New approaches and algorithms are developed for the identification and estimation of low order models that represent multipath channel effects in Code Division Multiple Access (CDMA) communication systems. Based on these parsimonious channel models, low complexity receivers such as RAKE receivers are considered to exploit these propagation effects and enhance the system performance. We consider the scenario where multipath is frequency selective slowly fading and where the channel components including delays and attenuation coefficients are assumed to be constant over one or few signalling intervals. We model the channel as a long FIR-like filter (or a tapped delay line filter) with the number of taps related to the ratio between the channel delay-spread and the chip duration. Due to the high data rate of new CDMA systems, the channel length in terms of the chip duration will be very large. With classical channel estimation techniques this will result in poor estimates of many of the channel parameters where most of them are zero leading to a reduction in the system performance. Unlike classical techniques which estimate directly the channel response given the number of taps or given an estimate of the channel length, the proposed techniques in this work will firstly identify the significant multipath parameters using model selection techniques, then estimate these identified parameters. Statistical tests are proposed to determine whether or not each individual parameter is significant. A low complexity RAKE receiver is then considered based on estimates of these identified parameters only. The level of significance with which we will make this assertion will be controlled based on statistical tests such as multiple hypothesis tests. Frequency and time domain based approaches and model selection techniques are proposed to achieve the above proposed objectives. / The frequency domain approach for parsimonious channel estimation results in an efficient implementation of RAKE receivers in DS-CDMA systems. In this approach, we consider a training based strategy and estimate the channel delays and attenuation using the averaged periodogram and modified time delay estimation techniques. We then use model selection techniques such as the sphericity test and multiple hypotheses tests based on F-Statistics to identify the model order and select the significant channel paths. Simulations show that for a pre-defined level of significance, the proposed technique correctly identifies the significant channel parameters and the parsimonious RAKE receiver shows improved statistical as well as computational performance over classical methods. The time domain approach is based on the Bootstrap which is appropriate for the case when the distribution of the test statistics required by the multiple hypothesis tests is unknown. In this approach we also use short training data and model the channel response as an FIR filter with unknown length. Model parameters are then estimated using low complexity algorithms in the time domain. Based on these estimates, bootstrap based multiple hypotheses tests are applied to identify the non-zero coefficients of the FIR filter. Simulation results demonstrate the power of this technique for RAKE receivers in unknown noise environments. Finally we propose adaptive blind channel estimation algorithms for CDMA systems. Using only the spreading code of the user of interest and the received data sequence, four different adaptive blind estimation algorithms are proposed to estimate the impulse response of frequency selective and frequency non-selective fading channels. Also the idea is based on minimum variance receiver techniques. Tracking of a frequency selective varying fading channel is also considered. / A blind based hierarchical MDL model selection method is also proposed to select non-zero parameters of the channel response. Simulation results show that the proposed algorithms perform better than previously proposed algorithms. They have lower complexity and have a faster convergence rate. The proposed algorithms can also be applied to the design of adaptive blind channel estimation based RAKE receivers.

Master of Engineering (Research) / Multiple antenna systems are an appealing candidate for emerging fourth-generation wireless networks due to its potential to exploit space diversity for increasing conveyed throughput without wasting bandwidth and power resources. Particularly, layered space-time architecture (LST) proposed by Foschini, is a technique to achieve a significant fraction of the theoretical capacity with a reasonable implementation complexity. There has been a great deal of challenges in the detection of space-time signal; especially to design a low-complexity detector, which can efficiently remove multi-layer interference and approach the interference free bound. The application of iterative principle to joint detection and decoding has been a promising approach. It has been shown that, the iterative receiver with parallel interference canceller (PIC) has a low linear complexity and near interference free performance. Furthermore, it is widely accepted that the performance of digital communication systems can be considerably improved once the channel state information (CSI) is used to optimize the transmit signal. In this thesis, the problem of the design of a power allocation strategy in LST architecture to simultaneously optimize coding, diversity and weighting gains is addressed. A more practical scenario is also considered by assuming imperfect CSI at the receiver. The effect of channel estimation errors in LST architecture with an iterative PIC receiver is investigated. It is shown that imperfect channel estimation at an LST receiver results in erroneous decision statistics at the very first iteration and this error propagates to the subsequent iterations, which ultimately leads to severe degradation of the overall performance. We design a transmit power allocation policy to take into account the imperfection in the channel estimation process. The transmit power of various layers is optimized through minimization of the average bit error rate (BER) of the LST architecture with a low complexity iterative PIC detector. At the receiver, the PIC detector performs both interference regeneration and cancellation simultaneously for all layers. A convolutional code is used as the constituent code. The iterative decoding principle is applied to pass the a posteriori probability estimates between the detector and decoders. The decoder is based on the maximum a posteriori (MAP) algorithms. A closed-form optimal solution for power allocation in terms of the minimum BER is obtained. In order to validate the effectiveness of the proposed schemes, substantial simulation results are provided.

