Blind Estimation of Carrier Frequency Offset for OFDM Systems using Maximum Likelihood Technique

A Multicarrier Communication system such as an Orthogonal Frequency Division Multiplexing OFDM has been shown to be an impressive approach to combat multipath fading in wireless communications. OFDM is a modulation scheme that allows digital data to be efficiently and reliably transmitted over a radio channel, even in multipath environments. OFDM transmits data by using a large number of narrow bandwidth carriers. These carriers are regularly spaced in frequency, forming a block of spectrum. The frequency spacing and time synchronization of the carriers is chosen in such a way that the carriers are orthogonal, meaning that they do not cause interference to each other. In spite of the success and effectiveness of the OFDM systems, it suffers from a well-known drawback of high sensitivity to Carrier Frequency Offset (CFO). The presence of the CFO in the received carrier will lose orthogonality among the carriers and causes a reduction of desired signal amplitude in the output decision variable and introduces Inter Carrier Interference (ICI). It then brings up an increase of Bit Error Rate (BER). This makes the problem of estimating the CFO an attractive and necessary research problem. In this thesis Blind Modified ML CFO estimation technique based on data symbol repetition is discussed to estimate the offset parameter

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

An enhanced multicarrier modulation system for mobile communications

PhD ThesisThe recent revolution in mobile communications and the increased demand on more efficient transmission systems influence the research to enhance and invent new modulation techniques. Orthogonal frequency division multiplexing with offset quadrature amplitude modulation (OFDM/OQAM) is one of the multicarrier modulations techniques that overcomes some of the weaknesses of the conventional OFDM in term of bandwidth and power efficiencies. This thesis presents a novel multicarrier modulation scheme with improved performance in mobile communications context. Initially, the theoretical principles behind OFDM and OFDM/OQAM are discussed and the advantages of OFDM/OQAM over OFDM are highlighted. The time-frequency localization of pulse shapes is examined over different types of pulses. The effect of the localization and the pulse choice on OFDM/OQAM performance is demonstrated. The first contribution is introducing a new variant of multicarrier modulation system based on the integration of the Walsh-Hadamard transform with the OFDM/OQAM modulator. The full analytical transmission model of the system is derived over flat fading and frequency selective channels. Next, because of the critical requirement of low implementation complexity in mobile systems, a new fast algorithm transform is developed to reduce the implementation complexity of the system. The introduced fast algorithm has demonstrated a remarkable 60 percent decrease in the hardware requirement compared to the cascaded configuration. Although, the problem of high peak to average power ratio (PAPR) is one of the main drawbacks that associated with most multicarrier modulation techniques, the new system achieved lower values compared to the conventional systems. Subsequently, three new algorithms to reduce PAPR named Walsh overlapped selective mapping (WOSLM) for a high PAPR reduction, simplified selective mapping (SSLM) for a very low implementation complexity and Walsh partial transmit sequence (WPTS), are developed. Finally, in order to assess the reliability of the presented system in this thesis at imperfect environments, the performance of the system is investigated in the presence of high power amplifier, channel estimation errors, and carrier frequency offset (CFO). Two channel estimations algorithms named enhanced pair of pilots (EPOP) and averaged enhanced pair of pilots (AEPOP), and one CFO estimator technique called frequency domain (FD) CFO estimator, are suggested to provide reliable performance.Ministry of Higher Education and Scientific Research (MOHSR) of Ira

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