Energy-Efficient Power Allocation in OFDM Systems with Wireless Information and Power Transfer

TL;DR

This paper develops an iterative power allocation algorithm for OFDM systems that maximizes energy efficiency while considering wireless power transfer, circuit power, and data rate constraints, demonstrating improved trade-offs through simulations.

Contribution

It introduces a novel optimization framework transforming a non-convex fractional problem into a subtractive form, enabling efficient power allocation in simultaneous wireless information and power transfer systems.

Findings

The algorithm converges to the optimal solution.

Wireless power transfer enhances energy efficiency in interference-limited regimes.

Trade-offs between capacity, energy efficiency, and power transfer are characterized.

Abstract

This paper considers an orthogonal frequency division multiplexing (OFDM) downlink point-to-point system with simultaneous wireless information and power transfer. It is assumed that the receiver is able to harvest energy from noise, interference, and the desired signals. We study the design of power allocation algorithms maximizing the energy efficiency of data transmission (bit/Joule delivered to the receiver). In particular, the algorithm design is formulated as a high-dimensional non-convex optimization problem which takes into account the circuit power consumption, the minimum required data rate, and a constraint on the minimum power delivered to the receiver. Subsequently, by exploiting the properties of nonlinear fractional programming, the considered non-convex optimization problem, whose objective function is in fractional form, is transformed into an equivalent optimization problem having an objective function in subtractive form, which enables the derivation of an efficient iterative power allocation algorithm. In each iteration, the optimal power allocation solution is derived based on dual decomposition and a one-dimensional search. Simulation results illustrate that the proposed iterative power allocation algorithm converges to the optimal solution, and unveil the trade-off between energy efficiency, system capacity, and wireless power transfer: (1) In the low transmit power regime, maximizing the system capacity may maximize the energy efficiency. (2) Wireless power transfer can enhance the energy efficiency, especially in the interference limited regime.