<p>In this thesis, a single carrier ATSC DTV baseband transmitter, part of the receiver(including channel estimator and channel equalizer), were modeled. Since multi-pathinduced ISI (inter symbol interference) is the most significant impact on theperformance of single carrier DTV reception, modeling and implementation of singlecarrier channel estimator and channel equalizer have been the focus of the thesis. Westarted with the investigation of channel estimation methods. Afterwards, severalchannel estimators and equalizers were modeled and the performance of each channelequalization methods in different scenarios was evaluated. Our results show that thefrequency domain equalizer can achieve low computing cost and handle long delaypaths. Another important issue to be considered in block equalization is Inter-BlockInterference (IBI). The impact of IBI was investigated via behavior modeling. In lastpart of our thesis, two methods for IBI cancellation are compared and the proposal forhardware implementation was given.</p>

<p>The thesis is largely built on a collection of published and submitted papers where the main focus is to analyze and optimize single-carrier adaptive coded modulation systems with and without antenna diversity. Multidimensional trellis codes are used as component codes. The majority of the analysis is done with both estimation and prediction errors being incorporated. Both channel estimation and prediction are performed using a pilot-symbol-assisted modulation scheme. Thus, known pilot symbols (overhead information) must be transmitted; which consumes power and also degrades system spectral efficiency. Both power consumption and pilot insertion frequency are optimized such that they are kept at necessary values to maximize system throughput without sacrificing the error rate performance. The results show that efficient and reliable system performance can be achieved over a wide range of the considered average channel quality. Going from a single-input single-output system to both spatially uncorrelated and correlated single-input multiple-ouput (SIMO) systems, and further to an uncorrelated multiple-input multiple-output (MIMO) diversity system, is the evolution of the thesis. In the SIMO case, maximum ratio combining is used to combine the incoming signals, whereas the signals are space-time combined in the MIMO diversity system. The multiple-input single-output system comes out as a special case of a MIMO system. Besides the spatially uncorrelated antenna array, the effect of spatial correlation is also considered in the SIMO case. In this case, only prediction error is considered and channel estimation is assumed to be perfect. At first, the impact of spatial correlation in a predicted system originally designed to operate on uncorrelated channels is quanitifed. Then, a maximum a posteriori (MAP)-optimal “space-time predictor” is derived to take spatial correlation into account. As expected, the results show that the throughput is still lower than the uncorrelated system, but the degradation is decreased when the MAP-optimal space-time predictor is used. Thus, by exploiting the correlation properly, the degradation can be reduced. By numerical examples, we demonstrate the potential effect of limiting the predictor complexity, of fixing the pilot spacing, as well as of assuming perfect estimation. The two first simplifications imply lower system complexity and feedback rate, whereas the last assumption is usually made to ease the mathematical analysis. The numerical examples indicate that all the simplifications can be done without serious impact on the predicted system performance.</p>

In the nineties, two major events brought about a revolution in the field of digital communications: the invention of turbo codes and the development of multiple-input multiple-output (MIMO) wireless systems. The turbo codes are error-correcting codes which enable communication systems to operate close to the channel capacity with a reasonable complexity. Multiple-antenna transceivers, a.k.a. MIMO wireless systems, mitigate the effect of multipath fading that occurs in most terrestrial wireless communications. MIMO transmissions are more reliable than single-input single-output transmissions. Moreover, the data rate achievable by MIMO systems is also much higher. This thesis combines these two recent breakthroughs in digital communications. Iterative decoding is a key factor in the success of turbo codes. This principle has proved to be so powerful that it has soon been applied to other schemes such as iterative channel equalization, also known as turbo equalization. In the first part of this thesis, we derived a low-complexity iterative equalizer for frequency-selective MIMO channels. Its robustness against channel estimation errors was also addressed. The second part of this thesis is devoted to the estimation of the channel impulse response and the noise variance for coded transmissions over MIMO channels. We proposed several new iterative estimators based on the expectation-maximization algorithm, the expectation-conditionalmaximization algorithm and the minimum mean square error criterion. Finally, we derived lower bounds on the mean square error of channel estimators. In particular, the Cramer-Rao bound for the estimation of frequency-selective MIMO channels were computed. We mainly investigated the case where no training sequence are transmitted and the case where the receiver has a partial knowledge of the transmitted sequence.

