Generalized quadrature spatial modulation and its application to vehicular networks with NOMA

Quadrature spatial modulation (QSM) is recently proposed to increase the spectral efficiency (SE) of SM, which extends the transmitted symbols into in-phase and quadrature domains. In this paper, we propose a generalized QSM (GQSM) scheme to further increase the SE of QSM by activating more than one transmit antenna in in-phase or quadrature domain. A low-complexity detection for GQSM is provided to mitigate the detection burden of the optimal maximum-likelihood (ML) detection method. An upper bounded bit error rate is analyzed to discover the system performance of GQSM. Moreover, by collaborating with the non-orthogonal multiple access (NOMA) technique, we investigate the practical application of GQSM to cooperative vehicular networks and propose the cooperative GQSM with OMA (C-OMA-GQSM) and cooperative GQSM with NOMA (C-NOMA-GQSM) schemes. Computer simulation results verify the reliability of the proposed low-complexity detection as well as the theoretical analysis, and show that GQSM outperforms QSM in the entire SNR region. The superior BER performance of the proposed C-NOMA-GQSM scheme make it a promising modulation candidate for next generation vehicular networks

Joint optimization of depth and ego-motion for intelligent autonomous vehicles

The three-dimensional (3D) perception of autonomous vehicles is crucial for localization and analysis of the driving environment, while it involves massive computing resources for deep learning, which can't be provided by vehicle-mounted devices. This requires the use of seamless, reliable, and efficient massive connections provided by the 6G network for computing in the cloud. In this paper, we propose a novel deep learning framework with 6G enabled transport system for joint optimization of depth and ego-motion estimation, which is an important task in 3D perception for autonomous driving. A novel loss based on feature map and quadtree is proposed, which uses feature value loss with quadtree coding instead of photometric loss to merge the feature information at the texture-less region. Besides, we also propose a novel multi-level V-shaped residual network to estimate the depths of the image, which combines the advantages of V-shaped network and residual network, and solves the problem of poor feature extraction results that may be caused by the simple fusion of low-level and high-level features. Lastly, to alleviate the influence of image noise on pose estimation, we propose a number of parallel sub-networks that use RGB image and its feature map as the input of the network. Experimental results show that our method significantly improves the quality of the depth map and the localization accuracy and achieves the state-of-the-art performance

CEP-based massive data processing approach for RFID Data

Complex Event Processing (CEP) is proposed in this paper to tackle the changeability, tense correlation, and association in massive data process. This paper takes RFID (Radio Frequency Identification) data for example to illustrate how CEP works on dealing with massive data processing. First, semantic expressions are introduced as the reason of modeling for CEP. Then, model solution based on semantic expressions is proposed. Finally, this methodology achieves good results in processing massive RFID data in terms of speed, efficiency and veracity. The experiment results demonstrate that it is better in getting rid of complexity data after comparing with traditional data management based on database with massive data. © (2011) Trans Tech Publications.Link_to_subscribed_fulltex

A Novel Deep-Learning-Based Enhanced Texture Transformer Network for Reference Image Super-Resolution

The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively