Challenges and Some New Directions in Channel Coding

Three areas of ongoing research in channel coding are surveyed, and recent developments are presented in each area: spatially coupled Low-Density Parity-Check (LDPC) codes, nonbinary LDPC codes, and polar coding.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/JCN.2015.00006

Challenges and some new directions in channel coding

Three areas of ongoing research in channel coding are surveyed, and recent developments are presented in each area: Spatially coupled low-density parity-check (LDPC) codes, nonbinary LDPC codes, and polar coding. © 2015 KICS

Polar-Coded OFDM with Index Modulation

Polar codes, as the first error-correcting codes with an explicit construction to provably achieve thesymmetric capacity of memoryless channels, which are constructed based on channel polarization, have recently become a primary contender in communication networks for achieving tighter requirements with relatively low complexity. As one of the contributions in this thesis, three modified polar decoding schemes are proposed. These schemes include enhanced versions of successive cancellation-flip (SC-F), belief propagation (BP), and sphere decoding (SD). The proposed SC-F utilizes novel potential incorrect bits selection criteria and stack to improve its error correction performance. Next, to make the decoding performance of BP better, permutation and feedback structure are utilized. Then, in order to reduce the complexity without compromising performance, a SD by using novel decoding strategies according to modified path metric (PM) and radius extension is proposed. Additionally, to solve the problem that BP has redundant iterations, a new stopping criterion based on bit different ratio (BDR) is proposed. According to the simulation results and mathematical proof, all proposed schemes can achieve corresponding performance improvement or complexity reduction compared with existing works. Beside applying polar coding, to achieve a reliable and flexible transmission in a wireless communication system, a modified version of orthogonal frequency division multiplexing (OFDM) modulation based on index modulation, called OFDM-in-phase/quadrature-IM (OFDM-I/Q-IM), is applied. This modulation scheme can simultaneously improve spectral efficiency and bit-error rate (BER) performance with great flexibility in design and implementation. Hence, OFDM-I/Q-IM is considered as a potential candidate in the new generation of cellular networks. As the main contribution in this work, a polar-coded OFDM-I/Q-IM system is proposed. The general design guidelines for overcoming the difficulties associated with the application of polar codes in OFDM-I/Q-IM are presented. In the proposed system, at the transmitter, we employ a random frozen bits appending scheme which not only makes the polar code compatible with OFDM-I/Q-IM but also improves the BER performance of the system. Furthermore, at the receiver, it is shown that the \textit{a posteriori} information for each index provided by the index detector is essential for the iterative decoding of polar codes by the BP algorithm. Simulation results show that the proposed polar-coded OFDM-I/Q-IM system outperforms its OFDM counterpart in terms of BER performance

Polar sampler: a novel Bernoulli sampler using polar codes with application to integer Gaussian sampling

Cryptographic constructions based on hard lattice problems have emerged as a front runner for the standardization of post-quantum public-key cryptography. As the standardization process takes place, optimizing specific parts of proposed schemes, e.g., Bernoulli sampling and integer Gaussian sampling, becomes a worthwhile endeavor. In this work, we propose a novel Bernoulli sampler based on polar codes, dubbed “polar sampler”. The polar sampler is information theoretically optimum in the sense that the number of uniformly random bits it consumes approaches the entropy bound asymptotically. It also features quasi-linear complexity and constant-time implementation. An integer Gaussian sampler is developed using multilevel polar samplers. Our algorithm becomes effective when sufficiently many samples are required at each query to the sampler. Security analysis is given based on Kullback–Leibler divergence and Rényi divergence. Experimental and asymptotic comparisons between our integer Gaussian sampler and state-of-the-art samplers verify its efficiency in terms of entropy consumption, running time and memory cost. We envisage that the proposed Bernoulli sampler can find other applications in cryptography in addition to Gaussian sampling

