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

Artificial Intelligence Aided Receiver Design for Wireless Communication Systems

Physical layer (PHY) design in the wireless communication field realizes gratifying achievements in the past few decades, especially in the emerging cellular communication systems starting from the first generation to the fifth generation (5G). With the gradual increase in technical requirements of large data processing and end-to-end system optimization, introducing artificial intelligence (AI) in PHY design has cautiously become a trend. A deep neural network (DNN), one of the population techniques of AI, enables the utilization of its ‘learnable’ feature to handle big data and establish a global system model. In this thesis, we exploited this characteristic of DNN as powerful assistance to implement two receiver designs in two different use-cases. We considered a DNN-based joint baseband demodulator and channel decoder (DeModCoder), and a DNN-based joint equalizer, baseband demodulator, and channel decoder (DeTecModCoder) in two single operational blocks, respectively. The multi-label classification (MLC) scheme was equipped to the output of conducted DNN model and hence yielded lower computational complexity than the multiple output classification (MOC) manner. The functional DNN model can be trained offline over a wide range of SNR values under different types of noises, channel fading, etc., and deployed in the real-time application; therefore, the demands of estimation of noise variance and statistical information of underlying noise can be avoided. The simulation performances indicated that compared to the corresponding conventional receiver signal processing schemes, the proposed AI-aided receiver designs have achieved the same bit error rate (BER) with around 3 dB lower SNR

Spreads, arcs, and multiple wavelength codes

AbstractWe present several new families of multiple wavelength (2-dimensional) optical orthogonal codes (2D-OOCs) with ideal auto-correlation λa=0 (codes with at most one pulse per wavelength). We also provide a construction which yields multiple weight codes. All of our constructions produce codes that are either optimal with respect to the Johnson bound (J-optimal), or are asymptotically optimal and maximal. The constructions are based on certain pointsets in finite projective spaces of dimension k over GF(q) denoted PG(k,q)

Neural Distributed Autoassociative Memories: A Survey

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension. The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons). Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints. Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.Comment: 31 page

Partial geometric designs and difference families

We examine the designs produced by different types of difference families. Difference families have long been known to produce designs with well behaved automorphism groups. These designs provide the elegant solutions desired for applications. In this work, we explore the following question: Does every (named) design have a difference family analogue? We answer this question in the affirmative for partial geometric designs

Representation Learning with Adversarial Latent Autoencoders

A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wisesimilarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon the explicit maximum likelihood training paradigm, as opposed to an implicit one. Likelihood maximization, coupled with poor decoder distribution leads to poor or blurry reconstructions at best. Generative Adversarial Networks (GANs) on the other hand, perform an implicit maximization of the likelihood by solving a minimax game, thus bypassing the issues derived from the explicit maximization. This provides GAN architectures with remarkable generative power, enabling the generation of high-resolution images of humans, which are indistinguishable from real photos to the naked eye. However, GAN architectures lack inference capabilities, which makes them unsuitable for training encoder-decoder maps, effectively limiting their application space.We introduce an autoencoder architecture that (a) is free from the consequences ofmaximizing the likelihood directly, (b) produces reconstructions competitive in quality with state-of-the-art GAN architectures, and (c) allows learning disentangled representations, which makes it useful in a variety of problems. We show that the proposed architecture and training paradigm significantly improves the state-of-the-art in novelty and anomaly detection methods, it enables novel kinds of image manipulations, and has significant potential for other applications