High data rate applications are the trend in today's wireless technology. W-CDMA standard was designed to support such high data rates of up to 3.84 Mcps. The main purpose of this research was to analyze the feasibility of a fixed-point implementation of the W-CDMA downlink receiver algorithm on a general-purpose digital signal processor (StarCore SC140 by Motorola). The very large instruction word architecture of SC140 core is utilized to generate optimal implementation, to meet the real time timing requirements of the algorithm. The other main aim of this work was to study and evaluate the performance of the W-CDMA downlink structure with incorporated space-time transmit diversity. The effect of the channel estimation algorithm used was extensively studied too.

The thesis is largely built on a collection of published and submitted papers where the main focus is to analyze and optimize single-carrier adaptive coded modulation systems with and without antenna diversity. Multidimensional trellis codes are used as component codes. The majority of the analysis is done with both estimation and prediction errors being incorporated. Both channel estimation and prediction are performed using a pilot-symbol-assisted modulation scheme. Thus, known pilot symbols (overhead information) must be transmitted; which consumes power and also degrades system spectral efficiency. Both power consumption and pilot insertion frequency are optimized such that they are kept at necessary values to maximize system throughput without sacrificing the error rate performance. The results show that efficient and reliable system performance can be achieved over a wide range of the considered average channel quality. Going from a single-input single-output system to both spatially uncorrelated and correlated single-input multiple-ouput (SIMO) systems, and further to an uncorrelated multiple-input multiple-output (MIMO) diversity system, is the evolution of the thesis. In the SIMO case, maximum ratio combining is used to combine the incoming signals, whereas the signals are space-time combined in the MIMO diversity system. The multiple-input single-output system comes out as a special case of a MIMO system. Besides the spatially uncorrelated antenna array, the effect of spatial correlation is also considered in the SIMO case. In this case, only prediction error is considered and channel estimation is assumed to be perfect. At first, the impact of spatial correlation in a predicted system originally designed to operate on uncorrelated channels is quanitifed. Then, a maximum a posteriori (MAP)-optimal “space-time predictor” is derived to take spatial correlation into account. As expected, the results show that the throughput is still lower than the uncorrelated system, but the degradation is decreased when the MAP-optimal space-time predictor is used. Thus, by exploiting the correlation properly, the degradation can be reduced. By numerical examples, we demonstrate the potential effect of limiting the predictor complexity, of fixing the pilot spacing, as well as of assuming perfect estimation. The two first simplifications imply lower system complexity and feedback rate, whereas the last assumption is usually made to ease the mathematical analysis. The numerical examples indicate that all the simplifications can be done without serious impact on the predicted system performance.

In wireless communications, channel estimation is necessary for coherent symbol detection. This thesis considers a network which consists of two transceivers communicating with the help of a relay applying the amplify-and-forward (AF) relaying scheme. The training based channel estimation technique is applied to the proposed network where the numbers of the training sequence transmitted by the two transceivers, are different. All three terminals are equipped with a single antenna for signal transmission and reception. Communication between the transceivers is carried out in two phases. In the first phase, each transceiver sends a transmission block of data embedded with known training symbols to the relay. In the second phase, the relay retransmits an amplified version of the received signal to both transceivers. Estimates of the channel coefficients are obtained using the Maximum Likelihood (ML) estimator. The performance analysis of the derived estimates are carried out in terms of the mean squared error (MSE) and we determine conditions required to increase the estimation accuracy. / UOIT

