Refine
Document Type
- Conference Proceeding (9)
- Article (4)
- Doctoral Thesis (1)
Has Fulltext
- no (14)
Keywords
Institute
This work introduces new signal constellations based on Eisenstein integers, i.e., the hexagonal lattice. These sets of Eisenstein integers have a cardinality which is an integer power of three. They are proposed as signal constellations for representation in the equivalent complex baseband model, especially for applications like physical-layer network coding or MIMO transmission where the constellation is required to be a subset of a lattice. It is shown that these constellations form additive groups where the addition over the complex plane corresponds to the addition with carry over ternary Galois fields. A ternary set partitioning is derived that enables multilevel coding based on ternary error-correcting codes. In the subsets, this partitioning achieves a gain of 4.77 dB, which results from an increased minimum squared Euclidean distance of the signal points. Furthermore, the constellation-constrained capacities over the AWGN channel and the related level capacities in case of ternary multilevel coding are investigated. Simulation results for multilevel coding based on ternary LDPC codes are presented which show that a performance close to the constellation-constrained capacities can be achieved.
The computational complexity of the optimal maximum likelihood (ML) detector for spatial modulation increases rapidly as more transmit antennas or larger modulation orders are employed. Hence, ML detection may be infeasible for higher bit rates. This work proposes an improved suboptimal detection algorithm based on the Gaussian approximation method. It is demonstrated that the new method is closely related to the previously published signal vector based detection and the modified maximum ratio combiner, but can improve the detection performance compared to these methods. Furthermore, the performance of different signal constellations with suboptimal detection is investigated. Simulation results indicate that the performance loss compared to ML detection depends heavily on the signal constellation, where the recently proposed Eisenstein integer constellations are beneficial compared to classical QAM or PSK constellations.
In diesem Beitrag wird die Hardware-Implementierung eines Datenkompressionsverfahrens auf einem FPGA vorgestellt. Das Verfahren wurde speziell für Kompression kurzer Datenblöcke in Flash-Speichern entwickelt. Dabei werden Quelldaten mithilfe eines Encoders komprimiert und mit einem Decoder verlustlos dekomprimiert. Durch die Reduktion der Datenrate kann in Flash-Speichern die Übertragungsdauer zum Lesen und Schreiben reduziert werden. Ebenso ist eine Kompression von Nutzdaten sinnvoll, um zusätzliche Redundanzen für einen Fehlerschutz einfügen zu können, ohne den Gesamtspeicherplatzbedarf zu erhöhen.
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.
This work proposes a lossless data compression algorithm for short data blocks. The proposed compression scheme combines a modified move-to-front algorithm with Huffman coding. This algorithm is applicable in storage systems where the data compression is performed on block level with short block sizes, in particular, in non-volatile memories. For block sizes in the range of 1(Formula presented.)kB, it provides a compression gain comparable to the Lempel–Ziv–Welch algorithm. Moreover, encoder and decoder architectures are proposed that have low memory requirements and provide fast data encoding and decoding.
This letter proposes two contributions to improve the performance of transmission with generalized multistream spatial modulation (SM). In particular, a modified suboptimal detection algorithm based on the Gaussian approximation method is proposed. The proposed modifications reduce the complexity of the Gaussian approximation method and improve the performance for high signal-to-noise ratios. Furthermore, this letter introduces signal constellations based on Hurwitz integers, i.e., a 4-D lattice. Simulation results demonstrate that these signal constellations are beneficial for generalized SM with two active antennas.
Nowadays, most digital modulation schemes are based on conventional signal constellations that have no algebraic group, ring, or field properties, e.g. square quadrature-amplitude modulation constellations. Signal constellations with algebraic structure can enhance the system performance. For instance, multidimensional signal constellations based on dense lattices can achieve performance gains due to the dense packing. The algebraic structure enables low-complexity decoding and detection schemes. In this work, signal constellations with algebraic properties and their application in spatial modulation transmission schemes are investigated. Several design approaches of two- and four-dimensional signal constellations based on Gaussian, Eisenstein, and Hurwitz integers are shown. Detection algorithms with reduced complexity are proposed. It is shown, that the proposed Eisenstein and Hurwitz constellations combined with the proposed suboptimal detection can outperform conventional two-dimensional constellations with ML detection.
Generalised concatenated (GC) codes are well suited for error correction in flash memories for high-reliability data storage. The GC codes are constructed from inner extended binary Bose–Chaudhuri–Hocquenghem (BCH) codes and outer Reed–Solomon codes. The extended BCH codes enable high-rate GC codes and low-complexity soft input decoding. This work proposes a decoder architecture for high-rate GC codes. For such codes, outer error and erasure decoding are mandatory. A pipelined decoder architecture is proposed that achieves a high data throughput with hard input decoding. In addition, a low-complexity soft input decoder is proposed. This soft decoding approach combines a bit-flipping strategy with algebraic decoding. The decoder components for the hard input decoding can be utilised which reduces the overhead for the soft input decoding. Nevertheless, the soft input decoding achieves a significant coding gain compared with hard input decoding.
This work proposes a construction for low-density parity-check (LDPC) codes over finite Gaussian integer fields. Furthermore, a new channel model for codes over Gaussian integers is introduced and its channel capacity is derived. This channel can be considered as a first order approximation of the additive white Gaussian noise channel with hard decision detection where only errors to nearest neighbors in the signal constellation are considered. For this channel, the proposed LDPC codes can be decoded with a simple non-probabilistic iterative decoding algorithm similar to Gallager's decoding algorithm A.
This paper proposes a novel transmission scheme for generalized multistream spatial modulation. This new approach uses one Mannheim error correcting codes over Gaussian or Eisenstein integers as multidimensional signal constellations. These codes enable a suboptimal decoding strategy with near maximum likelihood performance for transmission over the additive white Gaussian noise channel. In this contribution, this decoding algorithm is generalized to the detection for generalized multistream spatial modulation. The proposed method can outperform conventional generalized multistream spatial modulation with respect to decoding performance, detection complexity, and spectral efficiency.
