Non-Data-Aided Parameter Estimation in an Additive White Gaussian Noise Channel

Non-data-aided (NDA) parameter estimation is considered for binary-phase-shift-keying transmission in an additive white Gaussian noise channel. Cramer-Rao lower bounds (CRLBs) for signal amplitude, noise variance, channel reliability constant and bit-error rate are derived and it is shown how these parameters relate to the signal-to-noise ratio (SNR). An alternative derivation of the iterative maximum likelihood (ML) SNR estimator is presented together with a novel, low complexity NDA SNR estimator. The performance of the proposed estimator is compared to previously suggested estimators and the CRLB. The results show that the proposed estimator performs close to the iterative ML estimator at significantly lower computational complexity

Coded DS-CDMA Systems with Iterative Channel Estimation and no Pilot Symbols

In this paper, we describe direct-sequence code-division multiple-access (DS-CDMA) systems with quadriphase-shift keying in which channel estimation, coherent demodulation, and decoding are iteratively performed without the use of any training or pilot symbols. An expectation-maximization channel-estimation algorithm for the fading amplitude, phase, and the interference power spectral density (PSD) due to the combined interference and thermal noise is proposed for DS-CDMA systems with irregular repeat-accumulate codes. After initial estimates of the fading amplitude, phase, and interference PSD are obtained from the received symbols, subsequent values of these parameters are iteratively updated by using the soft feedback from the channel decoder. The updated estimates are combined with the received symbols and iteratively passed to the decoder. The elimination of pilot symbols simplifies the system design and allows either an enhanced information throughput, an improved bit error rate, or greater spectral efficiency. The interference-PSD estimation enables DS-CDMA systems to significantly suppress interference.Comment: To appear, IEEE Transactions on Wireless Communication

Adaptive Channel Estimation for Turbo Decoding

A new adaptive filter is proposed for the turbo decoding on Rayleigh fading channels with noisy channel estimates. The extrinsic information generated from the turbo decoder has some priority information about the transmitted data bits, which can help us better understand the channel characters. By using the soft extrinsic information after each iteration of decoding, we re-estimate the channel and the minimum mean square error (m.m.s.e.) and further update the channel reliability factor and decision variables at each iteration. Simulations show that signal to noise (SNR) gain is improved by up to about 1dB at bit error probability of 3.5&times10-4

Improved iterative detection techniques for slow-frequency-hop communications with Reed-Solomon codes

The performance of a packet-level iterative detection technique is examined for a slow-frequency-hop packet radio system using interleaved Reed-Solomon codes and per-dwell differential encoding. A per-dwell soft-input-soft-output detector along with successive-erasures decoding results in a system that performs better than previously considered detection techniques in the presence of partial-band interference. The log-MAP algorithm and two forms of its max-log-MAP approximation are considered for the soft-input-soft-output detector along with different channel estimators. The performance and detection complexity of the systems is compared. A limit on the number of erasures allowed in successive-erasures decoding is also considered, and its effect on the system\u27s performance and detection complexity is examined

Performance of Turbo Coded OFDM in Wireless Application

Orthogonal Frequency Division Multiplexing (OFDM) has become a popular modulation method in high speed wireless communications. By partitioning a wideband fading channel into flat narrowband channels, OFDM is able to mitigate the detrimental effects of multi path fading using a simple one- tap equalizer. There is a growing need to quickly transmit information wirelessly and accurately. Engineers have already combine techniques such as OFDM suitable for high data rate transmission with forward error correction (FEC) methods over wireless channels. In this thesis, we enhance the system throughput of a working OFDM system by adding turbo coding. The smart use of coding and power allocation in OFDM will be useful to the desired performance at higher data rates. Error control codes have become a vital part of modern digital wireless systems, enabling reliable transmission to be achieved over noisy channels. Over the past decade, turbo codes have been widely considered to be the most powerful error control code of practical importance. In the same time-scale, mixed voice/data networks have advanced further and the concept of global wireless networks and terrestrial links has emerged. Such networks present the challenge of optimizing error control codes for different channel types, and for the different qualities of service demanded by voice and data

Performance analysis of turbo coded OFDM in wireless application

Orthogonal Frequency Division Multiplexing (OFDM) has become a popular modulation method in high speed wireless communications. By partitioning a wideband fading channel into flat narrowband channels, OFDM is able to mitigate the detrimental effects of multi path fading using a simple one- tap equalizer. There is a growing need to quickly transmit information wirelessly and accurately.Engineers have already combine techniques such as OFDM suitable for high data rate transmission with forward error correction (FEC) methods over wireless channels. In this thesis, we enhance the system throughput of a working OFDM system by adding turbo coding. The smart use of coding and power allocation in OFDM will be useful to the desired performance at higher data rates.Error control codes have become a vital part of modern digital wireless systems,enabling reliable transmission to be achieved over noisy channels. Over the past decade,turbo codes have been widely considered to be the most powerful error control code of practical importance. In the same time-scale, mixed voice/data networks have advanced further and the concept of global wireless networks and terrestrial links has emerged. Such networks present the challenge of optimizing error control codes for different channel types,and for the different qualities of service demanded by voice and data

Raptor Codes for BIAWGN Channel: SNR Mismatch and the Optimality of the Inner and Outer Rates