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique which is robust against multipath fading and very easy to implement in transmitters and receivers using the inverse fast Fourier transform and the fast Fourier transform. A guard interval using cyclic prefix is inserted in each OFDM symbol to avoid the inter-symbol interference. This guard interval should be at least equal to, or longer than the maximum delay spread of the channel to combat against inter-symbol interference properly.<p> In coherent detection, channel estimation is required for the data detection of OFDM systems to equalize the channel effects. One of the popular techniques is to insert pilot tones (reference signals) in OFDM symbols. In conventional method, pilot tones are inserted into every OFDM symbols. Channel capacity is wasted due to the transmission of a large number of pilot tones. To overcome this transmission loss, incoherent data detection is introduced in OFDM systems, where it is not needed to estimate the channel at first. We use differential modulation based incoherent detection in this thesis for the data detection of OFDM systems. Data can be encoded in the relative phase of consecutive OFDM symbols (inter-frame modulation) or in the relative phase of an OFDM symbol in adjacent subcarriers (in-frame modulation). We use higher order differential modulation for in-frame modulation to compare the improvement of bit error rate. It should be noted that the single differential modulation scheme uses only one pilot tone, whereas the double differential uses two pilot tones and so on. Thus overhead due to the extra pilot tones in conventional methods are minimized and the detection delay is reduced. It has been observed that the single differential scheme works better in low SNRs (Signal to Noise Ratios) with low channel taps and the double differential works better at higher SNRs. Simulation results show that higher order differential modulation schemes don¡¯t have any further advantages. For inter-frame modulation, we use single differential modulation where only one OFDM symbol is used as a reference symbol. Except the reference symbol, no other overhead is required. We also perform channel estimation using differential modulation. Channel estimation using differential modulation is very easy and channel coefficients can be estimated very accurately without increasing any computational complexity. Our simulation results show that the mean square channel estimation error is about ¡¼10¡½^(-2) at an SNR of 30 dB for double differential in-frame modulation scheme, whereas channel estimation error is about ¡¼10¡½^(-4) for single differential inter-frame modulation. Incoherent data detection using classical DPSK (Differential Phase Shift Keying) causes an SNR loss of approximately 3 dB compared to coherent detection. But in our method, differential detection can estimate the channel coefficients very accurately and our estimated channel can be used in simple coherent detection to improve the system performance and minimize the SNR loss that happens in conventional method.

In recent years, the state-of-the-art in field programmable gate array (FPGA) technology has been advancing rapidly. Consequently, the use of FPGAs is being considered in many applications which have traditionally relied upon application-specific integrated circuits (ASICs). FPGA-based designs have a number of advantages over ASIC-based designs, including lower up-front engineering design costs, shorter time-to-market, and the ability to reconfigure devices in the field. However, ASICs have a major advantage in terms of computational resources. As a result, expensive high performance ASIC algorithms must be redesigned to fit the limited resources available in an FPGA. <p> Concurrently, coaxial cable television and internet networks have been undergoing significant upgrades that have largely been driven by a sharp increase in the use of interactive applications. This has intensified demand for the so-called upstream channels, which allow customers to transmit data into the network. The format and protocol of the upstream channels are defined by a set of standards, known as DOCSIS 3.0, which govern the flow of data through the network. <p> Critical to DOCSIS 3.0 compliance is the upstream demodulator, which is responsible for the physical layer reception from all customers. Although upstream demodulators have typically been implemented as ASICs, the design of an FPGA-based upstream demodulator is an intriguing possibility, as FPGA-based demodulators could potentially be upgraded in the field to support future DOCSIS standards. Furthermore, the lower non-recurring engineering costs associated with FPGA-based designs could provide an opportunity for smaller companies to compete in this market. <p> The upstream demodulator must contain complicated synchronization circuitry to detect, measure, and correct for channel distortions. Unfortunately, many of the synchronization algorithms described in the open literature are not suitable for either upstream cable channels or FPGA implementation. In this thesis, computationally inexpensive and robust synchronization algorithms are explored. In particular, algorithms for frequency recovery and equalization are developed. <p> The many data-aided feedforward frequency offset estimators analyzed in the literature have not considered intersymbol interference (ISI) caused by micro-reflections in the channel. It is shown in this thesis that many prominent frequency offset estimation algorithms become biased in the presence of ISI. A novel high-performance frequency offset estimator which is suitable for implementation in an FPGA is derived from first principles. Additionally, a rule is developed for predicting whether a frequency offset estimator will become biased in the presence of ISI. This rule is used to establish a channel excitation sequence which ensures the proposed frequency offset estimator is unbiased. <p> Adaptive equalizers that compensate for the ISI take a relatively long time to converge, necessitating a lengthy training sequence. The convergence time is reduced using a two step technique to seed the equalizer. First, the ISI equivalent model of the channel is estimated in response to a specific short excitation sequence. Then, the estimated channel response is inverted with a novel algorithm to initialize the equalizer. It is shown that the proposed technique, while inexpensive to implement in an FPGA, can decrease the length of the required equalizer training sequence by up to 70 symbols. <p> It is shown that a preamble segment consisting of repeated 11-symbol Barker sequences which is well-suited to timing recovery can also be used effectively for frequency recovery and channel estimation. By performing these three functions sequentially using a single set of preamble symbols, the overall length of the preamble may be further reduced.