Key Technologies Research Of Adaptive Array Signal Processing In Mobile Communication

Adaptive Array Antennas (AAA) can adjust its beam patterns based on the arrival direction of signal, tracking the anticipant signal, decreasing or counteracting the interferences and improving the SIR (Signal to Interference Ratio). The introduction of adaptive array antennas in the mobile communication system will develop the using of frequency spectrum, enlarge the capability of the system, expand the radiation of the base station, reduce the electromagnetic pollution and improve the communication quality of the system obviously. Adaptive array antennas have become one of the spotlights of the Third Generation mobile communication. Given the sampled data of each element, the signal processing of AAA consists of three parts: the determination of source number, the DOA (Direction of Arrival) multi-user detection and DBF (Digital Beamforming). They are not independent, but associated with one another. Having read a lot of transactions in the area of interest, the author aims at the subject on DOA and DBF in AAA technologies and makes efforts to study the problems below: 1. The conventional DOA methods are summarized. The DOA estimation performance of MUSIC algorithm in the scenario of correlated signals and uncorrelated signals are studied. Then the performances of MUSIC and that of the conventional methods are compared with the simulation results given. 2. MUSIC algorithm is the classical algorithm to Spectral analysis, and it has many virtues expect that Spatial Smoothing, which the algorithm needs to distinguish correlated signals, result in excess work. The spatial-temporal smoothing algorithm is proposed to distinguish each DOA of different path of different user effectively. Compared with the traditional Spatial Smoothing method, the spatial-temporal smoothing method can achieve higher resolution. 3. An algorithm based on the largest eigenvalue using temporal-spatial averaging is proposed. The algorithm integrates the DOA algorithm based on the largest eigenvalue and temporal-spatial averaging method. It keeps virtues of the former algorithm and through temporal-spatial averaging processing the performance of DOA estimation is improved greatly, especially on the condition that SNR (Signal-to-Noise Ratio) is low and the number of snapshot is not enough. 4. ESB (Eigenspace-based) beamforming algorithm is introduced and an improved algorithm based on the largest eigenvalue is proposed on the base of it. The improved algorithm eliminates the weight of interference users and multi-path signals, decreases the norm of the weight vector and the output noise power, improves SINR and converges more rapidly than the original ESB algorithm. It is not sensitive to the direction error, correlated inference and multi-path inference and has quite good stability and robustness.