Chinese Journal of Electronics

Large Spaceborne Deployable Antennas (LSDAs)-A Comprehensive Summary

DUAN Baoyan

2020, 29(1): 1-15. doi: 10.1049/cje.2019.09.001

Abstract(2375) PDF(4596)

Abstract:
This paper provides a survey of research activities of Large spaceborne deployable antennas (LSDAs) in the past, present and future. Firstly, three main kinds of spaceborne antennas, such as solid reflector, inflatable reflector and mesh reflector, are issued by showing the strengths and weaknesses. Secondly, a detailed research situation of LSDAs with mesh is discussed, for majority of the in-orbit large diameter and high frequency antennas are made in this type of structures. Thirdly, new conception of antenna is proposed as it does have both advantages of large aperture (high gain) and high precision (high frequency). Fourthly, the design theory and approach of LSDAs are concerned. It includes thermal-electromechanical multidisciplinary optimization, shaped beam design technique, performance testing technology and evaluation method, passive intermodulation of mesh, and application of new materials. Finally, the ultra large spaceborne deployable antennas of the next generation are presented, such as the deployable frame and inflatable reflector antennas, space-assembled ultra large antennas, smart array antennas and so on.

Research on Electromagnetic Compatibility of Chinese High Speed Railway System

WEN Yinghong, HOU Weixing

2020, 29(1): 16-21. doi: 10.1049/cje.2019.09.002

Abstract(865) PDF(985)

Abstract:
From technology importation to independent innovation, Chinese High speed railway (HSR) has been rapidly developed in the past a few years. However, this technology development mode introduces very complex electromagnetic environment of HSR system, which is based on various equipment with different Electromagnetic compatibility (EMC) design. Consequently, the suppression of electromagnetic disturbance and the electromagnetic protection is becoming an urgent issue for HSR. The characteristics of traction power supply technology, train operation control technology and other core technologies are quite unique in Chinese HSR. In this paper, the EMC and the electromagnetic safety issue of Chinese HSR is discussed, which is derived from technology development mode and core technology characteristics. The limitation of the high speed train's exportation due to the lack of test method for the train's emission is also analyzed. Combining the electromagnetic environment characteristic and the development planning of Chinese HSR, several suggestions in the research of antielectromagnetic interference, electromagnetic protection in the strong electromagnetic field and the whole large system's radiation emission test method are put forward.

Survey of Performance Evaluation Standardization and Research Methods on GNSS-Based Localization for Railways

CAI Baigen, WU Boqian, LU Debiao

2020, 29(1): 22-33. doi: 10.1049/cje.2019.09.003

Abstract(1005) PDF(938)

Abstract:
Global navigation satellite systems (GNSS) has been regarded as a key technology for train localization in the future, as GNSS-based localization can reduce the deployment of wayside equipment, and offer a high-accuracy determination in a cost effective way. However, the standardization of GNSS-based localization performance characteristics has not been done in railway. This can restrict the development of GNSS for safety related applications, which need critical requirement for the. The main objective of this paper is to provide a survey of research dealing with performance evaluation standardization. Train localization technique and performance requirement in Chinese train control system (CTCS), GNSS performance characteristics suggested in standards and guidelines, characteristics of accuracy/integrity/reliability/availability/safety for GNSS-based train localization and related evaluation approaches are presented. Some issues in GNSS-based train localization are highlighted based on the survey.

Optimized Fault Detection Algorithm Aided by BDS Baseband Signal for Train Positioning

WANG Ershen, YANG Di, WANG Chuanyun, Huang Yufeng, QU Pingping, PANG Tao

2020, 29(1): 34-40. doi: 10.1049/cje.2019.09.004

Abstract(626) PDF(883)

Abstract:
The satellite navigation integrity monitoring technology is closely related to the reliability of train positioning. It is of great significance to study satellite navigation Receiver autonomous integrity monitoring (RAIM) algorithm suitable for train positioning. Some existing conventional RAIM algorithms do not consider the influence of satellite navigation signal on algorithms performance. Aiming at the influence of satellite navigation signal on the propagation processes, such as shadow and multipath effects, the fault detection algorithm based on BeiDou navigation satellite system (BDS) baseband signal is proposed. The innovation parameters contained in the satellite navigation baseband signal are combined into the covariance matrix to set up a new test statistics of RAIM. A new fault detection algorithm based on optimized BDS baseband signal is discussed. And combined with weighted least square method, the noise power balance factor is established to realize satellite fault detection. Experimental results show that the proposed algorithm can improve the monitoring performance of train satellite positioning integrity, and lay the foundation for meeting the railway industry's security requirements for positioning services.

GNSS Fault Detection and Exclusion Based on Virtual Pseudorange-Based Consistency Check Method

LIU Jiang, ZHAO Xiaolin

2020, 29(1): 41-48. doi: 10.1049/cje.2019.09.005

Abstract(521) PDF(859)

Abstract:
Although there have been many implementations of Global navigation satellite system (GNSS) in rail transport systems, the integrity assurance of GNSS positioning is still not highly concerned by the users. Integrity is a significant issue for specific safety-relevant GNSS railway applications, especially the railway signaling system which requires a critical safety level. Classical integrity monitoring solutions require an acceptable satellite visibility level to carry out Fault detection and exclusion (FDE), and this restriction leads to a degraded availability of FDE and final position computation. In order to break the limitation by satellite visibility, a consistency check method for FDE is proposed based on the virtual satellite pseudorange data, which is established based on the track database. Results from simulations show that the presented method can detect and exclude different types of faults effectively. Compared with classical residualbased GNSS integrity monitoring algorithm, the presented method achieves a higher FDE availability and makes better use of the satellite measurements even under the limited GNSS observing conditions.

Hazard Rate Estimation for GNSS-Based Train Localization Using Model-Based Approach

LU Debiao, TANG Dezhang, SPIEGEL Dirk

2020, 29(1): 49-56. doi: 10.1049/cje.2019.09.006

Abstract(667) PDF(896)

Abstract:
GNSS (Global navigation satellite system) has shown its ability on the non-safety relevant applications in various applications. In railway domain, safety is the utmost concern, GNSS-based train localization unit is the carrier of safe localization. EN 50126 standard specifies the electric and electronic system performance requirements as Reliability, availability, maintainability and safety (RAMS). Safety performance characteristic is based on the reliability and availability analysis results. The formal method considering the risk identification and analysis is compulsory for the safety assessment. This paper provides the method for hazard analysis of the GNSS-based train localization function and further the hazard rate estimation of each hazard. The model-based approach using GNSS localization unit working principle is proposed to study railway localization behavior features. The hazard for the localization unit is formally stated, real collected data on circular test track in Beijing is studied using the proposed method. The result shows that the safety requirement of the performance can be transformed into safety integrity properly, the risks of GNSS in train station environment conditions are presented.

An APD-Based Evaluation on the Effect of Transient Disturbance over Digital Transmission

GENG Xin, ZHANG Jinbao

2020, 29(1): 57-65. doi: 10.1049/cje.2019.09.007

Abstract(793) PDF(866)

Abstract:
In railway electromagnetic environment, transient disturbances usually degenerate the function of railway wireless communication systems, thus may cause severe security issues. To protect communication services, measurement methodology to characterize disturbances keeps evolving. Nowadays, the statistical characteristics of disturbances, such as Amplitude probability distribution (APD), have been proven more efficient to assess the degradation of digital transmission than Quasipeak detection in most Electromagnetic Compatibility standards. To demonstrate the impact of disturbances on error performance more precisely, a more refined method to estimate Bit error probability (BEP) of digital modulation is necessary. The APD value is ideally obtained with the equivalent bandwidth of the radio service under threat, which means specific apparatus of certain radio service is consequently indispensable. This paper proposes a novel method to estimate the maximum BEP of multilevel amplitude modulations based on APD acquired by standard apparatus. The calculated BEP also provides reference for APD emission restriction.

A VMD Based Improved De-noising of Onboard BTM Receiving Signal

GENG Qi, WEN Yinghong, LIU Shanghe, ZHANG Dan

2020, 29(1): 66-72. doi: 10.1049/cje.2019.10.001

Abstract(725) PDF(861)

Abstract:
As a key equipment of the high-speed Electric multiple unit (EMU), the Balise transmission module (BTM) is tend to be affected by electromagnetic disturbances. However, the existing filter can not well eliminate electromagnetic disturbances. In order to improve the Signal to noise ratio (SNR) of BTM uplink signal, Variational mode decomposition (VMD) method is introduced into de-noising the uplink signal polluted by disturbance. The proposed de-noising method decomposes the noise-polluted signal into several narrow band subsignals. And then, the de-noised BTM transmitting signal is reconstructed by the sub-signals of the same frequency as the BTM carrier. To improve the accuracy of the VMD based signal decomposition, the Normalized short time fourier transform-Wigner ville distribution (NSTFTWVD) method are applied to determine the decomposition mode number of the target signal. The proposed method is used to deal with both simulation signal and measured BTM uplink signal. Results demonstrated good improvement in SNR by 20dB and 12.63dB, respectively.

A 10-bit 500-MS/s Current Steering DAC with Improved Random Layout

TONG Xingyuan, WANG Chaofeng

2020, 29(1): 73-81. doi: 10.1049/cje.2019.10.002

Abstract(621) PDF(1642)

Abstract:
According to the segmented current steering Digital-to-analog converter (DAC), the influence of current mismatch and output impedance of the current array on the linearity of DAC is discussed by theoretical analysis and derivation. An optimized layout plan for the current source array randomizes all these unit current sources corresponding to each thermometer code, which can significantly reduce the fist-order and second-order systematic mismatch errors in the current source array, and improve the linearity performance of DAC. With the segmented structure of 6 bit thermometer code and 4 bit binary code, a 10-bit DAC is realized in a 0.18-μm CMOS by using the above layout plan. The active area of this DAC is 620μm×340μm. Operated at 1V digital supply and 1.8V analog supply, with 500 MS/s sampling rate, the measured power consumption of this DAC is 14.3mW. The measurement results show that the Differential nonlinearity (DNL) and the Integral nonlinearity (INL) of the DAC with this random layout scheme are 0.71 LSB and 1.02 LSB. With 500 MS/s sampling rate and 1.465MHz input frequency, the Spurious free dynamic range (SFDR) and the Effective number of bits (ENOB) are 65.6dB and 9.2 bit, respectively.

VLSI Implementation of Area and Power Efficient Digital Control Circuit for HF RFID Tag Chip

WANG Deming, HU Jianguo, WANG Jianhui, DING Yanyu, WU Jing

2020, 29(1): 82-88. doi: 10.1049/cje.2019.10.003

Abstract(618) PDF(535)

Abstract:
A fully integrated area efficient digital control circuit based on the ISO/IEC 15693 protocol is proposed for high frequency RFID tag chip. The proposed circuit is mainly composed of pulse position modulation decoder, Manchester encoder, anticonllision, low power circuit and other control logic. It supports six different data rates, namely, low or high data rate with one subcarrier (6.62 or 26.48 Kbit/s), low or high data rate with two subcarriers (6.67 or 26.69 Kbit/s), fast data rate with one subcarrier (13.24 or 52.97 Kbit/s). The proposed digital control circuit was integrated in an RFID tag IC and was fabricated using a 0.18-μm 2P6M CMOS process with an area of 306μm by 326μm which is smaller than the existing designs. Besides of small area, the circuit has an advantage of low power with a power consumption of less than 50μW.

A Sample-Efficient Actor-Critic Algorithm for Recommendation Diversification

LI Shuang, YAN Yanghui, REN Ju, ZHOU Yuezhi, ZHANG Yaoxue

2020, 29(1): 89-96. doi: 10.1049/cje.2019.10.004

Abstract(812) PDF(606)

Abstract:
Diversifying recommendation results gains benefits from satisfying user's existing interests as well as exploring novel information needs. Recently proposed Monte-Carlo based reinforcement learning method suffers from sample inefficiency, large variance, and even failing to perform well in large action space. We propose a novel actor-critic reinforcement learning algorithm for recommendation diversification in order to solve the above mentioned problems. The actor acts as the ranking policy, while the introduced critic predicts the expected future rewards of each candidate action. The critic target is updated by full Bellman equation and the actor network is optimized using expected gradient in the whole action space. To further stabilize and improve the performance, we also add policy-filtered critic supervision loss. Experiments on MovieLens dataset well demonstrate the effectiveness of our approach over multiple competitive methods.

Divisor Class Halving Algorithms for Genus Three Hyperelliptic Curves

YOU Lin, YANG Yilin, GAO Shuhong

2020, 29(1): 97-105. doi: 10.1049/cje.2019.10.005

Abstract(447) PDF(517)

Abstract:
In an (hyper)elliptic curve cryptosystem, the most important operation or the most time-consuming operation is the divisor scalar multiplication which consists of a sequence of doubling (of divisor) and addition (of two divisors). Point halving algorithms for elliptic curve cryptosystem and divisor halving algorithms for genus-2 hyperelliptic curve cryptosystem had been successively put forward to take the place of doubling algorithms for speeding up (hyper)elliptic curve cryptosystem. We present an outline for an algorithm for divisor halving on genus-3 hyperelliptic curves over the binary field and give some explicit formulae for a class of genus-3 curves. Our algorithm improves previously known best doubling algorithms in most cases. A halve-and-add binary method for divisor scalar multiplications is presented.

Black-Box and Public Traceability in Multi-authority Attribute Based Encryption

ZHAO Qianqian, WU Gaofei, MA Hua, ZHANG Yuqing, WANG He

2020, 29(1): 106-113. doi: 10.1049/cje.2019.10.006

Abstract(614) PDF(572)

Abstract:
Ciphertext-policy Attribute-based encryption (CP-ABE) is a promising tool for implementing finegrained cryptographic access control. While the uniqueness of generating private keys brings extra security issues. The key escrow is inherent in CP-ABE systems because the trusted authority has the power to decrypt every ciphertext. The private keys are only associated with the attributes nor the user's identity. Some malicious users might be tempted to leak their decryption privileges for financial gain without the risk of being caught as the decryption privilege could be shared by multiple users who own the same set of attributes. We propose a new multiauthority CP-ABE with blackbox and public traceability, where the private keys are assigned by the cooperation between one central authority and multi-authorities. The performance and security analyses indicate that the proposed scheme is highly efficient and provably secure under the security model.

A Family of Constacyclic Codes over F_2^m + uF_2^m and Its Application to Quantum Codes

TANG Yongsheng, YAO Ting, ZHU Shixin, KAI Xiaoshan

2020, 29(1): 114-121. doi: 10.1049/cje.2019.10.007

Abstract(468) PDF(1119)

Abstract:
Let R be the ring F_2^m + uF_2^m, where u²=0. We introduce a Gray map from R to F₂²m and study (1 + u)-constacyclic codes over R. It is proved that the image of a (1 + u)-constacyclic code length n over R under the Gray map is a distance-invariant binary quasicyclic code of index m and length 2mn. We also prove that every code of length 2mn which is the Gray image of cyclic codes over R of length n is permutation equivalent to a binary quasi-cyclic code of index m. Furthermore, a family of quantum error-correcting codes obtained from the Calderbank-Shor-Steane (CSS) construction applied to (1 + u)-constacyclic codes over R.

Robust Semi-nonnegative Matrix Factorization with Adaptive Graph Regularization for Gene Representation

JIANG Wei, MA Tingting, FENG Xiaoting, ZHAI Yun, TANG Kewei, ZHANG Jie

2020, 29(1): 122-131. doi: 10.1049/cje.2019.11.001

Abstract(587) PDF(571)

Abstract:
Various data representation algorithms have been proposed for gene expression. There are some shortcomings in traditional gene expression methods, such as learning the ideal affinity matrix to effectively capture the geometric structure of genetic data space, and reducing noises and outliers influences of data input. We propose a novel matrix factorization algorithm called Robust semi-nonnegative matrix factorization (RSNMF) with adaptive graph regularization, which simultaneously performs matrix robust factorization with learning affinity matrix in a unified optimization framework. RSNMF also uses a loss function based on l_2,1-norm to improve the robustness of the model against noises and outliers. A novel Augmented Lagrange multiplier (ALM) is designed to obtain the optimal solution of RSNMF. The results of extensive experiments that were performed on gene expression datasets demonstrate that RSNMF outperforms the other algorithms, which validates the effectiveness and robustness of RSNMF.

Tiny YOLO Optimization Oriented Bus Passenger Object Detection

ZHANG Shuo, WU Yanxia, MEN Chaoguang, LI Xiaosong

2020, 29(1): 132-138. doi: 10.1049/cje.2019.11.002

Abstract(1058) PDF(1087)

Abstract:
The real-time collection of bus passenger object detection is an essential part of developing a smart bus system. The difficulty of object detection mainly lies in the objective factors, such as:clothing, hair style and accessories, light, etc. Traditional object detection methods with the artificial feature extraction suffers from insufficient strength in expression, generalization, and recognition rate. The object detection method based on deep learning mainly uses the convolutional neural network in deep learning to learn features from a large set of data. The learned features can describe the rich information inherent in the data, and improve the expression ability of the features as well as the recognition accuracy. Due to too many parameters of the Convolutional neural network (CNN) model, the amount of calculation is too large to be operated on the vehicle terminal. To reduce calculation burden and improve the operation speed, we employs the depthwise separable convolution method to optimize the convolutional layer of tiny YOLO network model. It decomposes a complete convolution operation into depthwise convolution and pointwise convolution, thus reducing the parameter amount of the CNN and improving the operation speed. The experiment results reveal that the speed of bus passenger object detection detected by our improved model is 4 times faster than the previous one but with the nearly same detection accuracy.

An Adaptive Time-Domain Kalman Filtering Approach to Acoustic Feedback Cancellation for Hearing Aids

LU Caixia, YANG Feiran, YANG Jun

2020, 29(1): 139-146. doi: 10.1049/cje.2019.11.003

Abstract(649) PDF(968)

Abstract:
The adaptive filtering approach has been widely used for acoustic feedback control in the hearing aids due to its excellent performance. The commonly used adaptive filtering algorithms employ a fixed step-size, which has to compromise between the initial convergence and the steady-state misalignment. Many variable stepsize adaptive algorithms have been proposed to handle this problem. In this paper, we propose a broadband Kalman filter to resolve this problem. The acoustic feedback path is modelled by a first-order Markov model, and the observation equation is constructed using more past data vector. A major issue in the hearing aids is the computational complexity. We thus present a simplified version to reduce the complexity, which bridges between the exact Kalman filter and the affine projection algorithm. The estimation of the process and measurement noise variance is discussed in detail. A two-feedbackpath model is adopted to improve the algorithm's lack of re-convergence. Simulation results confirm the proposed algorithm clearly outperforms the other variable step-size adaptive filtering approaches.

MMOS+ Ordering Search Method for Bayesian Network Structure Learning and Its Application

HE Chuchao, GAO Xiaoguang, WAN Kaifang

2020, 29(1): 147-153. doi: 10.1049/cje.2019.11.004

Abstract(536) PDF(572)

Abstract:
To address the problem of a reduced efficiency due to an increase of the search space, it has been proposed that priors could be added as constraints to the OS+ algorithm, which are Parent and children (PC) sets of each node obtained using the Max-min parent and children (MMPC) algorithm. Experimental results indicate that compared to other competitive methods, the proposed algorithm yields better solutions while maintaining high efficiency. Bayesian network (BN) sensitivity analysis is also proposed, which allows the network structure to be determined via a proposed ordering search method. We performed sensitivity analysis to determine the accuracy of the airborne avionics system, for which a simulation model is constructed to generate data samples, and the main effect of each error index is obtained using different sensitivity analysis methods. Experimental results indicate that the proposed BN method produces more accurate results when there is insufficient sample data, and this method can elucidate causal relationships that are present in the data.

Visualization Feature and CNN Based Homology Classification of Malicious Code

CHU Qianfeng, LIU Gongshen, ZHU Xinyu

2020, 29(1): 154-160. doi: 10.1049/cje.2019.11.005

Abstract(664) PDF(962)

Abstract:
The malicious code brings a serious security threat. Researchers have found that many new types of malicious code are variants of the existing one. The homology classification of the unknown malicious code can find its corresponding family in which all the code share inherent similarities from the database, so that the defenders can make rapid response and processing. We use the algorithm of malicious code visualization to translate the homology classification problem into the image classification problem. A convolution neural network for malicious code image is constructed. We train it to complete the malicious code homology classification on two different datasets. The results show that our work outperforms most of existing work with the accuracy of 98.60%.

A Novel Blind Motion Blur Restoration Algorithm for Text Images

WANG Simin, LI Xiaoguang, ZHANG Hui, ZHUO Li

2020, 29(1): 161-167. doi: 10.1049/cje.2019.12.001

Abstract(459) PDF(524)

Abstract:
Text images captured by the surveillance system or hand-hold cameras often suffer from motion blur due to the complex relative motion between the camera and the target during the exposure time. The accuracy of the kernel estimation and the effective priors for clear text images are two important keys in blind motion deblurring. A novel blind motion deblurring algorithm is proposed for text images in a complex scene. A criterion for selecting informative edges and an L₀ constraint are combined to improve the accuracy of the kernel estimation. Then, a fast non-blind deconvolution scheme is applied to accelerate the algorithm. Experimental results on text images show that the proposed method can achieve high-quality results with low computational complexity.

FDBST: Fast Discovery of Bursty Spatial-Temporal Topic

ZHU Chuangying, DU Junping, ZHANG Qiang, SHI Lei, Lee JangMyung

2020, 29(1): 168-176. doi: 10.1049/cje.2019.12.002

Abstract(592) PDF(544)

Abstract:
Discovering the hot topic among trends is an essential way to manage public opinion. But the dynamic property makes it a tough task as the existing methods of detection process are time-consuming. With the new Fast discovery of burst spatial-temporal topic (FDBST) method, the uptime of topic discovery is kept within one second and there is no sacrificing of topic quality, the noisy topics are all kept off, the time-varying problem and the sparsity problem are easily tackled by using spatial-temporal characteristics. How the FDBST works? It is triggered by a burst term based on social data trends, while the data is generated within a small time interval in a region while the irrelevant data is excluded during this process. By fusing the regional topics, the potential burst topic is obtained.The experiments show the preferable effects of the FDBST and it is an outperforms state-of-the-art approaches in terms of effectiveness.

Joint Channel Estimation and Power Allocation for the CRS-NOMA

TAN Yizhi, CHEN Baoren

2020, 29(1): 177-182. doi: 10.1049/cje.2019.12.003

Abstract(513) PDF(927)

Abstract:
We study the channel estimation and power allocation method for the Cooperative relaying system using Non-orthogonal multiple access (CRS-NOMA). Our object is maximizing the Average effective signal-tointerference-and-noise ratio (AESINR) of the weak user while guaranteeing bounded AESINR for the strong user. We transform the nonconvex optimization problem into a Difference of convex (DC) programming and propose a Sequential parametric convex approximation (SPCA) based iterative algorithm to obtain a local optimum. For comparison, we consider a similar optimization problem for a Non-cooperative NOMA (NC-NOMA) system. Simulation results show that the AESINR performance of the CRS-NOMA outperforms signicantly that of the NCNOMA system.

State Machine with Tracking Tree and Traffic Allocation Scheme Based on Cumulative Entropy for Satellite Network

LIN Wenliang, WANG Huijun, DENG Zhongliang, WANG Ke, ZHOU Xiaotian

2020, 29(1): 183-189. doi: 10.1049/cje.2019.06.024

Abstract(445) PDF(355)

Abstract:
Satellite network is regarded as an important component of 5th Generation (5G). New deployment scenarios have been designed in form of satellite network fused 5G terrestrial network. Service requests in Non terrestrial networks (NTN) are noncentralized in the time and space domain, which cause services modelling uncertain and lack of balance between latency and bandwidth. Targeted at those bottlenecks, a Satellite service model based on State machine with tracking tree (SM-SMTT) and traffic allocation scheme base on cumulative entropy is proposed. Enhanced mobile broadband service, low latency service and massive machine type communications in satellite will be modelled by SM-SMTT. Cumulative entropy is introduced as the criterion to allocate traffic. According to the lower bound from cumulative entropy, the balance of latency and bandwidth is achieved in different deployment scenarios. The simulation in OPNET, demonstrations the new scheme has promoted the performance of delay and bandwidth utilization by 37.8% and 12.0%.

Power Optimization in Cell-Free Massive MIMO with Non-ideal Hardware Transceiver

ZHANG Yao, CAO Haotong, ZHOU Meng, LI Long, YANG Longxiang

2020, 29(1): 190-198. doi: 10.1049/cje.2019.12.005

Abstract(835) PDF(474)

Abstract:
A downlink cell-free massive Multipleinput multiple-output (mMIMO) system is explored. To investigate the impact of hardware distortion on downlink cell-free mMIMO, this paper recalls a well-established model of hardware impairment and derives a new closedform downlink per-user Spectral efficiency (SE) expression for distributed conjugate beamforming precoding. Based on this trackable expression, two power control algorithms, namely max-total-SE and mixed Quality-of-service (QoS) algorithms, are proposed. Numerical results indicate that the downlink per-user SE is primarily limited by the hardware quality of the receiver, especially when the user corresponds to the receiver. In addition, the proposed max-total-SE algorithm has a fast convergence rate and can significantly improve the total SE compared to the Equal power control (EPC) scheme. What's more, our mixed QoS algorithm also performs well in many respects.

2020 Vol. 29, No. 1

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Chinese Journal of Electronics