Euiseok Hwang

Two-track joint detection for two-dimensional magnetic recording (TDMR)

The use of array readers (i.e., multiple read elements positioned to read stored data on multiple adjacent tracks) is one of the recent concepts to improve the areal density of magnetic recording systems. Array readers provide diversity gain to deal with noise and enable the handling of inter-track interference (ITI) as well as multi-track detection. In this paper, an array-reader two-track (AR2T) detection system is studied. We present the design of 2-D equalizer that jointly processes array readback signal streams over two tracks. Such a 2-D equalizer can be designed to approximate a 1-D or a 2-D partial-response (PR) target. It is shown that 2-D PR target can result in less residual ITI. For the detection of signals corresponding to 2-D PR target, we propose a novel symbol-based detection algorithm that processes two input signal streams jointly. We also implement a pattern-dependent noise-predictive (PDNP) version of proposed detector. Simulation results show that more than 4 dB performance gain can be achieved by the AR2T detection system compared to the traditional single-reader single-track detection system under aggressive track density conditions.

Secure Multiple Access Based on Multicarrier CDMA With Induced Random Flipping

In this paper, we study secure multiple access based on multicarrier (MC) code division multiple access (CDMA) systems with induced random (chip) flipping of spreading sequence, which is suitable for machine-type communications (MTC). In MTC, devices may use low-cost hardware-based random number generators for pseudo-random noise (PN) sequences in secure communications. Since PN sequences can be estimated by correlation attacks at an eavesdropper, induced random (chip) flipping of spreading sequences is proposed to make correlation attacks infeasible. However, induced random flipping can also degrade the performance of detection and decoding at a legitimate receiver that only knows original spreading sequences. To avoid the performance degradation, we derive low-complexity iterative receiver algorithms for multiuser signal detection and decoding based on the expectation-maximization (EM) algorithm.

Advanced channel signal processing for multi-track or multi-wordline data storage systems

In this paper, advanced channel signal processing schemes are proposed for multi-track or multi-wordline read architecture in the future hard disk drive and NAND flash memory systems. As the capacity increases with aggressive scaling of the data bit or memory cell, the interference or coupling between neighboring information causes severe inter-track (ITI) or inter-cell interference (ICI), and advanced signal processing accounting the neighboring tracks or wordlines is needed to alleviate the effect of these interference. In magnetic recording (MR), array-reader-based MR (ARMR) technology has shown a potential gain in the areal density capability (ADC) and the data transfer rate by employing multi-track channel signal processing, however skew effect causes complications in reliable data retrieval by widely varying the reader to reader distances. In thins study, a novel multi-track read channel signal processing approach that differentiates linear densities of jointly processing tracks is suggested and numerically evaluated. For an ARMR with the 3 reader 2 track setup shows around 4% of potential ADC gain over the constant linear density configuration under aggressive ITI condition, providing the doubled read throughput. Likewise, the multi-wordline signal processing schemes are investigated for NAND flash memory channel under severe ICI. Joint equalization can be modified for processing two or more wordlines soft information together, while it requires additional read operations with a channel customized set of slicing levels. Evaluations with numerical flash channel model show that the joint processing of two wordlines provides bit error rate (BER) performance gain around 0.1 order with 6 additional read operations per wordline.

Connected electric vehicles for flexible vehicle-to-grid (V2G) services

In this study, flexible vehicle-to-grid (V2G) coordination schemes are proposed for office buildings equipped with electric vehicle (EV) charging stations. EVs can be connected to electric grid during the rest hours and have a potential to provide V2G or vehicle-to-building (V2B) services such as electric load distributions and demand responses. Especially for smart buildings, the charging stations can be operated efficiently by integrating distributed energy resources (DER) such as Photovoltaic (PV) and battery energy storage systems (BESS). In this scenario, the charging coordination problem can be simplified as the integer linear programming (ILP), by assuming constant-rate charging with known schedules of visiting EVs at the station. Numerical evaluations are conducted with the buildings expected daily electricity load, PV generation, and electricity price datasets. Public service buildings are investigated for flexible V2G supports, based on the actual vehicles in and out patterns of the local office. The proposed coordination scheme shows potential gain of 14.3% of energy cost reduction compared to the first-come first-serve approach, under medium sized smart building scenario integrated with the PV and BESS.

