Non-linear echo cancellation - a Bayesian approach

Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel

Carrier frequency offset estimation for orthogonal frequency division multiplexing systems

Orthogonal frequency division multiplexing (OFDM) is an attractive modulation scheme used in wideband communications because it essentially transforms the frequency selective channel into a flat fading channel. Furthermore, the combination of multiple-input multiple-output (MIMO) signal processing and OFDM seems to be an ideal solution for supporting reliable high data rate transmission for future wireless communication systems. However, despite the great advantages OFDM systems offer, such systems present challenges of their own. One of the most important challenges is carrier frequency offset (CFO) estimation, which is crucial in building reliable wireless communication systems. In this thesis, we consider CFO estimation for the downlink and uplink OFDM systems. For the downlink channel, we focus on blind schemes where the cost functions are designed such that they exploit implicit properties associated with the transmitted signal where no training signal is required. By taking the unconditional maximum likelihood approach, we propose a virtual subcarrier based blind scheme for MIMO-OFDM systems in the presence of spatial correlation. We conclude that the presence of spatial correlation does not impact the CFO estimation significantly. We also propose a CFO estimator for OFDM systems with constant modulus signaling and extend it to MIMO-OFDM systems employing orthogonal space-time block coding. The curve fitting method is used which gives a closed-form expression for CFO estimation. Therefore, the proposed scheme provides an excellent trade-off between complexity and performance as compared to prominent existing estimation schemes. Furthermore, we design a blind CFO estimation scheme for differentially modulated OFDM systems based on the finite alphabet constraint. It can achieve better performance at high signal-to-noise ratios (SNRs) at the expense of some additional computational complexity as compared to the schemes based on the constant modulus constraint. The constrained Cramer-Rao lower bound (CRLB) is also derived for the blind estimation scheme. As for the uplink channel, which is a more challenging problem, we propose two training aided schemes. One is based on a scalar extended Kalman filter (EKF) and the other one is on the variable projection (VP) algorithm. For both schemes, we assume that the system uses an arbitrary subcarrier assignment scheme, which is more involved than the other two schemes, namely block and interleaved subcarrier assignment scheme. In the first scheme, to apply the scalar EKF algorithm, we represent the measurement equation as a function of a scalar state, i.e., each user's CFO, in lieu of a state vector which consists of both CFO and channel coefficients by replacing the unknown channel coefficients with a nonlinear function of CFO. This proposed scheme can achieve the CRLB at high SNR for two users with a complexity lower than that of the alternating-projection method. In the second scheme, the VP algorithm is used for CFO estimation which is followed with a robust minimum mean square error (MMSE) estimator for channel estimation. In the VP algorithm, the nonlinear least square cost function is optimized numerically by updating the CFOs and channel coefficients separately at each iteration. We demonstrate that this proposed scheme is superior to the existing methods in terms of convergence speed, computational complexity and estimation performance

Semi-blind CFO estimation and ICA based equalization for wireless communication systems

In this thesis, a number of semi-blind structures are proposed for Orthogonal Frequency Division Multiplexing (OFDM) based wireless communication systems, with Carrier Frequency Offset (CFO) estimation and Independent Component Analysis (ICA) based equalization. In the first contribution, a semi-blind non-redundant single-user Multiple-Input Multiple-Output (MIMO) OFDM system is proposed, with a precoding aided CFO estimation approach and an ICA based equalization structure. A number of reference data sequences are carefully designed and selected from a pool of orthogonal sequences, killing two birds with one stone. On the one hand, the precoding based CFO estimation is performed by minimizing the sum cross-correlations between the CFO compensated signals and the rest of the orthogonal sequences in the pool. On the other hand, the same reference data sequences enable the elimination of permutation and quadrant ambiguities in the ICA equalized signals. Simulation results show that the proposed semi-blind MIMO OFDM system can achieve a Bit Error Rate (BER) performance close to the ideal case with perfect Channel State Information (CSI) and no CFO. In the second contribution, a low-complexity semi-blind structure, with a multi-CFO estimation method and an ICA based equalization scheme, is proposed for multiuser Coordinated Multi-Point (CoMP) OFDM systems. A short pilot is carefully designed offline for each user and has a two-fold advantage. On the one hand, using the pilot structure, a complex multi-dimensional search for multiple CFOs is divided into a number of low-complexity mono-dimensional searches. On the other hand, the cross-correlation between the transmitted and received pilots is explored to allow the simultaneous elimination of permutation and quadrant ambiguities in the ICA equalized signals. Simulation results show that the proposed semi-blind CoMP OFDM system can provide a BER performance close to the ideal case with perfect CSI and no CFO. In the third contribution, a semi-blind structure is proposed for Carrier Aggregation (CA) based CoMP Orthogonal Frequency Division Multiple Access (OFDMA) systems, with an ICA based joint Inter-Carrier Interference (ICI) mitigation and equalization scheme. The CFO-induced ICI is mitigated implicitly via ICA based equalization, without introducing feedback overhead for CFO correction. The permutation and quadrant ambiguities in the ICA equalized signals can be eliminated by a small number of pilots. Simulation results show that with a low training overhead, the proposed semi-blind equalization scheme can provide a BER performance close to the ideal case with perfect CSI and no CFO

