Browsing by Author "Li, Boyuan"

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    Chaos Modulation and Equalization for Robust Wireless Communications
    (2022-01-28) Li, Boyuan; Leung, Henry; Messier, Geoffrey; Helaoui, Mohamed; Fapojuwo, Abraham; Kaddoum, Georges
    This thesis focuses on using chaos modulation and equalization to enhance the robustness of wireless communication. Our contributions are three-fold: For the Industrial Internet of Things (IIoT), a quadratic ergodic chaotic parameter modulation (QECPM) is proposed. We use software-defined radios (SDRs) to show that QECPM is robust against timing synchronization errors, which have a major effect on performance. The bit-error-rate (BER) performance of QECPM in Nakagami-m fading channels is derived and verified by simulations. In a multipath-rich channel, QECPM demonstrates superior performance to conventional modulations. Furthermore, we show that retransmissions causes misaligned packets; however, when using the proposed receiver, the error bits are sparse enough to utilize the non-retransmission mode to maintain stable link rates. In a denoise-and-forward (DNNF) two-way relay system, the signals are asynchronous. For most denoising and decoding methods, precise estimation of the delay is required by the relay and end users, which is not always available. Using the ergodic property of chaotic signals, we propose to address the asynchronous problem using ergodic chaotic parameter modulation (ECPM) and guarding intervals (GIs). The theoretical BER performance is analyzed and verified by simulations. A relay selection method is also proposed for two-way relay systems with multiple relays to achieve improved performance compared to using all relays. Chaos modulation signals can be blindly equalized using phase space volume (PSV). A maximum likelihood-PSV (ML-PSV) estimation and the Cramer Rao Lower Bound (CRLB) are derived. The ML-PSV algorithm is applied to blind system identification of autoregressive (AR) and moving average (MA) models as well as equalization. A method for ECPM to identify the constructive/destructive channel is developed. The destructive channel effect can be mitigated using the proposed equalization. Our approach is validated using SDRs. Our results show that ECPM with ML-PSV equalization is more robust than comparing methods.