Automatic modulation classification (AMC) plays a key role in non-cooperative communication systems. Feature-based (FB) methods have been widely studied in particular. Most existing FB methods are deployed at a fixed SNR level, and the pre-trained classifiers may no longer be effective when the SNR level changes. The classifiers may also need to be re-trained to be suitable for the varying channel environment. To address these problems, a robust AMC method under varying noise conditions is proposed in this paper. The method attempts to select noise-insensitive features from a large feature set to ensure that the trained classifiers will be robust to SNR variations. First, a feature set consisting of 25 types of features is extracted, and 4 features that are insensitive to noise are chosen through a feature selection method based on rough set theory. The generalizability of an SVM classifier trained on the 4 chosen features is evaluated based on numerical results. The classification accuracy remains reasonable when the SNR varies between 5 and 20 dB, indicating that the proposed method can be deployed under varying noise conditions.

Unmanned aerial vehicle (UAV) is suitable to improve connectivity of wireless communication networks in harsh environment due to its flexible deployment and high mobility. In this paper, we consider the case that a UAV is employed as a mobile relay to ferry data between two disconnected ground nodes. The amplify-and-forward mobile relay technique is studied. By exploiting the channel state variation induced by the movement of UAV, we consider maximizing the end-to-end throughput of this mobile relay system by optimizing the source/relay power allocation, as well as the UAV's trajectory. However, the formulated problem is non-convex and intractable to solve. To address this non-convex problem, we propose two iterative algorithms to optimize the source/relay power allocation with the fixed UAV's trajectory and the UAV's trajectory with fixed power allocation, by successive convex optimization, respectively. Then, an iterative algorithm is proposed to optimize the source/relay power allocation and UAV's trajectory in an alternate manner. By exploiting the predictable channel variation via the proposed power and trajectory optimization scheme, numerical results show that significant throughput gains can be achieved.

The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.

High-precision positioning scheme, an important part of indoor navigation, can be implemented using an ultra-wideband (UWB) based ranging system. Recently, solutions for precise positioning in dense multipath and non-line-of-sight (NLOS) conditions have attracted a lot of attentions. On the other hand, it is expected that waveform division multiple access (WDMA) technology for multiuser UWB positioning applications will be indispensable in the near future. In this regard, a WDMA-UWB-based positioning scheme is investigated in this paper, to enhance the positioning accuracy in multiuser applications. In accordance with practical requirements of indoor positioning, we propose a new indoor positioning scheme, termed as WDMA-UWB-based indoor positioning (WUB-IP). This scheme adopts WDMA for multiple access, and utilizes an entropy-based approach for the time of arrival estimation. Moreover, a transfer learning approach is used to mitigate the ranging error in NLOS conditions, in order to improve the positioning accuracy in NLOS conditions. System-level simulations demonstrate that the proposed scheme enhances the performance of indoor positioning for multiuser applications.

In this letter, we consider employing a non-orthogonal multiple access (NOMA) unmanned aerial vehicle (UAV) as relay to ferry data from a remote base station to multiple ground users. Our objective is to minimize the power consumption of UAV by jointly optimizing the location and transmission power of UAV. The formulated problem is non-convex with highly coupled binary discrete constraints, which is intractable to solve. Firstly, it is transformed into a more tractable form, where the logarithmic constraints are transformed into polynomial form, and the discrete constraints are transformed into continuous form. Then the non-convex equality constraints are handled by penalty method, while the non-convex inequality constraints are handled by successive convex approximation technique. Based on these measures, we propose a double-loop iterative algorithm to obtain the solution approximately. Numerical results show the effectiveness of our proposed algorithm.