Morteza Mehrnoush

Signal processing for two dimensional magnetic recording using Voronoi model averaged statistics

This paper considers a signal processing system for two-dimensional magnetic recording (TDMR) employing a random Voronoi grain model. The channel model also includes two-dimensional intersymbol interference (2D-ISI) and additive white Gaussian noise. The system uses a 2D-ISI BCJR detector and irregular repeat-accumulate (IRA) code decoder in a turbo-equalization approach. In order to transfer soft information to the IRA decoder a mapping function based on the 2D-ISI detector soft information statistics is used. Simulations employing the perturbed-bit-centers Voronoi grain model proposed in a previous paper by Hwang et al. show that the proposed system achieves a 6.5% increase in user bits/grain (U/G) and an 11.7 dB SNR gain compared to Hwang et al. Simulation results also indicate that our random Voronoi model is harder to equalize than the Hwang Voronoi model. For the random Voronoi model considered in this paper, the proposed system achieves a density of 0.4422 U/G, corresponding to an areal density of about 8.8 Terabits/in2 on typical magnetic hard disks; this is nearly an order of magnitude better than the best commercially available systems.

Iterative Detection and Decoding for TDMR With 2-D Intersymbol Interference Using the Four-Rectangular-Grain Model

This paper considers detection and error control coding for the 2-D magnetic recording (TDMR) channel modeled with the 2-D four-rectangular-grain model (FRGM). This simple model captures the effects of different 2-D grain sizes and shapes, as well as the TDMR grain overwrite effect. We construct a row-by-row Bahl-Cocke-Jelinek-Raviv-based detector that processes two rows at a time. Simulation results using the same coded bit density and channel code as a previous paper on FRGM detection show gains in user bits per grain of up to 13.4% when the detector and the decoder iteratively exchange soft information, resulting in densities higher than 0.5 user bits per grain under all scenarios simulated. When the proposed detector/decoder operates on coded bits read from a random Voronoi grain model, the achieved density drops to 0.25 user bits per grain due to model mismatch between the detector and the data. Finally, this paper considers an iterative detection and decoding scheme combining TDMR detection, 2-D-intersymbol interference (ISI) detection, and soft-in/soft-out channel decoding in a structure with two iteration loops. Simulation results for the concatenated FRGM and

Proactive spectrum handoff protocol for cognitive radio ad hoc network and analytical evaluation

2 \times 2

Interference mitigation in coexistence of WLAN network with radar

averaging mask ISI channel with 10 dB signal-to-noise ratio show that densities of 0.496 user bits per grain and above can be achieved over the entire range of FRGM grain probabilities.

Turbo Equalization for Two Dimensional Magnetic Recording Using Voronoi Model Averaged Statistics

Spectrum handoff in cognitive radio technology has been emerged as a new method for improving performance of the cognitive radio networks. The authors propose a proactive spectrum handoff protocol based on a single rendezvous (SRV) coordination scheme without common control channel. This protocol utilises a multi-user greedy channel selection method to select the best channel based on minimum service time for the secondary users to achieve a higher aggregate throughput. The authors proposed a novel theoretical analysis to evaluate the aggregate throughput of the secondary users in the proposed protocol. It uses Markov Chain to model the distributed channel selection scheme. The simulation results show the proposed protocol increases the throughput up to 38.7% in comparison with the study by Song and Xie, for 12 secondary users in the network with transmission rates of primary and secondary users equal to 5 (pkt/s) and 500 (pkt/s), respectively.

EXIT chart based IRA code design for TDMR

Coexistence between 802.11 wireless local area network (WLAN) and radars operating in co/adjacent channel scenarios (notably 5 GHz) is a problem of considerable importance that requires new innovations. We propose a modified Wi-Fi link design that mitigates the interference from a pulsed search radar such that the WLAN network continues to operate outside the exclusion region with no noticeable performance degradation. For low density parity check (LDPC) encoding adopted in high throughput WLANs such as 802.11n and .ac, the modified receiver includes a new interleaver and a log-likelihood ratio (LLR) mapping function to successfully mitigate the impact of radar interference. We evaluate, via simulations, the impact of interleaver length and LLR mapping function parameters to determine the optimum combination that yields the desirable frame error rate (FER) performance.

EXIT Chart-Based IRA Code Design for TDMR Turbo-Equalization System

This paper considers turbo equalization for 2-D magnetic recording. Magnetic grains are modeled as Voronoi regions of randomly distributed nuclei. Bits read from the magnetic grain model flow into a 2-D intersymbol interference (2D-ISI) model including additive white Gaussian noise. At high bit densities, some bits are not written on any grain, and hence are effectively “overwritten” by surrounding bits. The proposed system iteratively exchanges log-likelihood ratios (LLRs) between a 2D-ISI equalizer based on the forward-backward algorithm and an irregular repeat-accumulate (IRA) decoder. To combat bit overwrites, the system employs a non-linear function to map 2D-ISI extrinsic output LLRs to IRA decoder input LLRs. To pass back LLRs from the IRA decoder to the 2D-ISI equalizer, we design a simple likelihood-ratio-based LLR estimator. Simulations of the proposed system that employ the perturbed-bit-centers grain model proposed in a 2010 IEEE Transactions on Magnetics paper show a 6.5% increase in user bits per grain (U/G) and a 16.4 dB signal-to-noise ratio (SNR) gain compared with the previous paper, without iterative turbo equalization. Utilizing the LLR estimator to do iterative detection results in SNR gains of up to 1.7 dB compared with non-iterative detection. The random Voronoi model employed in this paper appears to be more difficult to equalize than the grain model in the 2010 paper. The proposed system with random Voronoi model achieves 0.4422 U/G at SNR =11.6 dB, i.e., about 8.8 Tb/in2 at (typically assumed future grain density) 20 Tgr/in2; this is almost ten times the density of current systems at 10 Tgr/in2.

