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Doctorate thesis defense of Imen Turki Boujemaa

Doctorate thesis defense on April 08th 2017 at 09H00 AM ,in Amphi II, Sup’Com.


Presented by : Imen Turki Boujemaa 



Ms. Monia Turki

Professor at ENIT, Tunisia





Ms. Fatma Abdelkefi

Associate Professor at Sup'Com, Tunisia



M. Mohamed Lassaad Ammari

Associate Professor at ENISO , Tunisia


Ms. Noura Sellami

Professor at ENIS, Tunisia



M. Mohamed Siala

Professor at Sup'Com, Tunisia



Ms. Ines Kammoun

Associate Professor at ENIS, Tunisia



MIMO systems can be used in different ways and for different purposes, namely spatial diversity, spatial multiplexing and transmit and/or receive beamforming, that can increase the signal to interference and noise ratio in one direction and decrease the effects of interference. This thesis focus on downlink beamforming for Single-User MIMO (SU-MIMO) systems, based on spatial diversity or spatial multiplexing, and different Multi- User MIMO (MU-MIMO) systems.

For SU-MIMO systems, we assume that the Channel State Information (CSI) is only available at the receiver and propose a limited feedback precoding. In this case, the precoder is designed at the receiver and conveyed to the transmitter. To reduce the signaling overhead on the uplink, the idea is to design in advance a finite codebook known to both the transmitter and the receiver. Thus, the receiver conveys the index of the chosen codebook element back to the transmitter according to the channel state. We consider three possible transmitters applying spatial diversity, spatial multiplexing and stacked space-time architecture. For the case of a SU-MIMO system based on spatial multiplexing, we propose a novel criterion for selecting among Grassmannian codebook matrices the optimum precoding matrix at the receiver, which maximizes the worst Signal to Interference and Noise Ratio (SINR) over the transmitted substreams. For the SU-MIMO system based on a stacked space-time architecture, precoding the stacked matrix is used to separate and decode efficiently the different components at a reasonable complexity and to improve the array gain. We propose a criterion for selecting the precoding matrix among Grassmannian codebook matrices that maximizes the worst SINR over all components of the stacked design.

For MU-MIMO systems, we first consider the precoding design at the transmitter for a MIMO cognitive radio system, where a secondary system coexists with a primary one. We treat the cases of perfect and imperfect CSI due to outdated quantized CSI and channel estimation errors. The aim of the proposed precoding is to reduce interference at the primary users and between the secondary users. Furthermore, we investigate the optimal power allocation at the Secondary Base Station (SBS) that maximizes the sum rate in the presence of feedback delay and channel estimation errors. Secondly, we consider a Multi-User Coordinated MultiPoint (MU-CoMP) system and propose a cooperative beamforming design that reduces the inter-cell-interference, investigating perfect and outdated quantized CSI cases. We also propose an optimal power allocation at the Base Stations (BSs) that maximizes the system sum rate in the case of outdated quantized CSI. In order to reduce the impact of feedback link errors on the system performance, we propose a bit-labelling of a codebook used for channel quantization, when one substream is transmitted to each user, that outperforms random labelling. Finally, we investigate downlink precoding, considering two promising technologies, Cloud-Radio Access Network (CRAN) and millimeter-wave (mmWave) transmission, which are expected to play an important role in 5G systems. Here, we assume that all CSIs are available in the cloud. We propose a mmWave coordinated beamforming design for C-RAN able to face the blockage problem and to improve the sum rate of the system.


MIMO, beamforming, limited feedback, spatial diversity, spatial multiplexing, stacked space-time architecture, cognitive radio, delay, channel estimation errors, power allocation, CoMP, bit-labelling, millimeter-wave, C-RAN, Grassmannian codebook