A Hybrid Integrated and Low-Cost Multi-Chip Broadband Doherty Power Amplifier Module for 5G Massive MIMO Application

In this paper,a hybrid integrated broadband Doherty power amplifier(DPA)based on a multi-chip module(MCM),whose active devices are fabricated using the gallium nitride(GaN)process and whose passive circuits are fabricated using the gallium arsenide(GaAs)integrated passive device(IPD)process,is proposed for 5G massive multiple-input multiple-output(MIMO)application.An inverted DPA structure with a low-Q output network is proposed to achieve better bandwidth performance,and a single-driver architecture is adopted for a chip with high gain and small area.The proposed DPA has a bandwidth of 4.4-5.0 GHz that can achieve a saturation of more than 45.0 dBm.The gain compression from 37 dBm to saturation power is less than 4 dB,and the average power-added efficiency(PAE)is 36.3%with an 8.5 dB peak-to-average power ratio(PAPR)in 4.5-5.0 GHz.The measured adjacent channel power ratio(ACPR)is better than50 dBc after digital predistortion(DPD),exhibiting satisfactory linearity.

用于5G多波束MIMO系统的先进低温共烧陶瓷封装的39 GHz双信道收发器芯片组

本文介绍了一种用于5G多输入多输出(MIMO)应用的39 GHz收发器前端芯片组。每个芯片包括两个可变增益的频率转换通道,可以同时支持两个独立波束,芯片还集成了一个本地振荡器链和数字模块,用于多芯片扩展和增益状态控制。为了提高射频性能,对前端系统的关键模块提出了几种电路级改进技术。此外,开发了一种先进的低温共烧陶瓷工艺来封装39 GHz双通道收发器芯片组,实现了低封装损耗和两个发射(TX)/接收(RX)通道之间的高隔离。进行了芯片级和系统级封装(SIP)测量,以演示收发器芯片组的性能。测量结果表明,TX SIP的最大增益为11 dB,饱和输出功率为10 dBm;RX SIP的最大增益为52 dB,噪声系数为5.4 dB,输出压缩点为7.2 dBm。该收发器的单通道通信链路测试表明,64正交调幅(QAM)调制的误差矢量幅度(EVM)为3.72%,频谱效率为3.25 bit·s^(−1)·Hz^(−1);256-QAM调制在1 m距离上的误差矢量幅度(EVM)为3.76%,频谱效率为3.9 bit·s^(−1)·Hz^(−1)。基于该芯片组,还开发了39 GHz多波束原型,用于执行5G毫米波应用的MIMO操作。单流和双流传输的空中通信链路表明,多波束原型机可以覆盖5~150 m的距离,吞吐量相当。

APPLICATION OF FRF ESTIMATOR BASED ON ERRORS-IN-VARIABLES MODEL IN MULTI-INPUT MULTI-OUTPUT VIBRATION CONTROL SYSTEM

The FRF estimator based on the errors-in-variables （EV） model of multi-input multi-output （MIMO） system is presented to reduce the bias error of FRF HI estimator. The FRF HI estimator is influenced by the noises in the inputs of the system and generates an under-estimation of the true FRF. The FRF estimator based on the EV model takes into account the errors in both the inputs and outputs of the system and would lead to more accurate FRF estimation. The FRF estimator based on the EV model is applied to the waveform replication on the 6-DOF （degree-of-freedom） hydraulic vibration table. The result shows that it is favorable to improve the control precision of the MIMO vibration control system.

A multi-input and multi-output design on automotive engine management system

Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069.

A TSE based design for MMSE and QRD of MIMO systems based on ASIP

A Taylor series expansion(TSE) based design for minimum mean-square error(MMSE) and QR decomposition(QRD) of multi-input and multi-output(MIMO) systems is proposed based on application specific instruction set processor(ASIP), which uses TSE algorithm instead of resource-consuming reciprocal and reciprocal square root(RSR) operations.The aim is to give a high performance implementation for MMSE and QRD in one programmable platform simultaneously.Furthermore, instruction set architecture(ISA) and the allocation of data paths in single instruction multiple data-very long instruction word(SIMD-VLIW) architecture are provided, offering more data parallelism and instruction parallelism for different dimension matrices and operation types.Meanwhile, multiple level numerical precision can be achieved with flexible table size and expansion order in TSE ISA.The ASIP has been implemented to a 28 nm CMOS process and frequency reaches 800 MHz.Experimental results show that the proposed design provides perfect numerical precision within the fixed bit-width of the ASIP, higher matrix processing rate better than the requirements of 5G system and more rate-area efficiency comparable with ASIC implementations.

Evaluation of Linear Precoding Schemes for Cooperative Multi-Cell MU MIMO in Future Mobile Communication Systems

In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear;this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.

Ordered successive noise projection cancellation algorithm for dual lattice-reduction-aided MIMO detection

A novel nonlinear multi-input multi-output MIMO detection algorithm is proposed which is referred to as an ordered successive noise projection cancellation OSNPC algorithm. It is capable of improving the computation performance of the MIMO detector with the conventional ordered successive interference cancellation OSIC algorithm. In contrast to the OSIC in which the known interferences in the input signal vector are successively cancelled the OSNPC successively cancels the known noise projections from the decision statistic vector. Analysis indicates that the OSNPC is equivalent to the OSIC in error performance but it has significantly less complexity in computation.Furthermore when the OSNPC is applied to the MIMO detection with the preprocessing of dual lattice reduction DLR the computational complexity of the proposed OSNPC-based DLR-aided detector is further reduced due to the avoidance of the inverse of the reduced basis of the dual lattice in computation compared to that of the OSIC-based one. Simulation results validate the theoretical conclusions with regard to both the performance and complexity of the proposed MIMO detection scheme.

Transfer-learning multi-input multi-output equalizer for mode-division multiplexing systems

We propose a transfer-learning multi-input multi-output(TL-MIMO)scheme to significantly reduce the required training complexity for converging the equalizers in mode-division multiplexing(MDM)systems.Based on a built three-mode(LP01,LP11a,and LP11b)multiplexed experimental system,we thoughtfully investigate the TL-MIMO performances on the three-typed data,collecting from different sampling times,launching optical powers,and inputting optical signal-to-noise ratios(OSNRs).A dramatic reduction of approximately 40%–83.33%in the required training complexity is achieved in all three scenarios.Furthermore,the good stability of TL-MIMO in both the launched powers and OSNR test bands has also been proved.

MIMO Soft-sensor Model of Nutrient Content for Compound Fertil- izer Based on Hybrid Modeling Technique

In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.

COMBINED TRANSMIT ANTENNA SELECTION AND DETECTION OVER SPATIAL CORRELATED MIMO SYSTEMS

In this paper a method that combines transmit antenna selection and reduced-constellation detection in spatially correlated Multi-Input Multi-Output （MIMO） fading channels is presented. To mitigate the performance degradation caused by the use of antenna selection that is based on correlation among columns, an iterative receiver scheme that uses only a subset of the constellation points close to the expected symbol vahle estimated in the previous iteration is proposed. The size of the subset can adapt to the maximum correlation of the sub-matrix after the simple antenna selection. Furthermore, the error rate performance of the scheme under linear Miniinutn Mean Square Error （MMSE） or Ordered Successive Interference Cancellation （OSIC） for the first run detection and different interleaver lengths is investigated while the transnlit antenna selection is considered. The simulation results show a significant advantage both for implementation complexity and for error rate performance under a fixed data rate.

MIMO RADAR IMAGING MODEL AND ALGORITHM

Based on the array architecture of multiple transmitting/receiving antennas, Multi-Input Multi-Output (MIMO) radar provides a new mechanism for radar imaging technology. In order to explore the processing approach to this imaging mechanism, the two dimensional (2D) imaging model of MIMO radar is established first, and the spatial sampling ability is analyzed from the concept of spatial convolution of the antenna elements. The target spatial spectral filling format of MIMO radar with monochromatic transmitting signal is described. High-resolution imaging capability of MIMO radar is analyzed according to spatial spectral coverage and the corresponding imaging algorithm is presented. Finally, field imaging experiment is used to demonstrate the superior imaging performance of MIMO radar.

MIMO Channel Measurement and Characterization at 6.0-6.4 GHz Under Typical Classroom Environment

An extensive 4 x 4 MIMO channel measurement is carried out at 6. 0-6. 4 GHz under a typical classroom environment with channel sounder based on vector network analyzer. Both LOS and NLOS scenarios are considered. The results on path loss, delay spread and spatial correlation are presented. The measurement shows that, for corridor coverage, 2x2 MIMO is more economical than 4x4 MIMO due to high correlation. In order to identify the unique characteristics at the high frequency band, the measured channel parameters at 6. 0-6.4 GHz are compared with those at 2. 45 GHz. The comparison shows that the shortened wavelength of this higher frequency band results in a great difference of channel characteristics. Therefore, our measurement results provide new gnidance for the design and development of the system working on 6. 0-6.4 GHz band.

A NOVEL ZERO-FORCING PRECODING METHOD FOR MULTI-CELL MIMO SYSTEMS

Precoding methods at the Base Station (BS) can be used to deal with the inter-cell interference and improve the signal quality of the user especially at the cell edge. In this paper, a novel Zero-Forcing (ZF) precoding method is proposed and investigated for multi-cell Multi-Input Multi-Output (MIMO) systems. We propose a relaxed ZF precoding method by relaxing the ZF criterion to some degree so that the inter-cell interference may not be zero. Complexity analysis shows that compared with the conventional ZF method, the additional computation complexity for the proposed method is trivial. Simulation results show that the proposed relaxed ZF method has better performance than the conventional ZF method in terms of the sum-rate, especially at low Signal to Noise Ratio (SNR).

SUBSPACE-BASED NOISE VARIANCE AND SNR ESTIMATION FOR MIMO OFDM SYSTEMS

This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.

Optimization of energy efficiency for the full-duplex massive MIMO systems

The energy efficiency（EE） for the full-duplex massive multi-input multi-output（MIMO） system is investigated. Given the transmit powers of both the uplink and the downlink, the closed-form solutions of the optimal number of antennas and the maximum EE are achieved in the high regime of the signal-to-noise ratio（SNR）. It is shown that the optimal number of antennas and the maximum EE gets larger with the increase in user numbers. To further improve the EE, an optimization algorithm with low complexity is proposed to jointly determine the number of antennas and the transmit powers of both the uplink and the downlink. It is shown that, the proposed algorithm can achieve the system performance very close to the exhaustive search.

ALGORITHMS AND ARCHITECTURE IMPLEMENTATIONS OF MIMO OFDM BASEBAND RECEIVER BASED ON THE SIMD DSP CORE

This letter presents a programmable single-chip architecture for Multi-lnput and Multi-Output （M1MO） OFDM baseband receiver. The architecture comprises a Single Instruction Multiple Data （SIMD） DSP core and three coprocessors that are used for synchronization, FFT and channel decoder. In this MIMO OFDM system, the Zero Correlation Zone （ZCZ） code is used as the synchronization word preamble of packet in the physical layer in order to avoid the interference from other transmitting antennas. Furthermore, a simple channel estimation algorithm is proposed which is appropriate tbr the SIMD DSP computation.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

Novel Grouped Probability Data Association Algorithm for MIMO Detection

To bridge the performance gap between original probability data association （PDA） algorithm and the optimum maximum a posterior （MAP） algorithm for multi-input multi-output （MIMO） detection, a grouped PDA （GP-PDA） detection algorithm is proposed. The proposed GP-PDA method divides all the transmit antennas into groups, and then updates the symbol probabilities group by group using PDA computations. In each group, joint a posterior probability （APP） is computed to obtain the APP of a single symbol in this group, like the MAP algorithm. Such new algorithm combines the characters of MAP and PDA. MAP and original PDA algorithm can be regarded as a special case of the proposed GP-PDA. Simulations show that the proposed GP-PDA provides a performance and complexity trade, off between original PDA and MAP algorithm.

INTERFERENCE SUPPRESSING FOR CELLULAR MIMO SYSTEM BASED ON TURBO RECEIVER

Space-Time Coding （STC）, which combines channel coding, modulation, and multiple transmit antennas, is a powerful scheme to achieve higher data rates and combat fading in wireless systems, in this paper, we propose a soil/cancellation turbo equalization scheme to suppress Co-Channel Interference （CCI） in STC systems. The simulation results show that the proposed method significantly improves the system＇s ability of interference suppression, while preserving the space-time structure.

Robust MIMO Precoding for Cognitive Multiuser Relay Networks with Imperfect Channel State Information

In this paper, a novel robust precoder with imperfect channel state information(CSI)is proposed for multi-input multi-output(MIMO)cognitive multiuser networks equipped with relays. In the proposed model, the secondary users(SUs)are allowed to share the spectrum with the primary users(PUs)when the interference temperature(IT)is below a specific threshold. The transmitting strategy of relays is amplify-and-forward(AF), and the CSI error is characterized in terms of spherical uncertainty region. A minmax problem for the transmit power of the relays is considered when the mean square error(MSE)of SUs and the IT of PU meet their corresponding thresholds, and it is transformed into a semi-definite programming(SDP)problem to search for the solution. Numerical simulations demonstrate the effectiveness of the proposed precoder.