Wireless Networking

The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods. 

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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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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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The data collection phase involves the collection of beacon frame characteristics and RSSI values from Wi-Fi APs using two Raspberry Pi devices. The purpose of this phase is to gather enough data to train the ML module of the proposed system to accurately determine the user's devices location based on these characteristics and values. To collect the data, we defined a threshold distance of 7 feet. This is the maximum distance between the user's devices that we consider acceptable for the purposes of this experiment.

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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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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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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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