Power Allocation Strategies in Energy Harvesting Wireless Cooperative Networks (1307.1630v2)

Abstract: In this paper, a wireless cooperative network is considered, in which multiple source-destination pairs communicate with each other via an energy harvesting relay. The focus of this paper is on the relay's strategies to distribute the harvested energy among the multiple users and their impact on the system performance. Specifically, a non-cooperative strategy is to use the energy harvested from the i-th source as the relay transmission power to the i-th destination, to which asymptotic results show that its outage performance decays as logSNR over SNR. A faster decaying rate, 1 over SNR, can be achieved by the two centralized strategies proposed this the paper, where the water filling based one can achieve optimal performance with respect to several criteria, with a price of high complexity. An auction based power allocation scheme is also proposed to achieve a better tradeoff between the system performance and complexity. Simulation results are provided to confirm the accuracy of the developed analytical results and facilitate a better performance comparison.

• The paper introduces a framework featuring non-cooperative, equal allocation, sequential water filling, and auction-based strategies for energy harvesting cooperative networks.
• It benchmarks the non-cooperative strategy with outage probability decaying as log(SNR)/SNR while equal and water filling methods achieve a decay of 1/SNR.
• The study shows that advanced auction-based and water filling schemes optimize channel use by prioritizing users with better conditions, reducing energy consumption overall.

Overview of Power Allocation Strategies in Energy Harvesting Wireless Cooperative Networks

The paper under discussion introduces a comprehensive framework for power allocation strategies in wireless cooperative networks, focusing on scenarios where multiple source-destination pairs communicate through an energy-harvesting relay. The central objective is to efficiently distribute power among multiple users and assess the impact of these allocation strategies on system performance.

Contributions

1. Non-Cooperative Individual Strategy: The authors first propose a non-cooperative strategy where the energy harvested from each source is exclusively used for its respective destination. This strategy serves as a benchmark, with asymptotic results illustrating that the outage performance decays as log(SNR)/SNR. While simple, it is relatively inefficient compared to other methods.
2. Equal Power Allocation: This method involves distributing the total harvested energy equally among all users. It enhances performance for users with poor channel conditions, substantially outperforming the non-cooperative strategy, with the outage probability decaying at a rate of 1/SNR.
3. Sequential Water Filling Strategy: Here, users with better channel conditions are prioritized. The relay first serves the user with the best channel, allocating remaining power sequentially. This strategy optimizes both the user with the best conditions and maximizes successful destinations while also minimizing the worst user outage probability.
4. Auction-Based Scheme: The authors propose an auction-based power allocation scheme to mitigate the need for extensive channel state information. This distributed method performs comparably with the centralized water filling strategy, providing a practical and efficient solution in multi-user systems.

Technical Results

• The non-cooperative strategy exhibits a slower decay rate in outage probability (log(SNR)/SNR), contrasting with equal and water filling strategies, which achieve a faster decay of 1/SNR.
• The water filling and auction-based strategies optimize performance concerning both the best and worst user outage probabilities.

Implications and Future Directions

The proposed schemes have significant implications for enhancing the efficiency and scalability of wireless networks. By optimizing power allocation, the need for extensive energy consumption is reduced, thus extending the operational lifespan of network components.

The exploration of energy harvesting techniques in cooperative networks opens new pathways towards sustainable wireless communication systems. The proposed strategies could be further extended to more complex network topologies, such as heterogeneous networks with diverse energy requirements and constraints.

Future research could also delve into the integration of machine learning techniques to facilitate real-time adaptive power allocation, further enhancing the performance and adaptability of these systems in dynamic environments.

Conclusion

This paper provides a systematic investigation into power allocation strategies in energy-harvesting cooperative networks. By introducing various strategies and analyzing their performance, it lays a solid foundation for energy-efficient wireless communication systems, offering insights into both theoretical advancements and practical implementations in energy-constrained networks.