Channel Coding in Molecular Communication

This dissertation establishes and analyzes a complete molecular transmission system from a communication engineering perspective. Its focus is on diffusion-based molecular communication in an unbounded three-dimensional fluid medium. As a basis for the investigation of transmission algorithms, an equivalent discrete-time channel model (EDTCM) is developed and the characterization of the channel is described by an analytical derivation, a random walk based simulation, a trained artificial neural network (ANN), and a proof of concept testbed setup. The investigated transmission algorithms cover modulation schemes at the transmitter side, as well as channel equalizers and detectors at the receiver side. In addition to the evaluation of state-of-the-art techniques and the introduction of orthogonal frequency-division multiplexing (OFDM), the novel variable concentration shift keying (VCSK) modulation adapted to the diffusion-based transmission channel, the lowcomplex adaptive threshold detector (ATD) working without explicit channel knowledge, the low-complex soft-output piecewise linear detector (PLD), and the optimal a posteriori probability (APP) detector are of particular importance and treated. To improve the error-prone information transmission, block codes, convolutional codes, line codes, spreading codes and spatial codes are investigated. The analysis is carried out under various approaches of normalization and gains or losses compared to the uncoded transmission are highlighted. In addition to state-of-the-art forward error correction (FEC) codes, novel line codes adapted to the error statistics of the diffusion-based channel are proposed. Moreover, the turbo principle is introduced into the field of molecular communication, where extrinsic information is exchanged iteratively between detector and decoder. By means of an extrinsic information transfer (EXIT) chart analysis, the potential of the iterative processing is shown and the communication channel capacity is computed, which represents the theoretical performance limit for the system under investigation. In addition, the construction of an irregular convolutional code (IRCC) using the EXIT chart is presented and its performance capability is demonstrated. For the evaluation of all considered transmission algorithms the bit error rate (BER) performance is chosen. The BER is determined by means of Monte Carlo simulations and for some algorithms by theoretical derivation

When Machine Learning Meets Information Theory: Some Practical Applications to Data Storage

Machine learning and information theory are closely inter-related areas. In this dissertation, we explore topics in their intersection with some practical applications to data storage. Firstly, we explore how machine learning techniques can be used to improve data reliability in non-volatile memories (NVMs). NVMs, such as flash memories, store large volumes of data. However, as devices scale down towards small feature sizes, they suffer from various kinds of noise and disturbances, thus significantly reducing their reliability. This dissertation explores machine learning techniques to design decoders that make use of natural redundancy (NR) in data for error correction. By NR, we mean redundancy inherent in data, which is not added artificially for error correction. This work studies two different schemes for NR-based error-correcting decoders. In the first scheme, the NR-based decoding algorithm is aware of the data representation scheme (e.g., compression, mapping of symbols to bits, meta-data, etc.), and uses that information for error correction. In the second scenario, the NR-decoder is oblivious of the representation scheme and uses deep neural networks (DNNs) to recognize the file type as well as perform soft decoding on it based on NR. In both cases, these NR-based decoders can be combined with traditional error correction codes (ECCs) to substantially improve their performance. Secondly, we use concepts from ECCs for designing robust DNNs in hardware. Non-volatile memory devices like memristors and phase-change memories are used to store the weights of hardware implemented DNNs. Errors and faults in these devices (e.g., random noise, stuck-at faults, cell-level drifting etc.) might degrade the performance of such DNNs in hardware. We use concepts from analog error-correcting codes to protect the weights of noisy neural networks and to design robust neural networks in hardware. To summarize, this dissertation explores two important directions in the intersection of information theory and machine learning. We explore how machine learning techniques can be useful in improving the performance of ECCs. Conversely, we show how information-theoretic concepts can be used to design robust neural networks in hardware

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&

Méthodes de codage et d'estimation adaptative appliquées aux communications sans fil

Les recherches et les contributions présentées portent sur des techniques de traitement du signal appliquées aux communications sans fil. Elles s’articulent autour des points suivants : (1) l’estimation adaptative de canaux de communication dans différents contextes applicatifs, (2) la correction de bruit impulsionnel et la réduction du niveau de PAPR (Peak to Average Power Ratio) dans un système multi-porteuse, (3) l’optimisation de schémas de transmission pour la diffusion sur des canaux gaussiens avec/sans contrainte de sécurité, (4) l’analyse, l’interprétation et l’amélioration des algorithmes de décodage itératif par le biais de l’optimisation, de la théorie des jeux et des outils statistiques. L’accent est plus particulièrement mis sur le dernier thème

