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

Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation

This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined. The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies

Optimizing LDPC codes for a mobile WiMAX system with a saturated transmission amplifier

In mobile communication, the user’s information is transmitted through a wireless communication link that is subjected to a range of deteriorating effects. The quality of the transmission can be presented by the rate of transfer and the reliability of the received stream. The capacity of the communication link can be reached through the use of channel coding. Channel coding is the method of adding redundant information to the user’s information to mitigate the deteriorating effects of the communication link. Mobile WiMAX is a technology that makes use of orthogonal frequency division multiplexing (OFDM) modulation to transmit information over a wireless communication channel. The OFDM physical layer has a high peak average to power ratio (PAPR) characteristic that saturates the transmitter’s amplifier quite easily when proper backoff is not made in the transmission power. In this dissertation an optimized graph code was used as an alternative solution to improve the system’s performance in the presence of a saturated transmission’s amplifier. The graph code was derived from a degree distribution given by the density evolution algorithm and provided no extra network overhead to implement. The performance analysis resulted in a factor of 10 improvement in the error floor and a coding gain of 1.5 dB. This was all accomplished with impairments provided by the mobile WiMAX standard in the construction of the graph code.Dissertation (MEng)--University of Pretoria, 2009.Electrical, Electronic and Computer Engineeringunrestricte

Advanced Statistical Signal Processing Methods in Sensing, Detection, and Estimation for Communication Applications

The applications of wireless communications and digital signal processing have dramatically changed the way we live, work, and learn over decades. The requirement of higher throughput and ubiquitous connectivity for wireless communication systems has become prevalent nowadays. Signal sensing, detection and estimation have been prevalent in signal processing and communications for many years. The relevant studies deal with the processing of information-bearing signals for the purpose of information extraction. Nevertheless, new robust and efficient signal sensing, detection and estimation techniques are still in demand since there emerge more and more practical applications which rely on them. In this dissertation work, we proposed several novel signal sensing, detection and estimation schemes for wireless communications applications, such as spectrum sensing, symbol-detection/channel-estimation, and encoder identification. The associated theories and practice in robustness, computational complexity, and overall system performance evaluation are also provided

Resource allocation software algorithms for AMC-OFDM systems

PhD ThesisIn recent years, adaptive modulation and coding (AMC) technologies, resource allocation strategies and user scheduling for single-cell downlink orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) systems have been widely researched in order to ensure that capacity and throughput are maximised. In terms of AMC technologies, the correlation between the channel coefficients corresponding to the transmitted sub-carriers has not been considered yet. In the literature of resource allocation and user scheduling, either channel coding is not considered or only a fixed code rate is specified. Consequently, with a fixed number of data sub-carriers for each user, all these criteria restrict the flexibility of exploiting the available channel capacity, which reflects negatively on system throughput. At the same time, the presented scheduling algorithms so far managed the data of each user regardless the fair services of all users. The philosophy of this thesis is to maximise the average system throughput by proposing novel AMC, resource allocation and user scheduling strategies for OFDM and OFDMA systems based on developed software engineering life cycle models. These models have been designed to guarantee the scalability, extendibility and portability of the proposed strategies. This thesis presents an AMC strategy that divides the transmitted frame into sub-channels with an equal number of sub-carriers and selects different modulation and coding schemes (MCSs) amongst them rather than considering the same MCS for the entire frame. This strategy has been combined with a pilot adjustment scheme that reduces the pilots used for channel estimation in each sub-channel depending on the measured coherence bandwidth, signal to noise ratio (SNR), and SNR fluctuation values. The reduced pilots are replaced with additional data sub-carriers in order to improve the throughput. Additionally, a novel resource allocation strategy has been introduced in order to maximise the system throughput by distributing the users, transmission power and information bit streams over the employed sub-channels. The introduced method utilises the proposed AMC strategy in combination with pilot adjustment scheme to tackle the problem of channel capacity exploiting efficiently. It presents the throughput as a new cost function in terms of spectral efficiency and bit-error rate (BER), in which both convolutional coding rates and modulation order can be varied. The investigated throughput maximisation problem has been solved by producing two approaches. Firstly, optimised approach that solves the adopted problem optimally using the well known Lagrange multipliers method. This approach requires a huge search processes to achieve the optimal allocation of the resources, which yields a high computational complexity. To overcome the complexity issue, the second approach decouples the considered maximisation problem into two sub-problems based on the decomposition method on the cost of performance particularly for low SNR values. The proposed resource allocation strategy has been developed to work with multi-input-multi-output (MIMO) based AMC-OFDMA systems. In this project, two MIMO transmission criteria are considered, i.e. traditional and eigen-mode. In contrast, a user scheduling algorithm combined with the proposed resource allocation and AMC strategies is presented. The user scheduling algorithm aims to maximize the average system throughput by arranging the users in distinct queues according to their priorities and selecting the best user of each queue individually in order to guarantee a fair user service amongst different priority levels. When the involved users are scheduled, the scheduled users have been passed to the resource allocation to implement the distribution of the available resources. The proposed strategies have been tested over different international telecommunication union (ITU) channel profiles. The obtained simulation results show the superior performance of the introduced approaches in comparison with the related conventional methods. Furthermore, the gradually improvement in the throughput performance of the AMC-OFDM/ODMA system throughout the combination of the proposed strategies is clearly explained.Ministry of Higher Education and Scientific Research/IRAQ

