369 research outputs found
Performance evaluation for ML sequence detection in ISI channels with Gauss Markov Noise
Inter-symbol interference (ISI) channels with data dependent Gauss Markov
noise have been used to model read channels in magnetic recording and other
data storage systems. The Viterbi algorithm can be adapted for performing
maximum likelihood sequence detection in such channels. However, the problem of
finding an analytical upper bound on the bit error rate of the Viterbi detector
in this case has not been fully investigated. Current techniques rely on an
exhaustive enumeration of short error events and determine the BER using a
union bound. In this work, we consider a subset of the class of ISI channels
with data dependent Gauss-Markov noise. We derive an upper bound on the
pairwise error probability (PEP) between the transmitted bit sequence and the
decoded bit sequence that can be expressed as a product of functions depending
on current and previous states in the (incorrect) decoded sequence and the
(correct) transmitted sequence. In general, the PEP is asymmetric. The average
BER over all possible bit sequences is then determined using a pairwise state
diagram. Simulations results which corroborate the analysis of upper bound,
demonstrate that analytic bound on BER is tight in high SNR regime. In the high
SNR regime, our proposed upper bound obviates the need for computationally
expensive simulation.Comment: This paper will appear in GlobeCom 201
A Two-Phase Maximum-Likelihood Sequence Estimation for Receivers with Partial CSI
The optimality of the conventional maximum likelihood sequence estimation
(MLSE), also known as the Viterbi Algorithm (VA), relies on the assumption that
the receiver has perfect knowledge of the channel coefficients or channel state
information (CSI). However, in practical situations that fail the assumption,
the MLSE method becomes suboptimal and then exhaustive checking is the only way
to obtain the ML sequence. At this background, considering directly the ML
criterion for partial CSI, we propose a two-phase low-complexity MLSE
algorithm, in which the first phase performs the conventional MLSE algorithm in
order to retain necessary information for the backward VA performed in the
second phase. Simulations show that when the training sequence is moderately
long in comparison with the entire data block such as 1/3 of the block, the
proposed two-phase MLSE can approach the performance of the optimal exhaustive
checking. In a normal case, where the training sequence consumes only 0.14 of
the bandwidth, our proposed method still outperforms evidently the conventional
MLSE.Comment: 5 pages and 4 figure
A communications system perspective for dynamic mode atomic force microscopy, with applications to high-density storage and nanoimaging
In recent times, the atomic force microscope (AFM) has been used in various fields like biology, chemistry, physics and medicine for obtaining atomic level images. The AFM is a high-resolution microscope which can provide the resolution on the order of fractions of a nanometer. It has applications in the field of material characterization, probe based data
storage, nano-imaging etc. The prevalent mode of using the AFM is the static mode where the cantilever is in continuous contact with the sample. This is harsh on the probe and the sample. The problem of probe and sample wear can be partly addressed by using the dynamic mode operation with the high quality factor cantilevers. In the dynamic mode operation, the cantilever is forced sinusoidally using a dither piezo. The oscillating cantilever gently taps the sample which reduces the probe-sample wear. In this dissertation, we demonstrate that viewing the dynamic mode operation from a communication systems perspective can yield huge gains in nano-interrogation speed and fidelity.
In the first part of the dissertation, we have considered a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per square inch. The usage of the static mode is harsh on the probe and the media. In this work, the high quality factor dynamic mode operation, which alleviates the probe-media wear, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state. We demonstrate an appropriate level of abstraction of this complex nanoscale system that obviates the need for an involved physical model. Next, a solution to the maximum likelihood sequence detection problem based on the Viterbi algorithm is devised. Experimental and simulation results demonstrate that the performance of this detector is several orders of magnitude better than the performance of other existing schemes.
In the second part of the dissertation, we have considered another interesting application of the dynamic mode AFM in the field of nano-imaging. Nano-imaging has played a vital role in biology, chemistry and physics as it enables interrogation of material with sub-nanometer
resolution. However, current nano-imaging techniques are too slow to be useful in the high speed applications of interest such as studying the evolution of certain biological processes over time that involve very small time scales. In this work, we present a high speed one-bit imaging technique using the dynamic mode AFM with a high quality factor cantilever. We propose a communication channel model for the cantilever based nano-imaging system. Next, we devise an imaging algorithm that incorporates a learned prior from the previous scan line while detecting the features on the current scan line. Experimental results demonstrate that our proposed algorithm provides significantly better image resolution compared to current nano-imaging techniques at high scanning speed.
While modeling the probe-based data storage system and the cantilever based nano-imaging system, it has been observed that the channel models exhibit the behavior similar to intersymbol-interference (ISI) channel with data dependent time-correlated noise. The Viterbi algorithm can be adapted for performing maximum likelihood sequence detection in such channels. However, the problem of finding an analytical upper bound on the bit error rate of the Viterbi detector in this case has not been fully investigated. In the third part of the dissertation, we have considered a subset of the class of ISI channels with data dependent Gauss-Markov noise. We derive an upper bound on the pairwise error probability (PEP) between the transmitted bit sequence and the decoded bit sequence that can be expressed as a product of functions depending on current and previous states in the (incorrect) decoded sequence and the (correct) transmitted sequence. In general, the PEP is asymmetric. The average BER over all possible bit sequences is then determined using a pairwise state diagram. Simulations results demonstrate that analytic bound on BER is tight in high SNR regime
Two dimensional signal processing for storage channels
Over the past decade, storage channels have undergone a steady increase in capacity.
With the prediction of achieving 10 Tb/in2 areal density for magnetic recording
channels in sight, the industry is pushing towards di erent technologies for
storage channels. Heat-assisted magnetic recording, bit-patterned media, and twodimensional
magnetic recording (TDMR) are cited as viable alternative technologies
to meet the increasing market demand. Among these technologies, the twodimensional
magnetic recording channel has the advantage of using conventional
medium while relying on improvement from signal processing. Capacity approaching
codes and detection methods tailored to the magnetic recording channels are
the main signal processing tools used in magnetic recording. The promise is that
two-dimensional signal processing will play a role in bringing about the theoretical
predictions.
The main challenges in TDMR media are as follows: i) the small area allocated
to each bit on the media, and the sophisticated read and write processes in shingled
magnetic recording devices result in signi cant amount of noise, ii) the twodimensional
inter-symbol interference is intrinsic to the nature of shingled magnetic
recording. Thus, a feasible two-dimensional communication system is needed to
combat the errors that arise from aggressive read and write processes.
In this dissertation, we present some of the work done on signal processing aspect
for storage channels. We discuss i) the nano-scale model of the storage channel,
ii) noise characteristics and corresponding detection strategies, iii) two-dimensional
signal processing targeted at shingled magnetic recording
An Information Theoretic Charachterization of Channel Shortening Receivers
Optimal data detection of data transmitted over a linear channel can always
be implemented through the Viterbi algorithm (VA). However, in many cases of
interest the memory of the channel prohibits application of the VA. A popular
and conceptually simple method in this case, studied since the early 70s, is to
first filter the received signal in order to shorten the memory of the channel,
and then to apply a VA that operates with the shorter memory. We shall refer to
this as a channel shortening (CS) receiver. Although studied for almost four
decades, an information theoretic understanding of what such a simple receiver
solution is actually doing is not available.
In this paper we will show that an optimized CS receiver is implementing the
chain rule of mutual information, but only up to the shortened memory that the
receiver is operating with. Further, we will show that the tools for analyzing
the ensuing achievable rates from an optimized CS receiver are precisely the
same as those used for analyzing the achievable rates of a minimum mean square
error (MMSE) receiver
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