Source-linked AI summary
Energy-Efficient Optimization for Wireless Information and Power Transfer in Large-Scale MIMO Systems Employing Energy Beamforming
Xiaoming Chen, Xiumin Wang, Xianfu Chen
TL;DR
The paper studies how a receiver can harvest wireless energy to support its own information transmission despite power and propagation constraints. It uses energy beamforming in a large-scale MIMO system and jointly optimizes transmit power and transfer duration for energy efficiency under QoS requirements. Numerical results show gains from joint optimization and larger antenna arrays, including QoS support at longer distances.
Problem
Wireless power transfer must overcome propagation loss while enabling the receiver to use harvested energy for information transmission under limited power and QoS requirements.
Method
The paper employs large-scale MIMO energy beamforming and jointly optimizes transmit power and transfer duration to maximize information-transmission energy efficiency subject to QoS constraints.
Results
About 1.5Kb/J gain is obtained at α = 0.05 over duration-only optimization, while increasing Nt from 50 to 100 yields about 2.5Kb/J gain at α = 0.05.
Takeaways & Limitations
The proposed scheme realizes long-distance and QoS-guaranteed wireless information and power transfer with energy-efficient resource allocation.
Abstract
from arXiv · showhide
In this letter, we consider a large-scale multiple-input multiple-output (MIMO) system where the receiver should harvest energy from the transmitter by wireless power transfer to support its wireless information transmission. The energy beamforming in the large-scale MIMO system is utilized to address the challenging problem of long-distance wireless power transfer. Furthermore, considering the limitation of the power in such a system, this letter focuses on the maximization of the energy efficiency of information transmission (bit per Joule) while satisfying the quality-of-service (QoS) requirement, i.e. delay constraint, by jointly optimizing transfer duration and transmit power. By solving the optimization problem, we derive an energy-efficient resource allocation scheme. Numerical results validate the effectiveness of the proposed scheme.
I. INTRODUCTION
The paper addresses wireless information and power transfer in large-scale MIMO systems, where energy beamforming improves transfer efficiency and supports receiver-side information transmission. It proposes jointly optimizing transmit power and transfer duration for energy-efficient QoS-guaranteed communication.
- Motivation: Wireless power transfer suffers propagation loss, making transfer efficiency a critical challenge.The losses include path loss, shadowing, and fast fading.
- Motivation: Large-scale MIMO uses many transmitter antennas and large array gain to improve wireless power transfer performance.The approach is intended to address limitations of traditional multi-antenna systems, especially over longer distances.
- Research gap: Previous wireless power transfer studies generally did not consider how receivers would use harvested energy for information transmission.This paper instead studies wireless powered communication, in which the receiver transmits information using harvested energy.
- Approach: The proposed system jointly considers wireless information and power transfer in a large-scale MIMO architecture employing energy beamforming.The paper focuses on energy-efficient resource allocation under QoS requirements.
- Evaluation: The paper evaluates the proposed resource allocation scheme with numerical results to validate its effectiveness.The study first models the system, then derives the allocation scheme and presents numerical validation.
II. SYSTEM MODEL
The system divides each slot between energy harvesting and information transmission: S1 beamforms energy to S2, then S2 uses the harvested energy to transmit data back. Large antenna arrays, MRT, and MRC support power transfer and information reception.
- System architecture: S1 uses Nt antennas for energy beamforming and receive combining, while single-antenna S2 serves as both power receiver and information transmitter.The large antenna count at S1 is central to improving transfer efficiency and information reception.
- System operation: Each time slot of length T allocates duration τ to wireless power transfer and T −τ to information transmission.S1 transfers energy first, after which S2 transmits information using the harvested energy.
- Channel model: The channel from S1 to S2 is modeled as αh, combining distance-dependent path loss α with i.i.d. Gaussian fast fading h.The channel model captures both distance effects and small-scale fading.
- Energy transfer: With full CSI, the unit-norm beamforming vector w = h/∥h∥ uses maximum ratio transmission to maximize harvested power.Channel reciprocity in TDD allows S1 to estimate the downlink channel from the preceding uplink.