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

Increasing the Capacity of Wireless Networks Using Beamforming

Wireless mobile communications are growing in an exponential manner, especially in terms of the number of users. Also, the demand for high Quality of Service (QoS) has become essential. Nowadays, subscribers are using more applications such as the internet, video conferencing, and high quality TV. These applications require high data rates. The Space Division Multiple Access (SDMA) is the key element that can enable reusing of the same channels among different users in the same cell to meet this demand. For the application of SDMA in an efficient way, it is required to identify the users’ positions and directions in the cell. The Direction of Arrival (DOA) algorithms can estimate the incident angles of all the received signals impinging on the array antenna. These algorithms give the DOAs of all relevant signals of the user sources and interference sources. However, they are not capable of distinguishing and identifying which one is the direction of the desired user. In this thesis, we have proposed to use a Reference Signal (RFS) known by the transmitter and the receiver to identify which one of the estimated DOAs is the DOA of the desired user in the cell. Using a RFS and applying the correlation concept, we can distinguish the desired signal from the others. Moreover, we have considered the Affine Projection Algorithm (APA) to enhance the accuracy of the estimated direction and to form a beam towards the desired user and nulls towards the interferers. Our simulation results assure that, in the presence of the RFS, the DOA algorithms can identify the direction of the desired user with high accuracy and resolution. We have investigated this concept on different DOA algorithms such as MUltiple Signal Classification (MUSIC), ROOT MUSIC, and Estimate the direction of arrival of Signals Parameters via Rotational Invariance Technique (ESPRIT) algorithms. Moreover , we have introduced an approach for using the smart antennas (SA) to exploit the space diversity for the next generations of mobile communication systems. We have applied a combination of the MUSIC and the Least Mean Squares (LMS) algorithms. We have proposed the MUSIC algorithm for finding the directions of the users in the cell. In addition, we have considered the LMS algorithm for enhancing the accuracy of the DOA, performing the beam generation process, and keeping track of the users in the cell. Furthermore, we have proposed a scheduling algorithm that performs the scheduling in terms of the generated beams. The space diversity, together with the time and frequency diversities of LTE (Long Term Evolution) results in a large capacity increase in the next generations of wireless mobile communication systems. Simulation results show that the proposed algorithm called MUltiple Signal Classification and Least Mean Squares (MLMS), has the capability to converge and completely follow the desired user signal with a very high resolution. The convergence and the accurate tracking of the desired signal user take place after 13 iterations while in the traditional LMS, the convergence needs 85 iterations to take place. This means an 84.7% improvement over the traditional LMS algorithm for the same number of calculations in each iteration. In contrast to the traditional LMS algorithm, the proposed algorithm can work in the presence of high level of interference. Furthermore, the proposed scheduling scheme based on beamforming shows a gain of 15% in the total aggregated throughput for each 10o decrease in the beam size. The proposed model provides an optimum, complete, and practical design for the next generations of the mobile communication systems. In this model, we have proposed a mechanism to find the direction of each user in the cell, enhance the accuracy of the obtained DOAs, and perform scheduling based on the generated beams. In addition, we have presented an approach for Frequency Reuse (FR) based on beamforming for 5G. We have implemented a synthesizer in order to smartly form the desired beam shape and make the nulls deeper. We have taken the advantage of the SAs, beamforming capabilities, and the radiation pattern (RP) synthesizing techniques to build up a FR scheme for 5G. Also, we have developed a formula for calculating the Signal to Interference and Noise Ratio (SINR) in terms of the desired and the interferers directions. The objective is to maintain the SINR at the minimum acceptable levels required by the LTE while reducing the beam sizes, and hence increase the FR factor. The simulation results show that with a Uniform Linear Antenna (ULA) of 11 elements, we can achieve the desirable SINR levels using beams of 100 width, which improves the FR factor from 1 to 18, and subsequently increases the number of mobile users

