Low-complexity SIC-MMSE for joint multiple-input multiple-output detection

Iterative detection and decoding based on a soft interference cancellation–minimum mean squared error (SIC-MMSE) scheme provides efficient performance for coded MIMO systems. The critical computational burden for a SIC-MMSE detector in a MIMO system lies in the multiple inverse operations of the complex matrix. In this paper, we present a new method to reduce the complexity of the SIC-MMSE scheme based on a MIMO detection scheme that uses a single universal matrix with a non-layer-dependent inversion process. We apply the Taylor series expansion approach and derive a simple non-layer-dependent inverse matrix. The simulation results reveal that the utilization of the universal matrices presented in this paper produces almost the same performance as the conventional SIC-MMSE scheme but with low computational complexity.     

Spatial modulation over partially coherent multiple-input/multiple-output channels

Communication over multiple-input/multiple-output (MIMO) channels of arbitrary coherence is considered in light of a mean square estimation error (MSEE) criterion. Earlier work in the field has focused on fully coherent channels and determined that use of a singular value decomposition (SVD) of the channel transfer function matrix can realize the capacity of the MIMO channel. More recently, research has shown that the use of arbitrary orthonormal channel excitation vectors can maximize expected capacity over fully incoherent Rayleigh fading MIMO channels. Partially coherent channels have generally been examined only in terms of their degrading influence on capacity. In this discussion, channel excitation techniques are proposed that minimize an MSEE criterion over an ensemble of MIMO channels of arbitrary coherence. The algorithms rely on only the second-and fourth-order moments of the channel transfer function. Two experiments were conducted to examine the new strategies. Using measured MIMO channel transfer function ensembles-one from an underwater acoustic channel and others from RF wireless channels-the performance of the strategies are compared. The new techniques outperform orthonormal signaling based on SINR or capacity metrics while requiring substantially less channel feedback than needed by a channel decomposition approach.     

A three-dimensional two-hemisphere model for unmanned aerial vehicle multiple-input multiple-output channels

The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three-dimensional multiple-input multiple-output concentric two-hemisphere model is proposed to characterize the scattering environment around a vehicle in an urban UAV-to-vehicle communication scenario. Multipath components of the model consisted of line-of-sight and single-bounced components. This study focused on the key parameters that determine the scatterer distribution. A time-variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space–time–frequency correlation function, Doppler power spectral density, level-crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level-crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level-crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry-based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.     

Multistep linear predictors-based blind identification andequalization of multiple-input multiple-output channels

Channel estimation and blind equalization of multiple-input multiple-output (MIMO) communications channels is considered using primarily the second-order statistics of the data. Such models arise when single receiver data from multiple sources is fractionally sampled (assuming that there is excess bandwidth) or when an antenna array is used with or without fractional sampling. We consider the estimation of (partial) channel impulse response and design of finite-length minimum mean-square error (MMSE) blind equalizers. We extend the multistep linear prediction approach to MIMO channels where the multichannel transfer function need not be column reduced. Moreover, we allow infinite impulse response (IIR) channels as well as the case where the “subchannel” transfer functions have common zeros. In the past, this approach has been confined to SIMO finite impulse response (FIR) channels with no common subchannel zeros. A related existing approach applicable to MIMO channels is restricted to FIR column-reduced systems with equal length subchannels. In our approach, the knowledge of the nature of the underlying model (FIR or IIR) or the model order is not required. Our approach works when the “subchannel” transfer functions have common zeros, as long as the common zeros are minimum-phase zeros. The sources are recovered up to a unitary mixing matrix and are further “unmixed” using higher order statistics of the data. Illustrative computer simulation examples are provided     

Channel prediction for precoded spatial multiplexing multiple-input multiple-output systems in time-varying fading channels

Conventional precoded spatial multiplexing multiple-input multiple-output (MIMO) systems using limited feedback are mainly based on the notion of time invariant channels throughout transmission. Consequently, the precoding matrix can be found during the training symbols and used over the subsequent data symbols. In this study, the authors consider a more practical system where the channel varies from one block of symbols to another. In such a scenario, the precoding matrix designed at the receiver based on the previous training symbols becomes outdated, which results in significant system performance degradation. In order to avoid this problem and reduce performance degradation, the authors propose the use of a Kalman filter linear predictor at the receiver to provide the transmitter with the precoding matrix for the next block of symbols. The performance of this method is assessed using computer simulation, and the obtained results for the proposed channel prediction demonstrate improved bit error rate performance for time-varying Rayleigh fading channels.     

RF multiple-input multiple-output switchless front-end

RF multiple-input multiple-output circuits are normally implemented with the beam-forming network between the amplifier and antenna. Here the amplifier is placed between the beam-forming network and antenna, thus improving the performance in terms of power loss and noise figure as well as allowing operation for beam steering or spatial diversity     

Macrocell multiple-input multiple-output system analysis

We develop a semi-deterministic semi-stochastic channel model for the multiple-input multiple-output (MIMO) system under the macrocell environment with local-to-mobile and local-to-base scatterers. We show that employing closely-spaced antennas (e.g., phased array) at the base station is capable of achieving diversity via the local-to-base scatterers, which avoids impractical large aperture requirement for the spatial diversity at the base station. We evaluate the system performance in terms of ergodic capacity, average pairwise error probability (PEP), and signal-to-noise ratio (SNR); derive closed-form expressions for lower and upper bounds on the capacity and PEP; and show that the capacity, multiplexing and diversity gains are limited by the number of multipaths around the base station. The base-station array affects the lower bound on the capacity and the upper bound on the error probability through the same metric; thus, optimal design of the base station array based on this metric will optimize the two different information theoretic measures simultaneously. The fading correlation matrix also appears in the two bounds in the same form. To improve the performance of the macrocell MIMO system, we propose using artificial scatterers and discuss optimal design issues. Numerical examples demonstrate the accuracy of our analytical results and tightness of performance bounds.     

Linear receivers for the multiple-input multiple-output multiple-access channel

We consider a multiuser multiple-input multiple-output (MIMO) communication system using code-division multiple access (CDMA) and multiuser detection to discriminate the different users. Our focus is on the CDMA uplink of a frequency-nonselective Rayleigh fading channel. We study two types of receivers: joint receivers, which address simultaneously both spatial and multiple-access interference; and separate receivers, addressing the two types of interference individually. This approach allows assessing the benefits of adding MIMO processing capabilities to existing multiuser single-input single-output systems. For both receiver types, we analyze solutions based on linear (matched filter, decorrelator, minimum mean-square error) and maximum-likelihood receivers. For all the receivers considered, we provide closed-form expressions (as expectations of given functions) of the resulting pairwise error probabilities. Performance results are obtained in terms of frame-error rate versus E/sub b//N/sub 0/, following two different approaches. An analytic approach using large-system asymptotic methods, whereby the system parameters (number of users and antennas, spreading gain) are assumed to grow to infinity with finite limiting ratios. A computer-simulation approach is used to illustrate the differences between asymptotic and simulation results.     

Blind adaptive multiple-input decision-feedback equalizer with aself-optimized configuration

This paper introduces a novel blind adaptive multiple-input decision-feedback equalizer (MI-DFE) which is basically characterized by its ability to self-optimize its configuration, in terms of both structure and criteria, according to the severity of the transmission medium. In the first running mode, the novel equalizer is recursive, linear and “blindly” adapted by criteria leading to a solution closely related to the minimum MSE solution. In the second running mode, it becomes the conventional MI-DFE. From the viewpoints of both robustness and spectral efficiency, this equalizer proves to be very attractive since it avoids pathological behaviors, often encountered with the conventional trained MI-DFE, while requiring no training sequence. Furthermore, its very high speed of convergence renders it competitive in various standard applications, even in the case of burst mode transmission systems. Finally, the novel blind MI-DFE has been successfully tested on underwater acoustic communications signals, in a very severe context. The results are clearly convincing     

SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems

This paper addresses the problem of multiple-input multiple-output (MIMO) frequency nonselective channel estimation. We develop a new method for multiple variable regression estimation based on Support Vector Machines (SVMs): a state-of-the-art technique within the machine learning community for regression estimation. We show how this new method, which we call M-SVR, can be efficiently applied. The proposed regression method is evaluated in a MIMO system under a channel estimation scenario, showing its benefits in comparison to previous proposals when nonlinearities are present in either the transmitter or the receiver sides of the MIMO system.     

Frequency-selective multiple-input multiple-output channel estimation with transmitter non-linearities

The authors propose an algorithm based on the knowledge of training sequences to obtain an asymptotically unbiased estimator of non-linear multiple-input multiple-output (MIMO) channels, which involves the radio frequency front-end non-linearity and linear frequency selective MIMO channels. Although the impact of non-linearity in the transmitter side has been widely studied, most work on the channel estimation assumes linear channel models and ignores the non-linear effects. In this study, we develop a nonlinear channel estimator that can simultaneously estimate the linear MIMO channel model and non-linearity of the transmitter is developed. With these two sets of parameters, the non-linear channel model can be fully described. This channel estimation algorithm is implemented over an empirical MIMO channel model using an orthogonal frequency division multiplexing system.     

Energy-efficient hybrid beamforming for millimeter-wave-based massive multiple-input multiple-output system

The advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave-based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub-connected with phase shifter selection (SPSS), and fully connected and sub-connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency.     

Independent component analysis for multiple-input multiple-output wireless communication systems

Independent component analysis (ICA), an efficient higher order statistics (HOS) based blind source separation technique, has been successfully applied in various fields. In this paper, we provide an overview of the applications of ICA in multiple-input multiple-output (MIMO) wireless communication systems, and introduce some of the important issues surrounding them. First, we present an ICA based blind equalization scheme for MIMO orthogonal frequency division multiplexing (OFDM) systems, with linear precoding for ambiguity elimination. Second, we discuss three peak-to-average power ratio (PAPR) reduction schemes, which do not introduce any spectral overhead. Third, we investigate the application of ICA to blind compensation for inphase/quadrature (I/Q) imbalance in MIMO OFDM systems. Finally, we present an ICA based semi-blind layer space-frequency equalization (LSFE) structure for single-carrier (SC) MIMO systems. Simulation results show that the ICA based equalization approach provides a much better performance than the subspace method, with significant PAPR reduction. The ICA based I/Q compensation approach outperforms not only the previous compensation methods, but also the case with perfect channel state information (CSI) and no I/Q imbalance, due to additional frequency diversity obtained. The ICA based semi-blind LSFE receiver outperforms its OFDM counterpart significantly with a training overhead of only 0.05%.     

Modified-rate-quantization algorithm for multiple-input multiple-output systems under imperfect channel knowledge

In this paper, a modified-rate-quantization algorithm for multiple input multiple output (MIMO) systems is proposed using singular-value decomposition (SVD). This low complexity scheme adapts the subchannel transmit power and spectral efficiency in the spatial and temporal domains under transmit power and instantaneous bit error rate (BER) constraints. It is shown that with five discrete-rate levels, the proposed scheme reaches a spectral efficiency performance similar to the scheme with a continuous rate. The robustness of the proposed scheme to channel state information (CSI) imperfections is also studied. The obtained results show that the spectral efficiency is unaffected up to a certain level, but the bit error rate (BER) performance is particularly sensitive to these imperfections, especially at high SNR levels. Indeed, this ideally designed MIMO system over-estimates the subchannels, which leads to a deterioration of the BER performance. A new version of this algorithm, which is suitable for vertical Bell Labs layered space–time (V-BLAST) systems, is also presented. Through simulation results, it appears that the extended algorithm allows to reach a better performance in terms of spectral efficiency than other known schemes, but it is more sensitive to imperfect CSI than the first version.     

Design of a mixed fuzzy controller for multiple-input multiple-output systems

Multiple-input multiple-output (MIMO) systems usually have characteristics of nonlinear dynamics coupling. Therefore, the difficulty in controlling MIMO systems is how to overcome the coupling effects between the degrees of freedom. The computational burden and dynamic uncertainty associated with MIMO systems make model-based decoupling impractical for real-time control. This work develops a mixed fuzzy controller (MFC) to solve this problem and improve control performance. This study first designs a traditional fuzzy controller (TFC) from the viewpoint of a single-input single-output (SISO) system for controlling each degree of freedom of a MIMO system. Then, an appropriate coupling fuzzy controller is also designed according to the characteristics of the system’s dynamics coupling and incorporated into a TFC to compensate for coupling effects between the degrees of freedom. This control strategy can not only simplify the implementation problem of fuzzy control, but also improve control performance. The state-space approach for analyzing the stability of fuzzy control systems is applied to evaluate the stability and robustness of this intelligent mixed fuzzy controller. To verify the applicability of the proposed mixed fuzzy controller, this work presents a two-link robotic manipulator with a complex dynamic model for a MIMO system to evaluate the stability and robustness of the MFC by numerical simulation, and to examine the control performance by comparing the simulation results of the MFC with those of a TFC for this MIMO system.     

Equal gain transmission in multiple-input multiple-output wireless systems

Multiple-input multiple-output (MIMO) wireless systems are of interest due to their ability to provide substantial gains in capacity and quality. The paper proposes equal gain transmission (EGT) to provide diversity advantage in MIMO systems experiencing Rayleigh fading. The applications of EGT with selection diversity combining, equal gain combining, and maximum ratio combining are addressed. It is proven that systems using EGT with any of these combining schemes achieve full diversity order when transmitting over a memoryless, flat-fading Rayleigh matrix channel with independent entries. Since, in practice, full channel knowledge at the transmitter is difficult to realize, a quantized version of EGT is proposed. An algorithm to construct a beamforming vector codebook that guarantees full diversity order is presented. Monte-Carlo simulation comparisons with various beamforming and combining systems illustrate the performance as a function of quantization.     

多输入多输出雷达发射阵列误差联合校正方法

针对多输入多输出雷达发射阵列提出了一种同时校正阵元位置误差、幅相误差和阵元互耦的新方法.该方法首先通过多次旋转天线阵列进行采样,获得来自多个方位的实验数据,然后对数据的协方差矩阵进行特征分解,得到实际阵列流型的估计值并构造代价函数,最后通过迭代算法估计所有的误差参数,并对误差进行校正.计算机仿真及实际天线阵列的校正实验均验证了该方法的有效性.     

On a whitening approach to partial channel estimation and blindequalization of FIR/IIR multiple-input multiple-output channels

Channel estimation and blind equalization of multiple-input multiple-output (MIMO) communications channels is considered using primarily the second-order statistics of the data. Such models arise when a single receiver data from multiple sources is fractionally sampled (assuming that there is excess bandwidth) or when an antenna array is used with or without fractional sampling. We consider estimation of (partial) channel impulse response and design of finite-length minimum mean-square error (MMSE) blind equalizers. The basis of the approach is the design of a zero-forcing equalizer that whitens the noise-free data. We allow infinite impulse response (IIR) channels. Moreover, the multichannel transfer function need not be column reduced. Our approaches also work when the “subchannel” transfer functions have common zeros as long as the common zeros are minimum-phase zeros. The channel length or model orders need not be known. The sources are recovered up to a unitary mixing matrix and are further “unmixed” using higher order statistics of the data. A linear prediction approach is also considered under the above conditions of possibly IIR channels, common subchannel zeros/factors, and not-necessarily column reduced channels. Four illustrative simulation examples are provided     

Hybrid optimization-based deep neuro-fuzzy network for designing m-user multiple-input multiple-output interference channel

Multiple-input multiple-outputs (MIMOs) have eminent quality in maximizing the throughput of wireless communication models. In MIMO, the antenna arrays can be utilized for fulfilling the needs of 5G by utilizing various spatial signatures of users. Even though 5G communication is imminent, there exist issues, like network interference that arise due to reused frequency spectrum resources. This delving presents an optimized deep model for suppressing interference occurring in the Rayleigh channel in the multiple-user MIMO (MU-MIMO) model. Here, an MU-MIMO model is employed with correlated interference wherein there exist various users around the base station (BS) with several antennas at the transmitter and receiver. Here, a deep neuro-fuzzy network (DNFN) is used to upgrade the performance of detectors underneath correlated interference. Here, the model comprises zero forcing-maximum likelihood detection (ZF-MLD) that assists to generate an initial estimate of broadcasted signals in a particular time slot. The DNFN is used to capture latent correlation among several symbols. Here, the DNFN training is performed using developed autoregressive Henry gas spider monkey optimization (RHGSMO), which is the combination of conditional autoregressive value at risk (CAViaR), Henry gas solubility optimization (HGSO), and spider monkey optimization (SMO). With the lowest symbol error rate (SER), bit error rate (BER), and signal to interference and noise ratio (SINR), the suggested RHGSMO-based DNFN performed better than existing approaches.     

Cross-layer based transmit antenna selection for decision-feedback detection in correlated Ricean MIMO channels

In this paper, we investigate a cross-layer transmit antenna selection (AS) approach for the decision-feedback detector (DFD) over spatially correlated flat Ricean fading multiple-input multiple-output (MIMO) channels. Closed-form expressions for the system throughput with both perfect and imperfect channel estimation are derived. Considering a training-based channel estimation technique, we show that the capacity-based AS is more robust to imperfect channel estimation. However, in all cases, the cross-layer AS delivers higher throughput gains than the capacity-based AS.