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Related Experiment Video

Updated: Sep 22, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

DDPG-Based Throughput Optimization with AoI Constraint in Ambient Backscatter-Assisted Overlay CRN.

Xueli Jia¹, Kechen Zheng¹, Kaikai Chi¹

¹School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

This study optimizes throughput in ambient backscatter (AB) communications and RF-powered cognitive radio networks (CRNs) using deep reinforcement learning. Results show a balance between data freshness (Age of Information) and high throughput, improving network performance.

Keywords:

DDPG age of information ambient backscatter cognitive radio networks

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Last Updated: Sep 22, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

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Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Area of Science:

Wireless Communications
Network Optimization
Data Freshness Metrics

Background:

Ambient Backscatter (AB) communications (ABCs) and RF-powered Cognitive Radio Networks (CRNs) address energy and spectrum challenges.
Growing demand for high-throughput and timely data in wireless networks necessitates advanced optimization techniques.
Age of Information (AoI) is a critical metric for data freshness in dynamic network environments.

Purpose of the Study:

To optimize long-term throughput in AB-assisted overlay CRNs (ABO-CRNs) under an Age of Information (AoI) constraint.
To investigate the trade-offs between throughput and data freshness in integrated ABCs and CRNs.
To develop a robust optimization framework for dynamic wireless network environments with incomplete information.

Main Methods:

Deep Reinforcement Learning (DRL), specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, was employed for throughput optimization.
Novel reward functions were developed considering time and energy allocation when AoI constraints are violated.
Analysis of the impact of minimum throughput requirements and maximum allowable AoI on network performance.

Main Results:

The proposed DRL-based scheme effectively optimizes throughput in ABO-CRNs while adhering to AoI constraints.
Simulation results demonstrate that the ABO-CRN achieves throughput close to the throughput-optimal (T-O) baseline.
The achieved AoI in the ABO-CRN closely approximates the AoI-optimal (A-O) baseline.

Conclusions:

The DRL-based approach provides an effective solution for balancing throughput and data freshness in complex wireless networks.
The integrated ABO-CRN framework demonstrates superior performance compared to traditional schemes, offering near-optimal throughput and data timeliness.
This research contributes a novel method for optimizing wireless networks facing energy and spectrum limitations.