Low-complexity iterative receiver design for high spectral efficiency communication systems

University of Technology Sydney. Faculty of Engineering and Information Technology.With the rapid development of the modern society, people have an increasing demand of higher data rate. Due to the limited available bandwidth, how to improve the spectral efficiency becomes a key issue in the next generation wireless systems. Recent researches show that, compared to the conventional orthogonal communication systems, the non-orthogonal system can transmit more information with the same resources by introducing non-orthogonality. The non-orthogonal communication systems can be achieved by using faster-than-Nyquist (FTN) signaling to transmit more data symbols in the same time period. On the other hand, by designing appropriate codebook, the sparse code multiple access (SCMA) system can support more users while preserving the same resource elements. Utilisation of these new technologies leads to challenge in receiver design, which becomes severer in complex channel environments. This thesis studies the receiver design for high spectral efficiency communication systems. The main contributions are as follows: 1. A hybrid message passing algorithm is proposed for faster-than-Nyquist, which solves the problem of joint data detection and channel estimation when the channel coefficients are unknown. To fully exploit the known ISI imposed by FTN signaling, the interference induced by FTN signaling and channel fading are intentionally separated. 2. Gaussian message passing and variational inference based estimation algorithms are proposed for faster-than-Nyquist signaling detection in doubly selective channels. Iterative receivers using mean field and Bethe approximations based on variational inference framework are proposed. Moreover, a novel Gaussian message passing based FTN signaling detection algorithm is proposed. 3. An energy minimisation based SCMA decoding algorithm is proposed and convergence analysis of the proposed algorithm is derived. Following optimisation theory and variational free energy framework, the posterior distribution of data symbol is derived in closed form. Then, the convergence property of the proposed algorithm is analysed. 4. A stretched factor graph is designed for MIMO-SCMA system in order to reduce the receiver complexity. Then, a convergence guaranteed message passing algorithm is proposed by convexifying the Bethe free energy. Finally, cooperative communication methods based on belief consensus and alternative direction method of multipliers are proposed. 5. A low complexity detection algorithm is proposed for faster-than-Nyquist SCMA system, which enables joint channel estimation, decoding and user activity detection in grant-free systems. The combination of FTN signaling with SCMA to further enhance the spectral efficiency is first considered. Then, a merging belief propagation and expectation propagation algorithm is proposed to estimate channel state and perform SCMA decoding

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

Channel Detection and Decoding With Deep Learning

In this thesis, we investigate the designs of pragmatic data detectors and channel decoders with the assistance of deep learning. We focus on three emerging and fundamental research problems, including the designs of message passing algorithms for data detection in faster-than-Nyquist (FTN) signalling, soft-decision decoding algorithms for high-density parity-check codes and user identification for massive machine-type communications (mMTC). These wireless communication research problems are addressed by the employment of deep learning and an outline of the main contributions are given below. In the first part, we study a deep learning-assisted sum-product detection algorithm for FTN signalling. The proposed data detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to compensate any detrimental effects that degrade the detection performance. By investigating the maximum-likelihood bit-error rate performance of a finite length coded FTN system, we show that the error performance of the proposed algorithm approaches the maximum a posterior performance, which might not be approachable by employing the sum-product algorithm on conventional FTN factor graph. After investigating the deep learning-assisted message passing algorithm for data detection, we move to the design of an efficient channel decoder. Specifically, we propose a node-classified redundant decoding algorithm based on the received sequence’s channel reliability for Bose-Chaudhuri-Hocquenghem (BCH) codes. Two preprocessing steps are proposed prior to decoding, to mitigate the unreliable information propagation and to improve the decoding performance. On top of the preprocessing, we propose a list decoding algorithm to augment the decoder’s performance. Moreover, we show that the node-classified redundant decoding algorithm can be transformed into a neural network framework, where multiplicative tuneable weights are attached to the decoding messages to optimise the decoding performance. We show that the node-classified redundant decoding algorithm provides a performance gain compared to the random redundant decoding algorithm. Additional decoding performance gain can be obtained by both the list decoding method and the neural network “learned” node-classified redundant decoding algorithm. Finally, we consider one of the practical services provided by the fifth-generation (5G) wireless communication networks, mMTC. Two separate system models for mMTC are studied. The first model assumes that low-resolution digital-to-analog converters are equipped by the devices in mMTC. The second model assumes that the devices' activities are correlated. In the first system model, two rounds of signal recoveries are performed. A neural network is employed to identify a suspicious device which is most likely to be falsely alarmed during the first round of signal recovery. The suspicious device is enforced to be inactive in the second round of signal recovery. The proposed scheme can effectively combat the interference caused by the suspicious device and thus improve the user identification performance. In the second system model, two deep learning-assisted algorithms are proposed to exploit the user activity correlation to facilitate channel estimation and user identification. We propose a deep learning modified orthogonal approximate message passing algorithm to exploit the correlation structure among devices. In addition, we propose a neural network framework that is dedicated for the user identification. More specifically, the neural network aims to minimise the missed detection probability under a pre-determined false alarm probability. The proposed algorithms substantially reduce the mean squared error between the estimate and unknown sequence, and largely improve the trade-off between the missed detection probability and the false alarm probability compared to the conventional orthogonal approximate message passing algorithm. All the aforementioned three parts of research works demonstrate that deep learning is a powerful technology in the physical layer designs of wireless communications