Interlaced Magnetic Recording

In this paper, an alternative magnetic recording architecture and corresponding signal processing schemes are presented, named interlaced magnetic recording (IMR). Tracks are recorded in an interlaced manner with different linear densities, which provides favorable tradeoff between areal density capability (ADC) and update-in-place write overhead. By predefining the recording order, tracks are under either double-sided squeeze or non-squeeze, and average ADC can be optimized by differentiating the linear density. Numerical evaluations with a micro-pixelated magnetic channel model show the ADC gain of IMR over conventional perpendicular magnetic recording (PMR), while manageable rewrite overhead compared with the shingled magnetic recording (SMR). For example, IMR provides 4.21% higher ADC over PMR near 1 Tb/in2 channel density scenario, while requires at most 1 rewrite overhead, negligible compared with the typical update overhead in SMR. Writing and reading of IMR can be efficiently managed by aggregating interlaced tracks as sub-zones and switching the channel configurations.

Poster: Electric vehicle network bidirectional charging for flexible vehicle-to-grid services

For flexible vehicle-to-grid (V2G) supports with a large scale electric vehicle (EV) network, a novel charging scheme is proposed in this study with a bidirectional constant-rate charging coordination (BCCC). Clustering EVs and coordinating their charging profiles have significant impact on overall V2G benefits however it is difficult to solve in general due to the uncertainty in EVs mobility. In the proposed scheme, minimizing the operation cost for EVs or EV charging stations can be formulated by the binary integer linear programming (ILP) by fixing the charging and discharging rates. BCCC also provides simple estimation of the battery wear cost from V2G supports, to inform the net benefit to EV owners. In numerical simulations for a residential complex charging station, 50 EVs are jointly coordinated to minimize the net charging costs, where BCCC provides ∼ 50

Hybrid Day-ahead Load Forecasting with Atypical Residue based Gaussian Process Regression

/day benefit over the uncoordinated first-come first-serve (FCFS) strategy.

Mitigation of Noise Correlation for Two-Dimensional Detection in Array-Reader Based Magnetic Recording

The prediction accuracy of electric power consumption plays a crucial role for the efficiency of a smart grid. Hybrid approaches that jointly account for the linear and nonlinear portions of the electric load have shown promising performance because of the mixture of memory effects and random environmental perturbations. Especially for day-ahead short-term prediction, the potentially long time gap between the measurements and prediction point degrades the linear prediction performance, while the nonlinear prediction based on the weather forecast may supplement the degradation. This paper proposes a residue-based hybrid model that uses linear prediction by auto-regressive modeling and nonlinear prediction by Gaussian process regression with atypical residue of the weather forecast, particularly the difference of weather station forecasted and linear predicted local temperatures. Since the typical memory effect of the temperature can be double counted by both models, atypical residue without its linear prediction contribution is employed for the Gaussian process regression step. To verify the performance of the proposed scheme, a GIST campus electric power consumption dataset is evaluated. As expected, the linear prediction residue shows larger correlation to the atypical residue of temperature than the temperature itself. Consequently, hybrid model with the atypical residue temperature based Gaussian process regression shows improved performance in the day ahead load prediction.

Secure multicarrier DS/SS with induced random flipping

Array-reader based two-dimensional magnetic recording (TDMR) is considered as a key technology for next-generation magnetic recording due to its advantages such as diversity gain against noise and inter-track interference suppression. In this work, a three-reader two-track (3R2T) system is studied where two two-dimensional equalizers and a joint detector are used to detect data from two tracks. Analysis shows strong cross-correlation between the noise components at the two equalizers outputs. Two approaches are presented to handle the cross-correlation in the equalizer and detector, respectively. Simulation results demonstrate that significant performance gain can be achieved using the two proposed approaches.

Cost benefit analysis of public service electric vehicles with vehicle-to-grid (V2G) capability

For secure transmissions, we consider direct sequence/spread spectrum (DS/SS) systems where pseudo-random (PN) sequences are used for spreading. To generate PN sequences, linear feedback shift registers (LFSRs) are employed, which are also used to generate keystreams in stream ciphers. For steam ciphers that use LFSRs, there are known attacks including correlation attacks that can regenerate keystreams. Since those attacks can also be used to regenerate spreading sequences, in this paper, in order to mitigate this problem, we consider induced random (chip) flipping of spreading sequences. While perturbed spreading sequences make correlation attacks infeasible, a legitimate receiver also suffers from random flipping. To take advantage of known spreading sequences for good performances, the maximum likelihood (ML) detection can be used, which is, however, computationally prohibitive as the complexity grows exponentially with the processing gain. To avoid this difficulty, we derive an expectation-maximization (EM) algorithm to perform the ML detection with low computational complexity in this paper.