Multi-Antenna Wireless Powered Communication with Energy Beamforming (1312.1450v3)

Abstract: The newly emerging wireless powered communication networks (WPCNs) have recently drawn significant attention, where radio signals are used to power wireless terminals for information transmission. In this paper, we study a WPCN where one multi-antenna access point (AP) coordinates energy transfer and information transfer to/from a set of single-antenna users. A harvest-then-transmit protocol is assumed where the AP first broadcasts wireless power to all users via energy beamforming in the downlink (DL), and then the users send their independent information to the AP simultaneously in the uplink (UL) using their harvested energy. To optimize the users' throughput and yet guarantee their rate fairness, we maximize the minimum throughput among all users by a joint design of the DL-UL time allocation, the DL energy beamforming, and the UL transmit power allocation plus receive beamforming. We solve this non-convex problem optimally by two steps. First, we fix the DL-UL time allocation and obtain the optimal DL energy beamforming, UL power allocation and receive beamforming to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users. This problem is shown to be in general non-convex; however, we convert it equivalently to a spectral radius minimization problem, which can be solved efficiently by applying the alternating optimization based on the non-negative matrix theory. Then, the optimal time allocation is found by a one-dimension search to maximize the minimum rate of all users. Furthermore, two suboptimal designs of lower complexity are proposed, and their throughput performance is compared against that of the optimal solution.

• The paper optimizes multi-antenna wireless powered communication networks to maximize minimum user throughput using a harvest-then-transmit protocol and energy beamforming.
• It formulates a non-convex problem involving joint optimization of time allocation, energy beamforming, and power allocation, solved using spectral radius minimization and alternating optimization.
• Numerical results show significant throughput gains with increased AP antennas and demonstrate how energy beamforming can mitigate the doubly near-far effect in WPCNs.

Multi-Antenna Wireless Powered Communication with Energy Beamforming

This paper investigates the optimization of wireless powered communication networks (WPCNs) where a multi-antenna access point (AP) facilitates energy and information transfer to a group of single-antenna users. The focus is on maximizing the minimum throughput for all users using a harvest-then-transmit protocol combined with energy beamforming.

Key Contributions and Approach

The research addresses the challenge of jointly optimizing downlink (DL) energy transfer and uplink (UL) information transmission. It proposes a solution following these steps:

1. Protocol Description: The AP first transmits energy to users using energy beamforming. Users then utilize the harvested energy to transmit information back to the AP.
2. Optimization Objective: The paper formulates a non-convex problem to maximize the minimum throughput, ensuring rate fairness. This involves optimizing:
   • DL-UL time allocation
   • DL energy beamforming
   • UL transmit power allocation and receive beamforming
3. Solution Methodology: The problem is tackled in two stages:
   • Stage 1: For a fixed DL-UL time allocation, the optimal DL energy beamforming and UL transmit power are derived to maximize the minimum SINR.
   • Stage 2: Optimal time allocation is found through a one-dimensional search.
4. Mathematical Framework: The authors convert the original problem to a spectral radius minimization problem using non-negative matrix theory, which is resolved using alternating optimization.
5. Suboptimal Designs: Two lower complexity suboptimal methods are proposed for practical implementation and compared against the optimal solution for throughput performance.

Numerical Results and Analysis

The paper reports comprehensive numerical results illustrating the effectiveness of the proposed methods. Key observations include:

• The intricate trade-off between DL energy transmission and UL information transmission time is evident, impacting throughput optimization.
• The proposed approach shows significant throughput improvements, especially with increased AP antenna count.
• Suboptimal solutions, though simpler, exhibit competitive performance relative to the optimal solution, especially in scenarios with high SNR.

Implications and Future Directions

The findings are significant for enhancing WPCNs by improving the efficiency of energy and information transfer. Key theoretical implications include:

• The use of energy beamforming can mitigate the doubly near-far effect, a unique challenge in WPCNs.
• Spectral radius minimization offers a novel approach to address non-convex optimization problems in communication networks.

Practical implications suggest potential applications in:

• Sensor networks where power constraints are prevalent.
• IoT devices to extend operational lifetime via energy harvesting.

The paper opens avenues for further research into more advanced beamforming strategies and the exploration of different network topologies. Additionally, the integration of machine learning techniques for dynamic optimization in real-time environments could be a worthwhile direction. As WPCNs evolve, addressing security aspects while maintaining energy efficiency could also become a priority.