Joint space time block code and modulation classification for MIMO systems

Non-cooperative identification of unknown communication signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to signal identification systems, such as the classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO signal identification by considering the modulation type and the STBC classification tasks as a joint classification problem

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

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