Polarization and Spatial Coupling:Two Techniques to Boost Performance

During the last two decades we have witnessed considerable activity in building bridges between the fields of information theory/communications, computer science, and statistical physics. This is due to the realization that many fundamental concepts and notions in these fields are in fact related and that each field can benefit from the insight and techniques developed in the others. For instance, the notion of channel capacity in information theory, threshold phenomena in computer science, and phase transitions in statistical physics are all expressions of the same concept. Therefore, it would be beneficial to develop a common framework that unifies these notions and that could help to leverage knowledge in one field to make progress in the others. A particularly striking example is the celebrated belief propagation algorithm. It was independently invented in each of these fields but for very different purposes. The realization of the commonality has benefited each of the areas. We investigate polarization and spatial coupling: two techniques that were originally invented in the context of channel coding (communications) thus resulting for the first time in efficient capacity-achieving codes for a wide range of channels. As we will discuss, both techniques play a fundamental role also in computer science and statistical physics and so these two techniques can be seen as further fundamental building blocks that unite all three areas. We demonstrate applications of these techniques, as well as the fundamental phenomena they provide. In more detail, this thesis consists of two parts. In the first part, we consider the technique of polarization and its resultant class of channel codes, called polar codes. Our main focus is the analysis and improvement of the behavior of polarization towards the most significant aspects of modern channel-coding theory: scaling laws, universality, and complexity (quantization). For each of these aspects, we derive fundamental laws that govern the behavior of polarization and polar codes. Even though we concentrate on applications in communications, the analysis that we provide is general and can be carried over to applications of polarization in computer science and statistical physics. As we will show, our investigations confirm some of the inherent strengths of polar codes such as their robustness with respect to quantization. But they also make clear in which aspects further improvement of polar codes is needed. For example, we will explain that the scaling behavior of polar codes is quite slow compared to the optimal one. Hence, further research is required in order to enhance the scaling behavior of polar codes towards optimality. In the second part of this thesis, we investigate spatial coupling. By now, there exists already a considerable literature on spatial coupling in the realm of information theory and communications. We therefore investigate mainly the impact of spatial coupling on the fields of statistical physics and computer science. We consider two well-known models. The first is the Curie-Weiss model that provides us with the simplest model for understanding the mechanism of spatial coupling in the perspective of statistical physics. Many fundamental features of spatial coupling can be simply explained here. In particular, we will show how the well-known Maxwell construction in statistical physics manifests itself through spatial coupling. We then focus on a much richer class of graphical models called constraint satisfaction problems (CSP) (e.g., K-SAT and Q-COL). These models are central to computer science. We follow a general framework: First, we introduce interpolation procedures for proving that the coupled and standard (un-coupled) models are fundamentally related, in that their static properties (such as their SAT/UNSAT threshold) are the same. We then use tools from spin glass theory (cavity method) to demonstrate the so-called phenomenon of threshold saturation in these coupled models. Finally, we present the algorithmic implications and argue that all these features provide a new avenue for obtaining better, provable, algorithmic lower bounds on static thresholds of the individual standard CSP models. We consider simple decimation algorithms (e.g., the unit clause propagation algorithm) for the coupled CSP models and provide a machinery to analyze these algorithms. These analyses enable us to observe that the algorithmic thresholds on the coupled model are significantly improved over the standard model. For some models (e.g., 3-SAT, 3-COL), these coupled algorithmic thresholds surpass the best lower bounds on the SAT/UNSAT threshold in the literature and provide us with a new lower bound. We conclude by pointing out that although we only considered some specific graphical models, our results are of general nature hence applicable to a broad set of models. In particular, a main contribution of this thesis is to firmly establish both polarization, as well as spatial coupling, in the common toolbox of information theory/communication, statistical physics, and computer science

one6G white paper, 6G technology overview:Second Edition, November 2022

6G is supposed to address the demands for consumption of mobile networking services in 2030 and beyond. These are characterized by a variety of diverse, often conflicting requirements, from technical ones such as extremely high data rates, unprecedented scale of communicating devices, high coverage, low communicating latency, flexibility of extension, etc., to non-technical ones such as enabling sustainable growth of the society as a whole, e.g., through energy efficiency of deployed networks. On the one hand, 6G is expected to fulfil all these individual requirements, extending thus the limits set by the previous generations of mobile networks (e.g., ten times lower latencies, or hundred times higher data rates than in 5G). On the other hand, 6G should also enable use cases characterized by combinations of these requirements never seen before, e.g., both extremely high data rates and extremely low communication latency). In this white paper, we give an overview of the key enabling technologies that constitute the pillars for the evolution towards 6G. They include: terahertz frequencies (Section 1), 6G radio access (Section 2), next generation MIMO (Section 3), integrated sensing and communication (Section 4), distributed and federated artificial intelligence (Section 5), intelligent user plane (Section 6) and flexible programmable infrastructures (Section 7). For each enabling technology, we first give the background on how and why the technology is relevant to 6G, backed up by a number of relevant use cases. After that, we describe the technology in detail, outline the key problems and difficulties, and give a comprehensive overview of the state of the art in that technology. 6G is, however, not limited to these seven technologies. They merely present our current understanding of the technological environment in which 6G is being born. Future versions of this white paper may include other relevant technologies too, as well as discuss how these technologies can be glued together in a coherent system