Microelectronic Implementation of Dicode PPM System Employing RS Codes

Optical fibre systems have played a key role in making possible the extraordinary growth in world-wide communications that has occurred in the last 25 years, and are vital in enabling the proliferating use of the Internet. Its high bandwidth capabilities, low attenuation characteristics, low cost, and immunity from the many disturbances that can afflict electrical wires and wireless communication links make it ideal for gigabit transmission and a major building block in the telecommunication infrastructure. A number of different techniques are used for the transmission of digital information between the transmitter and receiver sides in optical fibre system. One type of coding scheme is Pulse Position Modulation (PPM) in which the location of one pulse during 2M time slots is used to convey digital information from M bits. Although all the studies refer to advantages of PPM, it comes at a cost of large bandwidth and a complicated implementation. Therefore, variant PPM schemes have been proposed to transmit the data such as: Multiple Pulse Position Modulation (MPPM), Differential Pulse Position Modulation (DPPM), Pulse Interval Modulation (PIM), Digital Pulse Interval Modulation (DPIM), Dual Header Pulse Interval Modulation (DH-PIM), Dicode Pulse Position Modulation (DiPPM). The DiPPM scheme has been considered as a solution for the bandwidth consumption issue that other existing PPM formats suffer from. This is because it has a line rate that is twice that of the original data rate. DiPPM can be efficiently implemented as it employs two slots to transmit one bit of pulse code modulation (PCM). A PCM conversion from logic zero to logic one provides a pulse in slot RESET (R) and from one to zero provides a pulse in slot SET (S). No pulse is transmitted if the PCM data is unvarying. Like other PPM schemes, DiPPM suffers from three types of pulse detection errors wrong slot, false alarm, and erasure. The aim of this work was to build an error correction system, Reed Solomon (RS) code, which would overcome or reduce the error sources in the DiPPM system. An original mathematical program was developed using the Mathcad software to find the optimum RS parameters which can improve the DiPPM system error performance, number of photons and transmission efficiency. The results showed that the DiPPM system employing RS code offered an improvement over uncoded DiPPM of 5.12 dB, when RS operating at the optimum code rate of approximately ¾ and a codeword length of 25 symbols. Moreover, the error performance of the uncoded DiPPM is compared with the DiPPM system employing maximum likelihood sequence detector (MLSD), and RS code in terms of number of photons per pulse, transmission efficiency, and bandwidth expansion. The DiPPM with RS code offers superior performance compared to the uncoded DiPPM and DiPPM using MLSD, requiring only 4.5x103 photons per pulse when operating at a bandwidth equal to or above 0.9 times the original data rate. Further investigation took place on the DiPPM system employing RS code. A Matlab program and very high speed circuit Hardware Description language (VHDL) were developed to simulate the designed communication system. Simulation results were considered and agreed with the previous DiPPM theory. For the first time, this thesis presents the practical implementation for the DiPPM system employing RS code using Field Programmable Gate Array (FPGA)

The Telecommunications and Data Acquisition Report

Archival reports on developments in programs managed by the Jet Propulsion Laboratory's (JPL) Office of Telecommunications and Data Acquisition (TDA) are given. Space communications, radio navigation, radio science, and ground-based radio and radar astronomy, activities of the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) in planning, supporting research and technology, implementation, and operations are reported. Also included is TDA-funded activity at JPL on data and information systems and reimbursable Deep Space Network (DSN) work performed for other space agencies through NASA

Advances in Modeling and Signal Processing for Bit-Patterned Magnetic Recording Channels with Written-In Errors

In the past perpendicular magnetic recording on continuous media has served as the storage mechanism for the hard-disk drive (HDD) industry, allowing for growth in areal densities approaching 0.5 Tb/in2. Under the current system design, further increases are limited by the superparamagnetic effect where the medium's thermal energy destabilizes the individual bit domains used for storage. In order to provide for future growth in the area of magnetic recording for disk drives, a number of various technology shifts have been proposed and are currently undergoing considerable research. One promising option involves switching to a discrete medium in the form of individual bit islands, termed bit-patterned magnetic recording (BPMR).When switching from a continuous to a discrete media, the problems encountered become substantial for every aspect of the hard-disk drive design. In this dissertation the complications in modeling and signal processing for bit-patterned magnetic recording are investigated where the write and read processes along with the channel characteristics present considerable challenges. For a target areal density of 4 Tb/in2, the storage process is hindered by media noise, two-dimensional (2D) intersymbol interference (ISI), electronics noise and written-in errors introduced during the write process. Thus there is a strong possibility that BPMR may prove intractable as a future HDD technology at high areal densities because the combined negative effects of the many error sources produces an environment where current signal processing techniques cannot accurately recover the stored data. The purpose here is to exploit advanced methods of detection and error correction to show that data can be effectively recovered from a BPMR channel in the presence of multiple error sources at high areal densities.First a practical model for the readback response of an individual island is established that is capable of representing its 2D nature with a Gaussian pulse. Various characteristics of the readback pulse are shown to emerge as it is subjected to the degradation of 2D media noise. The writing of the bits within a track is also investigated with an emphasis on the write process's ability to inject written-in errors in the data stream resulting from both a loss of synchronization of the write clock and the interaction of the local-scale magnetic fields under the influence of the applied write field.To facilitate data recovery in the presence of BPMR's major degradations, various detection and error-correction methods are utilized. For single-track equalization of the channel output, noise prediction is incorporated to assist detection with increased levels of media noise. With large detrimental amounts of 2D ISI and media noise present in the channel at high areal densities, a 2D approach known as multi-track detection is investigated where multiple tracks are sensed by the read heads and then used to extract information on the target track. For BPMR the output of the detector still possesses the uncorrected written-in errors. Powerful error-correction codes based on finite geometries are employed to help recover the original data stream. Increased error-correction is sought by utilizing two-fold EG codes in combination with a form of automorphism decoding known as auto-diversity. Modifications to the parity-check matrices of the error-correction codes are also investigated for the purpose of attempting more practical applications of the decoding algorithms based on belief propagation. Under the proposed techniques it is shown that effective data recovery is possible at an areal density of 4 Tb/in2 in the presence of all significant error sources except for insertions and deletions. Data recovery from the BPMR channel with insertions and deletions remains an open problem