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Generalized concatenated (GC) codes with soft-input decoding were recently proposed for error correction in flash memories. This work proposes a soft-input decoder for GC codes that is based on a low-complexity bit-flipping procedure. This bit-flipping decoder uses a fixed number of test patterns and an algebraic decoder for soft-input decoding. An acceptance criterion for the final candidate codeword is proposed. Combined with error and erasure decoding of the outer Reed-Solomon codes, this bit-flipping decoder can improve the decoding performance and reduce the decoding complexity compared to the previously proposed sequential decoding. The bit-flipping decoder achieves a decoding performance similar to a maximum likelihood decoder for the inner codes.
The introduction of multiple-level cell (MLC) and triple-level cell (TLC) technologies reduced the reliability of flash memories significantly compared with single-level cell flash. With MLC and TLC flash cells, the error probability varies for the different states. Hence, asymmetric models are required to characterize the flash channel, e.g., the binary asymmetric channel (BAC). This contribution presents a combined channel and source coding approach improving the reliability of MLC and TLC flash memories. With flash memories data compression has to be performed on block level considering short-data blocks. We present a coding scheme suitable for blocks of 1 kB of data. The objective of the data compression algorithm is to reduce the amount of user data such that the redundancy of the error correction coding can be increased in order to improve the reliability of the data storage system. Moreover, data compression can be utilized to exploit the asymmetry of the channel to reduce the error probability. With redundant data, the proposed combined coding scheme results in a significant improvement of the program/erase cycling endurance and the data retention time of flash memories.
The Lempel-Ziv-Welch (LZW) algorithm is an important dictionary-based data compression approach that is used in many communication and storage systems. The parallel dictionary LZW (PDLZW) algorithm speeds up the LZW encoding by using multiple dictionaries. The PDLZW algorithm applies different dictionaries to store strings of different lengths, where each dictionary stores only strings of the same length. This simplifies the parallel search in the dictionaries for hardware implementations. The compression gain of the PDLZW depends on the partitioning of the address space, i.e. on the sizes of the parallel dictionaries. However, there is no universal partitioning that is optimal for all data sources. This work proposes an address space partitioning technique that optimizes the compression rate of the PDLZW using a Markov model for the data. Numerical results for address spaces with 512, 1024, and 2048 entries demonstrate that the proposed partitioning improves the performance of the PDLZW compared with the original proposal.
Acoustic Echo Cancellation (AEC) plays a crucial role in speech communication devices to enable full-duplex communication. AEC algorithms have been studied extensively in the literature. However, device specific details like microphone or loudspeaker configurations are often neglected, despite their impact on the echo attenuation or near-end speech quality. In this work, we propose a method to investigate different loudspeaker-microphone configurations with respect to their contribution to the overall AEC performance. A generic AEC system consisting of an adaptive filter and a Wiener post filter is used for a fair comparison between different setups. We propose the near-end-to-residual-echo ratio (NRER) and the attenuation-of-near-end (AON) as quality measures for the full-duplex AEC performance.
In this work, we investigate a hybrid decoding approach that combines algebraic hard-input decoding of binary block codes with soft-input decoding. In particular, an acceptance criterion is proposed which determines the reliability of a candidate codeword. For many received codewords the stopping criterion indicates that the hard-decoding result is sufficiently reliable, and the costly soft-input decoding can be omitted. The proposed acceptance criterion significantly reduces the decoding complexity. For simulations we combine the algebraic hard-input decoding with ordered statistics decoding, which enables near maximum likelihood soft-input decoding for codes of small to medium block lengths.
This work proposes an efficient hardware Implementation of sequential stack decoding of binary block codes. The decoder can be applied for soft input decoding for generalized concatenated (GC) codes. The GC codes are constructed from inner nested binary Bose-Chaudhuri-Hocquenghem (BCH) codes and outer Reed-Solomon (RS) codes. In order to enable soft input decoding for the inner BCH block codes, a sequential stack decoding algorithm is used.
Many resource-constrained systems still rely on symmetric cryptography for verification and authentication. Asymmetric cryptographic systems provide higher security levels, but are very computational intensive. Hence, embedded systems can benefit from hardware assistance, i.e., coprocessors optimized for the required public key operations. In this work, we propose an elliptic curve cryptographic coprocessors design for resource-constrained systems. Many such coprocessor designs consider only special (Solinas) prime fields, which enable a low-complexity modulo arithmetic. Other implementations support arbitrary prime curves using the Montgomery reduction. These implementations typically require more time for the point multiplication. We present a coprocessor design that has low area requirements and enables a trade-off between performance and flexibility. The point multiplication can be performed either using a fast arithmetic based on Solinas primes or using a slower, but flexible Montgomery modular arithmetic.
Today, many resource-constrained systems, such as embedded systems, still rely on symmetric cryptography for authentication and digital signatures. Asymmetric cryptography provide a higher security level, but software implementations of public-key algorithms on small embedded systems are extremely slow. Hence, such embedded systems require hardware assistance, i.e. crypto coprocessors optimized for public key operations. Many such coprocessor designs aim on high computational performance. In this work, an area efficient elliptic curve cryptography (ECC) coprocessor is presented for applications in small embedded systems where high performance coprocessors are too costly. We propose a simple control unit with a small instruction set that supports different ECC point multiplication (PM) algorithms. The control unit reduces the logic and number of registers compared with other implementations of ECC point multiplications.
It is well known that signal constellations which are based on a hexagonal grid, so-called Eisenstein constellations, exhibit a performance gain over conventional QAM ones. This benefit is realized by a packing and shaping gain of the Eisenstein (hexagonal) integers in comparison to the Gaussian (complex) integers. Such constellations are especially relevant in transmission schemes that utilize lattice structures, e.g., in MIMO communications. However, for coded modulation, the straightforward approach is to combine Eisenstein constellations with ternary channel codes. In this paper, a multilevel-coding approach is proposed where encoding and multistage decoding can directly be performed with state-of-the-art binary channel codes. An associated mapping and a binary set partitioning are derived. The performance of the proposed approach is contrasted to classical multilevel coding over QAM constellations. To this end, both the single-user AWGN scenario and the (multiuser) MIMO broadcast scenario using lattice-reduction-aided preequalization are considered. Results obtained from numerical simulations with LDPC codes complement the theoretical aspects.