IETR: Institut d’Electronique et de Télécommunications de Rennes
Start date: 1st February – duration: 6 months
The evolution of broadband and broadcast networks towards always higher data rates, leads operators to implement more and more complex baseband processing to satisfy high bandwidth-consuming services. From 2G to 4G cellular networks, the baseband processing was (and is still) done in base stations (BS) for the most part, leading to high operating costs. In order to reduce them, the telecommunication industry works on deporting some processing from BS to centralized server (a.k.a. the cloud) and to keep only radio-frequency segment in emitting BS. This paradigm implies to send the I/Q samples generated at the output of the OFDM modulator in the cloud to deported BSs in a high data link, called the fronthaul. Even if optical fiber is envisaged to support this huge data rate requirement, this could be not sufficient with the arrival of 5G . The solution is hence to reduce the data rate of I/Q samples in the fronthaul by some compressing techniques. One of the solution is to use non-uniform sampling in order to optimize the number of bits used to carry the information according to the probability density measure of their apparition. Lloyd-Max quantization algorithm is particularly well adapted to quantized Gaussian samples and can be used for OFDM-based signal . Vector-based quantization approach allows to deal with time correlation between output samples from IFFT and can achieve better compression rate than scalar quantization . Moreover, the non-uniform sampling can be jointly used with some time-spectral redundancy removal, e.g. cyclic prefix removal, and entropy-based encoding as well . On the other side, the compression rate on the Fronthaul link goes along with signal distortion measured with the error vector magnitude (EVM) which should be minimized.
This internship aims at analyzing some state-of-art non-uniform quantization algorithms and their impact on the EVM of OFDM-based signals. The candidate will first start analyzing the references given below. In a second time, he/she will be asked to implement and optimize some compression algorithms for the fronthaul link while keeping a low EVM as much as possible. Analytical and simulation results are expected since the data rate compression should be formulated as a global optimization problem under constraints.
 “C-RAN The Road Towards Green RAN”, White Paper China Mobile Research Institute, 2011.
 K. F. Nieman and B. L. Evans, “Time domain compression of complex baseband LTE signals for cloud radio access networks”, IEEE GlobalSIP, 2013
 H. Si, B. L. Ng, M. S. Rahman, J. Zhang, “A Novel and Efficient Vector Quantization Based CPRI Compression Algorithm”, IEEE Transactions on Vehicular Technology, 2017
 L. Ramalho, M. N. Fonseca, A. Klautau, C. Lu, M. Berg, E. Trojer, S. Höst, “An LPC-Based Fronthaul Compression Scheme”, IEEE Communications Letters, 2017.
The candidate should apply for an MSc degree, or equivalent, in one of the following field: signal processing, electrical engineering. He should have a strong background in probabilities as well as in signal processing for wireless communications. The candidate should be familiar with Matlab and C/C++ languages.
Digital communications, OFDM, sampling and quantization theory, probabilities.
Dr. Philippe Mary and Prof. Jean-François Hélard
INSA de Rennes / IETR UMR CNRS – 6164
(c) GdR 720 ISIS - CNRS - 2011-2018.