Unified Theory of QSTBCs for Four Transmit Antennas

Resource Overview

This work establishes a unified theoretical framework for Quadrature Space-Time Block Codes (QSTBCs) with four transmit antennas and one or more receive antennas. The research comprises two main components: Part I investigates QSTBC transmission without channel knowledge at the transmitter, while Part II explores transmission with partial Channel State Information (CSI). For both scenarios, we analyze QSTBC performance using Maximum Likelihood receivers and low-complexity Linear Zero-Forcing receivers over spatially correlated/uncorrelated frequency-flat MIMO channels. Spatial correlation is modeled using the Kronecker correlation model, and practical validation is performed through indoor measured channels.

Detailed Documentation

The primary objective of this research is to develop a unified theory of Quadrature Space-Time Block Codes (QSTBCs) for systems with four transmit antennas and one or more receive antennas. The thesis is structured into two principal parts: The first part analyzes QSTBC transmission without any channel knowledge at the transmitter, while the second part examines QSTBC transmission with partial Channel State Information (CSI) available at the transmitter. For both scenarios, we investigate QSTBC performance using Maximum Likelihood (ML) receivers and low-complexity Linear Zero-Forcing (ZF) receivers over frequency-flat MIMO channels under both spatially correlated and uncorrelated conditions. Spatial correlation is modeled through the Kronecker correlation model, which can be implemented computationally as R = R_tx ⊗ R_rx, where R_tx and R_rx represent the transmit and receive correlation matrices respectively, and ⊗ denotes the Kronecker product. Our simulations incorporate indoor measured channel data to demonstrate QSTBC performance in realistic propagation environments. The implementation typically involves channel matrix generation with correlation parameters, QSTBC encoding algorithms for 4xN antenna configurations, and receiver processing modules including ML detection (using sphere decoding algorithms for complexity reduction) and ZF equalization (implemented through pseudo-inverse operations on the channel matrix).