In order to transmit data and transfer energy to the low-power Internet of Things (IoT) devices, integrated data and energy networking (IDEN) system may be harnessed. In this context, we propose a bitwise end-to-end design for polar coded IDEN systems, where the conventional encoding/decoding, modulation/demodulation, and energy harvesting modules are replaced by the neural networks (NNs). In this way, the entire system can be treated as an AutoEncoder (AE) and trained in an end-to-end manner. Hence achieving global optimization. Additionally, we improve the common NN-based belief propagation (BP) decoder by adding an extra hypernetwork, which generates the corresponding NN weights for the main network under different number of iterations, thus the adaptability of the receiver architecture can be further enhanced. Our numerical results demonstrate that our BP-based end-to-end design is superior to conventional BP-based counterparts in terms of both the BER and power transfer, but it is inferior to the successive cancellation list (SCL)-based conventional IDEN system, which may be due to the inherent performance gap between the BP and SCL decoders.

Graph Convolution Networks (GCNs) have become the main-stream framework for skeleton-based action recognition tasks. Aiming at the problem of redundant spatial-temporal feature information and neighborhood constraints obtained in GCNs, we propose a novel method called Multi-Attention Network (MA-Net) to explore crucial skeleton information, including two main modules: Combined Attention Graph Convolution (CAGC) and Multi-layer Transposed Attention Encoding (MTAE). The CAGC utilizes multi-dimensional combination attention to capture more valuable information and enhance feature performance. The MTAE adopts self-attention to encode feature maps, effectively establishing long-range dependency and capturing global information. Centre on the attention mechanism, these two modules combine the complementary advantages of GCN (i.e., local topology and temporal dynamics) and Transformer (i.e., global context and dynamic attention). Extensive experiments on the challenging NTU-RGB+D 60 and Kinetics-Skeleton datasets demonstrate that our model performs excellently.