Turbo receivers for interleave-division multiple-access systems

In this paper several turbo receivers for Interleave-Division Multiple-Access (IDMA) systems will be discussed. The multiple access system model is presented first. The optimal, Maximum A Posteriori (MAP) algorithm, is then presented. It will be shown that the use of a precoding technique at the emitter side is applicable to IDMA systems. Several low complexity Multi-User Detector (MUD), based on the Gaussian approximation, will be next discussed. It will be shown that the MUD with Probabilistic Data Association (PDA) algorithm provides faster convergence of the turbo receiver. The discussed turbo receivers will be evaluated by means of Bit Error Rate (BER) simulations and EXtrinsic Information Transfer (EXIT) charts

Joint Communication and Positioning based on Channel Estimation

Mobile wireless communication systems have rapidly and globally become an integral part of everyday life and have brought forth the internet of things. With the evolution of mobile wireless communication systems, joint communication and positioning becomes increasingly important and enables a growing range of new applications. Humanity has already grown used to having access to multimedia data everywhere at every time and thereby employing all sorts of location-based services. Global navigation satellite systems can provide highly accurate positioning results whenever a line-of-sight path is available. Unfortunately, harsh physical environments are known to degrade the performance of existing systems. Therefore, ground-based systems can assist the existing position estimation gained by satellite systems. Determining positioning-relevant information from a unified signal structure designed for a ground-based joint communication and positioning system can either complement existing systems or substitute them. Such a system framework promises to enhance the existing systems by enabling a highly accurate and reliable positioning performance and increased coverage. Furthermore, the unified signal structure yields synergetic effects. In this thesis, I propose a channel estimation-based joint communication and positioning system that employs a virtual training matrix. This matrix consists of a relatively small training percentage, plus the detected communication data itself. Via a core semi- blind estimation approach, this iteratively includes the already detected data to accurately determine the positioning-relevant parameter, by mutually exchanging information between the communication part and the positioning part of the receiver. Synergy is created. I propose a generalized system framework, suitable to be used in conjunction with various communication system techniques. The most critical positioning-relevant parameter, the time-of-arrival, is part of a physical multipath parameter vector. Estimating the time-of-arrival, therefore, means solving a global, non-linear, multi-dimensional optimization problem. More precisely, it means solving the so-called inverse problem. I thoroughly assess various problem formulations and variations thereof, including several different measurements and estimation algorithms. A significant challenge, when it comes to solving the inverse problem to determine the positioning-relevant path parameters, is imposed by realistic multipath channels. Most parameter estimation algorithms have proven to perform well in moderate multipath environments. It is mathematically straightforward to optimize this performance in the sense that the number of observations has to exceed the number of parameters to be estimated. The typical parameter estimation problem, on the other hand, is based on channel estimates, and it assumes that so-called snapshot measurements are available. In the case of realistic channel models, however, the number of observations does not necessarily exceed the number of unknowns. In this thesis, I overcome this problem, proposing a method to reduce the problem dimensionality via joint model order selection and parameter estimation. Employing the approximated and estimated parameter covariance matrix inherently constrains the estimation problem’s model order selection to result in optimal parameter estimation performance and hence optimal positioning performance. To compare these results with the optimally achievable solution, I introduce a focused order-related lower bound in this thesis. Additionally, I use soft information as a weighting matrix to enhance the positioning algorithm positioning performance. For demonstrating the feasibility and the interplay of the proposed system components, I utilize a prototype system, based on multi-layer interleave division multiple access. This proposed system framework and the investigated techniques can be employed for multiple existing systems or build the basis for future joint communication and positioning systems. The assessed estimation algorithms are transferrable to all kinds of joint communication and positioning system designs. This thesis demonstrates their capability to, in principle, successfully cope with challenging estimation problems stemming from harsh physical environments

Semi-blind channel estimation for multiuser OFDM-IDMA systems.