Signal Processing and Coding System for TDMR Data from Grain Flipping Probability Model

We present an extrinsic information transfer (EXIT) chart-based design technique for irregular repeat-accumulate (IRA) codes used in 2-D magnetic recording (TDMR) turbo-equalization systems. The channel model includes Voronoi magnetic grains, 2-D intersymbol interference (2D-ISI) and additive white Gaussian noise (AWGN). The receiver uses a 2D-ISI BCJR equalizer and an IRA decoder. For one outer equalizer-decoder iteration, we propose theory and simulation-based methods for computing EXIT curves. The simulation method calculates experimental EXIT curves for the check node decoder (CND) and the combination of the variable node decoder (VND) and an equalizer. The theoretical approach recursively calculates CND and VND Gaussian mixture model parameters in order to calculate EXIT curves. We then fit the VND and CND EXIT curves to find optimized variable node degree distributions. Simulation results show that the TDMR-optimized IRA codes achieve up to a 6.2% density increase in user bits/grain (U/G) compared with IRA codes designed for AWGN channels. The theory-based code designs achieve the same or better U/G as the simulation-based designs, but require 98% less design computation time. We also derive optimized IRA codes for iterative turbo-equalization; these codes can achieve simultaneous U/G gains and SNR savings compared with AWGN-optimized codes.

Proactive SRV spectrum handoff protocol based on GCS scheme in cognitive radio adhoc network.

This paper presents an extrinsic information transfer (EXIT) chart based irregular repeat-accumulate (IRA) code design technique for a two-dimensional magnetic recording (TDMR) turbo-equalizer that employs a Voronoi magnetic grain model. The channel model also includes two-dimensional inter-symbol interference (2D-ISI) and additive white Gaussian noise (AWGN). At high bit densities (e.g., between 1 and 3 magnetic grains per coded bit (GPB)), occasionally a bit will not be written on any grain, and hence will effectively be “overwritten” (or erased) by bits on surrounding grains. The proposed code design takes into account the statistics of the TDMR channel to decrease overwrite effects. The proposed receiver uses a 2D-ISI BCJR equalizer and IRA decoder in a turbo-equalization approach. To design the IRA code, we find the experimental EXIT chart curves of the check node decoder (CND) and the combination of the variable node decoder (VND) with the 2D-ISI equalizer. We fit the VND and CND EXIT chart curves to find the IRA codes optimized variable node degree distribution for the TDMR channel. Simulation results show that the IRA codes optimized for the TDMR Voronoi grain model achieve up to a 3.3% density increase in user-bits/grain (U/G) compared to IRA codes designed for AWGN channels. At 1.2 GPB, the designed IRA codes achieve densities as high as 0.455 U/G, corresponding to an areal density of about 4.6 Terabits/in2 on typical magnetic hard disks with 10 Teragrains/in2; this is nearly a factor of 5 better than the best commercially available systems.

Association fairness in Wi-Fi and LTE-U coexistence

This paper presents a signal processing and coding system for processing realistic two-dimensional magnetic recording (TDMR) data generated by a grain-flipping probability model. The data set was generated at the Data Storage Institute, Singapore, and will be referred to as the DSI data. Three types of 2-D intersymbol interference (2-D-ISI) detectors with varying complexity are proposed. The first detector is a two-sided feedback (TSF) detector with known boundaries, which simultaneously detects the three tracks of received data. This detector uses only one of the two samples provided by the reader for each bit. The second detector is a joint TSF (JTSF) detector which uses both samples provided by the reader for each bit. The third detector is a 1-D state-input decision-feedback (ODSIDF) detector which detects the data based on a 2-D-ISI mask. Each of the 2-D-ISI detectors is utilized in a turbo iterative approach with an irregular repeat-accumulate (IRA) decoder in the proposed system. We use a coset coding approach in our IRA decoder for decoding the received data based on a known block of the data. A TDMR log-likelihood ratio (LLR) function is used to pass LLRs from the 2-D-ISI detector to the IRA decoder. The read head sensitivity function (2-D-ISI mask) is estimated based on the first sample alone (for TSF and ODSIDF detectors) and both samples (for JTSF detector) from the reader by using the least squares approach based on known data bits for a given set of reader outputs. The best simulation results show that the proposed signal processing and coding approach can achieve up to 1.7 Tb/in2 density at 18 nm track pitch (TP). Using a squeeze margin to account for the imperfect tracking of the track center by the read and write heads in a practical system, the proposed system achieves a realistic areal density of 1.25 Tb/in2 at TP = 18 nm.