This paper investigates energy-efficient resource allocation problem for the wireless power transfer (WPT) enabled multi-user massive multiple-input multiple-output (MIMO) systems. In the considered systems, the sensor nodes (SNs) are firstly powered by WPT from the power beacon (PB) with a large scale of antennas. Then, the SNs use the harvested energy to transmit the data to the base station (BS) with multiple antennas. The problem of optimizing the energy efficiency objective is formulated with the consideration of maximum transmission power of the PB and the quality of service (QoS) of the SNs. By adopting fractional programming, the energy-efficient optimization problem is firstly converted into a subtractive form. Then, a joint power and time allocation algorithm based on the block coordinate descent and Dinkelbach method is proposed to maximize energy efficiency. Finally, simulation results show the proposed algorithm achieves a good compromise between the spectrum efficiency and total power consumption.

A local program insertion (LPI) scheme for video broadcasting systems is proposed by using a novel rotate-and-forward strategy, which can be widely used when a local TV tower (LT) wants to insert its own TV signals into the signals from the main TV tower (MT) without any additional resources. In the proposed LPI scheme, the bit stream of MT is firstly modulated and transmitted through coordinated constellation mapping, Alamouti encoding and OFDM modulation. Then, the LT receives the MT signals and demodulates them into constellation symbols. Finally, the bit stream of LT is mapped as “rotate bit” to rotate the demodulated MT symbols and forward to the users. We show that our proposed LPI scheme does not require extra time or frequency resources and it is also a complexity-reduced scheme for the local TV tower (LT) since bit-level decoding is not required at the LT. In addition, it can increase the network exchanging capacity in term of bits per channel use (bpcu).

Medical images play an important role in medical diagnosis. However, acquiring a large number of datasets with annotations is still a difficult task in the medical field. For this reason, research in the field of image-to-image translation is combined with computer-aided diagnosis, and data augmentation methods based on generative adversarial networks are applied to medical images. In this paper, we try to perform data augmentation on unimodal data. The designed StarGAN V2 based network has high performance in augmenting the dataset using a small number of original images, and the augmented data is expanded from unimodal data to multimodal medical images, and this multimodal medical image data can be applied to the segmentation task with some improvement in the segmentation results. Our experiments demonstrate that the generated multimodal medical image data can improve the performance of glioma segmentation.