Internship projects

Here you find all the research projects proposed:

CP-DSSS Visible Light Communication waveform for Multiple Access in IoT

Period: minimum 3 months

Description of internship:

Cyclic prefix direct sequence spread spectrum is a new waveform that has recently been proposed [1], [2]as a two-tier waveform to alleviate the congested spectrum in the Internet of Things (IoT) paradigm. CP-DSSS is a versatile waveform that provides a variable rate, low interference solution as a femtocell network waveform to meet key next generation radio access network (NG-RAN) objectives, such as massive machine type communications (mMTC) and ultra-reliable low- latency communications (URLLC). CP-DSSS has also been shown to achieve similar capacity results when compared to orthogonal frequency division multiple access (OFDMA)[2]. Connection density has been identified by 3GPP as a key performance indicator (KPI) for mMTC use cases with an expected increase in connection density to 1,000,000 connected devices per km2[3]. Meanwhile, Visible light communications (VLC) have been proposed soon after white light emitting diodes (LEDs) where developed, about two decades ago. Since then, VLC have received extensive attention, mostly by the research community. Today, VLC is a rather mature wireless communications technology, and a highly qualified technology to be used in 5G and 6G.

The objective of this internship is to propose and simulate CP-DSSS in Matlab environment. The internship will be organized on three steps:

1. Cyclic prefix direct sequence spread spectrum for Optical wireless Communication simulation in Matlab

2. Evaluation of number of users based on CP-DSSS solution for Multiple Access

3. Scientific paper writing in colloaration with Doctorant in ISEP

Auto-Encoder AI algorithm FPGA implementation and performance evaluation

Period: minimum 3 months

Description of internship:

Autoencoder is an unsupervised artificial neural network that learns how to efficiently compress and encode data then learns how to reconstruct the data back from the reduced encoded representation to a representation that is as close to the original input as possible. Autoencoder, by design, reduces data dimensions by learning how to ignore the noise in the data [4].

In order to increase computing performance and simplify the hardwar e design. The objective of this internship is focusing on implmentation of a convolutional aoto-encoder (CAE) in a field programmable gate array (FPGA). The objective is to reduce hardware resources and maximum the number of channels. The internship will be organized on three steps:

1. CAE simulation and FPGA implementation
2. performance evaluation : hardware resource efficiency
3. collaborate with Doctorat in laboratory on “Visible light communication DCO-OFDM PAPR redunction” use case Via CAE. [5]

REF:

[1] A. Aminjavaheri, A. Rezazadehreyhani, R. Khalona, H. Moradi, and B. Farhang-Boroujeny, “Underlay control signaling for ultra-reliable low-latency IoT communications,” 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings, pp. 1–6, 2018.

[2] H. Moradi and B. Farhang-Boroujeny, “Underlay Scheduling Request for Ultra-Reliable Low-Latency Communications,” in 2019 IEEE 2nd 5G World Forum (5GWF). IEEE, sep 2019, pp. 28–33. [Online]. Available: https://ieeexplore.ieee.org/document/8911714/

[3] 3GPP and ETSI, “TS 122 261 - V15.5.0 - 5G; Service requirements for next generation new services and markets (Release 15),” Tech. Rep., 2018. [Online]. Available: https://www.etsi.org/deliver/etsi ts/122200 122299/122261/15.05.00 60/ts 122261v150500p.pdf

[4] https://towardsdatascience.com/auto-encoder-what-is-it-and-what-is-it-used-for-part-1-3e5c6f017726

[5] Lina Shi, Xun Zhang, and a.l, “On improving the accuracy of Visible Light Positioning system using deep autoencoder”, 2nd International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2020), 1-3 April 2020, Berlin, Germany