Paper proposes a new detector for SSDs with ICI, reducing bit error rates significantly.
problem Reducing bit error rate in SSDs with inter-cell interference.
method Proposes a novel channel model and uses sum-product algorithm for accurate posterior distributions, then passes them to a soft LDPC decoder. Introduces an iterative scheme to improve performance in quantized cases.
result Significant reduction in bit error rate, reaching 99.99% in simulations with strong diagonal coupling.
High error rates improve neural network performance and reduce power consumption.
problem Training neural networks with high Bit Error Rates (BERs).
method Trained three Binarized Convolutional Neural Network architectures on various datasets with high BERs.
result High BERs do not significantly degrade test accuracy, enabling more efficient hardware.
End-to-end deep learning boosts IM/DD fiber communication over dispersive channels.
problem Improving data transmission over dispersive IM/DD channels with memory.
method Bidirectional recurrent neural network (BRNN) for end-to-end deep learning of the communication system.
result End-to-end SBRNN achieves significant bit-error-rate reduction compared to FFNNs.
RFML systems are vulnerable to adversarial attacks, especially in OTA transmissions.
problem Vulnerability of RFML systems to adversarial machine learning attacks.
method Differentiated adversarial threats, developed methodology for evaluating vulnerabilities, used Fast Gradient Sign Method.
result RFML systems are vulnerable to adversarial examples, even in OTA attacks.
Improves bit error tolerance in RRAM-based BNNs without overfitting.
problem Bit errors in RRAM-based BNNs reduce accuracy and overfit to training error rates.
method Proposes straight-through gradient approximation and a novel regularizer.
result Improves BNNs' robustness to bit errors without overfitting.
Constrained sequence codes have been widely used in modern communication and data storage systems. Sequences encoded with constrained sequence codes satisfy constraints imposed by the physical channel, hence enabling efficient and reliable transmission of coded symbols. Traditional encoding and decoding of constrained …
In this paper, deep neural network (DNN) is utilized to improve the belief propagation (BP) detection for massive multiple-input multiple-output (MIMO) systems. A neural network architecture suitable for detection task is firstly introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed based on two m…
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than the…
Paper tackles invariance of demodulation in shallow water acoustic communications.
problem Frequency-selective signal distortion (Doppler effect) in shallow water environments.
method Developed ML-based demodulation methods using DBN-NN and DBN-CNN.
result Demonstrated invariance of the proposed method to Doppler effect with 2dB error margin.
Improved WBP decoding with simple scaling and SNR adaptation.
problem Efficiently decoding weighted Tanner graphs with reduced complexity.
method Simple-scaling models with machine learning for edge weights, and parameter adapter networks.
result Simple scaling with few parameters can achieve near-maximum-likelihood performance.
Adversarial machine learning hides 5G communications from eavesdroppers.
problem Hiding 5G communications from eavesdroppers using DL classifiers.
method Craft adversarial perturbations to fool DL classifiers.
result Adversarial perturbations can hide 5G communications with high probability.
This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption, but makes accurate channel estimation and…
In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applyin…
Deep learning improves SCMA decoding and codebook construction for 5G wireless networks.
problem Designing low complexity high accuracy decoding algorithms and constructing optimal SCMA codebooks.
method Proposed a deep neural network (DNN) called DL-SCMA and an autoencoder (AE-SCMA) to learn and reconstruct SCMA modulated signals.
result Deep learning SCMA decoder outperforms conventional algorithms in BER, SER, and computational complexity.
Paper tackles blind detection of molecular signals in non-coherent media.
problem Long-tail channel response causes ISI, deteriorating detection performance.
method Develops a high-dimensional non-coherent scheme combining different non-coherent metrics.
result Higher dimensionality in metric space achieves lower bit error rate (BER).
New MIMO constellation design for noncoherent communications reduces hardware complexity.
problem Designing efficient MIMO constellations for noncoherent communications over fading channels.
method Geodesic curves of the Grassmann manifold for structured constellation design.
result Achieves comparable error performance to unstructured designs with reduced hardware complexity.
Deep task-based quantization improves MIMO signal processing.
problem Improving performance of MIMO signal processing with scalar ADCs.
method Data-driven task-oriented quantization using deep learning.
result Deep task-based quantization can approach optimal performance limits.
For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates …
Optimizes QoS in FSO links over South Africa using ensemble learning.
problem Impact of weather on QoS in FSO links.
method Ensemble learning models (Random Forest, ADaBoost Regression, Stacking Regression, Gradient Boost Regression, Multilayer Neural Network) applied to meteorological data.
result Significant enhancement in QoS with RMSE and R-squared values of 0.0032 and 0.9906 respectively at George.