214 research outputs found
Design of Finite-Length Irregular Protograph Codes with Low Error Floors over the Binary-Input AWGN Channel Using Cyclic Liftings
We propose a technique to design finite-length irregular low-density
parity-check (LDPC) codes over the binary-input additive white Gaussian noise
(AWGN) channel with good performance in both the waterfall and the error floor
region. The design process starts from a protograph which embodies a desirable
degree distribution. This protograph is then lifted cyclically to a certain
block length of interest. The lift is designed carefully to satisfy a certain
approximate cycle extrinsic message degree (ACE) spectrum. The target ACE
spectrum is one with extremal properties, implying a good error floor
performance for the designed code. The proposed construction results in
quasi-cyclic codes which are attractive in practice due to simple encoder and
decoder implementation. Simulation results are provided to demonstrate the
effectiveness of the proposed construction in comparison with similar existing
constructions.Comment: Submitted to IEEE Trans. Communication
Lowering the Error Floor of LDPC Codes Using Cyclic Liftings
Cyclic liftings are proposed to lower the error floor of low-density
parity-check (LDPC) codes. The liftings are designed to eliminate dominant
trapping sets of the base code by removing the short cycles which form the
trapping sets. We derive a necessary and sufficient condition for the cyclic
permutations assigned to the edges of a cycle of length in the
base graph such that the inverse image of in the lifted graph consists of
only cycles of length strictly larger than . The proposed method is
universal in the sense that it can be applied to any LDPC code over any channel
and for any iterative decoding algorithm. It also preserves important
properties of the base code such as degree distributions, encoder and decoder
structure, and in some cases, the code rate. The proposed method is applied to
both structured and random codes over the binary symmetric channel (BSC). The
error floor improves consistently by increasing the lifting degree, and the
results show significant improvements in the error floor compared to the base
code, a random code of the same degree distribution and block length, and a
random lifting of the same degree. Similar improvements are also observed when
the codes designed for the BSC are applied to the additive white Gaussian noise
(AWGN) channel
CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning
Wi-Fi-based human activity recognition (HAR) has gained considerable attention recently due to its ease of use and the availability of its infrastructures and sensors. Channel state information (CSI) captures how Wi-Fi signals are transmitted through the environment. Using channel state
information of the received signals transmitted from Wi-Fi access points, human activity can be recognized with more accuracy compared with the received signal strength indicator (RSSI). However, in many scenarios and applications, there is a serious limit in the volume of training data because of
cost, time, or resource constraints. In this study, multiple deep learning models have been trained for HAR to achieve an acceptable accuracy level while using less training data compared to other machine learning techniques. To do so, a pre-trained encoder which is trained using only a limited
number of data samples, is utilized for feature extraction. Then, by using fine-tuning, this encoder is utilized in the classifier, which is trained by a fraction of the rest of the data, and the training is continued alongside the rest of the classifier’s layers. Simulation results show that by using only 50% of the training data, there is a 20% improvement compared with the case where the encoder is not used. We also showed that by using an untrainable encoder, an accuracy improvement of 11% using 50% of the training data is achievable with a lower complexity level
A CSI-based Human Activity Recognition using Canny Edge Detector
Human Activity Recognition (HAR) is one of the hot topics in the field of human-computer interaction. It has a wide variety of applications in different tasks such as health rehabilitation, smart houses, smart grids, robotics, and human action prediction. HAR can be carried out through different approaches such as vision-based, sensor-based, radar-based, and Wi-Fi-based. Due to the ubiquitous and easyto-deploy characteristic of Wi-Fi devices, Wi-Fi-based HAR has gained the interest of both academia and industry in recent years.WiFi-based HAR can be implemented by two channel metrics: Channel State Information (CSI) and Received Signal Strength Indicator (RSSI). Recently, converting the CSI data to images has led to increasing the accuracy level of activity prediction. However, none of the previous research has focused on extracting the features of converted images using image-processing techniques. In this study, we investigate three available datasets, gathered using CSI property, and took advantage of Deep Learning (DL), with convolutional layers and edge detection technique to increase overall system accuracy. The canny edge detector extracts the most important features of the image, and giving it to the DL
model empowers the prediction of activities. In all three datasets, we witnessed an improvement of 5%, 27%, and 37% in terms of accuracy
Binary CEO Problem under Log-Loss with BSC Test-Channel Model
In this paper, we propose an efficient coding scheme for the two-link binary
Chief Executive Officer (CEO) problem under logarithmic loss criterion. The
exact rate-distortion bound for a two-link binary CEO problem under the
logarithmic loss has been obtained by Courtade and Weissman. We propose an
encoding scheme based on compound LDGM-LDPC codes to achieve the theoretical
bounds. In the proposed encoding, a binary quantizer using LDGM codes and a
syndrome-coding employing LDPC codes are applied. An iterative joint decoding
is also designed as a fusion center. The proposed CEO decoder is based on the
sum-product algorithm and a soft estimator.Comment: 5 pages. arXiv admin note: substantial text overlap with
arXiv:1801.0043
Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques
Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers
Constrained Secrecy Capacity of Finite-Input Intersymbol Interference Wiretap Channels
We consider reliable and secure communication over intersymbol interference
wiretap channels (ISI-WTCs). In particular, we first examine the setup where
the source at the input of an ISI-WTC is unconstrained and then, based on a
general achievability result for arbitrary wiretap channels, we derive an
achievable secure rate for this ISI-WTC. Afterwards, we examine the setup where
the source at the input of an ISI-WTC is constrained to be a finite-state
machine source (FSMS) of a certain order and structure. Optimizing the
parameters of this FSMS toward maximizing the secure rate is a computationally
intractable problem in general, and so, toward finding a local maximum, we
propose an iterative algorithm that at every iteration replaces the secure rate
function by a suitable surrogate function whose maximum can be found
efficiently. Although the secure rates achieved in the unconstrained setup are
potentially larger than the secure rates achieved in the constraint setup, the
latter setup has the advantage of leading to efficient algorithms for
estimating achievable secure rates and also has the benefit of being the basis
of efficient encoding and decoding schemes.Comment: 32 pages, 6 figure
- …