Enhanced Fluid Index Modulation for Integrated Data and Energy Transfer
2026-06-03 • Information Theory
Information Theory
AI summaryⓘ
The authors study a way to send data and power wirelessly at the same time using a smart antenna system. They use something called fluid index modulation (FIM), where information is sent both through signals and antenna positions, to improve efficiency and reliability. The paper explains how to calculate data rates, error rates, and harvested power for this system, and then shows how to optimize the system’s parts to get the most power while keeping communication good. Their method performs better than existing ones and runs faster than checking every option.
Integrated Data and Energy Transfer (IDET)Fluid Index Modulation (FIM)Fluid Antenna System (FAS)Power SplittingBit Error Rate (BER)Finite-Alphabet SignalingPrecodingRiemannian Augmented Lagrangian Method (RALM)Block Coordinate Descent (BCD)Rate-Energy Trade-off
Authors
Long Zhang, Yizhe Zhao, Halvin Yang, Qiang Liu, Kai-Kit Wong
Abstract
Integrated data and energy transfer (IDET) is a promising technique for supporting sustainable low-power wireless networks. To improve both communication reliability and energy transfer efficiency, this paper investigates a fluid index modulation (FIM) assisted IDET system, where the base station employs a two-dimensional fluid antenna system (FAS) and the receiver adopts a power-splitting architecture. In FIM, the information bits are delivered not only from the modulation symbols, but also the index of antenna position. Under finite-alphabet signaling, the average harvested power, bit error rate (BER), and achievable data rate are derived in closed form. A joint optimization problem is formulated to maximize the average harvested power subject to BER and achievable rate constraints by jointly optimizing the port selection, precoding vector, and power splitting ratio. An alternating optimization framework is developed, where the precoding vector and port selection are obtained via a Riemannian augmented Lagrangian method (RALM) and block coordinate descent (BCD) algorithm, respectively. Simulation results demonstrate that the proposed scheme achieves a superior rate-energy trade-off over benchmark schemes, while the proposed algorithm attains near-optimal performance with significantly lower complexity than exhaustive search.