A Performance comparision of polar codes with convolutional turbo codes

Ankara : Department of Electrical and Electronic Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 134-136.Polar codes introduced recently by Arıkan are the first low-complexity codes achieving symmetric capacity for arbitrary binary-input discrete memoryless channels (B-DMCs). Although being theoretically significant, their practical significance is an issue that has not yet been fully explored. Previous studies have compared polar codes with Reed-Muller codes, where it was found that polar codes can outperform them. In this thesis, to investigate how polar codes perform against state-of-the-art forward error correction (FEC) codes used in practice, we implement a IEEE 802.16 based link-level Worldwide Interoperability for Microwave Access (WiMAX) simulator which incorporates several WiMAX FEC options, and polar codes. IEEE 802.16 standards family define standards for current and next generation broadband wireless access, which will make high data rate multimedia applications in mobile environments a reality. Next generation broadband access standard, pursued by the IEEE 802.16 Task Group m is a work in progress, and requires even more sophisticated error correction schemes so that higher throughput, better QOS, higher mobilities, wider ranges and lower latencies are supported. We perform performance comparison simulations with the convolutional turbo codes (CTC) configurations defined in IEEE 802.16e to see how much of a performance gap exists between polar codes and CTCs. The main findings of the thesis are that, although the polar codes achieve capacity for specific conditions, as expected, for the code lengths and channel conditions we have simulated, the performance of them cannot compete with that of the CTCs with equivalent rates and lengths. It remains a task to see whether polar codes can achieve similar performances with CTCs when used as component codes in other configurations and aid in the advancement of new communication technologies.Özgür, ÜstünM.S

Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications

Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel coding (NTSCC), extract the semantic latent features of source signal, and learn entropy model to guide the joint source-channel coding with variable rate to transmit latent features over wireless channels. In this paper, we propose a comprehensive framework for improving NTSCC, thereby higher system coding gain, better model versatility, and more flexible adaptation strategy aligned with semantic guidance are all achieved. This new sophisticated NTSCC model is now ready to support large-size data interaction in emerging XR, which catalyzes the application of semantic communications. Specifically, we propose three useful improvement approaches. First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain. On that basis, we further propose response network architectures to formulate versatile NTSCC, i.e., once-trained model supports various rates and channel states that benefits the practical deployment. Following this, we propose an online latent feature editing method to enable more flexible coding rate control aligned with some specific semantic guidance. By comprehensively applying the above three improvement methods for NTSCC, a deployment-friendly semantic coded transmission system stands out finally. Our improved NTSCC system has been experimentally verified to achieve considerable bandwidth saving versus the state-of-the-art engineered VTM + 5G LDPC coded transmission system with lower processing latency

Fuzzy logic in handovers of Mobile WiMAX

Projecte final de carrera fet en col.laboració amb el centre Czech Technical Universit

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&