tailieunhanh - 68 Array Processing for Mobile Communications

This chapter reviews the applications of antenna array signal processing to mobile networks. Cellular networks are rapidly growing aroundtheworldanda number of emerging technologies are seentobe critical to their improved economics and performance. | A. Paulraj et. Al. Array Processing for Mobile Communications. 2000 cRc Press LLC. http . Array Processing for Mobile Communications A. Paulraj Stanford University C. B. Papadias Stanford University Introduction and Motivation Vector Channel Model Propagation Loss and Fading Multipath Effects Typical Channels Signal Model Co-Channel Interference SignalPlus-Interference Model Block Signal Model Spatial and Temporal Structure Algorithms for STP Single-User ST-ML and ST-MMSE Multi-User Algorithms Simulation Example Applications of Spatial Processing Switched Beam Systems Space-Time Filtering Channel Reuse Within Cell Summary References Introduction and Motivation This chapter reviews the applications of antenna array signal processing to mobile networks. Cellular networks are rapidly growing around the world and a number of emerging technologies are seen to be critical to their improved economics and performance. Among these is the use of multiple antennas and spatial signal processing at the base station. This technology is referred to as Smart Antennas or more accurately as Space-Time Processing STP . STP refers to processing the antenna outputs in both space and time to maximize signal quality. A cellular architecture is used in a number of mobile portable communications applications. Cell sizes may range from large macrocells which serve high speed mobiles to smaller microcells or very small picocells which are designed for outdoor and indoor applications. Each of these offers different channel characteristics and therefore poses different challenges for STP. Likewise different service delivery goals such as grade of service and type of service voice data or video also need specific STP solutions. STP provides three processing leverages. The first is array gain. Multiple antennas capture more signal energy which can be combined to improve the signal-to-noise ratio SNR . Next is spatial diversity to combat .