Performance Enhancement Techniques for Next-Generation Multi-Service Communication and Medical Cyber-Physical Systems

Advances in science and technology have significantly improved our quality of life over the past decades. For example, wireless communication systems have evolved from basic voice services to delivering high-definition video for entertainment or business conferences at an accelerating pace. Furthermore, progress in cyber-physical systems has led to the development of brain-computer interfaces and wireless body area networks with the vision of advanced pervasive healthcare, anytime and anywhere. The primary motivation of this dissertation is to improve the performance of the next-generation multi-service communication and medical cyber-physical systems. The research has been concentrated in physical (PHY)/medium access control (MAC) layer aspects (such as channel modeling, waveform design, scheduling, and interference management) of wireless communication systems and signal processing algorithms for medical cyber-physical system. More specifically, the dissertation addresses the following topics: Inter-Numerology Interference Management for 5G Mobile Network: The next-generation communication technologies are evolving towards increased flexibility in various aspects. Although orthogonal frequency division multiplexing (OFDM) remains as the waveform of the upcoming fifth-generation (5G) standard, the new radio provides flexibility in waveform parametrization (a.k.a. numerology) to address diverse requirements. However, managing the peaceful coexistence of mixed numerologies is challenging due to inter-numerology interference (INI). The utilization of adaptive guards in both time and frequency domains is proposed as a solution along with a multi-window operation in the PHY layer. Since the allowed interference level depends on the numerologies operating in the adjacent bands, the potential of adaptive guards is further increased and exploited with a MAC layer scheduling technique as well in this study. In Vivo Channel Modeling for Wireless Body Area Networks: In vivo wireless body area networks (WBANs) and their associated technologies are shaping the future of healthcare by providing continuous health monitoring and noninvasive surgical capabilities, in addition to remote diagnostic and treatment of diseases. To fully exploit the potential of such devices, it is necessary to characterize the communication channel which will help to build reliable and high-performance communication systems. An in vivo wireless communication channel characterization for male torso both numerically and experimentally (on a human cadaver) is presented considering various organs. A statistical path loss model is introduced, and the anatomical region-specific parameters are provided. Multipath propagation characteristics are also investigated to facilitate proper waveform designs in the future wireless healthcare systems. Frequency Recognition for SSVEP-based Brain-computer Interfaces: Brain-computer interfaces (BCIs) and their associated technologies have the potential to shape future forms of communication, control, and security. Specifically, the steady-state visual evoked potential (SSVEP) based BCIs have the advantages of better recognition accuracy, and higher information transfer rate (ITR) compared to other BCI modalities. To fully exploit the capabilities of such devices, it is necessary to understand the underlying biological features of SSVEPs and design the system considering their inherent characteristics. Bio-inspired filter banks (BIFBs) are introduced for improved SSVEP frequency recognition. SSVEPs are frequency selective, subject-specific, and their power gets weaker as the frequency of the visual stimuli increases. Therefore, the gain and bandwidth of the filters are designed and tuned based on these characteristics while also incorporating harmonic SSVEP responses in this study