Coherent Detection of Non-Orthogonal Spectrally Efficient Multicarrier Signals Using a Decision Feedback Algorithm

Introduction. Spectrally efficient frequency division multiplexing (SEFDM) is a promising technology for improving spectral efficiency. Since SEFDM signals transmitted on subcarriers are not orthogonal, interchannel interference occurs due to the mutual influence of signals transmitted on adjacent subcarriers. Algorithms for receiving SEFDM signals can be distinguished into element-by-element coherent detection and maximum-likelihood sequence estimation (MLSE). The former method, although being simpler, is characterized by a low bit error rate performance. The latter method, although providing for a higher energy efficiency, is more complicated and does not allow high absolute message rates.Aim. To consider a trade-off solution to the problem of coherent detection of SEFDM signals under the condition of significant interchannel interference, namely, the use of an iterative algorithm of element-by-element processing with decision feedback at each subcarrier frequency.Materials and methods. Analytical expressions for the operation of a demodulator solver were derived. A simulation model for transmission of SEFDM signals was built in the MatLab environment, including element-by-element detection with decision feedback.Results. The simulation results confirmed the efficiency of the proposed algorithm. For error probabilities p =102…103, the energy gains reach values from 0.2 to 7.5 dB for different values of the non-orthogonal subcarrier spacing. At the same time, the efficiency of the detection algorithm with decision feedback turns out to be significantly lower than that when using the detection algorithm MLSE.Conclusion. The proposed detection algorithm can be used in future generations of mobile communications, which require high transmission rates. By reducing the computational complexity of the algorithm, it is possible to provide for a lower power consumption of mobile devices.

Erroneous pixel prediction for semantic image segmentation

We consider semantic image segmentation. Our method is inspired by Bayesian deep learning which improves image segmentation accuracy by modeling the uncertainty of the network output. In contrast to uncertainty, our method directly learns to predict the erroneous pixels of a segmentation network, which is modeled as a binary classification problem. It can speed up training comparing to the Monte Carlo integration often used in Bayesian deep learning. It also allows us to train a branch to correct the labels of erroneous pixels. Our method consists of three stages: (i) predict pixel-wise error probability of the initial result, (ii) redetermine new labels for pixels with high error probability, and (iii) fuse the initial result and the redetermined result with respect to the error probability. We formulate the error-pixel prediction problem as a classification task and employ an error-prediction branch in the network to predict pixel-wise error probabilities. We also introduce a detail branch to focus the training process on the erroneous pixels. We have experimentally validated our method on the Cityscapes and ADE20K datasets. Our model can be easily added to various advanced segmentation networks to improve their performance. Taking DeepLabv3+ as an example, our network can achieve 82.88% of mIoU on Cityscapes testing dataset and 45.73% on ADE20K validation dataset, improving corresponding DeepLabv3+ results by 0.74% and 0.13% respectively.

Deep space optical communications (DSOC) downlink simulation with varying PPM order

During the course of a typical deep space mission like the Mars Earth mission, there exist a wide range of operating points, due to the different changes in geometry that consequently cause different link budgets in terms of received signal and noise power. These changes include distance range, Sun-Earth-Probe angle, zenith angle and atmospheric conditions. The different operating points, with different losses (background noise, pointing losses and atmospheric losses), lead to different capacities and data rates over the course of a typical deep space mission. Consequently, different engineering parameters are adjusted and optimized to combat some of these varying losses in order to get acceptable data rates and bit error probabilities. This is a useful reason to analyze and simulate various operating conditions that occur with the varying spatial orbital time periods of the resulting received signal power level, noise power level, capacity, data rates and bit error probabilities. This paper details results of simulations of typical deep space optical communication link operation.

Read Reference Calibration and Tracking for Non-Volatile Flash Memories

The performance and reliability of nonvolatile NAND flash memories deteriorate as the number of program/erase cycles grows. The reliability also suffers from cell-to-cell interference, long data retention time, and read disturb. These processes effect the read threshold voltages. The aging of the cells causes voltage shifts which lead to high bit error rates (BER) with fixed predefined read thresholds. This work proposes two methods that aim on minimizing the BER by adjusting the read thresholds. Both methods utilize the number of errors detected in the codeword of an error correction code. It is demonstrated that the observed number of errors is a good measure for the voltage shifts and is utilized for the initial calibration of the read thresholds. The second approach is a gradual channel estimation method that utilizes the asymmetrical error probabilities for the one-to-zero and zero-to-one errors that are caused by threshold calibration errors. Both methods are investigated utilizing the mutual information between the optimal read voltage and the measured error values. Numerical results obtained from flash measurements show that these methods reduce the BER of NAND flash memories significantly.

Performance Errors Influence Voluntary Task Choices

Humans adjust their behavior after they committed an error, but it is unclear whether and how error commissions influence voluntary task choices. In the present article, we review different accounts on effects of errors in the previous trial (transient error effects) and overall error probabilities (sustained error effects) on behavioral adaptation. Based on this review, we derived five statistical models how errors might influence voluntary task choices. We analyzed the data of three experiments in which participants voluntarily selected one of two tasks before each trial whereby task difficulty, and concomitantly error probability, increased successively for the selected/performed tasks. Model comparison suggested that choice behavior was best explained by a combination of error probability of the performed task, error probability of the alternative task, and whether the previous response was correct or incorrect. The results revealed that participants were most likely to switch tasks in situations where the error probability of the performed task was high, the error probability of the alternative task was low, and after an error on the previous trial. We conclude that task selection processes are influenced by transient and sustained error effects.