Signaling for general MIMO channels

Wireless system designers are faced with a number of challenges. These include limited availability of radio frequency spectrum and a complex time-varying wireless environment (fading and multipath). Meeting the increasing demand for higher data rates, better quality of service (QoS), fewer dropped calls, higher network capacity, and user coverage calls for innovative techniques that improve spectral efficiency and link reliability. The use of multiple antennas at the receiver and transmitter in a wireless system, popularly known as MIMO (multiple-input multiple-output) wireless is an emerging cost-effective technology that promises significant improvements in these measures. A growing acknowledgment of the performance gains from MIMO technology has spurred the insertion of this technology into wireless standards, notably the mobile standards such as UMTS and CDMA 2000. MIMO techniques are also under study in IEEE 802.16 and 802.11 standards for fixed and WLAN applications respectively.; Broadly, multi-antenna signaling may be used to increase data rate through spatial multiplexing (SM) or link reliability through transmit diversity (TD). The performance of any MIMO signaling scheme depends strongly on matrix channel characteristics. The channel in turn depends on antenna heights and spacing, location (indoors/outdoors), richness of scattering, polarization of antennas, etc. The classical MIMO channel model assumes independent yet identically distributed frequency flat Rayleigh fading between any transmit-receive antenna pair. There has been much analysis of MIMO technology assuming this channel model. The classical model is reasonably accurate if scattering is rich and the antennas at transmitter and receiver are sufficiently spaced. However, channel measurements conducted at Stanford University and around the globe have shown that the MIMO channel can deviate significantly from the classical model.; In this thesis we introduce a general MIMO channel model capable of capturing any combination of real-world channel conditions such as Ricean fading, spatial fading correlation and gain imbalances between channel elements. We analyze the diversity performance of general MIMO channels and study the impact of varying channel conditions on the performance of SM and TD. Finally, we demonstrate how MIMO signaling may be optimized to improve performance based on knowledge of the channel conditions.