Spectrum has become an extremely scarce and congested resource. As a consequence, spectrum sensing enables the coexistence of different wireless technologies in shared spectrum bands. Most existing work requires spectrograms to classify signals. Ultimately, this implies that images need to be continuously created from I/Q samples, thus creating unacceptable latency for real-time operations. In addition, spectrogram-based approaches do not achieve sufficient granularity level as they are based on object detection performed on pixels and are based on rectangular bounding boxes. For this reason, we propose a completely novel approach based on semantic spectrum segmentation, where multiple signals are simultaneously classified and localized in both time and frequency at the I/Q level. Conversely from the state-of-the-art computer vision algorithm, we add non-local blocks to combine the spatial features of signals, and thus achieve better performance. In addition, we propose a novel data generation approach where a limited set of easy-to-collect real-world wireless signals are "stitched together" to generate large-scale, wideband, and diverse datasets. Experimental results obtained on multiple testbeds (including the Arena testbed) using multiple antennas, multiple sampling frequencies, and multiple radios over the course of 3 days show that our approach classifies and localizes signals with a mean intersection over union (IOU) of 96.70% across 5 wireless protocols while performing in real-time with a latency of 2.6 ms. Moreover, we demonstrate that our approach based on non-local blocks achieves 7% more accuracy when segmenting the most challenging signals with respect to the state-of-the-art U-Net algorithm. We will release our 17 GB dataset and code.

Due to the increased usage of spectrum caused by the exponential growth of wireless devices, detecting and avoiding interference has become an increasingly relevant problem to ensure uninterrupted wireless communications. In this paper, we focus our interest on detecting narrowband interference caused by signals that, despite occupying a small portion of the spectrum only, can cause significant harm to wireless systems. For example, in the case of interference with pilots and other signals that are used to equalize the effect of the channel or attain synchronization. Due to the small sizes of these signals, detection can be difficult due to their low energy footprint, while greatly impacting (or denying completely in some cases) network communications. We present a novel narrowband interference detection solution that utilizes convolutional neural networks (CNNs) to detect and locate these signals with high accuracy. To demonstrate the effectiveness of our solution, we have built a prototype that has been tested and validated on a real-world over-the-air large-scale wireless testbed. Our experimental results show that our solution is capable of detecting narrowband jamming attacks with an accuracy of up to 99%. Moreover, it is also able to detect multiple attacks affecting several frequencies at the same time even in the case of previously unseen attack patterns. Not only can our solution achieve a detection accuracy between 92% and 99%, but it does so by only adding an inference latency of 0.093ms.

With more than 65M people affected by epilepsy worldwide, early prediction and response to seizure onsets have become more important than ever. Cutting-edge research in implantable medical devices (IMDs) has shown that deep neural networks (DNNs) applied to intracranial electroencephalogram (iEEG) data can predict seizures up to an hour before onset. However, offloading of iEEG data to the edge/cloud is highly prohibitive, due to the sheer size of the generated data. Existing work either focuses on the DNN training phase only, or does not consider the severe energy/space limitations of IMDs. Moreover, the technical aspects of patient personalization, which allows for patient-specific hyper-parameter tuning, still remain unaddressed. In this paper, we propose a platform called AiEEG for in vivo early seizure prediction, whose DNN hardware circuitry can be reconfigured remotely without surgery. We prototype AiEEG on a system on chip (SoC) platform and demonstrate its end-to-end capabilities in seizure prediction with a population of 30 epileptic patients, with iEEG signals coming from a real-world dataset. Extensive experimental results shows that (i) our embedded and personalized DNN has an area under the curve (AUC) averaging at 0.97 and as low as zero false positives per hour (FPH) for over half the patients, an improvement of about 3.5x with respect to a non-personalized prediction method and the best for a dataset of this size when compared to the state-of-the-art; (ii) our AiEEG platform consumes 4.2x less energy than a cloud-based approach, leading to a 4x battery lifetime improvement; (iii) we are able to remotely fine-tune the DNN through partial reconfiguration as needed in about 10s.

Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) constraints; (ii) its underlying algorithms need to be extremely accurate, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. To the best of our knowledge, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper, we propose DeepSense, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver's baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform's hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected "in the wild" with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms. For reproducibility and benchmarking purposes, we pledge to share the code and the datasets used in this paper to the community.

We introduce X60, the first SDR-based testbed for 60 GHz WLANs, featuring fully programmable MAC/PHY/Network layers, multi-Gbps rates, and a user-configurable 12-element phased antenna array. These features provide us with an unprecedented opportunity to revisit the most important aspects of 60 GHz signal propagation and obtain new insights on performance expected from practical 60 GHz systems. X60's unique capabilities make it an ideal platform for experimentation and prototyping across layers.