Related papers: Multi-Label Wireless Interference Identification w…
Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by the intrinsic hardware impairments. State-of-the-art neural networks have been adopted for RFFI. However, many…
Complete awareness of the wireless environment, crucial for future intelligent networks, requires sensing all transmitted signals, not just the strongest. A fundamental barrier is estimating the target signal when it is buried under strong…
As wireless systems grow rapidly worldwide, one of the most important things, wireless systems designers and service providers faces is interference. Interference decreases coverage, capacity [1], and limits the effectiveness of both new…
The recent success in implementing supervised learning to classify modulation types suggests that other problems akin to modulation classification would eventually benefit from that implementation. One of these problems is classifying the…
Wireless networks have become the main form of Internet access. Statistics show that the global mobile Internet penetration should exceed 70\% until 2019. Wi-Fi is an important player in this change. Founded on IEEE 802.11, this technology…
In this paper, we propose a novel algorithm to detect anomaly in terms of Key Parameter Indicators (KPI)s over live cellular networks based on the combination of Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), as…
In this paper, we propose a distributed multi-hop interference avoidance algorithm, namely, IAA to avoid co-channel interference inside a wireless body area network (WBAN). Our proposal adopts carrier sense multiple access with collision…
In wireless communication networks, interference models are routinely used for tasks such as performance analysis, optimization, and protocol design. These tasks are heavily affected by the accuracy and tractability of the interference…
We consider an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) in which a multi antenna power beacon (PB) sends a dedicated energy signal to a wireless powered source. The source first harvests…
Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph convolution network…
The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To…
Anomaly detection has various applications including condition monitoring and fault diagnosis. The objective is to sense the environment, learn the normal system state, and then periodically classify whether the instantaneous state deviates…
Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing…
With the emergence of new technologies and a growing number of wireless networks, we face the problem of radio spectrum shortages. As a result, identifying the wireless channel spectrum to exploit the channel's idle state while also…
Interference management has the potential to improve spectrum efficiency in current and next generation wireless systems (e.g. 3GPP LTE and IEEE 802.11). Recently, new paradigms for interference management have emerged to tackle…
As the potential of molecular communication via diffusion (MCvD) systems at nano-scale communication increases, designing molecular schemes robust to the inevitable effects of molecular interference has become of vital importance. There are…
Radio frequency interference (RFI) detection and excision are key steps in the data-processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Because of its high sensitivity and large data rate, FAST requires…
This work proposes a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic. This approach requires deploying multiple sniffers across the network to capture wireless…
Predicting smartphone users activity using WiFi fingerprints has been a popular approach for indoor positioning in recent years. However, such a high dimensional time-series prediction problem can be very tricky to solve. To address this…
Wireless low-power transceivers used in sensor networks such as IEEE 802.15.4 typically operate in unlicensed frequency bands that are subject to external interference from devices transmitting at much higher power. Communication protocols…