Recently, the difficulty of placing multiple antennas onto a mobile terminal to exploit more diversity has been solved by using the cooperative communication technique, in which several relay nodes with a single antenna partner with each other to serve as virtual multiple antennas for providing the spatial diversity. Many existing researches in cooperative communication focuses on designing relay strategies to achieve better communication performance. However, most of their designs require the channel state information (CSI) being perfectly known. Unfortunately, CSI is generally unknown in practice. Therefore, before getting benefits brought by the relay-assisted network, it is necessary to obtain accurate channel state information (CSI) at the destination or relays. In this thesis, we also consider the training design for channel estimation in the AF relay network. The involvement of multiple relay nodes to exploit space diversity in cooperative communications requires sophisticated and complicated protocols, which poses a difficulty in avoiding all possible misbehaving relay nodes. Therefore, the channel estimation scheme in cooperative communication network needs to be robust against the possible relay misbehaviors. However, most prior works focused on developing channel estimation schemes by assuming perfect relayassisted communication protocol. By contrast, this work focuses on designing robust channel estimation schemes to combat the possible presence of the relay misbehaviors. Besides considering the robust design against relay misbehaviors, this work also considers more general channel model when designing the training sequence and channel estimation scheme. Specifically, in contrast to assume independent channels across relays, this thesis considers the correlated channels in both phases and the correlated noises in the first phase. Overall, the main problem of this work is to design robust channel estimation and training sequences against relay misbehaviors when the communication channels within the cooperative network are not restricted to be independent.

Bien que le traitement conjoint des mesures multimodales soit supposé conduire à de meilleures performances que celles obtenues en exploitant une seule modalité ou plusieurs modalités indépendamment, il existe des exemples en littérature qui prouvent que c'est pas toujours vrai. Dans cette thèse, nous analysons rigoureusement, en termes d'information mutuelle et d'erreur d'estimation, les différentes situations de l'analyse multimodale afin de déterminer les conditions conduisant à des performances optimales.Dans la première partie, nous considérons le cas simple de deux ou trois modalités, chacune étant associée à la mesure bruitée d'un signal, avec des liens entre modalités matérialisés par les corrélations entre les parties utiles du signal et par les corrélations les bruits. Nous montrons comment les performances obtenues sont améliorées avec l'exploitation des liens entre les modalités. Dans la seconde partie, nous étudions l'impact sur les performances d'erreurs sur les liens entre modalités. Nous montrons que ces fausses hypothèses dégradent les performances, qui peuvent alors devenir inférieure à celles atteintes avec une seule modalité.Dans le cas général, nous modélisons les multiples modalités comme un canal gaussien bruité. Nous étendons alors des résultats de la littérature en considérant l'impact d'erreurs sur les densités de probabilité du signal et du bruit sur l'information transmise par le canal. Nous analysons ensuite cette relation dans la cas d'un modèle simple de deux modalités. Nos résultats montrent en particulier le fait inattendu qu'une double inadéquation du bruit et du signal peuvent parfois se compenser et ainsi conduire à de très bonnes performances. / The joint processing of multimodal measurements is supposed to lead to better performances than those obtained using a single modality or several modalities independently. However, in literature, there are examples that show that is not always true. In this thesis, we analyze, in terms of mutual information and estimation error, the different situations of multimodal analysis in order to determine the conditions to achieve the optimal performances.In the first part, we consider the simple case of two or three modalities, each associated with noisy measurement of a signal. These modalities are linked through the correlations between the useful parts of the signal and the correlations between the noises. We show that the performances are improved if the links between the modalities are exploited. In the second part, we study the impact on performance of wrong links between modalities. We show that these false assumptions decline the performance, which can become lower than the performance achieved using a single modality.In the general case, we model the multiple modalities as a noisy Gaussian channel. We then extend literature results by considering the impact of the errors on signal and noise probability densities on the information transmitted by the channel. We then analyze this relationship in the case of a simple model of two modalities. Our results show in particular the unexpected fact that a double mismatch of the noise and the signal can sometimes compensate for each other, and thus lead to very good performances.