A new rate adaptive system: Controlled soft demodulation

Adaptive Demodulation (ADM) is a newly proposed rate-adaptive system which operates without requiring Channel State Information (CSI) at the transmitter (unlike adaptive modulation) by using adaptive decision region boundaries at the receiver and encoding the data with a rateless code. This paper addresses the design and performance of an ADM scheme for two common differentially coherent schemes: M-DPSK (M-ary Differential Phase Shift Keying) and M-DAPSK (M-ary Differential Amplitude and Phase Shift Keying) operating over AWGN and Rayleigh fading channels. The optimal method for determining the most reliable bits for a given differential detection scheme is presented. In addition, simple (near-optimal) implementations are provided for recovering the most reliable bits from a received pair of differentially encoded symbols for systems using 16-DPSK and 16- DAPSK. The new receivers offer the advantages of a rate-adaptive system, without requiring CSI at the transmitter and a coherent phase reference at the receiver. Bit error analysis for the ADM system in both cases is presented along with numerical results of the spectral efficiency for the rate-adaptive systems operating over a Rayleigh fading channel.

Soft demodulation is a basic module of traditional communication receivers. It converts received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the nonideal additive white Gaussian noise (AWGN) channel, it is difficult to accurately calculate the LLR. In this letter, we propose a demodulator, DemodNet, based on a fully convolutional neural network with variable input and output length. We use hard bit information to train the DemodNet, and we propose log probability ratio (LPR) based on the output layer of the trained DemodNet to realize soft demodulation. The simulation results show that under the AWGN channel, the performance of both hard demodulation and soft demodulation of DemodNet is very close to the traditional methods. In three non-ideal channel scenarios, i.e., the presence of frequency deviation, additive generalized Gaussian noise (AGGN) channel, and Rayleigh fading channel, the performance of channel decoding using the soft information LPR obtained by DemodNet is better than the performance of decoding using the exact LLR calculated under the ideal AWGN assumption.

The invention of Fountain codes is a major advance in the field of error correcting codes. The goal of this work is to study and develop algorithms for source and channel coding using a family of Fountain codes known as Raptor codes. From an asymptotic point of view, the best currently known sum-product decoding algorithm for non binary alphabets has a high complexity that limits its use in practice. For binary channels, sum-product decoding algorithms have been extensively studied and are known to perform well. In the first part of this work, we develop a decoding algorithm for binary codes on non-binary channels based on a combination of sum-product and maximum-likelihood decoding. We apply this algorithm to Raptor codes on both symmetric and non-symmetric channels. Our algorithm shows the best performance in terms of complexity and error rate per symbol for blocks of finite length for symmetric channels. Then, we examine the performance of Raptor codes under sum-product decoding when the transmission is taking place on piecewise stationary memoryless channels and on channels with memory corrupted by noise. We develop algorithms for joint estimation and detection while simultaneously employing expectation maximization to estimate the noise, and sum-product algorithm to correct errors. We also develop a hard decision algorithm for Raptor codes on piecewise stationary memoryless channels. Finally, we generalize our joint LT estimation-decoding algorithms for Markov-modulated channels. In the third part of this work, we develop compression algorithms using Raptor codes. More specifically we introduce a lossless text compression algorithm, obtaining in this way competitive results compared to the existing classical approaches. Moreover, we propose distributed source coding algorithms based on the paradigm proposed by Slepian and Wolf.

Automatic identification of digitally modulated signal has to be able to identify the digitally modulated signal correctly and accurately. Importance of automatic identification of digitally modulated signals are rising increasingly. In this paper an advanced technique is presented, that automatically identifies the multi-user chirp modulated signals in Additive White Gaussian Noise (AWGN) channel. The proposed technique is implementing high order moments (fourth, sixth, and eighth) of detail coefficients of discrete wavelet transform (DWT) as a feature extraction set. Adaptive Neural-Fuzzy Inference System (ANFIS) is proposed as a classifier. The proposed identification procedure is capable of identifying multi-user chirp modulated signals with high accuracy at 0dB, 5dB, and 10dB Signal to Noise Ratio (SNR), over AWGN channel.

