What: PhD position in CIFRE program with the company Teamcast.
Topic: Data rate compression for virtualized baseband processing in broadcasting applications
Where: Teamcast Rennes and Institute of Electronic and Telecommunication of Rennes (IETR)
Supervision: Dr. Matthieu Crussière and Dr. Philippe Mary
When: from October 1st, 2018
Company / Laboratory:
TeamCast and IETR: Institut d’Electronique et de Télécommunications de Rennes
Start date: 1st October 2018– duration:36 months
This PhD subject enters in the CIFRE program, in collaboration with TeamCast, a company specialized in the market of modem for professional broadcast networks. The digital terrestrial broadcast networks lie on dedicated architectures with specific hardware components such as RF transmitters, transport links and baseband processing. On the other hand, the telecommunication industry is experiencing a revolution by using remote servers interconnected in the cloud with high-rate internet protocol (IP) links. In this kind of architecture, the baseband processing that used to be realized on dedicated hardware devices are now executed in the common servers (a.k.a. the cloud) that can be located far away from the base stations. The latter only keep the RF components on their own. The interest of this architecture is the Capex, Opex reduction, the possibility to make the network functionalities evolve, the network reconfigurability.
The drawback of the virtualized architecture lies in the increase of the data rate in IP links. Indeed, the deportation of baseband processing, and in particular the fast Fourier transform (FFT), in a remote location w.r.t. the transmitting elements of the BS, implies important data rates on the fronthaul links between the cloud and BS. For broadband networks, even if optical fiber is envisaged to support this huge data rate requirement, this could be not sufficient with the arrival of 5G and the massive user connectivity . In the broadcast industry, high capacity optical fibers are not always available. The solution is hence to reduce the data rate of I/Q samples in the fronthaul by some compressing techniques. This PhD program aims at reducing the useful data rate, ideally with no signal quality loss or under a controlled loss. The signal quality is usually measured with the variance of the quantified signal w.r.t. the non-quantified one. This leads to criterion such as the error vector magnitude (EVM) that should be kept as low as possible. One of the solution is to use non-uniform quantization 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 . In addition to non-uniform quantization technique, the re-normalization procedure per packet which allows to use more efficiently the quantification dynamic and could optimize the data rate . Non-uniform scalar quantization is the simplest method to reduce the number of quantization bits, however it does not deal with time correlation between output samples from IFFT while a vector-based quantization approach does and it may achieve better compression rate than scalar quantization . Moreover, the non-uniform quantization can be jointly used with some time-spectral redundancy removal, e.g. cyclic prefix removal, and entropy-based encoding as well .
The PhD candidate will first start analyzing the available state-of-art on data rate reduction for cloud radio access networks (CRAN) and especially non-uniform quantization algorithms and their impact on the EVM of OFDM-based signals. Moreover, entropy-based quantization techniques will be studied in the context of ATSC3.0. In particular, considering the encoding process and the quantization as a joint processing , the optimal strategy satisfying the data rate constraint on the front haul and under signal distortion constraint (EVM) will be studied. The possible multiple flux option in ATSC3.0 will be used to compare the proposed strategy to known broadcast channel bounds.
 “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
 D. Samardzija, J. Pastalan, M. MacDonald, S. Walker, R. Valenzuela, “Compressed Transport of Baseband Signals in Radio Access Networks”, IEEE Transactions on Wireless Communications, Vol. 11, no. 9, September 2012.
 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.
 S. H. Park, O. Simeone, O. Sahin and S. Shamai, « Fronthaul Compression for Cloud Radio Access Networks. Signal Processing advances inspired by network information theory », IEEE Signal Processing Magazine, Nov. 2014.
The candidate should be have earnt an MSc degree, or equivalent, in one of the following field: signal processing, electrical engineering, telecommunications. He/She should have a strong background in signal processing for wireless communications, information theory, mathematics. The candidate should be familiar with Matlab and C/C++ languages and his/her skills on these languages should be supported by project realizations.
Cloud radio access network, digital communications, OFDM, sampling and quantization theory, data rate compression, probabilities.
Dr. Matthieu Crussière et Dr. Philippe Mary
INSA de Rennes / IETR UMR CNRS – 6164
e-mails: firstname.lastname@example.org and email@example.com
(c) GdR 720 ISIS - CNRS - 2011-2018.