The  database contains the raw range-azimuth measurements obtained from mmWave MIMO radars (IWR1843BOOST deployed in different positions around a robotic manipulator.


The database that contains the raw range-azimuth measurements obtained from mmWave MIMO radars inside a Human-Robot (HR) workspace environment. 


The database contains 5 data structures:

i) mmwave_data_test has dimension 900 x 256 x 63. Contains 900 FFT range-azimuth measurements of size 256 x 63: 256-point range samples corresponding to a max range of 11m (min range of 0.5m) and 63 angle bins, corresponding to DOA ranging from -75 to +75 degree. These data are used for testing (validation database). The corresponding labels are in label_test. Each label (from 0 to 5) corresponds to one of the 6 positions (from 1 to 6) of the operator as detailed in the image attached.


ii) mmwave_data_train has dimension 900 x 256 x 63. Contains 900 FFT range-azimuth measurements used for training. The corresponding labels are in label_train.


iii) label_test with dimension 900 x 1, contains the true labels for test data (mmwave_data_test), namely classes (true labels) correspond to integers from 0 to 5. 


iv) label_train with dimension 900 x 1, contains the true labels for train data (mmwave_data_train), namely classes (true labels) correspond to integers from 0 to 5. 


v) p (1 x 900) contains the chosen random permutation for data partition among nodes/device and federated learnig simulation (see python code).


Supplementary material for the paper: 'Adaptive Block Compressive Imaging: towards a real-time and low complexity implementation'


All instructions are in the attached readme document.


2D geometrically shaped constellations that are simultaneously robust to both residual phase noise and AWGN (GS-RPN) for 8, 16, 32 and 64-ary formats. The presented formats are optimised at the generalised mutual information (GMI) threshold of 0.96m bits/symbol, where m is the number of bits per symbol. Additionally, we added AWGN-only constellations (GS-AWGN) to serve as a reference.


Note: This is a supplementary dataset for a journal submission.


Each modulation order is placed in a separate folder, in which, every text file has the coordinates for the in-phase and quadrature components of each symbol in the first and the second column, respectively. The bit mapping for each symbol is natural mapping for the line number, i.e., 000 001 010 011 100 etc.


This MATLAB dataset (.mat) contains the collected real measurement data from a total of 470 access points (APs) deployed in the Linnanmaa campus of the University of Oulu, Finland. The measurements include IDs, dates of data collection, number of users, received traffic data, transmitted traffic data and location names of each AP. Each observation of traffic data and number of users provide the data value at every 10-minute interval between December 18, 2018 and February 12, 2019. Please cite this as: S. P. Sone & Janne Lehtomäki & Zaheer Khan.


Major component description: There are 3 main major components: number of users connected at collected time (numb_users), received traffic data in bytes (rxbytes) and transmitted traffic data in bytes (txbytes) of each AP in this dataset. Dates and times of data collection (date) can be converted into the serial date number by using datenum() function in MATLAB.


Received and transmitted traffic data are in the cumulative time series format so that differencing every 2 consecutive observations is required to get the actual values at every 10-minute. It can be done by using diff() function in MATLAB, for example, "diff(ap184016.txbytes)".


Setup and running instructions: First, MATLAB must be installed in the computer correctly. Then, the downloaded dataset should be placed in the folder whose path is already specified in MATLAB (see


Once the dataset (APs_dataset.mat) is loaded correctly in MATLAB, total 470 structure arrays with the IDs of each AP will appear in MATLAB Workspace. Then, the desired time series can be called in MATLAB, for example, "Tx_data = diff(ap184016.txbytes);".


Including the symbol under demodulation in data-aided reference/pilot recovery process, dominates the contribution of all the received sybmols. Therefore, excluding the contribution of the symbol under detection allows other symbols to equally contributeto the reference estimation.


Each folder is named by its corresponding figure.


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.