Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

Channel Estimation in Uplink of Long Term Evolution

Long Term Evolution is considered to be the fastest spreading communication standard in the world.To live up to the increasing demands of higher data rates day by day and higher multimedia services,the existing UMTS system was further upgraded to LTE.To meet their requirements novel technologies are employed in the downlink as well as uplink like Orthogonal Frequency Division Multiple Access (OFDMA) and Single Carrier- Frequency Division Multiple Access (SC-FDMA).For the receiver to perform properly it should be able to recover athe transmittedadata accurately and this is done through channel estimation.Channel Estimation in LTE engages Coherent Detection where a prior knowledge of the channel is required,often known as Channel State Information (CSI).This thesis aims at studying the channel estimation methods used in LTE and evaluate their performance in various multipath models specified by ITU like Pedestrian and Vehicular.The most commonly used channel estimation algorithms are Least Squarea(LS) and Minimum MeanaSquare error (MMSE) algorithms.The performance of these estimators are evaluated in both uplink as well as Downlink in terms of the Bit Error Rate (BER).It was evaluated for OFDMA and then for SC-FDMA,further the performance was assessed in SC-FDMA at first without subcarrier Mapping and after that with subcarrier mapping schemes like Interleaved SC-FDMA (IFDMA) and Localized SC-FDMA (lFDMA).It was found from the results that the MMSE estimator performs better than the LS estimator in both the environments.And the IFDMA has a lower PAPR than LFDMA but LFDMA has a better BER performance

Inter-carrier interference suppression in orthogonal frequency division multiple access (OFDMA) systems uplink

Ph.DDOCTOR OF PHILOSOPH

Techniques d’Estimation de Canal et de Décalage de Fréquence Porteuse pour Systèmes Sans-fil Multiporteuses en Liaison Montante

Multicarrier modulation is the common feature of high-data rate mobile wireless systems. In that case, two phenomena disturb the symbol detection. Firstly, due to the relative transmitter-receiver motion and a difference between the local oscillator (LO) frequency at the transmitter and the receiver, a carrier frequency offset (CFO) affects the received signal. This leads to an intercarrier interference (ICI). Secondly, several versions of the transmitted signal are received due to the wireless propagation channel. These unwanted phenomena must be taken into account when designing a receiver. As estimating the multipath channel and the CFO is essential, this PhD deals with several CFO and channel estimation methods based on optimal filtering. Firstly, as the estimation issue is nonlinear, we suggest using the extended Kalman filter (EKF). It is based on a local linearization of the equations around the last state estimate. However, this approach requires a linearization based on calculations of Jacobians and Hessians matrices and may not be a sufficient description of the nonlinearity. For these reasons, we can consider the sigma-point Kalman filter (SPKF), namely the unscented Kalman Filter (UKF) and the central difference Kalman filter (CDKF). The UKF is based on the unscented transformation whereas the CDKF is based on the second order Sterling polynomial interpolation formula. Nevertheless, the above methods require an exact and accurate a priori system model as well as perfect knowledge of the additive measurementnoise statistics. Therefore, we propose to use the H∞ filtering, which is known to be more robust to uncertainties than Kalman filtering. As the state-space representation of the system is non-linear, we first evaluate the “extended H∞ filter”, which is based on a linearization of the state-space equations like the EKF. As an alternative, the “unscented H∞ filter”, which has been recently proposed in the literature, is implemented by embedding the unscented transformation into the “extended H∞ filter” and carrying out the filtering by using the statistical linear error propagation approach.Multicarrier modulation is the common feature of high-data rate mobile wireless systems. In that case, two phenomena disturb the symbol detection. Firstly, due to the relative transmitter-receiver motion and a difference between the local oscillator (LO) frequency at the transmitter and the receiver, a carrier frequency offset (CFO) affects the received signal. This leads to an intercarrier interference (ICI). Secondly, several versions of the transmitted signal are received due to the wireless propagation channel. These unwanted phenomena must be taken into account when designing a receiver. As estimating the multipath channel and the CFO is essential, this PhD deals with several CFO and channel estimation methods based on optimal filtering. Firstly, as the estimation issue is nonlinear, we suggest using the extended Kalman filter (EKF). It is based on a local linearization of the equations around the last state estimate. However, this approach requires a linearization based on calculations of Jacobians and Hessians matrices and may not be a sufficient description of the nonlinearity. For these reasons, we can consider the sigma-point Kalman filter (SPKF), namely the unscented Kalman Filter (UKF) and the central difference Kalman filter (CDKF). The UKF is based on the unscented transformation whereas the CDKF is based on the second order Sterling polynomial interpolation formula. Nevertheless, the above methods require an exact and accurate a priori system model as well as perfect knowledge of the additive measurementnoise statistics. Therefore, we propose to use the H∞ filtering, which is known to be more robust to uncertainties than Kalman filtering. As the state-space representation of the system is non-linear, we first evaluate the “extended H∞ filter”, which is based on a linearization of the state-space equations like the EKF. As an alternative, the “unscented H∞ filter”, which has been recently proposed in the literature, is implemented by embedding the unscented transformation into the “extended H∞ filter” and carrying out the filtering by using the statistical linear error propagation approach

Synchronization Algorithms for FBMC Systems

Filter bank multicarrier (FBMC) systems, such as FMT and OFDM/OQAM systems, can provide reduced sensitivity to narrowband interference, high flexibility to allocate group of subchannels to different users and a high spectral containment. On the other hand, as all the multicarrier modulation schemes, one of their major drawbacks is their sensitivity to CFO and symbol timing errors. In this thesis the problem of CFO and symbol timing synchronization is examined and new data-aided and blind estimation techniques are proposed. Specifically, it is presented a new joint symbol timing and CFO synchronization algorithm based on the LS approach. Moreover, the joint ML phase offset, CFO and symbol timing estimator for a multiple access OFDM/OQAM system is considered. It is also proposed a closed-form CFO estimator based on the best linear unbiased estimation principle for FMT systems. Blind CFO estimators based on the ML principle for low SNR are also considered and, moreover, a closed-form CFO synchronization algorithm based on the LS method is derived. Finally, it is also proposed, under the assumption of low SNR, the joint ML symbol timing and phase offset estimator