Optimizations on Estimation and Positioning Techniques in Intelligent Wireless Systems

Wireless technologies across various applications aim to improve further by developing intelligent systems, where the performance is optimized through adaptive policy selections that efficiently adjust to the environment dynamics. As a result, accurate observation on the surrounding conditions, such as wireless channel quality and relative target location, becomes an important task. Although both channel estimation and wireless positioning problems have been well studied, with advanced wireless communications relying on complex technologies and being applied to diverse environments, optimization strategies tailored to their unique architectures and scenarios need to be further investigated. In this dissertation, four key research problems related to channel estimation and wireless positioning tasks for intelligent wireless systems are identified and studied. First, a channel denoising problem in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is addressed, and a Q-learning-based successive denoising scheme, which utilizes a channel curvature magnitude threshold to recover unreliable channel estimates, is proposed. Second, a pilot assignment problem in scalable open radio access network (O-RAN) cell-free massive MIMO (CFmMIMO) systems is studied, where a low-complexity pilot assignment scheme based on a multi-agent deep reinforcement learning (MA-DRL) framework along with a codebook search strategy is proposed. Third, sensor selection/placement problems for wireless positioning are addressed, and dynamic and robust sensor selection schemes that minimize the Cramér-Rao lower bound (CRLB) are proposed. Lastly, a feature selection problem for deep learning-based wireless positioning is studied, and a unique feature size selection method, which weights over the expected information gain and classification capability, along with a multi-channel positioning neural network is proposed.