Dynamic bandwidth allocation in CDMA-based passive optical networks

Fiber to the home (FTTH) technology is an attractive solution for providing high bandwidth from the Central Office (CO) to residences and small-and medium-sized businesses. The emergence of Internet Protocol-based communication within households such as VoIP, IPTV, video conferencing, and high definition multimedia shows that there is a need for high-capacity networks that can handle differentiated services. By providing an optical fiber link to a household where the optical network unit (ONU) is located, there will be a tremendous increase in information capacity with respect to Digital Subscriber Line and cable modem technologies that are currently in place. In access networks, Passive Optical Networks (PON) are rapidly replacing copper-based technologies due to a wide range of benefits, one of which is having the capability to transmit data at a higher rate and reach further distances without signal degradation. Under the PON family of technologies, Ethernet PON (EPON) was developed and is specified in the IEEE 802.3 standard outlining the framework that can deliver voice, data, and video over a native Ethernet port to businesses and residential customers. An increasingly important subject to network operators is Quality of Service (QoS). Although the EPON specification provides mechanisms for supporting QoS, it does not specify or define an algorithm for providing QoS. Rather it is up to the CO to design and implement an appropriate algorithm to meet the specifications of services that are offered to their clients. Researchers have extensively studied bandwidth allocation in EPON where the challenge is to develop bandwidth allocation algorithms that can fairly redistribute bandwidth among ONUs based on their demand. These algorithms were developed for the uplink direction, from ONUs to CO, in a network where only a single ONU is permitted to transmit at a time. Another well-established PON technology is Optical Code-Division Multiple Access PON (OCDMA-PON). In recent years, it has become more economical due to hardware advancements and it has gained a lot of attention due to its benefits over EPON. The most attractive benefit of OCDMA-PON is that multiple ONUs may transmit to the CO simultaneously, depending on a number of constraints, whereas EPON is limited to a single ONU transmission at a time. In this thesis, we develop a dynamic bandwidth allocation algorithm called Multi-Class Credit-Based Packet Scheduler (MCBPS) for OCDMA-PON in the uplink direction that supports the Internet Protocol (IP) Differentiated Services and takes advantage of the simultaneous nature of OCDMA. The IP Differentiated Services specifications stipulate the following traffic classifications: Expedited Forwarding for low latency, low packet loss, and low jitter applications; Assured Forwarding for services that require low packet loss; and Best Effort which are not guaranteed any bandwidth commitments. MCBPS incorporates the use of credit pools and the concept of a credit bank system to provide the same services as EPON by assigning ONUs specific timeslots to transmit data and also by specifying the amount of bytes from each class. MCBPS is a central office based algorithm that provides global fairness between Quality of Service (QoS) classes while also ensuring that at any given moment the desired number of simultaneous transmissions is not exceeded. We demonstrate through simulation that MCBPS algorithm is applicable in both EPON and OCDMA-PON environments. An in-house simulation program written in the C programming language is used to evaluate the effectiveness of the proposed algorithm. The MCBPS algorithm was tested alongside a benchmark algorithm called Interleaved Polling with Adaptive Cycle Time (IPACT) algorithm to compare network throughput, average packet delay, maximum packet delay, and packet loss ratio. From the simulation results it was observed that MCBPS algorithm is able to satisfy the QoS requirements and its performance is comparable to IPACT where the simultaneous transmission is limited to one. The simulation results also show that as the number of simultaneous transmissions within the network increases, so does the bandwidth. The MCBPS algorithm is able to re-distribute the scaling bandwidth while ensuring that a single ONU or QoS class does not monopolize all the available bandwidth. In doing so, through simulation results, as the simultaneous transmissions increases, the average packet delay decreases and the packet loss ratio improves

Improved tree species discrimination at leaf level with hyperspectral data combining binary classifiers

The purpose of the present thesis is to show that hyperspectral data can be used for discrimination between different tree species. The data set used in this study contains the hyperspectral measurements of leaves of seven savannah tree species. The data is high-dimensional and shows large within-class variability combined with small between-class variability which makes discrimination between the classes challenging. We employ two classification methods: G-nearest neighbour and feed-forward neural networks. For both methods, direct 7-class prediction results in high misclassification rates. However, binary classification works better. We constructed binary classifiers for all possible binary classification problems and combine them with Error Correcting Output Codes. We show especially that the use of 1-nearest neighbour binary classifiers results in no improvement compared to a direct 1-nearest neighbour 7-class predictor. In contrast to this negative result, the use of neural networks binary classifiers improves accuracy by 10% compared to a direct neural networks 7-class predictor, and error rates become acceptable. This can be further improved by choosing only suitable binary classifiers for combination

Error control techniques for satellite and space communications

The unequal error protection capabilities of convolutional and trellis codes are studied. In certain environments, a discrepancy in the amount of error protection placed on different information bits is desirable. Examples of environments which have data of varying importance are a number of speech coding algorithms, packet switched networks, multi-user systems, embedded coding systems, and high definition television. Encoders which provide more than one level of error protection to information bits are called unequal error protection (UEP) codes. In this work, the effective free distance vector, d, is defined as an alternative to the free distance as a primary performance parameter for UEP convolutional and trellis encoders. For a given (n, k), convolutional encoder, G, the effective free distance vector is defined as the k-dimensional vector d = (d(sub 0), d(sub 1), ..., d(sub k-1)), where d(sub j), the j(exp th) effective free distance, is the lowest Hamming weight among all code sequences that are generated by input sequences with at least one '1' in the j(exp th) position. It is shown that, although the free distance for a code is unique to the code and independent of the encoder realization, the effective distance vector is dependent on the encoder realization

Quantum key security : theory and analysis of experimental realisations

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