On the Impact of Phase Noise in Communication Systems –- Performance Analysis and Algorithms

The mobile industry is preparing to scale up the network capacity by a factor of 1000x in order to cope with the staggering growth in mobile traffic. As a consequence, there is a tremendous pressure on the network infrastructure, where more cost-effective, flexible, high speed connectivity solutions are being sought for. In this regard, massive multiple-input multiple-output (MIMO) systems, and millimeter-wave communication systems are new physical layer technologies, which promise to facilitate the 1000 fold increase in network capacity. However, these technologies are extremely prone to hardware impairments like phase noise caused by noisy oscillators. Furthermore, wireless backhaul networks are an effective solution to transport data by using high-order signal constellations, which are also susceptible to phase noise impairments. Analyzing the performance of wireless communication systems impaired by oscillator phase noise, and designing systems to operate efficiently in strong phase noise conditions are critical problems in communication theory. The criticality of these problems is accentuated with the growing interest in new physical layer technologies, and the deployment of wireless backhaul networks. This forms the main motivation for this thesis where we analyze the impact of phase noise on the system performance, and we also design algorithms in order to mitigate phase noise and its effects. First, we address the problem of maximum a posteriori (MAP) detection of data in the presence of strong phase noise in single-antenna systems. This is achieved by designing a low-complexity joint phase-estimator data-detector. We show that the proposed method outperforms existing detectors, especially when high order signal constellations are used. Then, in order to further improve system performance, we consider the problem of optimizing signal constellations for transmission over channels impaired by phase noise. Specifically, we design signal constellations such that the error rate performance of the system is minimized, and the information rate of the system is maximized. We observe that these optimized constellations significantly improve the system performance, when compared to conventional constellations, and those proposed in the literature. Next, we derive the MAP symbol detector for a MIMO system where each antenna at the transceiver has its own oscillator. We propose three suboptimal, low-complexity algorithms for approximately implementing the MAP symbol detector, which involve joint phase noise estimation and data detection. We observe that the proposed techniques significantly outperform the other algorithms in prior works. Finally, we study the impact of phase noise on the performance of a massive MIMO system, where we analyze both uplink and downlink performances. Based on rigorous analyses of the achievable rates, we provide interesting insights for the following question: how should oscillators be connected to the antennas at a base station, which employs a large number of antennas