This paper investigates Fountain codes and applications to wireless broadcast. In this paper, we extend Fountain codes on erasure channels to arbitrary channel types by first taking into account the symbol information and Belief Propagation (BP) algorithm. We propose a rateless-coding framework based on Fountain codes, which allow reliable wireless broadcast communications and asynchronous data access simultaneously. Considering the flexible code rate characteristic, we find a new measurement to evaluate the performance of Fountain codes in the wireless environment. Via comparing the performance of two types of Fountain codes: Luby Transform (LT) codes and Raptor codes within this framework, we get the conclusion that Fountain codes will be sufficient to fulfill both requirements in a wireless system. We also demonstrate such a framework has advantages in efficiency, reliability, and robustness.

Fountain codes are erasure correcting codes realizing reliable communication systems for the multicast on the Internet. The zigzag decodable fountain (ZDF) code is one of generalization of the Raptor code, i.e, applying shift operation to generate the output packets. The ZDF code is decoded by a two-stage iterative decoding algorithm, which combines the packet-wise peeling algorithm and the bit-wise peeling algorithm. By the bit-wise peeling algorithm and shift operation, ZDF codes outperform Raptor codes under iterative decoding in terms of decoding erasure rates and overheads. However, the bit-wise peeling algorithm spends long decoding time. This paper proposes a fast bit-wise decoding algorithm for the ZDF codes. Simulation results show that the proposed algorithm drastically reduces the decoding time compared with the existing algorithm.

A fundamental understanding of the relationship between delay performance and complexity in network coding is instrumental towards its application in practical systems. The main argument against delay-optimal random linear network coding (RLNC) is its decoding complexity, which is O(n 3 ) for n original packets. Fountain codes, such as LT and Raptor codes, reduce the decoding load on the receiver but at the cost of introducing additional, non-negligible delay. The source of this increased delay is the inherent sparsity of the code, which significantly reduces the impact of a new coded packet, i.e., the probability of a packet to be linearly independent from the knowledge at the receiver, as we approach the end of the transmission (when few degrees of freedom are missing). Thus, the additional overhead is mainly due to the transmission of the last data packets. Our key observation is that switching gears to denser codes as the transmission process progresses could considerably reduce the delay overhead while maintaining the complexity advantages of a sparse code. We propose tunable sparse network coding as a dynamic coding mechanism with a code structure with two regions: a sparse region, with various levels of sparsity, and a dense region, where packets are generated as per RLNC. We characterize the problem in multicast sessions on general networks and illustrate trade-offs in especial cases of interest. We also present a novel mechanism to perform efficient Gaussian elimination for sparse matrices that guarantees linear decoding complexity with high probability.

An analytical method for approximate performance evaluation of binary linear block codes using an additive white Gaussian noise channel model with binary phase-shift keying modulation is presented. We focus on the probability density function of the bit log-likelihood ratio (LLR), which is expressed in terms of the Gram-Charlier series expansion. This expansion requires knowledge of the statistical moments of the bit LLR. We introduce an analytical method for calculating these moments. This is based on some recursive calculations involving certain weight enumerating functions of the code. It is proved that the approximation can be as accurate as desired, if we use enough terms in the Gram-Charlier series expansion. Numerical results are provided for some examples, which demonstrate close agreement with simulation results.

Phase modulated holographic storage offers superior storage capacity and a longer life span compared with other storage technologies. However, its application is limited by its high raw bit error rate. We aimed to introduce low-density parity-check (LDPC) codes for data protection in phase modulated holographic storage systems. However, traditional LDPC codes can not fully exploit data error characteristics, causing inaccurate initial log-likelihood ratio (LLR) information, which degrades decoding performance, thus limiting the improvement degree of data reliability in phase modulated holographic storage. Therefore, we propose a reliable bit aware LDPC optimization method (RaLDPC) that analyzes and employs phase demodulation characteristics to obtain reliable bits. More accurate initial LLR weights are assigned to these reliable bits. Hence, the optimized initial LLR can reflect the reliability of the demodulated data more accurately. Experimental results show that RaLDPC can reduce the bit error rate by an average of 38.89% compared with the traditional LDPC code, improving the data reliability of phase modulated holographic storage.

The written-in errors in bit patterned media recording (BPMR) system cause the erroneous bits during the writing process leading to the performance degradation. In this work, we propose the pattern-dependent modified log-likelihood ratio (LLR) usage in low-density parity check (LDPC) decoder to reduce the written-in errors and also improve the write margin. Unlike the existing works, LLR computed based on the input data patterns is used at the LDPC decoder for the cascaded written-in error channel (WEC) with the additive white Gaussian noise (AWGN) channel. The proposed LDPC decoder outperforms the one with conventional LLR in terms of both the write margin, when the SNR is fixed at 5.5 dB, and the performance of LDPC decoder. The SNR gain is about 0.2 dB at the BER of 10- 6.

This paper examines the problem of carrier phase recovery in turbo-coded systems. We introduce a new concept of "a priori probability aided phase estimation", where the extrinsic information (log-likelihood ratio) obtained from turbo decoder is used to aid an iterative phase estimation process, which is based on a maximum-likelihood strategy. The phase estimator operates jointly with the turbo decoding rather than separately prior to the decoder as in traditional approaches. This technique provides reliable phase estimation with variance of estimation errors approaching the Cramer-Rao bound at very low signal-to-noise ratio and allows robust decoding with a wide range of phase errors. This paper addresses its application in turbo-coded binary phase-shift keying and quaternary phase-shift keying systems over the additive white Gaussian noise channel. The bit-error-rate performance is investigated and shows that the performance of this technique is very close to the optimally synchronised system and significantly outperforms the traditional non-data-aided method without using additional pilot symbols.

This paper addresses the prediction of error floors of low-density parity-check codes transmitted over the additive white Gaussian noise channel. Using a linear state-space model to estimate the behavior of the sum-product algorithm (SPA) decoder in the vicinity of trapping sets (TSs), we study the performance of the SPA decoder in the log-likelihood ratio (LLR) domain as a function of the LLR saturation level. When applied to several widely studied codes, the model accurately predicts a significant decrease in the error floor as the saturation level is allowed to increase. For nonsaturating decoders, however, we find that the state-space model breaks down after a small number of iterations due to the strong correlation of LLR messages. We then revisit Richardson's importance-sampling methodology for estimating error floors due to TSs when those floors are too low for Monte Carlo simulation. We propose modifications that account for the behavior of a nonsaturating decoder and present the resulting error floor estimates for the Margulis code. These estimates are much lower, significantly steeper, and more sensitive to iteration count than those previously reported.

In this letter, a new early termination method is proposed for Luby transform (LT) belief propagation (BP) decoder. The proposed method is decided whether BP decoder output converges to original data bits by observing only sign alterations of log-likelihood ratio (LLR) messages in BP decoder structure. Simulation results and complexity analyzes show that proposed method has low computational complexity and small average iteration amounts compared to conventional early termination methods in literature. In addition to this, the proposed method doesn't cause any performance degradation in BP decoder. The method can be easily applied to code families which can be decoded by BP algorithm such as low density parity check (LDPC) codes, polar codes and Raptor codes.

In this paper, rate adaptive forward error correction systems for land mobile satellite communication are studied. For link performance evaluation, a simplified L-band land mobile satellite three state communication channel is introduced. A code rate adaptive system using multilevel coded modulation schemes with iterative decoding is presented. A new cost function based on the E/sub b//N/sub 0/ is given for performance evaluation of such systems. Based on this cost function, we show that rate adaptive systems can achieve extra coding gains between 3.9 dB and 9.0 dB.

Multi-connectivity can increase the reliability of wireless communications by orders of magnitude due to an increased diversity. However, simply copying packets and sending them on multiple links in parallel, i. e., as standardized as packet duplication, will result in a wasteful scheme. For example, there is no benefit in receiving the same information more often than once if multiple links succeed. This drawback can be resolved by channel coding. In a previous work, we proposed packet transmission schemes based on rateless codes, which could already increase resource efficiency compared to packet duplication. Here, we extend our previous studies in two different ways. First, we replace the previously used random linear fountain code by a more sophisticated raptor code, namely the R10 code. Second, we investigate a scenario, where the rateless coding scheme, which typically does not need any feedback, has acknowledgement information available. Both proposals show significant benefits of up to 15 % compared with our previous work in our mathematical analysis and empirical simulations.