A practical alternative to the solution of the spectrum scarcity problem in wireless communication is the use of Cognitive Radio. The Primary users of which can be protected from secondary user interference by accurate prediction of TV White Spaces (TVWS) by using appropriate propagation modelling. In implementing any mobile communication system, the essential chore is to envisage the coverage of the projected system in a wide range. Also, the accurate determination of the propagation path loss leads to the development of efficient design and operation of quality networks.
Hardware tools used for drive test include:
1.Spectrum Analyzer (RF Explorer 3G combo model)
2.Personal Computer (HP Laptop)
3.Global Positioning System (GPS) receiver set
7.vehicle for mobility purposes.
The software tool used was the Touchstone-Pro software.
During the first half of 2020, the COVID-19 pandemic changed the social gathering lifestyle to online business and social interaction. The worldwide imposed travel bans and national lockdown prevented social gatherings, making learning institutions and businesses to adopt an online platform for learning and business transactions. This development led to the incorporation of video conferencing into daily activities. This data article presents broadband data usage measurement collected using Glasswire software on various conference calls made between July and August.
This data presents the Reference Signal Received power for LTE, GSM and HSPA. The Mobile signal measurement were taken around Covenant University, Nigeria. The experiment setup was on an indoor scenario. The data was collected to investigate the best network for Mobile subscribers on roaming services and local subscriber's high performance and data rates.
· The data contain screenshots of the raw data measured
· The data contain Excel files in CSV format of the raw data and CSV file of processed and classified data
· The read me file (.txt) is attached with the password for the raw data Excel workbook
The tool used to analyse the data is python juypter notebook. the scripts are uploaded.
The dataset contains:
1. We conducted a A 24-hour recording of ADS-B signals at DAB on 1090 MHz with USRP B210 (8 MHz sample rate). In total, we got the signals from more than 130 aircraft.
2. An enhanced gr-adsb, in which each message's digital baseband (I/Q) signals and metadata (flight information) are recorded simultaneously. The output file path can be specified in the property panel of the ADS-B decoder submodule.
3. Our GnuRadio flow for signal reception.
4. Matlab code of the paper, wireless device identification using the zero-bias neural network.
1. The "main.m" in Matlab code is the entry of simulation.
2. The "csv2mat" is a CPP program to convert raw records (adsb_records1.zip) of our gr-adsb into matlab manipulatable format. Matio library (https://github.com/tbeu/matio) is required.
3. The Gnuradio flowgraph is also provided with the enhanced version of gr-adsb, in which you are supposed to replace the original one (https://github.com/mhostetter/gr-adsb). And, you can specify an output file path in the property panel of the ADS-B decoder submodule.
4. Related publication: Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices, IEEE IoTJ (accepted for publication on 21 August 2020), DOI: 10.1109/JIOT.2020.3018677
Exact BER Analysis for Two-user NOMA uUing QAM with Arbitrary Modulation Order
This code was tested using Matlab 2019.b
There has been extensive modelling of the optical wireless channel, and the optimum modulation scheme for a particular channel is well-understood. However, this modelling has not taken into account the trade-offs that transmitter and receiver selection usually involve. For a particular type of transmitter, the modulation bandwidth and available power are closely related, as are receiver bandwidth, active area and sensitivity. In this paper, we present a design approach that takes this device selection into account.