Active Patch Array Design and Indoor Channel Modeling for Future Wireless Communications

Citation

Lu, Dai (2004) Active Patch Array Design and Indoor Channel Modeling for Future Wireless Communications. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/PVB9-TH19. https://resolver.caltech.edu/CaltechETD:etd-05212004-015508

Abstract

Indoor wireless LAN systems currently operate at ranges of up to 30 meters, with practical data rates of 10 Mbps. In order to achieve higher data rates, higher frequencies are under consideration. Smaller antennas are required at these frequencies, but path loss increases. To combat the large path loss and multipath, 24 GHz phased arrays are being considered. The advantage of a phased array is that it can form narrow beams to favored directions, and nulls to combat interference. A 24 GHz active antenna with a 5-element patch array is demonstrated that includes an integrated GaAs MMIC power amplifier and low noise amplifier chip. Bias switching is used for changing from transmit to receive. The measured active gain is 31 dB in receive and 35 dB in transmit. The measured noise figure in receive is 3.5 dB and the maximum output power in transmit is 22 dBm (158 mW). Indoor wireless channels are investigated at five different frequency bands. The understanding of the channel will help link budgeting and system planning for future wireless communication. An automatic testing system has been developed using remote control by LABVIEW. This increases testing efficiency and reduces near field interference from the operator. A combined E/H plane 2-D ray-tracing method is proposed to predict the channel performance. This approach accurately predicts path loss for both line-of-sight and non-line-of-sight paths. It predicts the delay spread in line-of-sight paths well but fails for non-line-of-sight paths. This could be due to the ignorance of some higher order paths with small amplitudes but near random phases. In addition, a 3-D simplified ray-tracing code is developed to for access point optimization and to predict human shadow effects.

Thesis Files