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Chaotic Waveform-based Signal Design for Noncoherent SWIPT Receivers

Priyadarshi Mukherjee, Constantinos Psomas, Ioannis Krikidis

TL;DR

The paper addresses simultaneous wireless information and power transfer (SWIPT) with chaotic, noncoherent signaling by introducing a SR-DCSK-based SIMO receiver that switches antennas between information transfer and energy harvesting. It provides closed-form BER and harvested DC expressions under generalized frequency-selective Nakagami-$m$ fading, revealing a fundamental BER-energy trade-off captured by a novel SR-$z_{ m DC}$ region. The work introduces optimal waveform design principles for different application requirements, including AWGN and fading scenarios, and demonstrates significant gains over conventional DCSK in the BER-energy trade-off. The findings highlight how transmit waveform design and antenna mode allocation critically influence both information throughput and harvested energy in chaotic SWIPT systems, offering practical guidelines for WPT-enhanced communications.

Abstract

This paper proposes a chaotic waveform-based multi-antenna receiver design for simultaneous wireless information and power transfer (SWIPT). Particularly, we present a differential chaos shift keying (DCSK)-based SWIPT multiantenna receiver architecture, where each antenna switches between information transfer (IT) and energy harvesting (EH) modes depending on the receiver's requirements. We take into account a generalized frequency-selective Nakagami-m fading model as well as the nonlinearities of the EH process to derive closed-form analytical expressions for the associated bit error rate (BER) and the harvested direct current (DC), respectively. We show that, both depend on the parameters of the transmitted waveform and the number of receiver antennas being utilized in the IT and EH mode. We investigate a trade-off in terms of the BER and energy transfer by introducing a novel achievable `success rate - harvested energy' region. Moreover, we demonstrate that energy and information transfer are two conflicting tasks and hence, a single waveform cannot be simultaneously optimal for both IT and EH. Accordingly, we propose appropriate transmit waveform designs based on the application specific requirements of acceptable BER or harvested DC or both. Numerical results demonstrate the importance of chaotic waveform-based signal design and its impact on the proposed receiver architecture.

Chaotic Waveform-based Signal Design for Noncoherent SWIPT Receivers

TL;DR

The paper addresses simultaneous wireless information and power transfer (SWIPT) with chaotic, noncoherent signaling by introducing a SR-DCSK-based SIMO receiver that switches antennas between information transfer and energy harvesting. It provides closed-form BER and harvested DC expressions under generalized frequency-selective Nakagami- fading, revealing a fundamental BER-energy trade-off captured by a novel SR- region. The work introduces optimal waveform design principles for different application requirements, including AWGN and fading scenarios, and demonstrates significant gains over conventional DCSK in the BER-energy trade-off. The findings highlight how transmit waveform design and antenna mode allocation critically influence both information throughput and harvested energy in chaotic SWIPT systems, offering practical guidelines for WPT-enhanced communications.

Abstract

This paper proposes a chaotic waveform-based multi-antenna receiver design for simultaneous wireless information and power transfer (SWIPT). Particularly, we present a differential chaos shift keying (DCSK)-based SWIPT multiantenna receiver architecture, where each antenna switches between information transfer (IT) and energy harvesting (EH) modes depending on the receiver's requirements. We take into account a generalized frequency-selective Nakagami-m fading model as well as the nonlinearities of the EH process to derive closed-form analytical expressions for the associated bit error rate (BER) and the harvested direct current (DC), respectively. We show that, both depend on the parameters of the transmitted waveform and the number of receiver antennas being utilized in the IT and EH mode. We investigate a trade-off in terms of the BER and energy transfer by introducing a novel achievable `success rate - harvested energy' region. Moreover, we demonstrate that energy and information transfer are two conflicting tasks and hence, a single waveform cannot be simultaneously optimal for both IT and EH. Accordingly, we propose appropriate transmit waveform designs based on the application specific requirements of acceptable BER or harvested DC or both. Numerical results demonstrate the importance of chaotic waveform-based signal design and its impact on the proposed receiver architecture.
Paper Structure (17 sections, 8 theorems, 59 equations, 10 figures, 2 tables)

This paper contains 17 sections, 8 theorems, 59 equations, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Chaotic signals
  5. Proposed SR-DCSK-based Receiver System Design
  6. Information and Power Transfer
  7. Bit Error Rate Analysis
  8. Average Energy Harvesting
  9. Waveform Design and BER-Energy Trade-off Characterization
  10. Achievable ${\rm SR}$ when $M=N$.
  11. Harvested DC corresponding to $K=N$.
  12. Numerical Results
  13. Conclusion
  14. Proof of Theorem \ref{['theoit1']}
  15. Proof of Theorem \ref{['theoit2']}
  16. ...and 2 more sections

Key Result

Theorem 1

If $M$$(\leq N)$ receiver antennas are utilized for the IT mode, the system's BER is where $\kappa=\sum\limits_{n=1}^{M}\sum\limits_{i=1}^L \alpha_{i,n}^2$, and $f\left( \kappa \right)$ is the probability distribution function of $\kappa$ given by

Figures (10)

  • Figure 1: Chaotic signal-based proposed receiver architecture.
  • Figure 2: (a) DCSK frame, and (b) SR-DCSK frame.
  • Figure 3: Impact of $M$ on BER; $\gamma_0=12$ dB and $\beta=80$; lines and markers correspond to analytical and simulation results, respectively.
  • Figure 4: Impact of $\gamma_0$ on BER; $\beta=80$ and $\phi=20$; lines and markers correspond to analytical and simulation results, respectively.
  • Figure 5: Impact of $K$ on $z_{\rm DC}$; $\beta=60$ and $m=4$; lines and markers correspond to analytical and simulation results, respectively.
  • ...and 5 more figures

Theorems & Definitions (14)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Theorem 3
  • proof
  • Proposition 1
  • proof
  • Corollary 1
  • Theorem 4
  • ...and 4 more