The development of an error-correcting scheme for use with a six-tone HF modem

This thesis describes the development of an error correcting system for a H.F. modem employing 6-tone Multi-Frequency Shift Keying (MFSK) as its modulation scheme. The modulation scheme was chosen to be compatible with equipment already in service and to eliminate the need to modify the existing communications infrastructure. A convolutional code together with either Viterbi decoding or Fano decoding is chosen to provide the error correction because of the potential power of such codes and because it is possible for these combinations of code and decoding method to work with any alphabet size. To detect whether correction has been successful a Cyclic Redundancy Check (CRC) is embedded within the data block before encoding.A method of using a convolutional code to provide variable rate is presented. The method uses a systematic code so that it is possible for the scheme to have a quick look to see if the first data transmission has been received error free. A search for good codes is undertaken and the effect the alphabet size has on the code spectra discussed. It is shown that a good generator sequence for a binary code is also a good generator sequence for non-binary codes.To decode the convolutional code both the Viterbi maximum likelihood decoder and the Fano sequential decoder are studied. It is argued that the Fano sequential decoder is the better choice for this application because it makes better use of system resources which will be limited in the field equipment. It is also shown that the performance of multi-level codes is better than binary codes and that an alphabet size of around 6 is optimum.The throughput of the variable rate scheme and a number of fixed rate schemes is examined. It is shown that the variable rate scheme provides the best throughput at all data rates and that the throughput improvement at the higher data rates is greatest. The effect of interleaving is also examined and results presented.To support the variable rate scheme a protocol is developed that can be used on practical H.F. channels. The potential problems with errors on both the forward and return channel are analysed and mechanisms to deal with these built-in

Some Notes on Code-Based Cryptography

This thesis presents new cryptanalytic results in several areas of coding-based cryptography. In addition, we also investigate the possibility of using convolutional codes in code-based public-key cryptography. The first algorithm that we present is an information-set decoding algorithm, aiming towards the problem of decoding random linear codes. We apply the generalized birthday technique to information-set decoding, improving the computational complexity over previous approaches. Next, we present a new version of the McEliece public-key cryptosystem based on convolutional codes. The original construction uses Goppa codes, which is an algebraic code family admitting a well-defined code structure. In the two constructions proposed, large parts of randomly generated parity checks are used. By increasing the entropy of the generator matrix, this presumably makes structured attacks more difficult. Following this, we analyze a McEliece variant based on quasi-cylic MDPC codes. We show that when the underlying code construction has an even dimension, the system is susceptible to, what we call, a squaring attack. Our results show that the new squaring attack allows for great complexity improvements over previous attacks on this particular McEliece construction. Then, we introduce two new techniques for finding low-weight polynomial multiples. Firstly, we propose a general technique based on a reduction to the minimum-distance problem in coding, which increases the multiplicity of the low-weight codeword by extending the code. We use this algorithm to break some of the instances used by the TCHo cryptosystem. Secondly, we propose an algorithm for finding weight-4 polynomials. By using the generalized birthday technique in conjunction with increasing the multiplicity of the low-weight polynomial multiple, we obtain a much better complexity than previously known algorithms. Lastly, two new algorithms for the learning parities with noise (LPN) problem are proposed. The first one is a general algorithm, applicable to any instance of LPN. The algorithm performs favorably compared to previously known algorithms, breaking the 80-bit security of the widely used (512,1/8) instance. The second one focuses on LPN instances over a polynomial ring, when the generator polynomial is reducible. Using the algorithm, we break an 80-bit security instance of the Lapin cryptosystem

Initial synchronisation of wideband and UWB direct sequence systems: single- and multiple-antenna aided solutions

This survey guides the reader through the open literature on the principle of initial synchronisation in single-antenna-assisted single- and multi-carrier Code Division Multiple Access (CDMA) as well as Direct Sequence-Ultra WideBand (DS-UWB) systems, with special emphasis on the DownLink (DL). There is a paucity of up-to-date surveys and review articles on initial synchronization solutions for MIMO-aided and cooperative systems - even though there is a plethora of papers on both MIMOs and on cooperative systems, which assume perfect synchronization. Hence this paper aims to ?ll the related gap in the literature

