Blind LDPC encoder identification

Nowadays, adaptive modulation and coding (AMC) techniques can facilitate flexible strategies subject to dynamic channel quality. The AMC transceivers select the most suitable coding and modulation mechanisms subject to the acquired channel information. Meanwhile, a control channel or a preamble is usually required to synchronously coordinate such changes between transmitters and receivers. On the other hand, low-density parity-check (LDPC) codes become more and more popular in recent years due to their promising capacity-approaching property. The broad range of variations in code rates and codeword lengths for LDPC codes makes them ideal candidates for future AMC transceivers. The blind encoder identification problem emerges when the underlying control channel is absent or the preamble is not allowed in AMC systems. It would be quite intriguing for one to build a blind encoder identification technique without spectrum-efficiency sacrifice. Therefore, in this thesis, we investigate blind LDPC encoder identification for AMC systems. Specifically, we would like to tackle the blind identification of binary LDPC codes (encoders) for binary phase-shift keying (BPSK) signals and nonbinary LDPC codes for quadrature-amplitude modulation (QAM) signals. We propose a novel blind identification system which consists of three major components, namely expectation-maximization (EM) estimator for unknown parameters (signal amplitude, noise variance, and phase offset), log-likelihood ratio (LLR) estimator for syndrome a posteriori probabilities, and maximum average-LLR detector. Monte Carlo simulation results demonstrate that our proposed blind LDPC encoder identification scheme is very promising over different signal-to-noise ratio conditions

New Identification and Decoding Techniques for Low-Density Parity-Check Codes

Error-correction coding schemes are indispensable for high-capacity high data-rate communication systems nowadays. Among various channel coding schemes, low-density parity-check (LDPC) codes introduced by pioneer Robert G. Gallager are prominent due to the capacity-approaching and superior error-correcting properties. There is no hard constraint on the code rate of LDPC codes. Consequently, it is ideal to incorporate LDPC codes with various code rate and codeword length in the adaptive modulation and coding (AMC) systems which change the encoder and the modulator adaptively to improve the system throughput. In conventional AMC systems, a dedicated control channel is assigned to coordinate the encoder/decoder changes. A questions then rises: if the AMC system still works when such a control channel is absent. This work gives positive answer to this question by investigating various scenarios consisting of different modulation schemes, such as quadrature-amplitude modulation (QAM), frequency-shift keying (FSK), and different channels, such as additive white Gaussian noise (AWGN) channels and fading channels. On the other hand, LDPC decoding is usually carried out by iterative belief-propagation (BP) algorithms. As LDPC codes become prevalent in advanced communication and storage systems, low-complexity LDPC decoding algorithms are favored in practical applications. In the conventional BP decoding algorithm, the stopping criterion is to check if all the parities are satisfied. This single rule may not be able to identify the undecodable blocks, as a result, the decoding time and power consumption are wasted for executing unnecessary iterations. In this work, we propose a new stopping criterion to identify the undecodable blocks in the early stage of the iterative decoding process. Furthermore, in the conventional BP decoding algorithm, the variable (check) nodes are updated in parallel. It is known that the number of iterations can be reduced by the serial scheduling algorithm. The informed dynamic scheduling (IDS) algorithms were proposed in the existing literatures to further reduce the number of iterations. However, the computational complexity involved in finding the update node in the existing IDS algorithms would not be neglected. In this work, we propose a new efficient IDS scheme which can provide better performance-complexity trade-off compared to the existing IDS ones. In addition, the iterative decoding threshold, which is used for differentiating which LDPC code is better, is investigated in this work. A family of LDPC codes, called LDPC convolutional codes, has drawn a lot of attentions from researchers in recent years due to the threshold saturation phenomenon. The IDT for an LDPC convolutional code may be computationally demanding when the termination length goes to thousand or even approaches infinity, especially for AWGN channels. In this work, we propose a fast IDT estimation algorithm which can greatly reduce the complexity of the IDT calculation for LDPC convolutional codes with arbitrary large termination length (including infinity). By utilizing our new IDT estimation algorithm, the IDTs for LDPC convolutional codes with arbitrary large termination length (including infinity) can be quickly obtained

Méthodes de codage et d'estimation adaptative appliquées aux communications sans fil

Les recherches et les contributions présentées portent sur des techniques de traitement du signal appliquées aux communications sans fil. Elles s’articulent autour des points suivants : (1) l’estimation adaptative de canaux de communication dans différents contextes applicatifs, (2) la correction de bruit impulsionnel et la réduction du niveau de PAPR (Peak to Average Power Ratio) dans un système multi-porteuse, (3) l’optimisation de schémas de transmission pour la diffusion sur des canaux gaussiens avec/sans contrainte de sécurité, (4) l’analyse, l’interprétation et l’amélioration des algorithmes de décodage itératif par le biais de l’optimisation, de la théorie des jeux et des outils statistiques. L’accent est plus particulièrement mis sur le dernier thème

Advances in parameter estimation, source enumeration, and signal identification for wireless communications

Parameter estimation and signal identification play an important role in modern wireless communication systems. In this thesis, we address different parameter estimation and signal identification problems in conjunction with the Internet of Things (IoT), cognitive radio systems, and high speed mobile communications. The focus of Chapter 2 of this thesis is to develop a new uplink multiple access (MA) scheme for the IoT in order to support ubiquitous massive uplink connectivity for devices with sporadic traffic pattern and short packet size. The proposed uplink MA scheme removes the Media Access Control (MAC) address through the signal identification algorithms which are employed at the gateway. The focus of Chapter 3 of this thesis is to develop different maximum Doppler spread (MDS) estimators in multiple-input multiple-output (MIMO) frequency-selective fading channel. The main idea behind the proposed estimators is to reduce the computational complexity while increasing system capacity. The focus of Chapter 4 and Chapter 5 of this thesis is to develop different antenna enumeration algorithms and signal-to-noise ratio (SNR) estimators in MIMO timevarying fading channels, respectively. The main idea is to develop low-complexity algorithms and estimators which are robust to channel impairments. The focus of Chapter 6 of this thesis is to develop a low-complexity space-time block codes (STBC)s identification algorithms for cognitive radio systems. The goal is to design an algorithm that is robust to time-frequency transmission impairments