Low Complexity Decoding for Higher Order Punctured Trellis-Coded Modulation Over Intersymbol Interference Channels

Trellis-coded modulation (TCM) is a power and bandwidth efficient digital transmission scheme which offers very low structural delay of the data stream. Classical TCM uses a signal constellation of twice the cardinality compared to an uncoded transmission with one bit of redundancy per PAM symbol, i.e., application of codes with rates $\frac{n-1}{n}$ when $2^{n}$ denotes the cardinality of the signal constellation. Recently published work allows rate adjustment for TCM by means of puncturing the convolutional code (CC) on which a TCM scheme is based on. In this paper it is shown how punctured TCM-signals transmitted over intersymbol interference (ISI) channels can favorably be decoded. Significant complexity reductions at only minor performance loss can be achieved by means of reduced state sequence estimation.Comment: 4 pages, 5 figures, 3 algorithms, accepted and published at 6th International Symposium on Communications, Control, and Signal Processing (ISCCSP 2014

Low Complexity Decoding for Punctured Trellis-Coded Modulation Over Intersymbol Interference Channels

Classical trellis-coded modulation (TCM) as introduced by Ungerboeck in 1976/1983 uses a signal constellation of twice the cardinality compared to an uncoded transmission with one bit of redundancy per PAM symbol, i.e., application of codes with rates $\frac{n-1}{n}$ when $2^{n}$ denotes the cardinality of the signal constellation. The original approach therefore only comprises integer transmission rates, i.e., $R=\left\{ 2,\,3,\,4\,\ldots \right\}$, additionally, when transmitting over an intersymbol interference (ISI) channel an optimum decoding scheme would perform equalization and decoding of the channel code jointly. In this paper, we allow rate adjustment for TCM by means of puncturing the convolutional code (CC) on which a TCM scheme is based on. In this case a nontrivial mapping of the output symbols of the CC to signal points results in a time-variant trellis. We propose an efficient technique to integrate an ISI-channel into this trellis and show that the computational complexity can be significantly reduced by means of a reduced state sequence estimation (RSSE) algorithm for time-variant trellises.Comment: 4 pages, 7 pictured, accepted for 2014 International Zurich Seminar on Communication

Optimal Sequence Estimation for Convolutionally Coded Signals With Binary Digital Modulation in ISI Channels

Decoding convolutional codes with binary digital modulation in intersymbol interference (ISI) channels is studied. The receiver structure is a whitened matched filter (WMF) whose transfer function is determined by the ISI channel. Decoding of the output sequence can be performed in two steps or one step. The two-step decoding first decodes the ISI corrupted coded sequence back to the ISI free coded sequence which is then decoded back to the uncoded message sequence. For one-step decoding, the entire encoder-channel-receiver system is modeled as a new encoder with combined memory length of the memory lengths of the original encoder and the channel, and followed by a weighted summation mapping from the binary symbols to real number symbols. The weighting coefficients are determined by the channel characteristic. In both two-step and one-step decoding, the Viterbi algorithm (VA) is used to perform the maximum likelihood decoding. Decoding error probability and complexity of both methods are analyzed, simulated and compared

Optimal Sequence Estimation for Convolutionally Coded Signals With Binary Digital Modulation in ISI Channels

Decoding convolutional codes with binary digital modulation in intersymbol interference (ISI) channels is studied. The receiver structure is a whitened matched filter (WMF) whose transfer function is determined by the ISI channel. Decoding of the output sequence can be performed in two steps or one step. The two-step decoding first decodes the ISI corrupted coded sequence back to the ISI free coded sequence which is then decoded back to the uncoded message sequence. For one-step decoding, the entire encoder-channel-receiver system is modeled as a new encoder with combined memory length of the memory lengths of the original encoder and the channel, and followed by a weighted summation mapping from the binary symbols to real number symbols. The weighting coefficients are determined by the channel characteristic. In both two-step and one-step decoding, the Viterbi algorithm (VA) is used to perform the maximum likelihood decoding. Decoding error probability and complexity of both methods are analyzed, simulated and compared

Sequential decoding on intersymbol interference channels with application to magnetic recording

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1990.Thesis (Master's) -- Bilkent University, 1990.Includes bibliographical references leaves 27-28In this work we treat sequential decoding in the problem of sequence estimation on intersymbol interference ( ISI ) channels. We consider the magnetic recording channel as the particular ISI channel and investigate the coding gains that can be achieved with sequential decoding for different information densities. Since the cutoff rate determines this quantity , we find lower bounds to the cutoff rate. The symmetric cutoff rate is computed as a theoretical lower bound and practical lower bounds are found through simulations. Since the optimum decoding metric is impractical, a sub-optimum metric has been used in the simulations. The results show that this metric can not achieve the cutoff rate in general, but still its performance is not far from that of the optimum metric. We compare the results to those of Immink[9] and see that one can achieve positive coding gains at information densities of practical interest where other practical codes used in magnetic recording show coding loss.Alanyalı, MuratM.S

Iterative receivers and multichannel equalisation for time division multiple access systems

