Bit-interleaved coded modulation

The principle of coding in the signal space follows directly from Shannon’s analysis of waveform Gaussian channels subject to an input constraint. The early design of communication systems focused separately on modulation, and error correcting codes. Bit-interleaved coded modulation (BICM) is a pragmatic approach combining the best out of both worlds: it takes advantage of the signal-space coding perspective, whilst allowing for the use of powerful families of binary codes with virtually any modulation format. As matter of fact, has established itself as a quasi-standard (de-facto) for bandwidth - and power - efficient communication, like DSL, wireless LANs, WiMax. The aim of this thesis is to describe the main aspects of the system, focusing the attention on model characteristics and on the error analysis (based on bit-error rate approximations). Finally I also consider the BICM with iterative decoding and I conclude with an overview of some applications of BIC

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

A Reliable Multiple Access Scheme Based on Chirp Spread Spectrum and Turbo Codes

Nowadays, smart devices are the indispensable part of everyone's life and they play an important role in the advancement of industries and businesses.These devices are able to communicate with themselves and build the super network of the Internet of Things(IoT). Therefore, the need for the underlying structure of wireless data communications gains momentum. We require a wireless communication to support massive connectivity with ultra-fast data transmission rate and ultra-low latency. This research explores two possible methods of tackling the issues of the current communication systems for getting closer to the realization of the IoT. First, a grant-free scheme for uplink communication is proposed. The idea is to the combine the control signals with data signals by superimposing them on top of each other with minimal degradation of both signals. Moreover, it is well-established that orthogonal multiple access schemes cannot support the massive connectivity. Ergo, the second part of this research investigates a Non-Orthogonal Multiple Access(NOMA) scheme that exploits the powerful notion of turbo codes for separating the signals in a slow fading channel. It has been shown that in spite of the simplicity of the design, it has the potentials to surpass the performance of Sparse Code Multiple Access(SCMA) scheme

Coding mechanisms for communication and compression : analysis of wireless channels and DNA sequencing

textThis thesis comprises of two related but distinct components: Coding arguments for communication channels and information-theoretic analysis for haplotype assembly. The common thread for both problems is utilizing information and coding theoretic principles in understanding their underlying mechanisms. For the first class of problems, I study two practical challenges that prevent optimal discrete codes utilizing in real communication and compression systems, namely, coding over analog alphabet and fading. In particular, I use an expansion coding scheme to convert the original analog channel coding and source coding problems into a set of independent discrete subproblems. By adopting optimal discrete codes over the expanded levels, this low-complexity coding scheme can approach Shannon limit perfectly or in ratio. Meanwhile, I design a polar coding scheme to deal with the unstable state of fading channels. This novel coding mechanism of hierarchically utilizing different types of polar codes has been proved to be ergodic capacity achievable for several fading systems, without channel state information known at the transmitter. For the second class of problems, I build an information-theoretic view for haplotype assembly. More precisely, the recovery of the target pair of haplotype sequences using short reads is rephrased as the joint source-channel coding problem. Two binary messages, representing haplotypes and chromosome memberships of reads, are encoded and transmitted over a channel with erasures and errors, where the channel model reflects salient features of highthroughput sequencing. The focus is on determining the required number of reads for reliable haplotype reconstruction.Electrical and Computer Engineerin

5G無線通信における誤り訂正符号化方式の評価に関する研究

早大学位記番号:新8267早稲田大

Iterative graphical algorithms for phase noise channels.

Doctoral Degree. University of KwaZulu-Natal, Durban.This thesis proposes algorithms based on graphical models to detect signals and charac- terise the performance of communication systems in the presence of Wiener phase noise. The algorithms exploit properties of phase noise and consequently use graphical models to develop low complexity approaches of signal detection. The contributions are presented in the form of papers. The first paper investigates the effect of message scheduling on the performance of graphical algorithms. A serial message scheduling is proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems in the presence of carrier frequency offset and phase noise. The algorithm is shown to have better convergence compared to non-serial scheduling algorithms. The second paper introduces a concept referred to as circular random variables which is based on exploiting the properties of phase noise. An iterative algorithm is proposed to detect Low Density Parity Check (LDPC) codes in the presence of Wiener phase noise. The proposed algorithm is shown to have similar performance as existing algorithms with very low complexity. The third paper extends the concept of circular variables to detect coherent optical OFDM signals in the presence of residual carrier frequency offset and Wiener phase noise. The proposed iterative algorithm shows a significant improvement in complexity compared to existing algorithms. The fourth paper proposes two methods based on minimising the free energy function of graphical models. The first method combines the Belief Propagation (BP) and the Uniformly Re-weighted BP (URWBP) algorithms. The second method combines the Mean Field (MF) and the URWBP algorithms. The proposed methods are used to detect LDPC codes in Wiener phase noise channels. The proposed methods show good balance between complexity and performance compared to existing methods. The last paper proposes parameter based computation of the information bounds of the Wiener phase noise channel. The proposed methods compute the information lower and upper bounds using parameters of the Gaussian probability density function. The results show that these methods achieve similar performance as existing methods with low complexity

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems