Millimeter-wave ( mmWave) and massive multiple-input multiple-output ( MIMO) are broadly recognized as key enabling technologies for the fifth generation (5G) communication systems. In this paper, a low-complexity angle- delay parameters estimation ( ADPE) algorithm was put forward for wideband mmWave systems with uniform planar arrays ( UPAs). In particular, the ADPE algorithm effectively decouples the angle-delay parameters and converts the angle-delay estimation problem into three independent subproblems. Accordingly, the ability to devise an off- grid method based on discrete Fourier transform ( DFT) with a closed-form solution for angle-delay estimation and potential path number acquisition can be realized. In actuality, only a limited number of potential paths are close to the true paths influenced by noise. Consequently, the removal of noise paths to acquire the corresponding true path gains through a sparsity adaptive path gains estimation ( APGE) algorithm is postulated. Finally, the simulation results substantiate the effectiveness of ADPE and APGE algorithms.

Since the release of the first version of the 5 th generation ( 5 G) mobile networks standard: Release-15 ( Rel- 15 ) , the 3 rd Generation Partnership Project ( 3 GPP) made significant efforts in the field of indoor and outdoor wireless positioning. Notably, Release-16 ( Rel-16 ) augmented support for enhanced mobile broadband ( eMBB) and ultra-reliable low-latency communication ( uRLLC) , particularly within complex indoor settings. To further meet the diverse application needs of positioning scenarios, the 3 GPP standards for Release-17 ( Rel-17 ) and Release-18 ( Rel-18 ) propose new enhancement measures to continuously provide more accurate positioning services. In this paper, the scholarly discourse on 5 G positioning was critically examined, providing a systematic review of the 5 G positioning standards as delineated in 3 GPP’s Rel-16 and Rel-17 , and extended the discussion to the anticipated enhancements in 5 G Rel-18 , along with their underlying motivations. Through these discussions, not only a comprehensive perspective on the current development of 5 G positioning technology was provided but also forward-looking analysis and predictions for the evolution of positioning technology in the upcoming 3 GPP Release-19 ( Rel-19 ) was offered. Additionally, it serves as a reference for researchers interested in understanding the development of positioning within the standard framework in the field of 5 G indoor positioning, which holds significant meaning for promoting research and application of 5 G positioning technology.

In this paper, a wideband high gain millimeter wave radar array antenna based on a wavy power divider was proposed. The radar antenna comprises a wavy power divider and a 10-element array antenna. By adjusting the wavy radius of the power divider, the surface current of the power divider is altered, resulting in better impedance matching with the antenna. This ultimately leads to a significant improvement in bandwidth performance. The 4 伊 10 millimeter wave radar antenna loaded with a wavy power divider exhibits an approximate enhancement of 3 GHz compared to traditional microstrip power divider antennas, and an average gain increase of 2.42 dB within the vehicle millimeter wave radar frequency band relative to the improved gradient power divider structure. The 4 伊 10 millimeter wave radar antenna loaded with wavy power divider possesses the characteristics of high gain and broad bandwidth.

Aiming at the problems of poor initial population quality, slow convergence, and long-running time of optical microscope algorithm ( OMA), a multiple-strategy improved OMA based on periodical variation and encircling mechanism, called MOMA, was proposed in this paper. Firstly, the good point set population initialization is introduced to obtain a uniform initial population. Secondly, the periodic mutation and encircling mechanism are successively used to improve the convergence speed. Finally, the MOMA’s running time is optimized by introducing the conversion factor and the corresponding threshold, while balancing the exploration and exploitation. Experimental and analytical comparisons are made with OMA and 7 other excellent optimizers on 21 benchmark functions. The results show that MOMA largely outperforms the original algorithm. Furthermore, by applying MOMA to the optimization experiments of the no-wait flow-shop scheduling problem ( NWFSP), MOMA can obtain the optimal completion time and the fastest convergence speed compared to modified particle swarm optimization ( PSO) using adaptive strategy, grey wolf optimizer ( GWO), golden jackal optimization ( GJO), and OMA.

