Conventional construction of non-binary polar codes divides the synthesized channels into the frozen channels and information channels. Each information channel carries one symbol i.e.$q$bits. However, there are many middle channels with insufficient polarization, which can not carry one symbol of$q$bits but only$i$bits,$1 \leq i

Recently, deep convolutional neural networks have been widely applied in target detection of synthetic aperture radar (SAR) images. However, the regular convolution kernel cannot effectively establish dependency between features of SAR image with geometric distortion. Meanwhile, SAR images contain a small number of vehicle targets, and the imbalance problem between foreground-background class is serious during training. To solve these problems, we propose a you only look once (YOLO) detector based on graph convolutional network (GCN) called GCN-YOLO. First, a multilayer GCN model called vision GNN (ViG) is used as feature extractor to model the local area and build long-term dependencies between features. In addition, a convolutional block attention module (CBAM) is embedded into the last layer to enhance semantic features. Then, we introduce the VariFocal loss (VFL) as confidence loss to relief the imbalance problem between positive and negative samples. The experimental results on the miniSAR data demonstrate the effectiveness of the proposed method.

Being one of infrastructures for modern cities, urban roads suffer from potential subsurface disasters, which results in unexpected vital loss of economy and life. Thanks to the superiority of nondestructive detection with high efficiency, ground penetrating radar (GPR) has been widely applied to underground disaster detection; however, fenced by lack of labelled GPR data, automatic detection methods, especially the ones based on deep neural networks, have to be trained by synthetic GPR images and few real ones, which impedes the further application of deep neural networks in underground disaster detection. We proposed a network based on generative adversarial network with multiscale discrimination strategy to generate GPR b-scan images from the synthetic images, i.e., the forward GPR b-scan images generated by gprMax. Because sharing the same physical laws with real b-scan images and carrying label information of subsurface disaster, the simulated images could be utilized to augment training dataset for detection networks. The associated experiments show that the simulated images by our network are very similar to the real GPR b-scan images in appearance; meanwhile, the detection networks trained on the data set mixing the b-scan images simulated by our network and real ones could achieve better performance. Using our network as an augmentation method for GPR b-scan images contributes to the extensive application of deep neural networks in intelligent processing of GPR data.

With the development of the high-speed trains (HST), high spectral efficiency and low latency in high mobility scenarios have become an urgent demand. In this paper, we consider bandwidth-efficient multilevel coding (MLC) based on polar codes under high mobility scenarios. Since the use of a lot of binary coding levels and large list sizes will lead to a high latency for polar decoding, we first introduce a new kind of nonbinary polar codes based on multiplicative repetition method. Then, we employ the proposed codes as component codes for MLC scheme, and optimize the design for high mobility scenarios with low latency. Simulation results show that nonbinary polar coded MLC scheme (with less coding levels) outperform LTE turbo codes with high-order modulations, and can exhibit similar performance of binary polar coded bit-interleaved coded modulation scheme but with a smaller list size (reflecting low latency) over HST channels.

In this paper, we consider the detection of faster-than-Nyquist (FTN) signaling. By noticing that the whitening filter for FTN signaling cannot be directly derived when the symbol rate exceeds the signal bandwidth, we propose a new reduced-complexity M-algorithm BCJR (M-BCJR) algorithm based on the Ungerboeck observation model. By taking some “future” symbols into account, the proposed algorithm is able to select the M best states in the maximum a posteriori sense. We further simplify the above algorithm by choosing the key path from each possible state, which successfully reduces the complexity while maintaining a good bit error rate performance. Simulation results show that, with the use of the proposed methods, great gains can be obtained in terms of spectral efficiency (up to 186%) or signal-to-noise ratio (up to 4.5 dB) compared with the Nyquist signaling.

With the development of high-speed trains (HST), efficient and reliable communication services in high mobility scenarios have become an urgent demand. As one of the strong candidates in 5G wireless system, polar codes along with its optimized design should also be investigated under high mobility scenarios. In this paper, a scheme of hash-concatenated polar codes is proposed to reduce the false alarm rate, which is a key performance in 5G enhanced mobile broadband control channel. Then, for data channels, hash-based cyclic redundancy check (CRC)-aided polar codes with a joint successive cancellation list decoding method is introduced to improve the error-correcting performance. Simulation results show that the hash-concatenated polar codes can achieve both the lower false alarm rate and better error-correcting performance than conventional CRC-aided polar codes in both the AWGN and high mobility channels. Furthermore, with the joint decoding approach, hash-based CRC-aided polar codes perform better than LTE turbo codes for high-order modulations in terms of the frame error rate over the HST channel.

Spinal codes, a new class of rateless codes, have received considerable attention for their capacity‐approaching performance over noisy channels. Hash function is the core of a spinal encoder to generate infinite coded symbols, which has higher hardware complexity. In this study, the authors propose a multiplicative repetition‐based method instead of the hash function to generate innumerable symbols with low encoding complexity. Furthermore, both frozen‐aided and cyclic redundancy check‐aided decoding methods are proposed to improve the spectral efficiency. Simulation results show that the proposed spinal codes have lower computational complexity and can achieve higher spectral efficiency in the high signal‐to‐noise ratio region compared with the conventional ones over both additive white Gaussian noise and Rayleigh fading channels.

This demo abstract describes an initial design of a new adaptive video streaming protocol for device-to-device WiFi-based mobile platforms and its software implementation. For the demonstration, two mobile servers and two mobile users will be deployed verifying that our device-to-device adaptive video streaming implementation works with desirable user experience.