Modeling and Digital Mitigation of Transmitter Imperfections in Radio Communication Systems

To satisfy the continuously growing demands for higher data rates, modern radio communication systems employ larger bandwidths and more complex waveforms. Furthermore, radio devices are expected to support a rich mixture of standards such as cellular networks, wireless local-area networks, wireless personal area networks, positioning and navigation systems, etc. In general, a "smart'' device should be flexible to support all these requirements while being portable, cheap, and energy efficient. These seemingly conflicting expectations impose stringent radio frequency (RF) design challenges which, in turn, call for their proper understanding as well as developing cost-effective solutions to address them. The direct-conversion transceiver architecture is an appealing analog front-end for flexible and multi-standard radio systems. However, it is sensitive to various circuit impairments, and modern communication systems based on multi-carrier waveforms such as Orthogonal Frequency Division Multiplexing (OFDM) and Orthogonal Frequency Division Multiple Access (OFDMA) are particularly vulnerable to RF front-end non-idealities.This thesis addresses the modeling and digital mitigation of selected transmitter (TX) RF impairments in radio communication devices. The contributions can be divided into two areas. First, new modeling and digital mitigation techniques are proposed for two essential front-end impairments in direct-conversion architecture-based OFDM and OFDMA systems, namely inphase and quadrature phase (I/Q) imbalance and carrier frequency offset (CFO). Both joint and de-coupled estimation and compensation schemes for frequency-selective TX I/Q imbalance and channel distortions are proposed for OFDM systems, to be adopted on the receiver side. Then, in the context of uplink OFDMA and Single Carrier FDMA (SC-FDMA), which are the air interface technologies of the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) and LTE-Advanced systems, joint estimation and equalization techniques of RF impairments and channel distortions are proposed. Here, the challenging multi-user uplink scenario with unequal received power levels is investigated where I/Q imbalance causes inter-user interference. A joint mirror subcarrier processing-based minimum mean-square error (MMSE) equalizer with an arbitrary number of receiver antennas is formulated to effectively handle the mirror sub-band users of different power levels. Furthermore, the joint channel and impairments filter responses are efficiently approximated with polynomial-based basis function models, and the parameters of basis functions are estimated with the reference signals conforming to the LTE uplink sub-frame structure. The resulting receiver concept adopting the proposed techniques enables improved link performance without modifying the design of RF transceivers.Second, digital baseband mitigation solutions are developed for the TX leakage signal-induced self-interference in frequency division duplex (FDD) transceivers. In FDD transceivers, a duplexer is used to connect the TX and receiver (RX) chains to a common antenna while also providing isolation to the receiver chain against the powerful transmit signal. In general, the continuous miniaturization of hardware and adoption of larger bandwidths through carrier aggregation type noncontiguous allocations complicates achieving sufficient TX-RX isolation. Here, two different effects of the transmitter leakage signal are investigated. The first is TX out-of-band (OOB) emissions and TX spurious emissions at own receiver band, due to the transmitter nonlinearity, and the second is nonlinearity of down-converter in the RX that generates second-order intermodulation distortion (IMD2) due to the TX in-band leakage signal. This work shows that the transmitter leakage signal-induced interference depends on an equivalent leakage channel that models the TX path non-idealities, duplexer filter responses, and the RX path non-idealities. The work proposes algorithms that operate in the digital baseband of the transceiver to estimate the TX-RX non-idealities and the duplexer filter responses, and subsequently regenerating and canceling the self-interference, thereby potentially relaxing the TX-RX isolation requirements as well as increasing the transceiver flexibility.Overall, this thesis provides useful signal models to understand the implications of different RF non-idealities and proposes compensation solutions to cope with certain RF impairments. This is complemented with extensive computer simulations and practical RF measurements to validate their application in real-world radio transceivers