Fountain codes are a class of rateless codes with two interesting properties, first, they can generate potentially limitless numbers of encoded symbols given a finite set of source symbols, and second, the source symbols can be recovered from any subset of encoded symbols with cardinality greater than the number of source symbols. Raptor codes are the first implementation of fountain codes with linear complexity and vanishing error floors on noisy channels. Raptor codes are designed by the serial concatenation of an inner Luby trans-form (LT) code, the first practical realization of fountain codes, and an outer low-density parity-check (LDPC) code. Raptor codes were designed to operate on the binary erasure channel (BEC), however, since their invention they received considerable attention in or-der to improve their performance on noisy channels, and especially additive white Gaussiannoise (AWGN) channels. This dissertation considers two issues that face Raptor codes on the binary input additive white Gaussian noise (BIAWGN) channel: inaccurate estimation of signal to noise ratio (SNR) and the optimality of inner and outer rates. First, for codes that use a belief propagation algorithm (BPA) in decoding, such as Raptor codes on the BIAWGN channel, accurate estimation of the channel SNR is crucial to achieving optimal performance by the decoder. A difference between the estimated SNR and the actual channel SNR is known as signal to noise ratio mismatch (SNRM). Using asymptomatic analysis and simulation, we show the degrading effects of SNRM on Raptor codes and observe that if the mismatch is large enough, it can cause the decoding to fail. Using the discretized density evolution (DDE) algorithm with the modifications required to simulate the asymptotic performance in the case of SNRM, we determine the decoding threshold of Raptor codes for different values of SNRM ratio. Determining the threshold under SNRM enables us to quantify its effects which in turn can be used to reach important conclusions about the effects of SNRM on Raptor codes. Also, it can be used to compare Raptor codes with different designs in terms of their tolerance to SNRM. Based on the threshold response to SNRM, we observe that SNR underestimation is slightly less detrimental to Raptor codes than SNR overestimation for lower levels of mismatch ratio, however, as the mismatch increases, underestimation becomes more detrimental. Further, it can help estimate the tolerance of a Raptor code, with certain code parameters when transmitted at some SNR value, to SNRM. Or equivalently, help estimate the SNR needed for a given code to achieve a certain level of tolerance to SNRM. Using our observations about the performance of Raptor codes under SNRM, we propose an optimization method to design output degree distributions of the LT part that can be used to construct Raptor codes with more tolerance to high levels of SNRM. Second, we study the effects of choosing different values of inner and outer code rate pairs on the decoding threshold and performance of Raptor codes on the BIAWGN channel. For concatenated codes such as Raptor codes, given any instance of the overall code rate R, different inner (Ri) and outer (Ro) code rate combinations can be used to share the available redundancy as long asR=RiRo. Determining the optimal inner and outer rate pair can improve the threshold and performance of Raptor codes. Using asymptotic analysis, we show the effect of the rate pair choice on the threshold of Raptor codes on the BIAWGN channel and how the optimal rate pair is decided. We also show that Raptor codes with different output degree distributions can have different optimal rate pairs, therefore, by identifying the optimal rate pair we can further improve the performance and avoid suboptimal use of the code. We make the observation that as the outer rate of Raptor codes increases the potential of achieving better threshold increases, and provide the reason why the optimal outer rate of Raptor codes cannot occur at lower values. Finally, we present an optimization method that considers the optimality of the inner and outer rates in designing the output degree distribution of the inner LT part of Raptor codes. The designed distributions show improvement in both the decoding threshold and performance compared to other code designs that do not consider the optimality of the inner and outer rates

On performance analysis and implementation issues of iterative decoding for graph based codes

There is no doubt that long random-like code has the potential to achieve good performance because of its excellent distance spectrum. However, these codes remain useless in practical applications due to the lack of decoders rendering good performance at an acceptable complexity. The invention of turbo code marks a milestone progress in channel coding theory in that it achieves near Shannon limit performance by using an elegant iterative decoding algorithm. This great success stimulated intensive research oil long compound codes sharing the same decoding mechanism. Among these long codes are low-density parity-check (LDPC) code and product code, which render brilliant performance. In this work, iterative decoding algorithms for LDPC code and product code are studied in the context of belief propagation. A large part of this work concerns LDPC code. First the concept of iterative decoding capacity is established in the context of density evolution. Two simulation-based methods approximating decoding capacity are applied to LDPC code. Their effectiveness is evaluated. A suboptimal iterative decoder, Max-Log-MAP algorithm, is also investigated. It has been intensively studied in turbo code but seems to be neglected in LDPC code. The specific density evolution procedure for Max-Log-MAP decoding is developed. The performance of LDPC code with infinite block length is well-predicted using density evolution procedure. Two implementation issues on iterative decoding of LDPC code are studied. One is the design of a quantized decoder. The other is the influence of mismatched signal-to-noise ratio (SNR) level on decoding performance. The theoretical capacities of the quantized LDPC decoder, under Log-MAP and Max-Log-MAP algorithms, are derived through discretized density evolution. It is indicated that the key point in designing a quantized decoder is to pick a proper dynamic range. Quantization loss in terms of bit error rate (BER) performance could be kept remarkably low, provided that the dynamic range is chosen wisely. The decoding capacity under fixed SNR offset is obtained. The robustness of LDPC code with practical length is evaluated through simulations. It is found that the amount of SNR offset that can be tolerated depends on the code length. The remaining part of this dissertation deals with iterative decoding of product code. Two issues on iterative decoding of\u27 product code are investigated. One is, \u27improving BER performance by mitigating cycle effects. The other is, parallel decoding structure, which is conceptually better than serial decoding and yields lower decoding latency