Stochastic Modeling and Statistical Inferences of Adaptive Antennas in Wireless Communications

Wireless Ad-hoc networks can be considered as a means of linking portable user terminals that meet temporarily in locations where connection to a network infrastructure is difficult. Hence, techniques are needed that contribute to the development of high-performance receiving antennas with the capability of automatically eliminating surrounding interference. Solutions to this problem for the conventional linear antenna arrays meet nevertheless complex architectures resulting in high power dissipation. We consider in the current paper novel algorithms for the analog aerial beamforming of a reactively controlled adaptive antenna array as a non-linear spatial filter by variable parameters. Being based on the Stochastic Approximation Theory, such algorithms have great potentials for use in mobile terminals and provide therefore important support for wireless communication networks. The resulting unconventional adaptive antennas can lead to dramatically simplified architectures leading in turn to significantly lower power dissipation and fabrication costs.