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Traditional deep neural network (DNN) based process monitoring methods only use the deep features of the last layer and residuals to achieve fault detection. However, the features in different hidden layers are different representations of the input data, which may be beneficial to process monitoring. Only using the deepest features for process monitoring will cause the problems of information loss and low monitoring performance. To obtain more useful information for fault detection, this paper considers the features in all hidden layers and proposed an ensemble monitoring model based on multi-subspace partition of deep features. Firstly, a DNN model is established based on the collected faultless data to obtain the features in all hidden layers and residuals. Secondly, a new feature matrix is constructed based on the retained deep features and residuals. Then, the multi-subspace partition of the new feature matrix is realized by combining correlation analysis and cluster analysis. Finally, the monitoring statistics that are established based on the features in each subspace are fused to realize process monitoring. The proposed method can not only reduce information loss but also enrich the fault-related information. The monitoring performance is verified through two benchmark processes and one actual industrial process.

Energy efficiency is one of the key performance indicators in 5G New Radio (NR) networks targeted to support diversified use cases including enhanced mobile broadband (eMBB), massive machine type communications (mMTC) and ultra-reliable and low latency communications (URLLC). Trade-offs have to be carefully considered between energy efficiency and other performance aspects such as latency, throughput, connection densities and reliability. Energy efficiency is important for both user equipment (UE) side and base station side. On UE side, UE battery life has great impact on user experience. It is challenging to improve UE experience in other performance aspects without affecting battery life of 5G handsets. On the base station side, efficient network implementation is critical in both environmental and operation cost standpoints. To adapt different requirements and trade-offs, the 5G NR standard is designed to have great flexibility on network operation modes. This paper provides an overview on power saving techniques supported by 5G NR standards according to the current 5G standardization progress. It provides the 5G evolution path of the power saving techniques from the first release of 5G standard to the future beyond-5G releases. In addition to the existing standardized techniques, some major development trends of green communication and the future potential enhancements expected in the beyond-5G standards are discussed.

In the differential evolution (DE) algorithm, numerous studies have independently performed strategy adaptation and parameter adaptation. However, the strategy and parameters are interrelated in their impact on algorithm performance. It is well known that different problems and evolutionary stages require different appropriate parameters and strategies, but the fact that the same is true for different individuals is ignored. Few studies have focused on the difference in fitness values between two successive generations, which contains substantial evolution information. This study proposes a potential-based DE algorithm with joint adaptation of parameters and strategies (JAPSPDE). In JAPSPDE, a new population classification scheme, a new classification evolution mechanism, and a new joint adaptation mechanism are proposed to circumvent the three abovementioned issues. In the population classification scheme, individuals are divided into potential and unpotential individuals according to the improvement in fitness values between two generations. A classification evolution mechanism is applied by evolving potential individuals and unpotential individuals in two ways. In addition, a three-dimensional probability array is constructed to achieve joint adaptation of parameters and strategies. Finally, after properly combining the above algorithmic components, JAPSPDE can find the most appropriate combination of control parameters and mutation strategies for specific problems, stages, and individuals. The performance of JAPSPDE is evaluated in comparison with five well-known DE algorithms on BBOB2012 and CEC2014 and with six up-to-date evolution algorithms on CEC2014. The comparison results demonstrate the competitive performance of JAPSPDE.

In this paper, the dynamic channel characteristics at 23.5 GHz in an indoor scenario are investigated according to measurement and deterministic simulation. In order to obtain accurate channel realizations, the ray tracing (RT) software is calibrated on both the power delay profile and the path levels. For the measurement data, the propagation paths are identified using the non-parametric peak detection algorithm. The cluster-alike behaviors of these paths and the influence of the antenna radiation pattern are also studied through the comparison with the RT simulated paths. Subsequently, the evolutionary traces of channel with regard to the user equipment's movement are identified by associating the samples, which have the similar parameters at adjacent locations. The features of these traces are analyzed in both statistical and individual ways. Results show that the life durations of most traces are within 5 m. The line of sight and reflected paths with significant power survive longer than the others. These observations confirm the feasibility of designing adaptive beam tracking algorithms based on the spatial consistency of the dominant propagation paths. Moreover, high correlations among the variations of different parameters in the same trace are revealed.

The use of large-size antenna arrays to implement pencil-beam forming techniques is becoming a key asset to cope with the very high throughput density requirements and high path-loss of future millimeter-wave (mm-wave) gigabit-wireless applications. Suboptimal beamforming (BF) strategies based on search over discrete set of beams (steering vectors) are proposed and implemented in present standards and applications. The potential of fully adaptive advanced BF strategies that will become possible in the future, thanks to the availability of accurate localization and powerful distributed computing, is evaluated in this paper through system simulation. After validation and calibration against mm-wave directional indoor channel measurements, a 3-D ray tracing model is used as a propagation-prediction engine to evaluate performance in a number of simple, reference cases. Ray tracing itself, however, is proposed and evaluated as a real-time prediction tool to assist future BF techniques.