Nonlinear receivers for DS-CDMA

The growing demand for capacity in wireless communications is the driving force behind improving established networks and the deployment of a new worldwide mobile standard. Capacity calculations show that the direct sequence code division multiple access (DS-CDMA) technique has more capacity than the time division multiple access technique. Therefore, most 3rd generation mobile systems will incorporate some sort of DS-CDMA. In this thesis DS-CDMA receiver structures are investigated from the view point of pattern recognition which leads to new DS-CDMA receiver structures. It is known that the optimum DS-CDMA receiver has a nonlinear structure with prohibitive complexity for practical implementation. It is also known that the currently implemented receiver in 2nd generation DSCDMA mobile handsets has poor performance, because it suffers from multiuser interference. Consequently, this work focuses on sub-optimum nonlinear receivers for DS-CDMA in the downlink scenario. First, the thesis reviews DS-CDMA, established equalisers, DS-CDMA receivers and pattern recognition techniques. Then the new receivers are proposed. It is shown that DS-CDMA can be considered as a pattern recognition problem and hence, pattern recognition techniques can be exploited in order to develop DS-CDMA receivers. Another approach is to apply known equaliser structures for DS-CDMA. One proposed receiver is based on the Volterra series expansion and processes the received signal at the chip rate. Another receiver is a symbol rate radial basis function network (RBFN) receiver with reduced complexity. Subsequently, a receiver is proposed based on linear programming (LP) which is especially tailored for nonlinearly separable scenarios. The LP based receiver performance is equivalent to the known decorrelating detector in linearly separable scenarios. Finally, a hybrid receiver is proposed which combines LP and RBFN and which exploits knowledge gained from pattern recognition. This structure has lower complexity than the full RBF and good performance, and has a large potential for further improvements. Monte-Carlo simulations compare the proposed DS-CDMA receivers against established linear and nonlinear receivers. It is shown that all proposed receivers outperform the known linear receivers. The Volterra receiver’s complexity is relatively high for the performance gain achieved and might not suit practical implementation. The other receiver’s complexity was greatly reduced but it performs nearly as well as an optimum symbol by symbol detector. This thesis shows that DS-CDMA is a pattern recognition problem and that pattern recognition techniques can simplify DS-CDMA receiver structures. Knowledge is gained from the DSCDMA signal patterns which help to understand the problem of a DS-CDMA receiver. It should be noted that from the large number of known techniques, only a few pattern recognition techniques are considered in this work, and any further work should look at other techniques. Pattern recognition techniques can reduce the complexity of existing DS-CDMA receivers while maintaining performance, leading to novel receiver structures

Optimization of multidimensional equalizers based on MMSE criteria for multiuser detection

PhD ThesisThis thesis is about designing a multidimensional equalizer for uplink interleaved division multiple access (IDMA) transmission. Multidimensional equalizer can be classified into centralized and decentralized multidimensional equalizer. Centralized multidimensional equalizer (MDE) have been used to remove both inter-symbol interference (ISI) and multiaccess interference (MAI) effects from the received signal. In order to suppress MAI effects, code division multiple access (CDMA) has been used with MDE to minimize the correlation between users' signals. The MDE structure can be designed using linear equalizer (MLE) or decision feedback equalizer (MDFE). Previous studies on MDE employed adaptive algorithms to estimate filter co-effi cients during the training mode, i.e. the symbol equalization was not optimal, for two users. In our work, we applied MDE on IDMA receiver for multipath selective fading channels and also derived new equations to obtain the optimal filter taps for both types of MDE equalizers, i.e. MDFE and MLE, based on the minimum mean square error (MMSE) criterion. The optimal filter taps are calculated for more than two users. Moreover, we investigated the performance of the optimal MDFE using both IDMA (MDFE-IDMA) and CDMA (MDFE-CDMA) detectors. Generally, the MDE equalizer suffers from residual MAI interference effects at low signal-to-noise-ratios (SNR) due to the delay inherent in the convergence of the crossover filter taps. Therefore, a new decentralized multidimensional equalizer has been proposed to IDMA detector. Within design of decentralized equalizer, the convergence problem has been resolved by replacing the crossover filters with parallel interference canceler (PIC) for removing MAI dispersion. The proposed decentralized multidimensional equalizer shows a higher efficiency in removing MAI interference when compared with existing receivers in the literature. However, this is achieved at the expense of higher computational complexity compared to centralized multidimensional equalization

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

Blind channel identification/equalization with applications in wireless communications

Ph.DDOCTOR OF PHILOSOPH

Survey of FPGA applications in the period 2000 – 2015 (Technical Report)

Romoth J, Porrmann M, Rückert U. Survey of FPGA applications in the period 2000 – 2015 (Technical Report).; 2017.Since their introduction, FPGAs can be seen in more and more different fields of applications. The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware. Nevertheless, every application field introduces special requirements to the used computational architecture. This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Development of Fuzzy System Based Channel Equalisers

Channel equalisers are used in digital communication receivers to mitigate the effects of inter symbol interference (ISI) and inter user interference in the form of co-channel interference (CCI) and adjacent channel interference (ACI) in the presence of additive white Gaussian noise (AWGN). An equaliser uses a large part of the computations involved in the receiver. Linear equalisers based on adaptive filtering techniques have long been used for this application. Recently, use of nonlinear signal processing techniques like artificial neural networks (ANN) and radial basis functions (RBF) have shown encouraging results in this application. This thesis presents the development of a nonlinear fuzzy system based equaliser for digital communication receivers. The fuzzy equaliser proposed in this thesis provides a parametric implementation of symbolby-symbol maximum a-posteriori probability (MAP) equaliser based on Bayes’s theory. This MAP equaliser is also called Bayesian equaliser. Its decision function uses an estimate of the noise free received vectors, also called channel states or channel centres. The fuzzy equaliser developed here can be implemented with lower computational complexity than the RBF implementation of the MAP equaliser by using scalar channel states instead of channel states. It also provides schemes for performance tradeoff with complexity and schemes for subset centre selection. Simulation studies presented in this thesis suggests that the fuzzy equaliser by using only 10%-20% of the Bayesian equaliser channel states can provide near optimal performance. Subsequently, this fuzzy equaliser is modified for CCI suppression and is termed fuzzy–CCI equaliser. The fuzzy–CCI equaliser provides a performance comparable to the MAP equaliser designed for channels corrupted with CCI. However the structure of this equaliser is similar to the MAP equaliser that treats CCI as AWGN. A decision feedback form of this equaliser which uses a subset of channel states based on the feedback state is derived. Simulation studies presented in this thesis demonstrate that the fuzzy–CCI equaliser can effectively remove CCI without much increase in computational complexity. This equaliser is also successful in removing interference from more than one CCI sources, where as the MAP equalisers treating CCI as AWGN fail. This fuzzy–CCI equaliser can be treated as a fuzzy equaliser with a preprocessor for CCI suppression, and the preprocessor can be removed under high signal to interference ratio condition