Fan Yang

Unsupervised domain adaptation (UDA) aims to transfer knowledge from the labeled source domain to the fully unlabeled target domain, thus improving the classification performance of the target domain. Recently, self-training methods have shown their effectiveness on UDA. It iteratively trains target data using the generated target pseudo-labels. However, the feature space for generating pseudo-labels contains a large amount of source information, which traps the model in the source domain, making it challenging for the generator to learn discriminative features of the target domain. In this article, we propose a self-training domain adaptation (DA) model with bidirectional transmission generators (BDTGs). Specifically, we design a bidirectional transmission structure for generators, using exponential moving average (EMA) as the bridge between two generators. The structure has two advantages: 1) by transmitting weight parameters to each other during the training process, it promotes the shift of the feature space, thereby alleviating the difficulty of the model in adapting target domain features and 2) the transmission disturbs the classification boundary and is able to expose unreliable target samples near the boundary. We design a cosine similarity-based filter to identify such samples, to reduce the influence of noisy pseudo-labels with incorrect semantic information on the model. Extensive experiments conducted on five benchmark UDA datasets show that our approach has superior classification performance.

D2D communications are considered as effective means to achieve vehicle-to-vehicle (V2V) communications, which can be used to improve the performance in vehicular ad hoc networks (VANETs). In this paper, we investigate the resource allocation and interference management based on clustering mechanism in the D2D communications under-laying VANETs aiming to optimize resource utilization efficiency. Firstly, given the transmission resources of intra-cluster for D2D communications, we design a vehicle clustering algorithm to improve intra-cluster resource efficiency and nullify the intra-cluster interference. Secondly, by analyzing the wireless service in VANETs, a cooperative communication scheme with optimized resource utilization is proposed to maximize intra-cluster spectral efficiency. Thirdly, when uplink spectrums are shared, an interference neutralization (IN) model is developed to manage inter-cluster interference, while each vehicle is equipped with one antenna and there is no extra antenna for interference cancellation. In accordance with the IN model, a cross-layer optimization frame for multi-hop VANETs is proposed to maximize the end-to-end throughput of multiple coexisting communication sessions. To evaluate the performance, we select the method without cooperative communication and IN as a benchmark and compare it against the method with cooperative communication only and the method with both cooperative communication and IN. The simulation results show that the method that uses cooperative communication and IN can dramatically increase the throughput. Furthermore, the throughput gains increase along with vehicle density growing in VANETs.