The Road From Classical to Quantum Codes: A Hashing Bound Approaching Design Procedure

Powerful Quantum Error Correction Codes (QECCs) are required for stabilizing and protecting fragile qubits against the undesirable effects of quantum decoherence. Similar to classical codes, hashing bound approaching QECCs may be designed by exploiting a concatenated code structure, which invokes iterative decoding. Therefore, in this paper we provide an extensive step-by-step tutorial for designing EXtrinsic Information Transfer (EXIT) chart aided concatenated quantum codes based on the underlying quantum-to-classical isomorphism. These design lessons are then exemplified in the context of our proposed Quantum Irregular Convolutional Code (QIRCC), which constitutes the outer component of a concatenated quantum code. The proposed QIRCC can be dynamically adapted to match any given inner code using EXIT charts, hence achieving a performance close to the hashing bound. It is demonstrated that our QIRCC-based optimized design is capable of operating within 0.4 dB of the noise limit

Analysis and Design of Tuned Turbo Codes

It has been widely observed that there exists a fundamental trade-off between the minimum (Hamming) distance properties and the iterative decoding convergence behavior of turbo-like codes. While capacity achieving code ensembles typically are asymptotically bad in the sense that their minimum distance does not grow linearly with block length, and they therefore exhibit an error floor at moderate-to-high signal to noise ratios, asymptotically good codes usually converge further away from channel capacity. In this paper, we introduce the concept of tuned turbo codes, a family of asymptotically good hybrid concatenated code ensembles, where asymptotic minimum distance growth rates, convergence thresholds, and code rates can be traded-off using two tuning parameters, {\lambda} and {\mu}. By decreasing {\lambda}, the asymptotic minimum distance growth rate is reduced in exchange for improved iterative decoding convergence behavior, while increasing {\lambda} raises the asymptotic minimum distance growth rate at the expense of worse convergence behavior, and thus the code performance can be tuned to fit the desired application. By decreasing {\mu}, a similar tuning behavior can be achieved for higher rate code ensembles.Comment: Accepted for publication in IEEE Transactions on Information Theor

Error-Correction Coding and Decoding: Bounds, Codes, Decoders, Analysis and Applications

Coding; Communications; Engineering; Networks; Information Theory; Algorithm

Kodierungstheorie

[no abstract available

Applications of iterative decoding to magnetic recording channels.

Finally, Q-ary LDPC (Q-LDPC) codes are considered for MRCs. Belief propagation decoding for binary LDPC codes is extended to Q-LDPC codes and a reduced-complexity decoding algorithm for Q-LDPC codes is developed. Q-LDPC coded systems perform very well with random noise as well as with burst erasures. Simulations show that Q-LDPC systems outperform RS systems.Secondly, binary low-density parity-check (LDPC) codes are proposed for MRCs. Random binary LDPC codes, finite-geometry LDPC codes and irregular LDPC codes are considered. With belief propagation decoding, LDPC systems are shown to have superior performance over current Reed-Solomon (RS) systems at the range possible for computer simulation. The issue of RS-LDPC concatenation is also addressed.Three coding schemes are investigated for magnetic recording systems. Firstly, block turbo codes, including product codes and parallel block turbo codes, are considered on MRCs. Product codes with other types of component codes are briefly discussed.Magnetic recoding channels (MRCs) are subject to noise contamination and error-correcting codes (ECCs) are used to keep the integrity of the data. Conventionally, hard decoding of the ECCs is performed. In this dissertation, systems using soft iterative decoding techniques are presented and their improved performance is established