Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer (1403.3991v2)

Abstract: This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number $M$ of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame). To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-$M$ regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system. We define a metric, namely, the massive MIMO degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by $\log(M)$. We show that the proposed WET-MM system is optimal in terms of MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.

• The paper introduces a throughput-optimized massive MIMO system using wireless energy transfer to enhance both energy efficiency and uplink rates.
• It employs a tri-phasic frame with pilot-based channel estimation and energy beamforming, achieving asymptotically optimal performance in large antenna regimes.
• It ensures rate fairness among users by overcoming the double near-far problem and doubling the MM-DoRG compared to non-beamforming systems.

Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer

This paper explores an innovative approach in leveraging wireless energy transfer (WET) to power massive MIMO systems, specifically the interaction between a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. This system, referred to as the WET-MM, incorporates a tri-phasic transmission frame, encompassing uplink channel estimation (CE), downlink WET, and uplink wireless information transmission (WIT). The purpose of this paper is to optimize the throughput of such systems while ensuring rate fairness among users.

The authors establish a framework where users, after harvesting energy in prior frames, initially transmit pilots to facilitate channel estimation by the H-AP, exploiting channel reciprocity. Subsequently, the H-AP performs energy beamforming to transfer energy to users based on the estimated channels. Finally, users employ the harvested energy to transmit data uplink. The core objective is to maximize the minimum rate amongst users—a critical fairness criterion in massive MIMO networks.

Key Findings

The paper presents several pivotal results:

1. Performance Analysis: In the large-M antenna regime, asymptotically optimal solutions are derived, revealing insights into system design. The results showcase that both optimized resource allocation and channel estimation significantly contribute to improved energy transfer efficiency and uplink rate.
2. Massive MIMO Degree-of-Rate-Gain (MM-DoRG): The paper introduces the concept of MM-DoRG, quantifying the scaling behavior of uplink rates with respect to the logarithm of the number of antennas. For the proposed WET-MM systems, MM-DoRG is shown to be double that of systems without beamforming support (OP-MM).
3. Rate Fairness: The optimal design ensures that all users asymptotically achieve a common rate, thereby addressing the “double near-far” problem prevalent in WPCNs, where distant users typically experience inferior service quality.
4. Numerical Results: Simulation results corroborate the analytical findings, demonstrating that the proposed system requires significantly fewer antennas to achieve target rates compared to non-beamforming systems, with desirable common rates achieved at relatively small antenna numbers.

Implications and Future Directions

The implications of this research are profound. The formulation and solutions presented lay the groundwork for practical deployment strategies of massive MIMO systems in energy-constrained settings, where reliability and fairness are critical. Furthermore, understanding the channel estimation's role and optimizing time allocation for CE, WET, and WIT have tangible effects on improving system efficiency and scalability.

The paper offers a methodological basis for future research focused on enhancing massive MIMO efficiency, particularly in exploring the domain of wireless power transfer. Future work could include investigating the impacts of real-world constraints such as channel estimation inaccuracies, spatial correlation, and hardware imperfections. Additionally, exploring the interaction between multiple carriers or enhancing robustness in dynamic environments through adaptive algorithms would be valuable.

In conclusion, this work advances the field's understanding of integrated wireless power and communication networks, revealing substantial potential for improving the operational efficiency of future wireless systems while addressing the energy constraints and fairness issues inherent to massive internet of everything (IoE) applications.