Wireless Networking

We conducted a massive MIMO channel measurement experiment in an indoor setting on the Rice University campus. We used a 64-antenna RENEW software-defined massive MIMO base station and seven software-defined clients in a large open area inside a building hall. We fixed six of the clients in a circle,15maway from the base station. The seventh node was placed on a robot where we moved the robot across the hall starting from the location of the first client to the last. We moved the robot along the path with different speeds, i.e. with0.5m/s,1m/s, and2m/s.

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Bistatic MIMO Radar Sensing

This dataset includes the synthetic aperture measurement data and code accompanying the publication "Bistatic MIMO Radar Sensing of Specularly Reflecting Surfaces for Wireless Power Transfer" [1].

Description:

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The dataset Provides S-parameter measurements of two ISO/ICE 14443-1 Coils with series capacitance compensation at 13.56 MHz under different spatial configurations of vertical and horizontal misalignment, inter-coil distance, and azimuthal tilt as indicated in the image. The dataset  can be used for training of neural networks controlling adaptive impedance matching networks.

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Air corridors are considered as a promising solution to traffic management of a large number of aircrafts (both manned and unmanned). To enable such a system, we developped an emulation of an air corridor system based on a software in the loop (SITL) from ArduPilot (a popular open source autopilot).

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This dataset is made of the Channel Impulse Response (CIR) data collected in 9 different environments in Ghent city, Belgium. These environments include:

1.  Fourth floor at iGent Tower in the premises of Gent University

2. Zwijnaarde Open Area

3. Stadhuis Street and Nearby

4. Zuid Mall

5. Portus Ganda

6. Sint-Pieters Railway Station

7. Krook library

8. Citadel Park

9. Graffiti Straat

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388 Views

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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544 Views

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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504 Views

 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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