M. Sc. Eng. University of KwaZulu-Natal, Durban 2014.Over the last decade, the data rate and spectral efficiency of wireless mobile communications have been significantly enhanced. OFDM technology has been used in the development of advanced systems such as 3GPP LTE and terrestrial digital TV broadcasting. In general, bits of information in mobile communication systems are conveyed through radio links to receivers. The radio channels in mobile radio systems are usually multipath fading channels, which cause inter-symbol interference (ISI) in the received signal. The ability to know the channel impulse response (CIR) and Channel State Information (CSI) helps to remove the ISI from the signal and make coherent detection of the transmitted signal at the receiver end of the system easy and simple. The information about CIR and CSI are primarily provided by channel estimation. This thesis is focused on the development of multiple access communication technique, Multicarrier Interleave Division Multiple Access (MC-IDMA) and the corresponding estimation of the system channel. It compares various efficient channel estimation algorithms. Channel estimation of OFDM-IDMA scheme is important because the emphasis from previous studies assumed the implementation of MC-IDMA in a perfect scenario, where Channel State Information (CSI) is known. MC-IDMA technique incorporates three key features that will be common to the next generation communication systems; multiple access capability, resistance to multipath fading and high bandwidth efficiency. OFDM is almost completely immune to multipath fading effects and IDMA has a recently proposed multiuser capability scheme which employs random interleavers as the only method for user separation. MC-IDMA combines the features of OFDM and IDMA to produce a system that is Inter Symbol Interference (ISI) free and has higher data rate capabilities for multiple users simultaneously. The interleaver property of IDMA is used by MC-IDMA as the only means by which users are separated at the receiver and also its entire bandwidth expansion is devoted to low rate Forward Error Correction (FEC). This provides additional coding gain which is not present in conventional Multicarrier Multiuser systems, (MC-MU) such as Code Division Multiple Access (CDMA), Multicarrier-Code Division Multiple Access (MC-CDMA) systems, and others. The effect of channel fading and both cross-cell and intra-cell Multiple Access Interference (MAI) in MC-IDMA is suppressed efficiently by its low-cost turbo-type Chip-by-Chip (CBC) multiuser detection algorithm. We present the basic principles of OFDM-IDMA transmitter and receiver. Comparative studies between Multiple Access Scheme such as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), CDMA and IDMA are carried out. A linear Minimum Mean Square Error (MMSE)-based estimation algorithm is adopted and implemented. This proposed algorithm is a non-data aided method that focuses on obtaining the CSI, remove ISI and reduce the complexity of the MMSE algorithm. However, to obtain a better and improved system performance, an improved MMSE algorithm and simplified MMSE using the structured correlation and reduced auto-covariance matrix are developed in this thesis and proposed for implementation of semi-blind channel estimation in OFDM-IDMA communication systems. The effectiveness of the adopted and proposed algorithms are implemented in a Rayleigh fading multipath channel with varying mobile speeds thus demonstrating the performance of the system in a practical scenario. Also, the implemented algorithms are compared to ascertain which of these algorithms offers a better and more efficient system performance, and with less complexity. The performance of the channel estimation algorithm is presented in terms of the mean square error (MSE) and bit error rate (BER) in both slow fading and fast fading multipath scenarios and the results are documented as well

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

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

Frequency synchronization in multiuser OFDM-IDMA systems.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2013.Various multiuser schemes have been proposed to efficiently utilize the available bandwidth while ensuring an acceptable service delivery and flexibility. The multicarrier CDMA became an attractive solution to the major challenges confronting the wireless communication system. However, the scheme is plagued with multiple access interference (MAI), which causes conspicuous performance deterioration at the receiver. A low-complexity multiuser scheme called the Interleave Division Multiple Access (IDMA) was proposed recently as a capable solution to the drawback in the multicarrier CDMA scheme. A combined scheme of OFDM-IDMA was later introduced to enhance the performance of the earlier proposed IDMA scheme. The multicarrier IDMA scheme therefore combats inter-symbol interference (ISI) and MAI effectively over multipath with low complexity while ensuring a better cellular performance, high diversity order, and spectral efficiency. Major studies on the OFDM-IDMA scheme emphasis only on the implementation of the scheme in a perfect scenario, where there are no synchronization errors in the system. Like other multicarrier schemes, the OFDM-IDMA scheme however suffers from carrier frequency offset (CFO) errors, which is inherent in the OFDM technique. This research work therefore examines, and analyzes the effect of synchronization errors on the performance of the new OFDM-based hybrid scheme called the OFDM-IDMA. The design of the OFDM-IDMA system developed is such that the cyclic prefix duration of the OFDM component is longer than the maximum channel delay spread of the multipath channel model used. This effectively eliminates ISI as well as timing offsets in the system. Since much work has not been done hitherto to address the deteriorating effect of synchronization errors on the OFDM-IDMA system, this research work therefore focuses on the more challenging issue of carrier frequency synchronization at the uplink. A linear MMSE-based synchronization algorithm is proposed and implemented. The proposed algorithm is a non-data aided method that focuses on the mitigation of the ICI induced by the residual CFOs due to concurrent users in the multicarrier system. However, to obtain a better and improved system performance, the Kernel Least Mean Square (KLMS) algorithm and the normalized KLMS are proposed, implemented, and effectively adapted to combat the degrading influence of carrier frequency offset errors on the OFDM-IDMA scheme. The KLMS synchronization algorithm, which involves the execution of the conventional Least Mean Square (LMS) algorithm in the kernel space, utilizes the modulated input signal in the implementation of the kernel function, thereby enhancing the efficacy of the algorithm and the overall output of the multicarrier system. The algorithms are applied in a Rayleigh fading multipath channel with varying mobile speed to verify their effectiveness and to clearly demonstrate their influence on the performance of the system in a practical scenario. Also, the implemented algorithms are compared to ascertain which of these algorithms offers a better and more efficient system performance. Computer simulations of the bit error performance of the algorithms are presented to verify their respective influence on the overall output of the multicarrier system. Simulation results of the algorithms in both slow fading and fast fading multipath scenarios are documented as well

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