Wireless Networking

Unlicensed coexistence networks and spectrum sharing are two relatively new technological paradigms in cellular technology. These wireless systems are standardized and adopted to help cellular operators meet the ever-increasing mobile data demand by efficient utilization of unlicensed bands. However, several incumbents are already operational in these frequencies such as military, radar, and navy systems rendering the wireless environment extremely dynamic and unpredictable.

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 The drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. Thus, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet). 

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Database of energy consumption (Eihop) and Transmission Power P0, resulting from the manipulation of the variables: Nb (Number of bits per frame), i (Number of hops to the destination) and d (Distance between origin and destination) in Tmote Sky device Ultra-low power IEEE 802.15.4 (Moteiv). DataSet used in the learning process, via Machine Learning, of the transmission behavior of this device.

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437 Views

This data file contains simulated channel mat files and MATLAB code for performance evaluation of L-shape array based technique for massive MIMO systems to reduce cross user correlation of tightly coupled MTs.

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This dataset contains supporting information regarding the LCA modeling carried out in the scientific paper "Technical and Ecological Limits of 2.45-GHz Wireless Power Transfer for Battery-Less Sensors".

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271 Views

We collected data to train the ML module to determine the user’s device's location based on beacon frame characteristics and RSSI values from Wi-Fi APs. To collect the data, we defined a threshold distance of 7 feet as the maximum allowable distance between the user’s devices. We then collected two datasets: one with data collected while the two Raspberry Pis were within 7 feet or less of each other named ”authentic”, and another with data collected while the distance between the two Raspberry Pis was over 7 feet named ”unauthorized”.

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579 Views

Although Unmanned Aerial Vehicles (UAVs) have long been recognized as systems to effectively deliver advanced and innovative services, their practical application to Beyond Line-of-Sight (BLOS) use cases is still largely missing due to safety concerns and regulations. Indeed, BLOS applications for UAVs require a reliable infrastructure that is capable of ensuring ubiquitous connectivity, low latency, and high data rates.

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501 Views

Most machine learning (ML) proposals in the Internet of Things (IoT) space are designed and evaluated on pre-processed datasets, where the data acquisition and cleaning steps are often considered a black box. Therefore, the data acquisition stage requires additional data cleaning/anomaly techniques, which translate to additional resources, energy, and storage.

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1279 Views

This is a data set for Radio Frequency fingerprinting, which is a kind of identification of wireless devices based on their intrinsic physical features. The data set is composed by GSM bursts collected from 12 GSM mobile phones while transmitting.  The samples have been collected using a Software Defined Radio with a sample rate at 20 MS/s. The content information has been removed from the bursts to remove the risk of bias due to content. The data set is in MATLAB format.

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885 Views

In this dataset, based on a beam sweeping experiment in the 60 GHz band in an indoor environment, we provide the acquired IQ data samples (containing the announced TX antenna weighting vectors (AWV) index as information) for the given RX AWV index, location, and carrier frequency. We also include the information obtained after processing the PPDU in IQ data.

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768 Views

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