Kaiser Window Based Side Lobe Level Suppression in 3D Beamforming for Massive MIMO Structure

In this paper, a novel integrated 3D beamforming (3DBF) algorithm has been reported with Kaiser window based Side Lobe Level (SLL) suppression technique for 12 x 8 Massive Multiple Input Multiple Output (MIMO) receiving (R) antenna structure. The 3DBF can steer the main-lobe towards the desired direction and place null towards the undesired directions. The incorporation of the SLL suppression technique makes 3DBF algorithm less vulnerable to unforeseen and undesired signals in comparison with conventional 3DBF strategies having significant SLL. Accordingly, the proposed method is more suitable for 5G applications in terms of user tracking. In this work, a conventional beamforming strategy is employed firstly to evaluate the appropriate antenna weights for directing the main lobe towards the Desired Signal (DS) and nulls in the direction of the Undesired Signals (UDSs) followed by an algorithm that invokes SLL suppression. The proposed SLL suppression procedure exploits independent and individual low-pass filter based Kaiser windowing technique in the vertical and horizontal axes of the Rx antenna for minimizing the vertical and horizontal side lobes of the Radiation Pattern (RP) separately. This SLL suppression technique re-evaluates the antenna weights in such a manner that it can jointly form the desired 3D beam with suppressed SLL without changing the direction of the main beam. The elevation, azimuth, and 3D RPs have been generated to test the performance of the proposed algorithm in terms of SLL reduction and robustness. Approximately 72% improvement has been observed in the first SLL of the RP. This 3DBF algorithm based on SLL reduction methodology is much faster than any evolutionary optimization algorithm which needs an iterative technique for SLL suppression.