Likelihood-based Research Articles

A Survey of Blind Modulation Classification Techniques for OFDM Signals.

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels

Automatic modulation classification (AMC) is a significant part of cognitive communication systems. In early researches, likelihood-based (LB) and feature-based (FB) solutions were proposed for the AMC problem. With the developments in the data-driven approaches, a third method based on deep learning (DL) has recently gained prominence among AMC researchers. It is shown that convolutional neural network based classifiers are very efficient in the AMC for both single input single output (SISO) and multiple-input multiple-output (MIMO) systems. However, for most of the works in MIMO-AMC, the channel considered is full rank. This work addresses the problem of AMC over rank deficient channels such as a keyhole channel using a DL-based classifier. The proposed DL-classifier utilizes a CNN, which does not employ pooling layers or dropouts in the convolutional layers. To further improve the classification accuracy, decision cooperation as well as feature fusion is employed. In addition to the keyhole effect, this work investigates the effect of antenna correlation on DL-based AMC. A comparative study of the proposed method and the existing FB AMC method for the MIMO keyhole channel is also presented.

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

SummaryAutomatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency‐selective multipath fading, and time‐varying channels in real‐world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood‐based (LB) and feature‐based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal‐to‐noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC.

Multiple imputation strategies for a bounded outcome variable in a competing risks analysis.

In patient follow‐up studies, events of interest may take place between periodic clinical assessments and so the exact time of onset is not observed. Such events are known as “bounded” or “interval‐censored.” Methods for handling such events can be categorized as either (i) applying multiple imputation (MI) strategies or (ii) taking a full likelihood‐based (LB) approach. We focused on MI strategies, rather than LB methods, because of their flexibility. We evaluated MI strategies for bounded event times in a competing risks analysis, examining the extent to which interval boundaries, features of the data distribution and substantive analysis model are accounted for in the imputation model. Candidate imputation models were predictive mean matching (PMM); log‐normal regression with postimputation back‐transformation; normal regression with and without restrictions on the imputed values and Delord and Genin's method based on sampling from the cumulative incidence function. We used a simulation study to compare MI methods and one LB method when data were missing at random and missing not at random, also varying the proportion of missing data, and then applied the methods to a hematopoietic stem cell transplantation dataset. We found that cumulative incidence and median event time estimation were sensitive to model misspecification. In a competing risks analysis, we found that it is more important to account for features of the data distribution than to restrict imputed values based on interval boundaries or to ensure compatibility with the substantive analysis by sampling from the cumulative incidence function. We recommend MI by type 1 PMM.

Likelihood-based estimation of the effective population size using temporal changes in allele frequencies: a genealogical approach.

A new genetic estimator of the effective population size (N(e)) is introduced. This likelihood-based (LB) estimator uses two temporally spaced genetic samples of individuals from a population. We compared its performance to that of the classical F-statistic-based N(e) estimator (N(eFk)) by using data from simulated populations with known N(e) and real populations. The new likelihood-based estimator (N(eLB)) showed narrower credible intervals and greater accuracy than (N(eFk)) when genetic drift was strong, but performed only slightly better when genetic drift was relatively weak. When drift was strong (e.g., N(e) = 20 for five generations), as few as approximately 10 loci (heterozygosity of 0.6; samples of 30 individuals) are sufficient to consistently achieve credible intervals with an upper limit <50 using the LB method. In contrast, approximately 20 loci are required for the same precision when using the classical F-statistic approach. The N(eLB) estimator is much improved over the classical method when there are many rare alleles. It will be especially useful in conservation biology because it less often overestimates N(e) than does N(eLB) and thus is less likely to erroneously suggest that a population is large and has a low extinction risk.