The use of practically non-repeating spreading codes to generate sequence-based spread spectrum waveforms is a strong method to improve transmission security, by limiting an observers opportunity to cross-correlate snapshots of the signal into a coherent gain. Such time-varying codes, particularly when used to define multi-bit resolution arbitrary-phase waveforms, also present significant challenges to the intended receiver, which must synchronize correlator processing to match the code every time it changes. High-order phase shift keying (PSK) spread modulations do, however, provide an overall whiter spectral response than legacy direct sequence spread spectrum (DSSS) signals. Further, the unique ability to color the output signal spectrum offers new advantages to optimize transmission in a non-white frequency channel and to mitigate observed interference. In high data rate applications, the opportunity to inject a time-aligned co-channel underlay-based watermark for authentication at the receiver is an effective method to enhance physical layer (PHY) security for virtually any primary network waveform. This thesis presents a series of options to enhance the implementation of arbitrary-phase chaotic sequence-based spread spectrum waveforms, including techniques to significantly reduce fallthrough correlator hardware resources in low-power sensing devices for only minor performance loss, capabilities for programming chosen frequency domain spectra into the resulting spread spectrum signal, and design considerations for underlay watermark-based PHY-layer firewalls. A number of hardware validated prototypes were built on an Intel Arria 10 SoC FPGA to provide measurable results, achieving substantial computational resource gains and implementation flexibility. / Master of Science / This thesis presents a series of options for enhancing the implementation of arbitrary-phase spread spectrum waveforms, a highly-secure class of wireless technologies, in order to reduce design complexity with minimal loss, provide methods for real-time performance adaptations, and extend the traditional application space for increased security of communications in other networks. A number of enhanced hardware prototypes were implemented to provide measurable results, achieving substantial computational resource gains and design flexibility. Given the computational resources and power constraints of devices in the Internet of Things (IoT), the signal detection loss of 2.10 dB for reducing the hardware logic utilization of the brute force fallthrough correlator by more than 76% (and eliminating the need to dedicate computationally-expensive embedded multipliers) is a very reasonable trade. While the waveform is fundamentally designed for increased security, adapting to widespread and/or commercial use may allow some sacrifice of the signal’s ability to avoid interception/detection to improve performance in undesirable operating conditions. In a similar, yet reversed, case, injecting a watermarking signature at the physical layer (PHY) of less-secure wireless technologies for receiver-side authentication also proves to be beneficial.

En esta Tesis se aborda la estimación óptima de señales caóticas generadas por mapas unidimensionales y contaminadas por ruido aditivo blanco Gaussiano, desde el punto de vista de los dos marcos de inferencia estadística más extendidos: máxima verosimilitud (ML) y Bayesiano. Debido al elevado coste computacional de estos estimadores, se proponen asimismo diversos estimadores subóptimos, aunque computacionalmente eficientes, con un rendimiento similar al de los óptimos. Adicionalmente se analiza el problema de la estimación de los parámetros de un mapa caótico explotando la relación conocida entre muestras consecutivas de la secuencia caótica. Por último, se considera la aplicación de los estimadores anteriores al diseño de receptores para dos esquemas de comunicaciones caóticas diferentes: conmutación caótica y codificación simbólica o caótica. / This Thesis studies the optimal estimation of chaoticsignals generated iterating unidimensional maps and contaminated by additive white Gaussian noise, from the point of view of the two most common frameworks in statistical inference: maximum likelihood (ML) and Bayesian. Due to the high computational cost of optimum estimators, several suboptimal but computationally efficient estimators are proposed, which attain a similar performance as the optimum ones. Additionally, the estimation of the parameters of a chaotic map is analyzed, exploiting the known relation between consecutive samples of the chaotic sequence. Finally, we consider the application of the estimators developed in the design of receivers for two different schemes of chaotic communications: chaotic switching and symbolic or chaotic coding.