In the process of obtaining information from the actual traffic network, the incomplete data set caused by missing data reduces the validity of the data and the performance of the data-driven model. A traffic flow repair model based on a k-nearest neighbor ( KNN) spatio-temporal attention ( STA) graph convolutional network ( KAGCN) was proposed in this paper. Firstly, the missing data is initially interpolated by the KNN algorithm, and then the complete index set ( CIS) is constructed by combining the adjacency matrix of the network structure. Secondly, a STA mechanism is added to the CIS to capture the spatio-temporal correlation between the data. Then, the graph neural network ( GNN) is used to reconstruct the data by spatio-temporal correlation, and the reconstructed data set is used to correct and optimize the initial interpolation data set to obtain the final repair result. Finally, the PEMSD4 data set is used to simulate the missing data in the actual road network, and experiments are carried out under the missing rate of 30% , 50% , and 70% respectively. The results show that the mean absolute error ( MAE), root mean square error ( RMSE), and mean absolute percentage error ( MAPE) of the KAGCN model increased by at least 3.83% , 2.80% , and 5.33% , respectively, compared to the other baseline models at different deletion rates. It proves that the KAGCN model is effective in repairing the missing data of traffic flow.

As a fundamental component of intelligent transportation systems, existing urban traffic flow forecasting models tend to overlook the spatio-temporal and long-term time-dependent patterns that characterize transportation networks. Among these, the long sequence time-series forecasting ( LSTF) model is susceptible to the issue of gradient disappearance, which can be attributed to the influence of a multitude of intricate factors. Accordingly, in this paper, the standpoint of multi-feature fusion was studied, and a traffic flow forecasting network model based on feature fusion and spatio-temporal transformer ( S-T transformer) ( STFFN) was proposed. The model combined predictive recurrent neural network ( Pred RNN) and S-T transformer to dynamically capture the spatio-temporal dependence and long-term time-dependence of traffic flow, thereby achieving a certain degree of model interpretability. A novel gated residual network-2 ( GRN-2) was proposed to investigate the potential relationship between multivariate features and target values. Furthermore, a hybrid quantile loss function was devised to alleviate the gradient disappearance in LSTF problems effectively. In extensive real experiments, the rationality and effectiveness of each network of the model were demonstrated, and the superior forecasting performance was verified in comparison to existing benchmark models.

With the rapid iteration of neural network algorithms, higher requirements were placed on the computational performance and memory access bandwidth of neural network accelerators. Simply increasing bandwidth cannot improve energy efficiency, so improving the data reuse rate is a hot research topic. From the perspective of supporting data reuse, a reconfigurable convolutional neural network ( CNN) accelerator based on elastic storage ( RCAES) was designed in this paper. Supporting elastic memory access and flexible data flow reduces data movement between the processor and memory, eases the bandwidth pressure and enhances CNN acceleration performance. The experimental results indicate that by conducting 1 × 1 convolution and 3 × 3 convolution when performing convolution calculations, the execution speed increased by 25.00% and 61.61% , respectively. The   3 × 3 maximum pooling speed was increased by 76.04% .

A new design for a transparent low-pass filter ( LPF) using a flexible polyethylene terephthalate ( PET) material and metallic copper was proposed in this paper. The transparent microstrip filter employs an asymmetric Π-shaped structure with dimensions of 260.000 mm × 13.000 mm × 0.148 mm, which owns a narrow transition band and achieves an insertion loss of less than 1. 0 dB below 0. 26 GHz. Furthermore, the insertion loss of the transparent microstrip filter can exceed 30 dB in the stopband from 1. 02 GHz to 6. 50 GHz. Square holes are added to the copper layer to improve the transparency of the LPF, and the filter performance can be adjusted by changing the square hole area and resolution. Additionally, multiple identical structures are cascaded to increase the insertion loss in the stopband while maintaining the passband. The transparent microstrip LPF has the characteristics of flexibility, high transparency, and significant insertion loss in the stopband, making it suitable for applications that require electromagnetic protection in a clear field of vision.