This paper introduces and uses spider monkey optimization (SMO) for synthesis sparse linear arrays, which are composed of a uniformly spaced core subarray and an extended sparse subarray. The amplitudes of all the elements and the locations of elements in the extended sparse subarray are optimized by the SMO algorithm to reduce the side lobe levels of the whole array, under a set of practical constraints. To show the efficiency of SMO, different examples are presented and solved. Simulation results of the sparse arrays designed by SMO are compared with published results to verify the effectiveness of the SMO method.

In this paper, a self optimization beamforming null control (SOBNC) scheme is proposed. There is a need of maintaining signal to interference plus noise ratio (SINR) threshold to control modulation and coding schemes (MCS) in recent technologies like Wi-Fi, Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A). Selection of MCS depends on the SINR threshold that allows maintaining key performance index (KPI) like block error rate (BLER), bit error rate (BER) and throughput at certain level. The SOBNC is used to control the antenna pattern for SINR estimation and improve the SINR performance of the wireless communication systems. The nulling comes with a price; if wider nulls are introduced, i.e. more number of nulls are used, the 3dB beam-width and peak side lobe level (SLL) in antenna pattern changes critically. This paper proposes a method which automatically controls the number of nulls in the antenna pattern as per the changing environment based on adaptive-network based fuzzy interference system (ANFIS) to maintain output SINR level higher or equal to the required threshold. Finally, simulation results show a performance superiority of the proposed SOBNC compared with minimum mean square error (MMSE) based adaptive nulling control algorithm and conventional fixed null scheme.

The performance in terms of the user separation of multi-user multiple-input multiple-output (MU-MIMO) depends on not only the spatial correlation but also the location of the mobile stations (MSs). In order to take into account the performance in terms of the user separation, we need to consider the granularity of the beam and null width of the precoded antenna pattern in addition to the spatial correlation to determine the base station (BS) antenna configuration. In this paper, we propose Smart Vertical MIMO (SV-MIMO) as the best antenna configuration that achieves both spatial correlation and granularity of the beam and null width of the precoded antenna pattern. We evaluate SV-MIMO in a field experiment using a downlink 4-by-2 MU-MIMO configuration focusing on the dependency of the location of the MSs in Yokosuka, Japan. The majority of the measurement course is under line-of-sight (LOS) conditions in a single cell environment. The MSs are almost uniformly set 30 to 60 degrees in azimuth and 12 to 30 degrees in elevation and the distance from the BS antennas is approximately 150m at maximum. We also evaluate the performance of 4-by-2 MU-MIMO using the conventional type of horizontal array antenna and show the difference. The field experimental results show that throughput of greater than 1Gbps is achieved at the Cumulative Distribution Function (CDF) of 14% by employing SV-MIMO for Rank-4 MU-MIMO. The throughput of SV-MIMO is 30% higher than that for the horizontal array antenna configuration at the CDF of 50%.