Content area
In this thesis, we develop a novel adaptive MIMO space-time processing method which adapts to changes in the wireless environment for the maximization of wireless link performance.
First, we develop a fully simulated MIMO wireless environment in Matlab ® to facilitate algorithm development and performance benchmarking. Second, we develop the theory supporting an adaptive MIMO layered space-time processing method called adaptive BLAST (A-BLAST). Thirdly, known non-adaptive diversity and spatial multiplexing layered space-time processing techniques are implemented and benchmarked under simulated MIMO channels using two element, four element, and eight element antenna arrays respectively. Fourth, we implement and benchmark an A-BLAST approach under equivalent simulated MIMO channels. Finally, simulation results are provided which demonstrate a performance improvement using A-BLAST over the non-adaptive benchmarks.
The main contributions of this thesis include the creation of a highly parameterized MIMO simulation environment in Matlab®, as well as, the development of a novel A-BLAST layered spacetime processing algorithm complemented with adaptation based on reference BER data obtained under MIMO channels of varying spatial rank using the developed simulation environment. The A-BLAST code structure developed provides additional space-time codeword mappings not previously defined through traditional non-adaptive BLAST methods. These additional codeword mappings are shown to provide more granular control over the relative weighting of spatial multiplexing gain and diversity order and are better suited to a broader range of MIMO channel environments. Using estimates of the MIMO channel spatial rank and receive signal-to-noise ratio, combined with a residual bit error rate threshold, the developed adaptation algorithm is shown to be able to automatically select an A-BLAST codeword mapping from the available A-BLAST codeword set, improving MIMO link performance when compared with non-adaptive BLAST techniques which are optimized for the spatial multiplexing and diversity encoding respectively.