- Energy transfer: The harvested energy is Qharv = ηαP∥h∥2τ, and S2 uses it to transmit during the remaining slot interval.η denotes the conversion efficiency and P denotes S1's transmit power.
- Information transfer: S1 applies maximum ratio combining under perfect CSI to maximize the information transmission rate.The received signal includes the normalized Gaussian transmit signal, additive white Gaussian noise, and the uplink channel θg.
- System implications: Large-scale MIMO can support relatively long-distance, high-QoS transmission with low transmit power by adding antennas at the transmitter.Traditional MIMO is described as mainly suitable for short-distance wireless information and power transfer because of path loss.
III. ENERGY-EFFICIENT POWER ALLOCATION
The paper formulates energy efficiency as a joint transmit-power and transfer-duration optimization under power, time, and QoS constraints. It converts the fractional objective into an iterative problem solved using convex optimization, Lagrange multipliers, KKT conditions, and Dinkelbach-inspired updates.
- Optimization formulation: Energy efficiency is maximized by jointly allocating transmit power P and transfer duration τ under power, time, and QoS constraints.Energy efficiency is defined as information-transmission bits per Joule, while rmin specifies the minimum rate for the QoS requirement.
- Optimization formulation: The harvested-power constraint becomes P ≤ P2,max(T − τ) because of channel hardening in large-scale MIMO systems.Combining this with P ≤ P1,max yields an upper bound τmax = ηαNtP1,max/(ηαNtP1,max + P2,max).
- Fractional-program transformation: The fractional objective is transformed into R̄(P, τ)T − q⋆(P0T + Pτ), where q⋆ is the optimal energy efficiency.This transformation addresses the generally nonconvex fractional-programming formulation.
- Solution method: The transformed problem is treated as a convex optimization problem and solved with the Lagrange multiplier method.The Lagrangian uses multipliers associated with the transformed power, duration, and QoS constraints.
- Solution method: An iterative algorithm updates the multipliers, jointly solves KKT conditions for P⋆ and τ⋆, and updates q⋆ until convergence.The algorithm initializes the variables, recomputes the optimal power and duration, and stops when the fractional-program improvement is no greater than ε.
IV. NUMERICAL RESULTS
Numerical experiments evaluate convergence, joint power-duration optimization, and antenna-number effects on energy efficiency. The proposed scheme outperforms duration-only optimization and benefits from larger antenna arrays, especially under larger path-loss conditions.
- Simulation setup and convergence: No more than 20 iterative computations are required for convergence across all simulated scenarios.The simulations use W = 10 KHz, T = 5 ms, η = 0.8, rmin = 12 Kb/s, P0 = 45 Watt, and P1,max = P2,max = 15 Watt.
- Comparison of allocation schemes: About 1.5 Kb/J gain occurs at α = 0.05 for joint power-duration optimization over duration-only optimization.The performance gain becomes larger as α increases.
- Effect of BS antennas: About 2.5 Kb/J gain occurs at α = 0.05 when Nt increases from 50 to 100.The antenna-number performance gain is especially significant for large α.
- Effect of BS antennas: When α ≤ 0.02, energy efficiency with Nt = 20 falls to zero because harvested power cannot support the given QoS requirement.With Nt = 50, the system supports QoS-guaranteed service even at α = 0.01.
V. CONCLUSION
The letter applies large-scale MIMO and energy beamforming to wireless information and power transfer, targeting long-distance, QoS-guaranteed operation. It proposes joint transmit-power and transfer-duration optimization for energy efficiency, with numerical results confirming effectiveness.
- V. CONCLUSION: Large-scale MIMO is introduced into wireless information and power transfer to realize long-distance and QoS-guaranteed services.The approach exploits large-scale MIMO while considering green-communication demands.
- V. CONCLUSION: Joint optimization of transmit power and transfer duration produces an energy-efficient resource allocation scheme.The scheme is designed for wireless information and power transfer under the paper's QoS setting.
- V. CONCLUSION: Numerical results confirm the effectiveness of the proposed resource allocation scheme.The conclusion summarizes the numerical validation reported for the proposed approach.