Exploiting Gaussian Mixture Model Clustering for Full-Duplex Transceiver Design

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital domain channel estimations and SIC. The frame structure of the designed FDT contains two successive phases: labelling phase and data transmission phase. In particular, the designed FDT performs cluster labelling in the labelling phase and performs GMM clustering based on expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the labelling symbol design is optimized for the overhead reduction and clustering accuracy improving. Finally, simulation results show that, the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge, meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) channel estimator.

In full-duplex orthogonal frequency-division multiplexing systems with IQ imbalances, a frequency-domain least-squares (FD-LS) channel estimator is proposed to estimate both source-to-destination (intended) and destination-to-destination (self-interference) channels. Subsequently, an optimal closed-form pilot matrix is derived to minimize the sum of mean square errors (sum-MSE) of the proposed FD-LS channel estimator. Then, an improved FD-LS estimator is presented and proved to further improve the performance of the FD-LS by exploiting the time-domain property of the channel. In the presence of channel estimation error, an optimal low-complexity maximum likelihood (ML) detector is developed by using eigenvalue and singular value decompositions and whitening the residual self-interference plus noise. Simulation results show that given the proposed FD-LS estimator, the optimal pilot matrix performs much better than those nonsingular pilot matrices with larger conditional numbers (CN). To be specific, the former achieves about 10-dB signal-to-noise ratio (SNR) gain over the latter with CN = 10, the improved FD-LS channel estimator provides about 6-dB SNR gain over the FD-LS estimator at a fixed bit error rate (BER), and the proposed whitening-filter ML detector performs at least 0.6 dB better than the conventional ML detector at a given BER of $10^{-3}$ in the medium-and high-SNR regions.

We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

In this work we propose a strategy to jointly estimate activity and borders of oncological lesions in PET/CT. The starting step is constituted by a lesion contouring on PET image volume and by a Gaussian Mixture Model (GMM) clustering which individuates a set of regions in the lesion area (lesion, uncertainty, lesion spillout, organ). A maximum likelihood (A WOSEM) reconstruction step refines regions' borders and estimates a mean convergence activity for the lesion region. It applies a model of the scanner Point Spread Function (PSF) to recover blurring and it contemporaneously works on regional basis functions and single voxels. The area outside the four regions is frozen (i.e. not updated). The algorithm was validated on an anthropomorphic phantom in which lesions have been simulated with zeolites (clinoptilolite samples, volume 0.6 - 5.2 ml) loaded with 18F-FDG. Zeolite borders for ground truth definition were derived segmenting zeolites on coregistered CT images. For each zeolite, three different initial contouring were considered, corresponding to volumes about 100%, 60% and 140% of the true volume: the GMM clustering was able to robustly delineate regions independently from the initial contouring (variations 0.75) and in estimating zeolite activity (activity error 1ml). Suboptimal results were found for zeolites at the border of the axial FOV, since the PSF model, supposed invariant to axial shift, was inadequate at the axial borders. The proposed strategy appears promising and can be proposed as a general approach for a semi-automatic quantification and segmentation of lesions previously detected on standard clinical images. It will be further validated on data sets provided with a ground truth.

This study aimed to assess the impact and vulnerability of climate change by classifying 26 clusters of meteorologically homogeneous regions. To determine the optimal clustering method, both K-means and Gaussian Mixture Model (GMM) clustering were analyzed using the effective storage capacity to watershed area ratio. The optimal number of clusters was derived based on several evaluation metrics, including the Silhouette Score, Calinski-Harabasz Index, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Ultimately, GMM clustering was identified as the optimal method, with the best clustering results obtained at k = 4 for an effective storage capacity of 100,000 to 400,000 tons and k = 5 for an effective storage capacity of 400,000 to 10,000,000 tons. Additionally, standard reservoirs applicable to agricultural production infrastructure design standards were identified based on homogeneous weather region clusters, the optimal clustering method, and centroid results. The findings of this study can serve as fundamental data for the development and revision of design standards, contributing to more climate-resilient agricultural infrastructure.

In this paper a comparison is made between the convergence and tracking properties of Least Squares (LS) and Least Mean Squares (LMS) algorithms as high frequency (HF) channel estimators. Theoretical results are derived for the asymptotic error achieved by the LS algorithms under white-input conditions in the HF channel. This result is more accurate than previous analyses of LS algorithms in a nonstationary enviroment [5,8,9]. Utilising a state space definition of the channel model a minimum variance Kalman estimator is derived using the a-priori knowledge of the parameters which define the Markov process.

우리나라 식생활에 밀접한 관련을 가지고 있는 채소인 양파의 수급불균형 해결을 위한 생산량 예측 모형 개발의 노력이 많은 연구를 통해 이뤄지고 있다. 하지만 양파의 수확기와 저장 가능성을 고려해 봤을 때 생산량 예측만으로는 수급불균형 해결이 어렵다. 따라서 본 논문에서는 양파의 생산량 정보와 가격의 다양한 요인이 포함되어 있으며 일상에서 쉽게 접할 수 있는 인터넷 기사를 이용하여 가격 예측을 위한 감성사전을 구축하고자 한다. 양파 기사는 2012년부터 2016년까지의 데이터를 사용하였고 도매시장 가격을 통한 문서구분을 통해 4가지 TF-IDF를 비교하여 적합한 TF-IDF를 사용하였다. 분석을 위하여 분할적 군집분석 중 k-means 군집, 밀도기반군집(DBSCAN; density based spatial cluster applications with noise), 가우시안혼합분포군집(GMM; Gaussian mixture model) 군집을 통하여 가격에 대한 긍정/부정 단어를 구분한 결과 GMM 군집이 의미 있는 긍정, 부정, 무정의 3개의 사전으로 구성되었다. 구축된 사전의 합리성을 비교하기 위하여 가격 상승 기사와 가격 하락 기사의 분류에 로지스틱 회귀분석을 적용한 결과 85.7%의 정확도로 구축된 사전의 합리성을 확인할 수 있었다.Many researches are accomplished as a result of the efforts of developing the production predicting model to solve the supply imbalance of onions which are vegetables very closely related to Korean food. But considering the possibility of storing onions, it is very difficult to solve the supply imbalance of onions only with predicting the production. So, this paper s purpose is trying to build a sentiment dictionary to predict the price of onions by using the internet articles which include the informations about the production of onions and various factors of the price, and these articles are very easy to access on our daily lives. Articles about onions are from 2012 to 2016, using TF-IDF for comparing with four kinds of TF-IDFs through the documents classification of wholesale prices of onions. As a result of classifying the positive/negative words for price by k-means clustering, DBSCAN (density based spatial cluster application with noise) clustering, GMM (Gaussian mixture model) clustering which are partitional clustering, GMM clustering is composed with three meaningful dictionaries. To compare the reasonability of these built dictionary, applying classified articles about the rise and drop of the price on logistic regression, and it shows 85.7% accuracy.

A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency partition multiplexing (OFDM) modulation technique can achieve reliable high data rate show over broadband wireless channels.Channel state information for both single-input single-output (SISO) and MIMO systems based on pilot aided arrangement is investigated in this paper.The estimate of channel at pilot frequencies with conventional Least Square (LS) and Minimum Mean Square (MMSE) estimation algorithms is carried out through Matlab simulation.For outdoor communicationscenarios, where wireless channels are sparse in nature, pathinterruptions of different transmit-receive antenna couples share a jointsparse decoration due to the spatial correlation of MIMO channels.By simultaneously exploiting those MIMOchannel characteristics, the proposed scheme performs better thanpresent state-of-the-art systems.Furthermore, by joint processingof signals associated with different antennas, the pilot overheadcan be reduced the performance of MIMO OFDM and SISO OFDM are assessed on the basis of Bit Error Rate (BER) and Mean Square Error (MSE) level.Further enhancement of presentation can be attained through maximum diversity Space Time Block Coding and Maximum Likelihood Detection at transmission and reception ends respectively.MMSE estimation has been shown to perform much better than LS but is more complex than LS for the MIMO system using pilot carriers.

