Learning To Simulate Asymmetric Encryption With Adversarial Neural Networks


Digital signature is an encryption mechanism used to verify the authenticity and integrity of message, which has higher complexity and security than traditional handwritten signature. However, the two main challenges of digital signature are security and computing speed. It then imposes a problem - how to quickly verify and sign digital signatures under the premise of ensuring security.


Drive test measurements of deployed LTE base stations located at and around the campus of The Technical Unversity of Denmark. Metrics of signal quality are obtained using TSMW equipment with a vehicle driving around the area. In addition to the radio measurements, a GNSS receiver is utilized for additional localization metrics. Approximate altitude information is provided by the GNSS. 


The csv contains the necessary headers for the data. This includes specifically the following columns:


[Date,Time,UTC,Latitude,Longitude,Altitude,Speed,Heading, #Sat, EARFCN, Frequency, PCI, MCC, MNC, TAC, CI, eNodeB-ID, cellID, BW,SymPerSlot,Power,SINR,RSRP,RSRQ,4G-Drift,Sigma-4G-Drift,TimeOfArrival,TimeOfArrivalFN]


Visible Light Positioning is an indoor localization technology that uses wireless transmission of visible light signals to obtain a location estimate of a mobile receiver. 

This dataset can be used to validate supervised machine learning approaches in the context of Received Signal Strength Based Visible Light Positioning. 

The set is acquired in an experimental setup that consists of 4 LED transmitter beacons and a photodiode as receiving element that can move in 2D.


This RSSI Dataset is a comprehensive set of Received Signal Strength Indicator (RSSI) readings gathered from three different types of scenarios. Three wireless technologies were used which consisted of:

  • Zigbee (IEEE 802.15.4),
  • Bluetooth Low Energy (BLE), and
  • WiFi (IEEE 802.11n 2.4GHz band).

The scenarios took place in three rooms with different sizes and inteference levels. For the experimentation, the equipment utilized consisted of Raspberry Pi 3 Model Bs, Gimbal Series 10 Beacons, and Series 2 Xbees with Arduino Uno microcontrollers.



A set of tests was conducted to determine the accuracy between multiple types of system designs including: Trilateration, Fingerprinting with K-Nearest Neighbor (KNN) processing, and Naive Bayes processing while using a running average filter. For the experiments, all tests were done on tables which allowed tests to be simulated at a height where a user would be carrying a device in their pocket. Devices were also kept in the same orientation throughout all the tests in order to reduce the amount of error that would occur in the measuring of RSSI values.


Three different experimental scenarios were utilized with varying conditions in order to determine how the proposed system will function according to the environmental parameters.

Scenario 1 was a 6.0 x 5.5 m wide meeting room. The environmental area was cleared of all transmitting devices to create a clear testing medium where all the devices can transmit without interference. Transmitters were placed 4 m apart from one another in the shape of a triangle. Fingerprint points were taken with a 0.5 m spacing in the center between the transmitters. This created 49 fingerprints that would comprise the database. For testing, 10 points were randomly selected.

Scenario 2 was a 5.8 x 5.3 m meeting room. This area was a high noise environment as additional transmitting devices were placed around the environment in order to create interference in the signals. There were 16 fingerprints gathered with a larger distance selected between the points. In this Scenario, 6 testing points were randomly selected to be used for comparing the algorithms.

Scenario 3 was a 10.8 x 7.3 m computer lab. This lab was a large area with a typical amount of noise occurring due to the WiFi and BLE transmitting that were in the area. The large space also allowed for signals to experience obstructions, reflections, and interference. Transmitters were placed so Line-of-Sight (LoS) was available between the transmitters to the receiver. In total, 40 fingerprints were gathered with an alternating pattern occurring between the points. Points were taken to be 1.2 m apart in one direction, and 0.6 m apart in the other. For testing 16 randomly selected points were taken.


In the testing environment, fingerprints were gathered to be used in the creation of a database, while test points were selected to be used against the database for the comparison. The figures of each topology can be found inside the dataset folder. In the figures, the black dots represent the location of the transmitters and the red dots represent the locations where fingerprints and test points were gathered where appropriate. 

Related Publication

S. Sadowski, P. Spachos, K. Plataniotis, "Memoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things", IEEE Internet of Things Journal.


The RSSI dataset contains a folder for each experimental scenario and furthermore on wireless technology (i.e. Zigbee, BLE, and WiFi). Each folder contains three additional folders where the data was gathered (Pathloss, Database, and Tests). Pathloss contains 18 files measuring the RSSI at varying distances from the devices. The number of files located in Database and Tests varies based on the scenario.

For each technology, the file name corresponds to the point as to where the data was gathered. For specific locations, the (x,y) coordinates can be seen in the appropriate .xlsx file.

For the files in the Database and Tests folders, there are approximately 300 reading. In the Pathloss folder, there are approximately 50 only occurring from a single node. Readings appear in the format "Node LetterValue" where:

Letter corresponds to the transmitter that signal was sent from, represented by 'A', 'B', or 'C'.

Value is the RSSI reading.


Encoding and decoding tables for 6b8b encoder/decoder for sefl-syncrhonized improved RMII protocol. Proposed encoder/decoder garantee that 2-bit TXD/RXD will change each data transmission cycle, making it possible for RMII interface to work without REF_CLK, TX_EN and CRS_DV lines.


This work develops a novel power control framework for energy-efficient powercontrol in wireless networks. The proposed method is a new branch-and-boundprocedure based on problem-specific bounds for energy-efficiency maximizationthat allow for faster convergence. This enables to find the global solution forall of the most common energy-efficient power control problems with acomplexity that, although still exponential in the number of variables, is muchlower than other available global optimization frameworks.


This package only contains data which belongs to the work "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" (B. Matthiesen, A. Zappone, K.-L. Besser, E. Jorswieck, and M. Debbah, IEEE Transactions on Signal Processing, vol. 68, pp. 3887-3902).To use it, the source code included with this data set is required. The most recent version is available on GitHub.


The "results" directory contains the trained models.

  • "final4users" contains the big model used for the numerical evalution in the paper cited above.

  • "final16" is the smaller model analyzed in the last subsection of the numerical evaluation.

  • "final7users" contains the models of the 7 user scenario.


The "data" directory contains the channel data.

  • "channels-4.h5": Channels used for training and validation for 4 users

  • "channels-7.h5": Channels used for training and validation for 7 users


This file contains the experimental results for the research paper "Optimization-Based Offloading and Routing Strategies for Sensor-Enabled Video Surveillance Networks". Just open the excel file! then you can view the data.


This dataset is associated with the paper entitled "DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems", accepted by IEEE Transactions on Wireless Communications. It has synthetic and real-word IEEE 802.11ax OFDM symbols. The synthetic dataset has around 110 million OFDM symbols and the real-world dataset has more than 14 million OFDM symbols. Our comprehensive synthetic dataset has specifically considered typical indoor channel models and RF impairments. The real-world dataset was collected under a wide range of signal-to-noise ratio (SNR) levels and at va


Detailed documentations and instructions will be found in the uploaded files.


This is source code for the paper "Analog Self-Interference Cancellation Using Auxiliary Transmitter Considering IQ Imbalance and Amplifier Nonlinearity" submitted to IEEE Transactions on Wireless Communications.