Wireless Energy and Information Transfer Tradeoff for Limited Feedback Multi-Antenna Systems with Energy Beamforming (1307.6285v1)

Abstract: In this paper, we consider a multi-antenna system where the receiver should harvest energy from the transmitter by wireless energy transfer to support its wireless information transmission. In order to maximize the harvesting energy, we propose to perform adaptive energy beamforming according to the instantaneous channel state information (CSI). To help the transmitter to obtain the CSI for energy beamforming, we further propose a win-win CSI quantization feedback strategy, so as to improve the efficiencies of both power and information transmission. The focus of this paper is on the tradeoff of wireless energy and information transfer by adjusting the transfer duration with a total duration constraint. Through revealing the relationship between transmit power, transfer duration and feedback amount, we derive two wireless energy and information transfer tradeoff schemes by maximizing an upper bound and an approximate lower bound of the average information transmission rate, respectively. Moreover, the impact of imperfect CSI at the receiver is investigated and the corresponding wireless energy and information transfer tradeoff scheme is also given. Finally, numerical results validate the effectiveness of the proposed schemes.

• The paper introduces adaptive energy beamforming to optimize harvested energy and the average transmission rate under fixed time and feedback constraints.
• It proposes two tradeoff schemes based on upper and lower bound maximization to balance energy transfer duration with information delivery efficiency.
• The study demonstrates that even a small amount of CSI feedback can yield substantial performance gains, mitigating challenges posed by imperfect channel information.

Insights into Wireless Energy and Information Transfer Tradeoff in Multi-Antenna Systems

The paper by Xiaoming Chen, Chau Yuen, and Zhaoyang Zhang addresses a sophisticated concept within the domain of wireless communication systems: the tradeoff between energy and information transfer in multi-antenna systems. This research is particularly focused on systems that employ energy beamforming supported by limited feedback. The researchers aim to optimize the performance of such systems by proposing strategic methodologies that enhance both the energy transfer efficiency and the average information transmission rate.

Summary of Key Contributions

The core contribution of the paper lies in its analysis and proposal of a trade-off strategy that maximizes the average information transmission rate. This involves dynamically adjusting the duration of energy and information transfer under a fixed time constraint. The authors propose two novel schemes to approximate this optimal tradeoff by maximizing an upper bound and a lower bound of the information transmission rate. The main insights and methodologies can be highlighted as follows:

1. Adaptive Energy Beamforming: The paper introduces an adaptive energy beamforming strategy to maximize harvested energy at the receiver end. This strategy involves real-time adjustment based on the instantaneous channel state information (CSI), which is essential for spatial alignment and efficient energy transfer.
2. CSI Quantization Feedback: To facilitate efficient energy beamforming, the authors propose a CSI quantization feedback strategy that uses quantization codebooks to convey channel states. This allows the transmitter to adaptively align its energy beams, significantly improving energy transfer efficiency while considering feedback limitations.
3. Tradeoff Schemes: Two tradeoff strategies are introduced: (a) maximizing an upper bound, constructed using Jensen's inequality, which considers a simplified expression of the system's achievable rate, and (b) maximizing a lower bound, which is derived from an approximation that simplifies the impact of channel conditions on the transmission rate.
4. Impact of Imperfect CSI: The paper also explores the implications of imperfect CSI at the power receiver, proposing adjustments in the tradeoff strategy to mitigate potential performance degradation due to channel estimation errors.

Numerical Results and Implications

The presented numerical results substantiate the effectiveness of the proposed schemes. The results indicate that the upper-bound-based strategy closely approximates the optimal achievable rate, particularly in high-power regions. The numerical analysis further demonstrates that even a small feedback overhead can result in substantial performance gains, emphasizing the viability of the proposed feedback strategy in improving transmission effectiveness.

Theoretical and Practical Implications

Theoretical Implications: The work extends the understanding of the interplay between wireless energy and information transmission in multi-antenna systems. It offers theoretical models that provide foundational insights into optimal resource allocation under reciprocal constraints of energy and information transfer duration.

Practical Implications: These findings have significant implications for the design of wireless systems, particularly in applications such as IoT networks and medical implants, where devices require efficient wireless power transmission alongside data communication capabilities.

Prospective Developments

Looking forward, this research sets the stage for further exploration into refining energy beamforming techniques and feedback strategies to accommodate more sophisticated network scenarios, such as those involving multiple users and varying spatial configurations. Future work could also explore integration with advanced machine learning techniques for more dynamic and intelligent resource management in real-time scenarios.

Overall, this paper's approach towards optimizing energy and information transfer tradeoffs offers valuable insights and practical frameworks that can be leveraged for enhancing the operational efficiency of next-generation wireless systems.