Manifold Discriminative Learning Inspired Hybrid Beamforming for Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mmWave) massive MIMO (multiple-input multiple-output) is a promising technology as it provides significant beamforming gains and interference reduction capabilities due to the large number of antennas. However, mmWave massive MIMO is computationally demanding, as the high antenna count results in high-dimensional matrix operations when conventional MIMO processing is applied. Hybrid precoding is an effective solution for the mmWave massive MIMO systems to significantly decrease the number of radio frequency (RF) chains without an apparent sum-rate loss. In this paper, we propose user clustering hybrid precoding to enable efficient and low-complexity operation in high-dimensional mmWave massive MIMO, where a large number of antennas are used in low-dimensional manifolds. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. The manifold discriminative learning seek to learn the embedding low-dimensional manifolds, where manifolds with different user cluster labels are better separated, and the local spatial correlation of the high-dimensional channels within each manifold is enhanced. Most of the high-dimensional channels are embedded in the low-dimensional manifolds by manifold discriminative learning, while retaining the potential spatial correlation of the high-dimensional channels. The nonlinearity of high-dimensional channel is transformed into global and local nonlinearity to achieve dimensionality reduction. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions

Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.

Empirical Comparative Study of Wearable Service Trust Based on User Clustering

Users of wearable services are different in age, occupation, income, education, personality, values and lifestyle, which also determine their different consumption patterns. Therefore, for the trust of wearable services, the influencing factors or strength may not be the same for different users. This article starts with the resource and motivation dimensions of VALSTM model, and the clustering model and questionnaire scale for consumers of wearable services were constructed. And then the users and potential users of wearable service are clustered by an improved clustering algorithm based on adaptive chaotic particle swarm optimization. Through clustering analysis of 535 valid questionnaires, users are grouped into three types of consumers with different lifestyles, respectively named: trend-following users, fashion-leading users and economic-rational users. Finally, this paper analyzes and compares the trust subgroup models of three clusters, and draws some conclusions.

Empirical Comparative Study of Wearable Service Trust Based on User Clustering

Users of wearable services are different in age, occupation, income, education, personality, values and lifestyle, which also determine their different consumption patterns. Therefore, for the trust of wearable services, the influencing factors or strength may not be the same for different users. This article starts with the resource and motivation dimensions of VALSTM model, and the clustering model and questionnaire scale for consumers of wearable services were constructed. And then the users and potential users of wearable service are clustered by an improved clustering algorithm based on adaptive chaotic particle swarm optimization. Through clustering analysis of 535 valid questionnaires, users are grouped into three types of consumers with different lifestyles, respectively named: trend-following users, fashion-leading users and economic-rational users. Finally, this paper analyzes and compares the trust subgroup models of three clusters, and draws some conclusions.

Clustering Users Based on Hearing Aid Use: An Exploratory Analysis of Real-World Data

While the assessment of hearing aid use has traditionally relied on subjective self-reported measures, smartphone-connected hearing aids enable objective data logging from a large number of users. Objective data logging allows to overcome the inaccuracy of self-reported measures. Moreover, data logging enables assessing hearing aid use with a greater temporal resolution and longitudinally, making it possible to investigate hourly patterns of use and to account for the day-to-day variability. This study aims to explore patterns of hearing aid use throughout the day and assess whether clusters of users with similar use patterns can be identified. We did so by analyzing objective hearing aid use data logged from 15,905 real-world users over a 4-month period. Firstly, we investigated the daily amount of hearing aid use and its within-user and between-user variability. We found that users, on average, used the hearing aids for 10.01 h/day, exhibiting a substantial between-user (SD = 2.76 h) and within-user (SD = 3.88 h) variability. Secondly, we examined hearing aid use hourly patterns by clustering 453,612 logged days into typical days of hearing aid use. We identified three typical days of hearing aid use: full day (44% of days), afternoon (27%), and sporadic evening (26%) day of hearing aid use. Thirdly, we explored the usage patterns of the hearing aid users by clustering the users based on the proportion of time spent in each of the typical days of hearing aid use. We found three distinct user groups, each characterized by a predominant (i.e., experienced ~60% of the time) typical day of hearing aid use. Notably, the largest user group (49%) of users predominantly had full days of hearing aid use. Finally, we validated the user clustering by training a supervised classification ensemble to predict the cluster to which each user belonged. The high accuracy achieved by the supervised classifier ensemble (~86%) indicated valid user clustering and showed that such a classifier can be successfully used to group new hearing aid users in the future. This study provides a deeper insight into the adoption of hearing care treatments and paves the way for more personalized solutions.

Deep Neural Network (DNN) for Efficient User Clustering and Power Allocation in Downlink Non-Orthogonal Multiple Access (NOMA) 5G Networks

Non-orthogonal multiple access (NOMA) emerges as a promising candidate for 5G, which radically alters the way users share the spectrum. In the NOMA system, user clustering (UC) becomes another research issue as grouping the users on different subcarriers with different power levels has a significant impact on spectral utilization. In previous literature, plenty of works have been devoted to solving the UC problem. Recently, the artificial neural network (ANN) has gained considerable attention due to the availability of UC datasets, obtained from the Brute-Force search (BF-S) method. In this paper, deep neural network-based UC (DNN-UC) is employed to effectively characterize the nonlinearity between the cluster formation with channel diversity and transmission powers. Compared to the ANN-UC, the DNN-UC is more competent as UC is a non-convex NP-complete problem, which cannot be entirely captured by the ANN model. In this work, the DNN-UC is first trained with the training samples and then validated with the testing samples to examine its mean square error (MSE) and throughput performance in an asymmetrical fading NOMA channel. Unlike the ANN-UC, the DNN-UC model offers greater room for hyper-parameter optimizations to maximize its learning capability. With the optimized hyper-parameters, the DNN-UC can achieve near-optimal throughput performance, approximately 97% of the throughput of the BF-S method.