37 research outputs found
Coding for Racetrack Memories
Racetrack memory is a new technology which utilizes magnetic domains along a
nanoscopic wire in order to obtain extremely high storage density. In racetrack
memory, each magnetic domain can store a single bit of information, which can
be sensed by a reading port (head). The memory has a tape-like structure which
supports a shift operation that moves the domains to be read sequentially by
the head. In order to increase the memory's speed, prior work studied how to
minimize the latency of the shift operation, while the no less important
reliability of this operation has received only a little attention.
In this work we design codes which combat shift errors in racetrack memory,
called position errors. Namely, shifting the domains is not an error-free
operation and the domains may be over-shifted or are not shifted, which can be
modeled as deletions and sticky insertions. While it is possible to use
conventional deletion and insertion-correcting codes, we tackle this problem
with the special structure of racetrack memory, where the domains can be read
by multiple heads. Each head outputs a noisy version of the stored data and the
multiple outputs are combined in order to reconstruct the data. Under this
paradigm, we will show that it is possible to correct, with at most a single
bit of redundancy, deletions with heads if the heads are
well-separated. Similar results are provided for burst of deletions, sticky
insertions and combinations of both deletions and sticky insertions
Error correction for asynchronous communication and probabilistic burst deletion channels
Short-range wireless communication with low-power small-size sensors has been broadly applied in many areas such as in environmental observation, and biomedical and health care monitoring. However, such applications require a wireless sensor operating in always-on mode, which increases the power consumption of sensors significantly. Asynchronous communication is an emerging low-power approach for these applications because it provides a larger potential of significant power savings for recording sparse continuous-time signals, a smaller hardware footprint, and a lower circuit complexity compared to Nyquist-based synchronous signal processing.
In this dissertation, the classical Nyquist-based synchronous signal sampling is replaced by asynchronous sampling strategies, i.e., sampling via level crossing (LC) sampling and time encoding. Novel forward error correction schemes for sensor communication based on these sampling strategies are proposed, where the dominant errors consist of pulse deletions and insertions, and where encoding is required to take place in an instantaneous fashion. For LC sampling the presented scheme consists of a combination of an outer systematic convolutional code, an embedded inner marker code, and power-efficient frequency-shift keying modulation at the sensor node. Decoding is first obtained via a maximum a-posteriori (MAP) decoder for the inner marker code, which achieves synchronization for the insertion and deletion channel, followed by MAP decoding for the outer convolutional code. By iteratively decoding marker and convolutional codes along with interleaving, a significant reduction in terms of the expected end-to-end distortion between original and reconstructed signals can be obtained compared to non-iterative processing. Besides investigating the rate trade-off between marker and convolutional codes, it is shown that residual redundancy in the asynchronously sampled source signal can be successfully exploited in combination with redundancy only from a marker code. This provides a new low complexity alternative for deletion and insertion error correction compared to using explicit redundancy. For time encoding, only the pulse timing is of relevance at the receiver, and the outer channel code is replaced by a quantizer to represent the relative position of the pulse timing. Numerical simulations show that LC sampling outperforms time encoding in the low to moderate signal-to-noise ratio regime by a large margin.
In the second part of this dissertation, a new burst deletion correction scheme tailored to low-latency applications such as high-read/write-speed non-volatile memory is proposed. An exemplary version is given by racetrack memory, where the element of information is stored in a cell, and data reading is performed by many read ports or heads. In order to read the information, multiple cells shift to its closest head in the same direction and at the same speed, which means a block of bits (i.e., a non-binary symbol) are read by multiple heads in parallel during a shift of the cells. If the cells shift more than by one single cell location, it causes consecutive (burst) non-binary symbol deletions.
In practical systems, the maximal length of consecutive non-binary deletions is limited. Existing schemes for this scenario leverage non-binary de Bruijn sequences to perfectly locate deletions. In contrast, in this work binary marker patterns in combination with a new soft-decision decoder scheme is proposed. In this scheme, deletions are soft located by assigning a posteriori probabilities for the location of every burst deletion event and are replaced by erasures. Then, the resulting errors are further corrected by an outer channel code. Such a scheme has an advantage over using non-binary de Bruijn sequences that it in general increases the communication rate
Codes for Correcting Asymmetric Adjacent Transpositions and Deletions
Codes in the Damerau--Levenshtein metric have been extensively studied
recently owing to their applications in DNA-based data storage. In particular,
Gabrys, Yaakobi, and Milenkovic (2017) designed a length- code correcting a
single deletion and adjacent transpositions with at most
bits of redundancy. In this work, we consider a new setting where both
asymmetric adjacent transpositions (also known as right-shifts or left-shifts)
and deletions may occur. We present several constructions of the codes
correcting these errors in various cases. In particular, we design a code
correcting a single deletion, right-shift, and left-shift errors
with at most bits of redundancy where . In
addition, we investigate codes correcting -deletions, right-shift,
and left-shift errors with both uniquely-decoding and list-decoding
algorithms. Our main contribution here is the construction of a list-decodable
code with list size and with at most bits of redundancy, where . Finally, we construct
both non-systematic and systematic codes for correcting blocks of -deletions
with -limited-magnitude and adjacent transpositions
Quantum Deletion Codes Derived From Quantum Reed-Solomon Codes
This manuscript presents a construction method for quantum codes capable of
correcting multiple deletion errors. By introducing two new alogorithms, the
alternating sandwich mapping and the block error locator, the proposed method
reduces deletion error correction to erasure error correction. Unlike previous
quantum deletion error-correcting codes, our approach enables flexible code
rates and eliminates the requirement of knowing the number of deletions
Improved constructions of permutation and multi-permutation codes correcting a burst of stable deletions
Permutation codes and multi-permutation codes have been widely considered due
to their various applications, especially in flash memory. In this paper, we
consider permutation codes and multi-permutation codes against a burst of
stable deletions. In particular, we propose a construction of permutation codes
correcting a burst stable deletion of length , with redundancy . Compared to the previous known results, our improvement
relies on a different strategy to retrieve the missing symbol on the first row
of the array representation of a permutation. We also generalize our
constructions for multi-permutations and the variable length burst model.
Furthermore, we propose a linear-time encoder with optimal redundancy for
single stable deletion correcting permutation codes.Comment: Accepted for publication in IEEE Trans. Inf. Theor