The thesis introduces receiver algorithms improving the performance of TDMA mobile radio systems. Particularly, we consider receivers utilising side information, which can be obtained from the error control coding or by having a priori knowledge of interference sources. Iterative methods can be applied in the former case and interference suppression techniques in the latter. Convolutional coding adds redundant information into the signal and thereby protects messages transmitted over a radio channel. In the coded systems the receiver is usually comprised of separate channel estimation, detection and channel decoding tasks due to complexity restrictions. This suboptimal solution suffers from performance degradation compared to the optimal solution achieved by optimising the joint probability of information bits, transmitted symbols and channel impulse response. Conventional receiver utilises estimated channel state information in the detection and detected symbols in the channel decoding to finally obtain information bits. However, the channel decoder provides also extrinsic information on the bit probabilities, which is independent of the received information at the equaliser input. Therefore it is beneficial to re-perform channel estimation and detection using this new extrinsic information together with the original input signal. We apply iterative receiver techniques mainly to Enhanced General Packet Radio System (EGPRS) using GMSK modulation for iterative channel estimation and 8-PSK modulation for iterative detection scheme. Typical gain for iterative detection is around 2 dB and for iterative channel estimation around 1 dB. Furthermore, we suggest two iteration rounds as a reasonable complexity/performance trade-off. To obtain further complexity reduction we introduce the soft trellis decoding technique that reduces the decoder complexity significantly in the iterative schemes. Cochannel interference (CCI) originates from the nearby cells that are reusing the same transmission frequency. In this thesis we consider CCI suppression by joint detection (JD) technique, which detects simultaneously desired and interfering signals. Because of the complexity limitations we only consider JD for two binary modulated signals. Therefore it is important to find the dominant interfering signal (DI) to achieve the best performance. In the presence of one strong DI, the JD provides major improvement in the receiver performance. The JD requires joint channel estimation (JCE) for the two signals. However, the JCE makes the implementation of the JD more difficult, since it requires synchronised network and unique training sequences with low cross-correlation for the two signals.reviewe

Reduced Receivers for Faster-than-Nyquist Signaling and General Linear Channels

Fast and reliable data transmission together with high bandwidth efficiency are important design aspects in a modern digital communication system. Many different approaches exist but in this thesis bandwidth efficiency is obtained by increasing the data transmission rate with the faster-than-Nyquist (FTN) framework while keeping a fixed power spectral density (PSD). In FTN consecutive information carrying symbols can overlap in time and in that way introduce a controlled amount of intentional intersymbol interference (ISI). This technique was introduced already in 1975 by Mazo and has since then been extended in many directions. Since the ISI stemming from practical FTN signaling can be of significant duration, optimum detection with traditional methods is often prohibitively complex, and alternative equalization methods with acceptable complexity-performance tradeoffs are needed. The key objective of this thesis is therefore to design reduced-complexity receivers for FTN and general linear channels that achieve optimal or near-optimal performance. Although the performance of a detector can be measured by several means, this thesis is restricted to bit error rate (BER) and mutual information results. FTN signaling is applied in two ways: As a separate uncoded narrowband communication system or in a coded scenario consisting of a convolutional encoder, interleaver and the inner ISI mechanism in serial concatenation. Turbo equalization where soft information in the form of log likelihood ratios (LLRs) is exchanged between the equalizer and the decoder is a commonly used decoding technique for coded FTN signals. The first part of the thesis considers receivers and arising stability problems when working within the white noise constraint. New M-BCJR algorithms for turbo equalization are proposed and compared to reduced-trellis VA and BCJR benchmarks based on an offset label idea. By adding a third low-complexity M-BCJR recursion, LLR quality is improved for practical values of M. M here measures the reduced number of BCJR computations for each data symbol. An improvement of the minimum phase conversion that sharpens the focus of the ISI model energy is proposed. When combined with a delayed and slightly mismatched receiver, the decoding allows a smaller M without significant loss in BER. The second part analyzes the effect of the internal metric calculations on the performance of Forney- and Ungerboeck-based reduced-complexity equalizers of the M-algorithm type for both ISI and multiple-input multiple-output (MIMO) channels. Even though the final output of a full-complexity equalizer is identical for both models, the internal metric calculations are in general different. Hence, suboptimum methods need not produce the same final output. Additionally, new models working in between the two extremes are proposed and evaluated. Note that the choice of observation model does not impact the detection complexity as the underlying algorithm is unaltered. The last part of the thesis is devoted to a different complexity reducing approach. Optimal channel shortening detectors for linear channels are optimized from an information theoretical perspective. The achievable information rates of the shortened models as well as closed form expressions for all components of the optimal detector of the class are derived. The framework used in this thesis is more general than what has been previously used within the area

Coded Modulation Assisted Radial Basis Function Aided Turbo Equalisation for Dispersive Rayleigh Fading Channels

In this contribution a range of Coded Modulation (CM) assisted Radial Basis Function (RBF) based Turbo Equalisation (TEQ) schemes are investigated when communicating over dispersive Rayleigh fading channels. Specifically, 16QAM based Trellis Coded Modulation (TCM), Turbo TCM (TTCM), Bit-Interleaved Coded Modulation (BICM) and iteratively decoded BICM (BICM-ID) are evaluated in the context of an RBF based TEQ scheme and a reduced-complexity RBF based In-phase/Quadrature-phase (I/Q) TEQ scheme. The Least Mean Square (LMS) algorithm was employed for channel estimation, where the initial estimation step-size used was 0.05, which was reduced to 0.01 for the second and the subsequent TEQ iterations. The achievable coding gain of the various CM schemes was significantly increased, when employing the proposed RBF-TEQ or RBF-I/Q-TEQ rather than the conventional non-iterative Decision Feedback Equaliser - (DFE). Explicitly, the reduced-complexity RBF-I/Q-TEQ-CM achieved a similar performance to the full-complexity RBF-TEQ-CM, while attaining a significant complexity reduction. The best overall performer was the RBF-I/Q-TEQ-TTCM scheme, requiring only 1.88~dB higher SNR at BER=10-5, than the identical throughput 3~BPS uncoded 8PSK scheme communicating over an AWGN channel. The coding gain of the scheme was 16.78-dB