In a wireless ad hoc network (MANET), nodes can form a centerless, self-organizing, multi-hop dynamic network without any centralized control function, while hidden and exposed terminals seriously affect the network performance. Meanwhile, the wireless network node is evolving from single communication function to jointly providing a self precise positioning function, especially in indoor environments where GPS cannot work well. However, the existing medium access control (MAC) protocols only deal with collision control for data transmission without positioning function. In this paper, we propose a MAC protocol based on 802.11 standard to enable a node with self-positioning function, which is further used to solve hidden and exposed terminal problems. The location of a network node is obtained through exchanging of MAC frames jointly using a time of arrival (TOA) algorithm. Then, nodes use the location information to calculate the interference range, and judge whether they can transmit concurrently. Simulation shows that the positioning function of the proposed MAC protocol works well, and the corresponding MAC protocol can achieve higher throughput, lower average end-to-end delay and lower packet loss rate than that without self-localization function.

Concatenated zigzag (CZ) codes are classified as one kind of parallel-concatenated codes with powerful performance and low complexity. This kind of codes has flexible implementation methods and a good application prospect. We propose a modified turbo-type decoder and adaptive extrinsic information scaling method based on the Max-Log-APP (MLA) algorithm, which can provide a performance improvement also under the relatively low decoding complexity. Simulation results show that the proposed method can effectively help the sub-optimal MLA algorithm to approach the optimal performance. Some contrasts with low-density parity-check (LDPC) codes are also presented in this paper.

Cross-project defect prediction (CPDP) is a hot research topic in recent years. The inconsistent data distribution between source and target projects and lack of labels for most of target instances bring a challenge for defect prediction. Researchers have developed several CPDP methods. However, the prediction performance still needs to be improved. In this paper, we propose a novel approach called Joint Domain Adaption and Pseudo-Labeling (JDAPL). The network architecture consists of a feature mapping sub-network to map source and target instances into a common subspace, followed by a classification sub-network and an auxiliary classification sub-network. The classification sub-network makes use of the label information of labeled instances to generate pseudo-labels. The auxiliary classification sub-network learns to reduce the distribution difference and improve the accuracy of pseudo-labels for unlabeled instances through loss maximization. Network training is guided by the adversarial scheme. Extensive experiments are conducted on 10 projects of the AEEEM and NASA datasets, and the results indicate that our approach achieves better performance compared with the baselines.

Along with the fast development of blue economy, wireless communication in oceans has received extensive attention in recent years, and opportunistic networks without any aid from fixed infrastructure or centralized management are expected to play an important role in such highly dynamic environments. Here, link prediction can help nodes to select proper links for data forwarding to reduce transmission failure. The existing prediction schemes are mainly based on analytical models with no adaptability, and consider relatively simple and small terrestrial wireless networks. In this paper, we propose a new link prediction algorithm based on machine learning, which is composed of an extractor of convolutional layers and an estimator of long short-term memory to extract useful representations of time-series data and identify effective long-term dependencies. The experiments manifest that the proposed scheme is more effective and flexible compared with the other link prediction schemes.

In this paper, a new scheme is presented for ground moving target indication for multichannel high-resolution wide-swath (HRWS) SAR systems with modified reconstruction filters. The conventional steering vector is generalized for moving targets through taking into account the additional Doppler centroid shift caused by the across-track velocity. Two modified steering vectors with symmetric velocity information are utilized to produce two images for the same scene. Due to the unmatched steering vectors, the stationary backgrounds are defocused but they still hold the same intensities in both images but moving targets are blurred to different extents. The ambiguous components of the moving targets can also be suppressed due to the beamforming in the reconstruction procedure. Therefore, ground moving target indication can be carried out via intensity comparison between the two images. The effectiveness of the proposed method is verified by both simulated and real airborne SAR data.