Search

Near-field antennas have been successfully adopted in several wireless applications. To exploit the high reconfigurability of array antennas, multiple synthesis techniques for arrays operating in the near-field region have been proposed. Building upon previous works on eigenmode expansions of the radiated fields, two synthesis methods for the excitations of near-field arrays based on the definition of an inner product on the electromagnetic fields are investigated: the "maximum norm" and "minimum error field norm" methods. The "maximum norm" method computes the array excitations that maximize either the active power flow through a target surface or the electric/magnetic energy stored in an assigned volume, depending on the adopted inner product. The performance of the maximum active power flow method is compared with the one of the simpler conjugate phase method. Furthermore, the limit solution achieved when the target surface reaches the far-field region is compared against the "maximum Beam Collection Efficiency" method. The "minimum error field norm" method allows to synthesize a given target field. As an example, the latter method is used to find the optimal excitation of a Plane Wave Generator with a spherical quiet zone. The effectiveness and performance of the discussed synthesis methods are validated through numerical simulations.

In the present work, a reinforcement learning (RL) based adaptive algorithm to optimise the transmit beampattern for a colocated massive MIMO radar is presented. Under the massive MIMO regime, a robust Wald type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been exploited to improve the detection performance by learning and interacting with the surrounding unknown environment. Building upon previous findings, we develop here a fully adaptive and data driven scheme for the selection of the hyper-parameters involved in the RL algorithm. Such an adaptive selection makes the Wald RL based detector independent of any ad hoc, and potentially suboptimal, manual tuning of the hyper-parameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values.