Predictive Wireless Antenna Selection for High Mobility Conditions

Accurate channel knowledge is indispensable to the practical success of channel-aware wireless communication technologies. However, channel estimates obtained from pilot symbols rapidly become outdated due to fast time variations of multipath fading channels. To reduce system cost, antenna subset selection reduces radio frequency (RF) chain components. For systems selecting a subset of a plurality of antennas for reception, this outdated channel information is a significant impediment to selection and data decoding reliability. In this thesis, training-based schemes for antenna selection (AS) for time-varying channels which account for practical constraints such as training, packetization and antenna switching time are proposed based on discrete prolate spheroidal sequences. They only operate with knowledge or estimates of the Doppler frequency and the channel signal-to-noise ratio (SNR), but do not require detailed statistical correlation knowledge. A pilot-based AS scheme for time-varying frequency-flat channels for single input– multiple output (SIMO) systems selecting one of a plurality of antennas using packe