This paper is focusing on the performance of the proposed exponentially weight recursive least square (EW-RLS) and normalized least mean square (NLMS) channel estimator using simulations, in the context of the communication system with MIMO antenna operating in outdoor environments. Different training rates and different Doppler frequencies are used to track time-variations of the channel. Three adaptive algorithms are considered; namely the least squares (LS), RLS algorithm and the least mean square (LMS) algorithm. In channel estimation, RLS is widely used especially for slow time-varying. Even though RLS is little bit complex compare to LMS, but it has faster convergence speed. The performance is evaluated in terms of the mean square error (MSE) of the channel estimate, and the system bit error rate (BER), for different Doppler frequencies (correspond to different mobility speeds). Simulation results have demonstrated that time-domain adaptive channel estimation and tracking in MIMO OFDM systems based on the decision directed (DD) EW-RLS and DD-NLMS is very effective in slowly to moderate time-varying fading channels. This paper provides analysis, evaluation and computer simulations in MATLAB.

In wireless full-duplex communication systems, selfinterference (SI) cancellation is a key technique to ensure simultaneous transmission and reception on the same carrier frequency. During the SI cancellation, it has been shown that the oscillator phase noise dramatically affects the cancellation capability and thus needs consideration and suppression. In this paper, a novel time-domain digital SI cancellation approach is proposed to cancel the transmitter and receiver phase noises along with the incoming SI signal, without estimating either the SI channel response or the phase noise associated coefficients. For proofof-concept evaluation, simulations are performed on orthogonal frequency division multiplexing (OFDM)-modulated signals of 20 MHz bandwidth with 16 QAM constellations to verify the effectiveness of the proposed method. It is demonstrated that the proposed approach provides at least 3 dB improvement on the SI cancellation capability and at least 5 dB increase on the interference tolerance over the conventional cancellation approach that uses the frequency domain SI channel estimation.

A low complexity maximum likelihood (ML) detection method is proposed for the square quadrature amplitude modulation (QAM) constellation. In order to reduce the implementation complexity of ML detection, set-partition, subset-selection, and subset-shift are recursively applied to the square QAM constellation. The estimated square QAM symbol is finally given in a closed form as a function of the received signal and the estimated channel. Complexity analysis and numerical simulation confirm that the proposed detection method reduces the implementation complexity substantially and achieves exactly the same performance as the conventional ML detection irrespective of the channel estimation error.

Research on FT-transformer-based intelligent discrimination model for roof stability classification of retreating coal roadways

