Reduced Order Estimation of Time Varying Wireless Channels in Real Life Scattering Environment

This thesis deals with theoretical study and numerical simulation of 2x1 MISO system with Alamouti coding and imperfect channel estimation at the receiver. We adopt two channel models to represent scattering environment. One is Sum of Sinusoids model, which is simple, but does not properly reflect the geometry of scattering environment. The second model uses a set of Modulated Discrete Prolate Spheroidal Sequences to represent the channel in a scenario with scattering from one or more clusters with predefined geometry. The effect of clusters location on estimation quality is examined. Furthermore, we derive reduced complexity Wiener filters for slow flat fading channel estimation in pilot aided receiver. Our approach is based on the approximation of the channel covariance function to zero and second order Taylor series to reduce computational effort of the filter design. Theoretical MMSE is developed, verified through simulation and compared to one of a full Wiener filter

Towards Efficient and Secure IIoT: Solutions for the Sensing Domain

This work explores reduced complexity solutions for the increased efficiency and safety of the industrial Internet of Things (IIoT) sensing domain. Resource virtualization, security, and predictive modeling are the main subjects of these studies. The first solution is a joint throughput and time-resource allocation scheme for virtualization of IEEE 802.15.4-based wireless sensor networks. Virtualization is realized through the utilization of the guaranteed time slot mechanism for scheduling on the medium access control (MAC) layer. The solution abstracts resources into logical units that are allocated to segregated applications with different service requirements. The problem is formulated in a linear optimization framework and solved with a heuristic fair resource allocation (FRA) algorithm. The proposed scheduling approach provides fast and efficient resource management for low-power networks. The second solution performs a reduced complexity symmetric group rekeying in low-power wireless networks. The novel pseudorandom key chaining (PRKC) scheme uses pseudorandom sequences generated at lower layers of the communications stack to enable synchronous refresh of encryption keys in network nodes during broadcasting. The suitability of generated keys for cryptographic applications is validated using the National Institute of Standards and Technology Special Publication 800-22 (NIST SP 800-22) statistical suit. When implemented on a Raspberry Pi board, the PRKC algorithm runs faster and requires smaller CPU effort to refresh keys than the reference schemes. Our final contribution is a concept drift-aware solution for adaptive modeling of multivariate time series in nonstationary environments of the IIoT sensing domain. In the proposed three-layered three-state (TriLS) system, the gateway and the cloud collaborate to accurately model industrial process trends towards intelligent factory automation. In this scheme, all computationally demanding functionality of a model building and concept drift detection is shifted to the cloud side. The gateway uses a trained model for prediction in real-time and fine-tunes it to changing data. When tested on synthetic and real datasets, the TriLS system demonstrates a better prediction quality in nonstationary conditions than the conventional approaches. It also requires a reduced computational effort on the gateway side and smaller communications overhead for adjusting the model to drifting concept