Tracking Mobile User through Adaptive Beamforming for Wireless Cellular Networks in a 2D Urban Environment

Abstract

The interference reduction capability of array antennas and the power control (PC) algorithms have been considered separately as means to increase the capacity in wireless communication networks. The capability of the smart antenna systems to track the user with the main lobe and interference with the nulls creates a significant impact on the current and future wireless sensor networks. In this paper, we use constrained least mean square (CLMS) and conjugate gradient adaptive beam-forming (CGBF) algorithms for narrowband adaptive beam-forming for tracking mobile user in a 2D urban environment without using PC algorithm. The CLMS algorithm is capable of efficiently adapting according to the environment and able to permanently maintain the chosen frequency response in the look direction while minimizing the output power of the array. In addition, with the CGBF algorithm, the desired users' signal in an arbitrary path is passed and the inter-path interference (IPI) is canceled in other paths in each RAKE finger. The adaptability of the algorithms is closely observed for uniformly spaced linear array. Also in this paper, we present switched-beam (SB) technique. In the SB technique by using a number of fixed, independent, or directional antennas we increase the uplink capacity of the wireless systems. Simulation results indicate that the SB technique is able to considerably increase the signal to interference plus noise ratio (SINR) of mobile user in comparison with other algorithms. In addition, we observe that the SINR in the CLMS algorithm is lower than the CGBF algorithm. Finally, we discuss perfect power control and path loss parameter in urban environments and their effects on system capacity by simulations.