This thesis investigates the integration of machine learning techniques into computational fluid dynamics (CFD) to improve simulation accuracy and computational performance. It proposes the use of the Gaussian Mixture Model (GMM) and unsupervised clustering as novel tools for mesh adaptation and flow feature detection. The methodology applies GMM to segment the computational domain into regions with distinct physical behaviour, viscous/turbulent and inviscid/irrotational. This clustering-based p-adaptation approach removes the need for predefined thresholds, enabling a fully automated and localised adjustment of the solution's polynomial degree. As a result, mesh refinement becomes more efficient and precise. A series of numerical experiments, including simulations of flow past a cylinder at varying Reynolds numbers, demonstrate the ability of the method to balance computational cost and solution fidelity. The framework is also tested on real-world engineering cases, such as wind turbine wake simulations, confirming its scalability and robustness. The results show substantial gains in efficiency and adaptability, establishing this approach as a promising solution for a wide range of CFD applications. By embedding machine learning into high-order solvers, this work advances the state-of-the-art and opens new directions for adaptive simulation strategies. It also lays the groundwork for future research in error-driven refinement, 3D turbulence modeling, and the broader application of AI in fluid mechanics. Keywords: Computational fluid dynamics, machine learning, Gaussian Mixture Model, p-adaptation, flow feature detection, mesh refinement, high-order methods. RESUMEN Esta tesis examina la integración de técnicas de aprendizaje automático en la dinámica de fluidos computacional (CFD) para mejorar la precisión de las simulaciones y reducir el coste computacional. Se propone el uso del Modelo de Mezclas Gaussianas (GMM) y métodos de agrupamiento no supervisado para desarrollar nuevas estrategias de adaptación de malla y detección de regiones del flujo sin recurrir a umbrales predefinidos. La metodología aplica GMM para segmentar automáticamente el dominio en regiones con diferentes características físicas, como zonas viscosas/turbulentas y no viscosas/irrotacionales. Esta clasificación guía un proceso de p-adaptación, ajustando el orden polinomial de la solución numérica de forma localizada, lo que permite un refinamiento automático, preciso y eficiente. Diversos experimentos numéricos, incluyendo simulaciones del flujo alrededor de un cilindro para distintos números de Reynolds, demuestran que el enfoque propuesto equilibra de manera eficaz el coste computacional y la calidad de la solución. Además, su aplicabilidad se valida en casos reales como las estelas de aerogeneradores, evidenciando la escalabilidad del método. Los resultados confirman que estas técnicas mejoran la eficiencia y adaptabilidad de los solucionadores de alto orden, lo que las convierte en herramientas prometedoras para aplicaciones complejas en CFD. La tesis contribuye así al estado del arte al integrar aprendizaje automático con métodos numéricos avanzados, y sienta las bases para investigaciones futuras sobre refinamiento adaptativo basado en errores, simulaciones tridimensionales de turbulencia y uso extendido de IA en mecánica de fluidos. Palabras clave: dinámica de fluidos computacional, aprendizaje automático, Modelo de Mezclas Gaussianas, p-adaptación, detección de características, refinamiento de mallas, métodos de alto orden. RÉSUMÉ Cette thèse explore l’intégration des techniques d’apprentissage automatique dans la dynamique des fluides numérique (CFD) afin d’améliorer la précision des simulations et la surcharge de calcul. En utilisant le modèle de mélanges gaussiens (GMM) et des méthodes de regroupement non supervisé, cette recherche introduit des approches innovantes pour l’adaptation du maillage et la détection des structures d’écoulement. La méthodologie se concentre sur l’application du GMM pour partitionner les champs d’écoulement en régions distinctes, régions distinctes, différenciant les zones visqueuses/turbulentes des zones inviscides/irrotationnelles. Cette technique de p-adaptation basée sur le regroupement élimine le besoin de seuils prédéfinis, permettant ainsi un raffinement du maillage automatisé, efficace et précis. De nombreuses expériences numériques sur divers cas tests, notamment l’écoulement autour d’un cylindre à différents nombres de Reynolds, démontrent la capacité de la méthode proposée à équilibrer le coût computationnel et la fidélité de la solution. Des applications en ingénierie, telles que la simulation du sillage d’une éolienne, illustrent davantage l’évolutivité des méthodes proposées. Les résultats montrent des améliorations significatives en termes du coût computationnel et d’adaptabilité, rendant ces techniques particulièrement adaptées à un large éventail d’applications en CFD. Ce travail contribue à l’état de l’art en intégrant de manière fluide l’apprentissage automatique dans les solveurs CFD d’ordre élevé, ouvrant ainsi la voie à de futures avancées dans les méthodes de simulation adaptative. Les méthodologies proposées établissent une base pour l’exploration du raffinement basé sur l’erreur, la modélisation tridimensionnelle de la turbulence et l’adoption plus large de l’apprentissage automatique dans la recherche en dynamique des fluides.

International audience

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR.