MIMO array capacity optimization using a genetic algorithm - Manuel OsÃrio Binelo (2013)
One challenging task in multiple input multiple output (MIMO) systems design is to accommodate the multiple antennas in the mobile device without compromising the system capacity, due to spatial and electrical constraints. In this work, an experimental MIMO wireless channel characterization in an outdoor environment is performed in order to study the different factors that affect MIMO capacity. The data acquired during wideband channel measurement campaigns made in Stockholm, Sweden, were used in order to predict the impact of direction of arrival (DOA) distribution and polarization diversity on the channel capacity, choosing speciﬁc measurement routes and locations as well as different MIMO antenna array conﬁgurations. This thesis proposes a genetic algorithm (GA) to obtain the position and orientation of each MIMO array antenna that maximizes the ergodic capacity for a given propagation scenario. The simulations of the GA use the characterized experimental channel model, as a case of study, in order to evaluate the impact of different characteristics of the propagation environment in the capacity. Based on an interface between the antenna model and the propagation channel model, the ergodic capacity is considered as the objective function of the MIMO array optimization. Simulation results corroborate the importance of polarization and antenna pattern diversities for MIMO in small terminals. The results also show that the electromagnetic coupling effect can be exploited by the optimizer in order to decrease signal correlation and increase MIMO capacity. A comparison among uniform linear array (ULA), uniform circular array (UCA) and the GA-optimized array is also carried out, showing that the topology given by the optimizer is superior to that of the standard ULA and UCA arrays for the considered propagation channel. This work also presents a method for optimizing the capacity of MIMO antenna array systems with antenna selection, evolving the antenna array best suited for antenna selection in a given scenario. As a result of the proposed GA optimizer, different array conﬁgurations were obtained for cases with and without antenna selection, showing that true polarization diversity (TPD) schemes are particularly suited for antenna selection systems.