Hover-in-place unmanned aerial vehicles (UAVs), which are used frequently as filming equipment in media production, also have potential use cases as hovering relay stations for microwave and millimeter-wave wireless links. For efficient frequency usage, it is desirable to utilize in-band full-duplex relays. In addition, to reduce the processing load of a UAV relay station, amplify-and-forward relays are desirable. In this case, it would be difficult to take countermeasures using signal processing if there is mutual interference between multiple nearby UAV relay stations. Therefore, we evaluated how much interference should be suppressed to avoid degradation in the average channel capacity. The results enabled us to quantitatively determine the required interference suppression for dual-polarized MIMO.

To efficiently compress the sign information of images, we address a sign retrieval problem for the block-wise discrete cosine transformation (DCT): reconstruction of the signs of DCT coefficients from their amplitudes. To this end, we propose a fast sign retrieval method on the basis of binary classification machine learning. We first introduce 3D representations of the amplitudes and signs, where we pack amplitudes/signs belonging to the same frequency band into a 2D slice, referred to as the sub-band block. We then retrieve the signs from the 3D amplitudes via binary classification, where each sign is regarded as a binary label. We implement a binary classification algorithm using convolutional neural networks, which are advantageous for efficiently extracting features in the 3D amplitudes. Experimental results demonstrate that our method achieves accurate sign retrieval with an overwhelmingly low computation cost.