Wavelet based image compression integrating error protection via arithmetic coding with forbidden symbol and map metric sequential decoding with ARQ retransmission

The phenomenal growth of digital multimedia applications has forced the communication

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

Embedded coding algorithms applicable to time variable channels

This thesis investigates new design and implementation techniques applicable to modern communication systems operating over time variable channels. Three areas of interest are investigated. These include, source coding in conjunction with real-time channel evaluation, channel coding and modem design. An investigation of source coding methods has led to the development of a new embedded real time channel evaluation, based on statistical techniques. The performance of this technique is examined using simulation techniques for channels with and without memory. Existing channel coding schemes applicable to time variable channels have been examined. This led to the formulation of a new coding technique, termed embedded encoding. Two implementations of such codes, embedded array codes and embedded convolutional codes, were developed. The theoretical and practical performance of these codes has been investigated. The final area of investigation has been the development of a 4-tone multi-frequency shift keying modem. In keeping with the intention of totally digital system design, the demodulator has been im­plemented on a single digital signal processing card. The demodulation method developed employs an embedded synchronisation technique, termed Code-Assisted Bit Synchronisation. The demodulator performs symbol synchronisation by utilising the convolutional code used for the purpose of channel coding. It thus performs the combined functions of the demodulator, decoder and symbol timing recovery, which are normally found as separate sub­systems. In combining these subsystems a more efficient modem has been developed

Application of wavelets and artificial neural network for indoor optical wireless communication systems

Abstract This study investigates the use of error control code, discrete wavelet transform (DWT) and artificial neural network (ANN) to improve the link performance of an indoor optical wireless communication in a physical channel. The key constraints that barricade the realization of unlimited bandwidth in optical wavelengths are the eye-safety issue, the ambient light interference and the multipath induced intersymbol interference (ISI). Eye-safety limits the maximum average transmitted optical power. The rational solution is to use power efficient modulation techniques. Further reduction in transmitted power can be achieved using error control coding. A mathematical analysis of retransmission scheme is investigated for variable length modulation techniques and verified using computer simulations. Though the retransmission scheme is simple to implement, the shortfall in terms of reduced throughput will limit higher code gain. Due to practical limitation, the block code cannot be applied to the variable length modulation techniques and hence the convolutional code is the only possible option. The upper bound for slot error probability of the convolutional coded dual header pulse interval modulation (DH-PIM) and digital pulse interval modulation (DPIM) schemes are calculated and verified using simulations. The power penalty due to fluorescent light interference (FL I) is very high in indoor optical channel making the optical link practically infeasible. A denoising method based on a DWT to remove the FLI from the received signal is devised. The received signal is first decomposed into different DWT levels; the FLI is then removed from the signal before reconstructing the signal. A significant reduction in the power penalty is observed using DWT. Comparative study of DWT based denoising scheme with that of the high pass filter (HPF) show that DWT not only can match the best performance obtain using a HPF, but also offers a reduced complexity and design simplicity. The high power penalty due to multipath induced ISI makes a diffuse optical link practically infeasible at higher data rates. An ANN based linear and DF architectures are investigated to compensation the ISI. Unlike the unequalized cases, the equalized schemes don‘t show infinite power penalty and a significant performance improvement is observed for all modulation schemes. The comparative studies substantiate that ANN based equalizers match the performance of the traditional equalizers for all channel conditions with a reduced training data sequence. The study of the combined effect of the FLI and ISI shows that DWT-ANN based receiver perform equally well in the present of both interference. Adaptive decoding of error control code can offer flexibility of selecting the best possible encoder in a given environment. A suboptimal ?soft‘ sliding block convolutional decoder based on the ANN and a 1/2 rate convolutional code with a constraint length is investigated. Results show that the ANN decoder can match the performance of optimal Viterbi decoder for hard decision decoding but with slightly inferior performance compared to soft decision decoding. This provides a foundation for further investigation of the ANN decoder for convolutional code with higher constraint length values. Finally, the proposed DWT-ANN receiver is practically realized in digital signal processing (DSP) board. The output from the DSP board is compared with the computer simulations and found that the difference is marginal. However, the difference in results doesn‘t affect the overall error probability and identical error probability is obtained for DSP output and computer simulations