## 1 Introduction  
The demand for secure communication systems has grown exponentially in recent decades. This surge is driven by the critical role of telecommunications in global connectivity and the increasing prevalence of devices in the Internet of Things (IoT). Secure and robust data transfer has become essential, especially considering growing cyber threats and privacy concerns. Traditional methods of ensuring secure communication often rely on encryption techniques, which can be vulnerable to advances in processing power, such as those provided by quantum computing. Encryption typically covers higher protocol layers, leaving the physical communication layer relatively unprotected. This is where chaotic communication can provide a promising avenue, leveraging the inherent unpredictability and nonlinearity of chaotic signals to enhance security and mitigate eavesdropping risks. Chaotic systems possess distinctive characteristics that make them suitable for secure communication [1]. Their nonlinear, ergodic, and noise-like behavior ensures unpredictability. Their sensitivity to initial conditions and deterministic nature provides inherent encryption capabilities, adding complexity for intruders attempting to synchronize or decode the system without precise parameter knowledge. These systems also exhibit low autocorrelation, low inter-signal correlation, and a wide bandwidth, further reinforcing their potential as secure communication carriers with resilience to interference, providing some level of enhanced physical link security for wireless data transmission. To translate theoretical advancements into practical applications, the hardware implementation of chaos oscillators must be considered. The traditional implementation of chaos oscillator involves an analog electronic circuit (e.g., Chua's circuit [2], Rossler circuit [3], Colpitts oscillator [4]), which is a feasible and simple way to generate chaotic dynamics. However, the use of analog chaos generators in communication systems poses challenges, including component value drift caused by temperature, humidity, supply voltage fluctuations, and other environmental or operational factors. Even the slightest change in the resistance or capacity of elements will lead to different behaviors of the chaos dynamics. These parameter variations during operation are difficult to compensate for, complicating chaotic synchronization, which requires two chaos generators with identical parameters and proper coupling. Moreover, analog chaos generators lack control over their initial state. As the chaotic oscillator system can be described by a simple system of ordinary differential equations (ODEs), solving these equations in a discrete, digital form is another viable option. This can be carried out on any computer, but a much more efficient solution to this task can be provided by such a digital hardware platform as a field-programmable gate array (FPGA). FPGAs combine flexibility, high computational power, and robust real-time processing through pipelining, making them ideal for advanced digital signal processing tasks. Their capability to synthesize fully deterministic discrete chaos generators eliminates component drift issues while addressing stringent performance and resource constraints. Additionally, their reconfigurable nature enables rapid prototyping with hardware description languages and potential adaptability to evolving communication standards, positioning FPGAs as a powerful platform for secure chaotic communication system implementations [5; 6]. As FPGA technology becomes increasingly available for prototyping and deploying digital systems, this has led to a renewed interest in chaotic systems, given the potential for efficient digital realization. However, this approach also has several design challenges that require addressing, such as the efficient utilization of FPGA hardware resources and optimization for stable performance at higher clock speeds.