Advanced transceivers for spectrally-efficient communications

In this thesis, we will consider techniques to improve the spectral efficiency of digital communication systems, operating on the whole transceiver scheme. First, we will focus on receiver schemes having detection algorithms with a complexity constraint. We will optimize the parameters of the reduced detector with the aim of maximizing the achievable information rate. Namely, we will adopt the channel shortening technique. Then, we will focus on a technique that is getting very popular in the last years (although presented for the first time in 1975): faster-than-Nyquist signaling, and its extension which is time packing. Time packing is a very simple technique that consists in introducing intersymbol interference on purpose with the aim of increasing the spectral efficiency of finite order constellations. Finally, in the last chapters we will combine all the presented techniques, and we will consider their application to satellite channels.Comment: PhD Thesi

Synchronization in digital communication systems: performance bounds and practical algorithms

Communication channels often transfer signals from different transmitters. To avoid interference the available frequency spectrum is divided into non-overlapping frequency bands (bandpass channels) and each transmitter is assigned to a different bandpass channel. The transmission of a signal over a bandpass channel requires a shift of its frequency-content to a frequency range that is compatible with the designated frequency band (modulation). At the receiver, the modulated signal is demodulated (frequency shifted back to the original frequency band) in order to recover the original signal. The modulation/demodulation process requires the presence of a locally generated sinusoidal signal at both the transmitter and the receiver. To enable a reliable information transfer, it is imperative that these two sinusoids are accurately synchronized. Recently, several powerful channel codes have been developed which enable reliable communication at a very low signal-to-noise ratio (SNR). A by-product of these developments is that synchronization must now be performed at a SNR that is lower than ever before. Of course, this imposes high requirements on the synchronizer design. This doctoral thesis investigates to what extent (performance bounds) and in what way (practical algorithms) the structure that the channel code enforces upon the transmitted signal can be exploited to improve the synchronization accuracy at low SNR

Advanced low-complexity multiuser receivers

It tema centrale di questa tesi è la rivelazione multi-utente per sistemi di comunicazione wireless ad elevata efficienza spettrale. Lo scopo del lavoro è quello di proporre nuovi ricevitori multi-utente a bassa complessità con elevate prestazioni. Sono considerati sistemi satellitari basati su FDM (Frequency Division Multiplexing), in cui ogni utente adotta una modulazione CPM (Continuous Phase Modulation) concatenata serialmente con un codificatore tramite un interlacciatore e decodifica iterativa. Si considerano, inoltre, canali lineari in presenza di AWGN (additive white Gaussian noise). In particolare, si studiano sistemi FDM, in cui i canali adiacenti possono sovrapporsi in frequenza per aumentere l'efficienza spettrale, e sistemi CDMA (code division multiple access). Per gli scenari presi in esame, proponiamo schemi di rivelazione con un eccellente compromesso tra prestazioni e complessità computazionale, che permettono di implementare schemi di trasmissione con straordinaria efficienza spettrale, al prezzo di un limitato aumento di complessità rispetto ad un classico ricevitore singolo-utente che ignora l'interferenza.This thesis deals with multiuser detection (MUD) for spectrally-efficient wireless communication systems. The aim of this work is to propose new advanced low-complexity multiuser receivers with near-optimal detection performance. We consider frequency division multiplexing (FDM) satellite systems where each user employs a continuous phase modulation (CPM), serially concatenated with an outer code through an interleaver, and iterative detection/decoding. We also consider linear channels impaired by additive white Gaussian noise (AWGN), focusing on FDM systems where adjacent channels are allowed to overlap in frequency, and on code division multiple access systems (CDMA). For the considered scenarios, we propose detection schemes with an excel- lent performance/complexity tradeoff which allow us to implement transmission schemes with unprecedented spectral efficiency at a price of a limited complexity increase with respect to a classical single-user receiver which neglects the interference

Conditioned pilots for ISI channels

One of the proposals to increase the spectral efficiency of the DVB-S2 standard is based on time-frequency packing. This technique causes intersymbol and interchannel interferences to arise, requiring a significant growth of the number of pilots used to carry out frequency and phase synchronization. Therefore, a new pilot design will be introduced and suited optimal and suboptimal reduced-complexity algorithms derived. We will show that the proposed pilot strategy may outperform the classical one in terms of bit error rate and spectral efficiency