Collision detection (CD) is a key capability of carrier sense multiple access (CSMA) based medium access control (MAC) protocol. Applying CD, the transmitter can abort transmission immediately so that the power can be saved. This technique does not need the peer receiver to give feedback on whether there is a packet collision, and hence, the overall overhead is significantly low. The challenge, however, is to operate in transmit time and instantly detect the week colliding signal in the presence of strong self-interference (SI).


Instant collision detection (CD) can be achieved at the transmitter side more efficiently. To detect the collision, though, the device has to overcome the strong self-interference (SI) in such a way that it can listen to the channel in transmit time. This capability is feasible by in-band full-duplex (IBFD) technology, which allows two nodes to communicate concurrently over the same frequency channel. Recent works have shown the network-level benefits of using IBFD for collision detection, in the sense of power efficiency, throughput, and delay performance. By any means, the performance of these MAC protocols highly depends on the rapidity and precision of the CD method, although the collision detection in this context has still not been investigated thoroughly. By leveraging multiple hidden convolutional layers, modern machine learning techniques have confirmed their effectiveness in a wide range of applications, such as automatic image recognition, and network optimization. Motivated by its remarkable success in various fields as well as its real-time functionality, in this work we investigate whether a convolutional neural network (CNN) can be exploited to accelerate CD without sacrificing the detection accuracy. Meanwhile, we realize that the CD problem can be mapped to traditional SNR estimation problem. When there is a collision, the signal SNR will drop. Lots of domain knowledge are there with regard to signal demodulation and SNR estimation. On the contrary, CNN could be regarded as a kind of domain-specific knowledge less method. It will be interesting to see the performance comparison between the two methodologies. This kind of comparison will inspire the research community to study further about how should we combine the domain-specific knowledge (DSK) with CNN. Besides, to encourage future studies, we offer free access to the dataset and programs in IEEE DataPort, which allows researchers to reproduce our results out of the box or investigate different approaches.


Visual representations are always better than narrations in accordance to children, for better understanding. This is quite advantageous in learning school lessons and it eventually helps in engaging the children and enhancing their imaginative skills.


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A Traffic Light Controller PETRI_NET (Finite State Machine) Implementation.

An implementation of FSM approach can be followed in systems whose tasks constitute a well-structured list so all states can be easily enumerated. A Traffic light controller represents a relatively complex control function


The analysis is based on two kinds of measured dataset. In both cases, uplink data are measured (A-UL0) i.e. the transmitters are UBSs and the receiver is UBSC and FDD is used. The first dataset has been collected from August 17, 2018, to August 20, 2018. The experiment has been carried over two separate distances, i.e., 1 km, and 3 km between the transmitter (Tx) and receiver (Rx) in Mohang Port (Taean-gun).  



While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compressing efficiency, its computational complexity dramatically increases. This dataset provides a thoroug analisis on complexity of both encoder and decoder of VVC Test Model 6.0. Encoding and decoding operations have been performed for six video sequences of 720p, 1080p, and 2160p resolutions, and under Low-Delay (LD), Random-Access (RA), and All-Intra (AI) conditions (a total of 320 encoding/decoding operations).


This repository includes two sub-folders:

1- VTune_CSV_reports, contains csv reports of VTune for encoding and decoding of 6 sequences using HM 16 and VTM 6. Sub-folders contain reports for LD, RA, and AI configurations, and QP values of 22, 27, 32, and 37. Please refere to VTune documentation for more details on the report format. 

2- Encoded_files contains bitstream outputs for all video sequences and all coding conditions.

Moreover, a tabulated report of complexity is extracted from the dataset and is provided here in Tabulated_report.docx.


This dataset covers cellular communication signals in the SCF format. There is a total of 60000 signal instances, 36000 of them are reserved as training data and the rest is for the test. The SNR levels are between 1 dB and 15 dB.


For each SNR level, the training dataset has four files. The user can concatenate these files. The same procedure is valid for the test dataset.


The labels mean:


0 -> AWGN (no signal in the spectrum)


1 -> UMTS


2 -> LTE


3 -> GSM


A distributed protocol based on network exploration to discover multiple disjoint paths.


In the fifth generation (5G) wireless communication, high-speed railway (HSR) communication is one of the most challenging scenarios. By adopting massive multi-input multi-output (mMIMO) technology in HSR communication, the design of the underlying communication system becomes more challenging. Some new channel characteristics must be studied, such as non-stationarity in space, time and frequency domains. In this paper, two models are established for the two states of HSR.


the measurement data  simulated data of Hd-TCP and its comparisons' performance on the real high-speed railways scenario


Authors’ multimedia video of VO2 patch with resistive heater electrically isolated by a thin SiO2 layer and increase in temperature (by Joule heating) inducing phase transition, observed by a change in color.