al. 2024

IEEE Access

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“…To evaluate the freshness of status updates, researchers have introduced the concept of the age of information (AoI) [ 1 ], which is defined as the time elapsed since the the generation of the most recent status update. Previous studies [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] have focused on developing scheduling algorithms that minimize the average AoI across the network in the generate-at-will scenario, which means the generation of status update packets can be controlled. Various low-complexity algorithms were presented in [ 2 ] to minimize the average AoI in the network, demonstrating their proximity to the theoretical lower bound.…”

Section: Introductionmentioning

confidence: 99%

Age of Synchronization Minimization Algorithms in Wireless Networks with Random Updates under Throughput Constraints

He,

Chen,

Chen

et al. 2023

Entropy

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This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) through a shared bandwidth-limited channel. Considering the random arrival of status updates, we measure the data freshness with the age of synchronization (AoS) metric; specifically, we use the time elapsed since the latest synchronization as a metric. The objective of this study is to minimize the weighted sum of the average AoS of the entire network while meeting the minimum throughput requirement of each node. We consider both the central scheduling scenario and the distributed scheduling scenario. In the central scheduling scenario, we propose the optimal stationary randomized policy when the transmission feedback is unavailable and the max-weight policy when it is available. In the distributed scenario, we propose a distributed policy. The complexity of the three scheduling policies is significantly low. Numerical simulations show that the policies can satisfy the throughput constraint in the central controlling scenario and the AoS performance of the max-weight policy is close to the lower bound.

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Age of information minimization in hybrid cognitive radio networks under a timely throughput constraint

El-Sherif,

Rabia,

El-Malek

et al. 2024

Performance Evaluation

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Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks

Cited by 5 publications

References 48 publications

AoI-Oriented Resource Allocation for NOMA-Based Wireless Powered Cognitive Radio Networks Based on Multi-Agent Deep Reinforcement Learning

AoI-Oriented Resource Allocation for NOMA-Based Wireless Powered Cognitive Radio Networks Based on Multi-Agent Deep Reinforcement Learning

Age of Synchronization Minimization Algorithms in Wireless Networks with Random Updates under Throughput Constraints

Age of information minimization in hybrid cognitive radio networks under a timely throughput constraint

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