Implementation of WiMAX physical layer baseband processing blocks in FPGA

This project thesis elaborates on designing a baseband processing blocks for Worldwide Interoperability for Microwave Access (WiMAX) physical layer using an FPGA. WiMAX provides broadband wireless access and uses OFDM as the essential modulation technique. The channel performance is badly affected due to synchronization mismatches between the transmitter and receiver ends so the transmitted signal received is not reliable as the OFDM deals with high data rate. This thesis includes the theory and concepts behind OFDM, WiMAX IEEE 802.16d standard and other blocks algorithms, its architectures used for designing as well as a presentation of how they are implemented. Here Altera’s FPGA has been used for targeting to the EP4SGX70HF35C2 device of the Stratix IV family. WiMAX use sophisticated digital signal processing techniques, which typically require a large number of mathematical computations. Here Stratix IV devices are ideally suited for these kinds of complex tasks because the DSP blocks have a combination of dedicated elements that perform multiplication, addition, subtraction, accumulation, summation, and dynamic shift operations. The WiMAX physical layer baseband processing architecture consists of various major modules which were simulated block wise in order to check its giving the correct output as required. The coding style used here is VHDL. The sub-blocks have been synthesized using Altera Quartus II v11. 0 and simulated using ModelSim Altera Edition 6.6d

Influence du mapping sur la reconnaissance d'un système de communication

Le contexte de cette thèse est la reconnaissance de systèmes de communication dans un contexte non coopératif. Nous nous intéressons au problème de la reconstruction de codes convolutifs et à la reconstruction du mapping (la bijection utilisée pour associer une séquence binaire à un signal modulé). Nous avons élaboré une nouvelle méthode statistique qui à partir d'une séquence binaire bruitée observée permet de détecter si une séquence binaire est codée par un codeur convolutif. Cette méthode consiste à former des blocs de séquence suffisamment grands pour contenir le support d'une équation de parité et à compter le nombre de blocs identiques. Elle a l'avantage de fournir la longueur du code utilisé lorsque le mapping est inconnu. Cette méthode peut également être utilisée pour reconstruire le dual d'un code convolutif lorsque le mapping est connu. Nous proposons par ailleurs un algorithme de reconnaissance de mapping basé sur le parcours de classes d'équivalences. Deux types de classes sont définies. Nous disposons d'un signal bruité partiellement démodulé (démodulé avec un mapping par défaut) et supposons que les données sont codées par un codeur convolutif. Nous utilisons la reconnaissance d'un tel code comme testeur et parcourons enfin les classes d'équivalences faisant apparaître une structure de codes convolutifs. Cette classification améliore la complexité de la recherche pour les petites constellations (4 et 8-PSK). Dans le cas des constellations 16 à 256-QAM l'algorithme est appliqué aux mappings Gray ou quasi-Gray. L'algorithme ne fournit pas un résultat unique mais il permet de trouver un ensemble de mappings possibles à partir de données bruitées.The context of this thesis is the recognition of communication systems in a non-cooperative context. We are interested in the convolutional code reconstruction problem and in the constellation labeling reconstruction (the mapping used to associate a binary sequence to a modulated signal). We have defined a new statistical method for detecting if a given binary sequence is a noisy convolutional code-word obtained from an unknown convolutional code. It consists in forming blocks of sequence which are big enough to contain the support of a parity check equation and counting the number of blocks which are equal. It gives the length of the convolutional code without knowledge of the constellation labeling. This method can also be used to reconstruct the dual of a convolutional code when the constellation labeling is known. Moreover we propose a constellation labeling recognition algorithm using some equivalence classes. Two types of classes are defined: linear and affine. We observe a noisy signal which is partially demodulated (with a default labeling) and assume that the data are coded by a convolutional encoder. Thus we use the reconstruction of a code as a test and run through the classes which reveal a code structure. This classification improves the complexity of the search for small constellations (4-PSK and 8-PSK). In case of 16-QAM to 256-QAM constellations we apply the algorithm to Gray or quasi-Gray labelings. The algorithm does not give a unique result but it allows to find a small set of possible constellation labelings from noisy data.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF