Performance of Serial Concatenated Convolutional Codes on faded channels

A coded wideband MC-CDMA system is considered for high-bit-rate data transmission in a typical outdoor environment with Rayleigh multipath fading. First, two powerful interleaved concatenated coding techniques, turbo and serially concatenated convolutional codes (SCCC) codes are applied in the downlink system and important conclusions are derived on their relative performance behavior in this type of system. Simulation results indicate some of the relative merits of SCCC on achieving very low error rates, with relative mitigation of error floor behavior at the typical BER range of interest for wireless data applications. Next, the more critical uplink system is considered, which requires a multiuser detection technique in combination with powerful coding to achieve similar performance. Successive interference cancellation with SCCC coding is presented, with computation of optimal log-likelihood ratios required for this type of interference cancellation followed by SCCC decoding. Application of SCCC coding and interference cancellation has improved the system performance significantly even in the fully loaded case. Based on these results, the combination of SCCC coding and successive cancellation is found to be attractive for high-rate data transmission in the uplink MC-CDMA.

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When channel state information (CSI) is not available, unitary space-time symbol-based parallel concatenated convolutional code (UST-SB-PCCC) can give a good performance and remain an acceptable calculation complexity, however it also meet with the error floor phenomenon. We propose a scheme utilizing UST-SB serial concatenated convolutional code (SCCC) for multiple-antenna wireless communication systems over rapid fading channels which can avoid the error floor. We also give the corresponding multi-ary MAP decoding algorithm for both the systematic and the parity symbols. With iterative decoding and/or multi-antennas, it can further improve the system performance, which helps to give an acceptable bit error rate (BER).

The additive white Gaussian noise (AWGN) model is commonly used in the development of communication systems, and adequately models many noisy environments. However the impulsive noise from snapping shrimp is poorly approximated by this model. The mismatch in model has an adverse impact on the performance of conventional communication systems operating in warm shallow waters. The AWGN model may be replaced by the more general additive white symmetric α-stable noise (AWSαSN) model, which better approximates the heavy tailed noise due to snapping shrimp. When converted to the complex baseband representation, the resulting noise for the AWSαSN case is radically different from its Gaussian counterpart. In this talk some properties of baseband noise for the general AWSαSN case are investigated. These properties can be used to guide design decisions for coherent communication systems operating in warm shallow waters. The baseband noise is generally not isotropic and furthermore the real and imaginary components may be dependent. By varying certain physical parameters different non-isotropic distributions may be attained. The resulting properties can be exploited to design communication systems that are able to provide robust performance in the presence of snapping shrimp noise.

Concatenation is a method of building long codes out of shorter ones, it attempts to meet the problem of decoding complexity by breaking the required computation into manageable segments. Concatenated Continuous Phase Frequency Shift Keying (CPFSK) facilitates powerful error correction. CPFSK also has the advantage of being bandwidth efficient and compatible with non-linear amplifiers. Bandwidth efficient concatenated coded modulation schemes were designed for communication over Additive White Gaussian noise (AWGN), and Rayleigh fading channels. An analytical bounds on the performance of serial concatenated convolutional codes (SCCC), and parallel concatenated convolutionalcodes (PCCC), were derived as a base of comparison with the third category known as hybrid concatenated trellis codes scheme (HCTC). An upper bound to the average maximum-likelihood bit error probability of the three schemes were obtained. Design rules for the parallel, outer, and inner codes that maximize the interleaver's gain were discussed. Finally, a low complexity iterative decoding algorithm that yields a better performance is proposed.

Generalized signal-dependent noise model and parameter estimation for natural images

The paper presents a novel cooperative coding scheme using punctured serial concatenated convolutional codes (SCCC). We evaluate its performance in terms of frame error rate (FER) under two cooperation protocols: a decode-and-forward protocol in conjunction with automatic repeat request (ARQ), by which the relay sends an acknowledge bit (ACK) back to the source to indicate whether its data packet is correctly decoded; an adaptive amplify-and-forward protocol, where the amplifying relay node cooperates to forward the message only when the inter-user link gain is above a certain threshold. In both cases, the destination makes the decision on each transmitted bit after appropriately combining signals and performing iterative decoding. Simulation results show that SCCC-based cooperative coding achieves full spatial diversity and exhibits a similar FER performance to that using parallel concatenated convolutional codes (PCCC). At the price of channel feedback, the adaptive decode-and-forward algorithm demonstrates a relatively lower FER than the adaptive amplify-and-forward scheme. Amplified cooperation experiences severe performance degradation due to the inefficiency of repetition coding and error propagation, especially when the quality of the source-relay link deteriorates; however, if the threshold for carrying cooperation is carefully selected, the adaptive amplify-and-forward protocol can perform comparably to the decode-and-forward protocol.

In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources. In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a “classical” feedback power control strategy, which aims at “equalizing” the SNRs at the AP over all communication links, and (ii) an “optimal” feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest energy consumption. These strategies will be referred to as balanced SNR and unbalanced SNR, respectively. While they require unlimited power control range at the sources (the sensors), we then propose “practical” implementations by limiting the power control range. We evalute the benefits brought by the use of the proposed feedback power control strategies considering joint channel decoding (JCD) schemes, where the sensors use channel coding and a proper iterative decoding algorithm is considered at the AP. The analysis is carried out considering the use of low-density parity-check (LDPC) codes and a serially concatenated convolutional code (SCCC).

Medical diagnosis is often hampered by the quality of the images. This happens in a wide range of image modalities. Image noise reduction is a crucial step, however difficult to be accomplished. Bayesian algorithms have been commonly used with success, namely with additive white Gaussian noise (AWGN) model. In fact, the noise corrupting some of the most used medical imaging modalities is not additive neither Gaussian but multiplicative described by Poisson or Rayleigh distributions. This paper proposes a unified framework with automatic hyper parameters estimation. The proposed framework deals with AWGN but also with both Poisson and Rayleigh distributions. The algorithm proposed herein, is based on a maximum a posteriori (MAP) criterion with the edge preserving prior based on the total variation (TV), which avoids the distortion of relevant anatomical details. The denoising technique is performed via single parametric iterative scheme parameterized for each noise model considered. Tests with real data from several medical imaging modalities testify the performance of the algorithm.

We propose an enhanced log-likelihood ratio (E-LLR) technique for soft-decision forward error correction (SD-FEC) in intensity-modulation direct-detection (IMDD) systems with optical amplifiers and demonstrate its effectiveness in 7-core weakly coupled fiber transmission. In amplified IMDD, square-law photodetection and modulator nonlinearities cause amplified spontaneous emission noise to produce non-Gaussian signal statistics, invalidating conventional LLR assumptions and degrading SD-FEC performance. To address this issue, we abandon the classic additive white Gaussian noise (AWGN) model and introduce a channel model based on multi-parameter chi-square distributions that more accurately reflect optical channel noise. Unlike previous Gaussian and chi-square approaches, E-LLR uses a training sequence to estimate the variance and non-centrality parameters for each pulse-amplitude modulation (PAM) level, generating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> distinct noncentral chi-square distributions for precise LLR computation in FEC decoding. Experimental results show that the proposed approach improves the receiver sensitivity by 1.13dB and reduces the decoding iterations of FEC compared with AWGN-model based schemes. These improvements contribute to reduced system complexity and decoding latency, while still achieving the required bit error rate threshold.

Localization of sensor node is imperative in Wireless Sensor Networks (WSN) as data gathered at the user end become meaningless without location information of the node. In this work, impact of anchor position errors on WSN localization using Mobile Anchor Positioning (MAP) Algorithm has been investigated by evaluating two deployment strategies. In deployment I sensor nodes are randomly spread in the entire chosen area (1000 χ 1000) whilst deployment II has nodes spread in the central region of the chosen area. Firstly, MAP Algorithm employing three anchor nodes is investigated for the two deployments. To study the impact of mobile anchors on MAP, anchor number is increased to four and the two deployments are investigated again. Further, this work also investigates the effect of anchor position errors on localization accuracy of sensor nodes. Error term is modelled using Additive White Gaussian Noise (AWGN) Model. Performance metrices considered for validation are Average Root Mean Square Error and Execution time.

The capacity limits of fiber-optic communication systems in the nonlinear regime are not yet well understood. In this paper, we study the capacity of amplitude modulated first-order soliton transmission, defined as the maximum of the so-called time-scaled mutual information. Such definition allows us to directly incorporate the dependence of soliton pulse width to its amplitude into capacity formulation. The commonly used memoryless channel model based on noncentral chi-squared distribution is initially considered. Applying a variance normalizing transform, this channel is approximated by a unit-variance additive white Gaussian noise (AWGN) model. Based on a numerical capacity analysis of the approximated AWGN channel, a general form of capacity-approaching input distributions is determined. These optimal distributions are discrete comprising a mass point at zero (off symbol) and a finite number of mass points almost uniformly distributed away from zero. Using this general form of input distributions, a novel closed-form approximation of the capacity is determined showing a good match to numerical results. Finally, mismatch capacity bounds are developed based on split-step simulations of the nonlinear Schrdinger equation considering both single soliton and soliton sequence transmissions. This relaxes the initial assumption of memoryless channel to show the impact of both inter-soliton interaction and Gordon–Haus effects. Our results show that the inter-soliton interaction effect becomes increasingly significant at higher soliton amplitudes and would be the dominant impairment compared to the timing jitter induced by the Gordon–Haus effect.

Upper and lower bounds on the capacity and minimum energy-per-bit for general additive white Gaussian noise (AWGN) and frequency-division AWGN (FD-AWGN) relay channel models are established. First, the max-flow min-cut bound and the generalized block-Markov coding scheme are used to derive upper and lower bounds on capacity. These bounds are never tight for the general AWGN model and are tight only under certain conditions for the FD-AWGN model. Two coding schemes that do not require the relay to decode any part of the message are then investigated. First, it is shown that the "side-information coding scheme" can outperform the block-Markov coding scheme. It is also shown that the achievable rate of the side-information coding scheme can be improved via time sharing. In the second scheme, the relaying functions are restricted to be linear. The problem is reduced to a "single-letter" nonconvex optimization problem for the FD-AWGN model. The paper also establishes a relationship between the minimum energy-per-bit and capacity of the AWGN relay channel. This relationship together with the lower and upper bounds on capacity are used to establish corresponding lower and upper bounds on the minimum energy-per-bit that do not differ by more than a factor of 1.45 for the FD-AWGN relay channel model and 1.7 for the general AWGN model.

We consider the additive white Gaussian noise (AWGN) cognitive fading interference channel consisting of one primary and multiple secondary transmitter-receiver pairs. The secondary transmitters have non-causal knowledge of the primary message. We find a tuple of achievable rates by utilizing the discrete superposition model (DSM), which is a simplified, deterministic channel model. The coding scheme devised for the DSM can be translated into a coding scheme for the AWGN model, where the rate achieved in the AWGN model is at most a constant gap below the rate achieved in the DSM. We then calculate the average throughput of the secondary pairs under the assumption of Rayleigh fading channels. The main result is that our scheme performs well in the weak interference regime. The sum-throughput increases with the number of secondary pairs.

We present a new adaptive approach for iterative turbo decoding using a pseudorandom training sequence with the distribution patterns known by both the transmitter and receiver, which can be adaptively sent to the receiver whenever the channel status changes. The branch detector computes the conditional distributions of extrinsic values produced by both arms at each iterative decoding with the aid of the training sequence. These updated probabilities are obtained instead of the traditional Gaussian law for further iterative decoding of information bits over additive white Gaussian noise and various fading channels. Such an adaptive decoding has considerable gains, investigated by the numerical simulation, over the traditional approach under the same conditions.