CDMbased 4Channel Digital Beamforming Transmitter Using a Single DAC In recent years, digital beamforming (DBF) is gaining more attention for next generation directional communication systems such as 5G, thanks to its flexibility for beamstearing compared with that of both analog and hybridbeamforming. However, conventional digital beamforming requires the use of independent Digital to Analog converter (DAC) and RF chain for each antenna element, which poses significant challenges in cost, power and heat management. In this work, we report for the first time a novel lowcost solution with reduced hardware complexity in DBF. Digital beamformed multiple baseband signal is designed to feed different antenna elements through a single DAC. Coding schemes were implemented in digital baseband domain, while decoding was done in analog RF domain. The decoded signals are tested before connected with antenna for proofofconcept. The demonstrated technique provides cost and size reduction of the array system, while keeping the integrity of the signals and beam pattern.

Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels The use of beamforming for efficient transmission has already been successfully implemented in practical systems and is absolutely necessary to even further increase spectral and energy efficiencies in some configurations of the nextgeneration wireless systems and for low earth orbit satellites. A remarkable capacity increase is then achieved and spectral congestion is minimized. In this context, this article proposes a novel complex multipleinput multipleoutput radial basis function neural network (CMMRBF) for transmitter beamforming, based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed CMMRBF is compared with the least mean square (LMS) algorithm for beamforming with six dipoles arranged in a uniform and circular array and with 16 dipoles in a 2Dgrid array. Simulation results show that the proposed solution presents lower steadystate mean squared error, faster convergence rate and enhanced halfpower beamwidth (HPBW) when compared with the LMS algorithm in a nonlinear scenario.