Robust mean-squared error estimation in the presence of model uncertainties

We consider the problem of estimating an unknown parameter vector x in a linear model that may be subject to uncertainties, where the vector x is known to satisfy a weighted norm constraint. We first assume that the model is known exactly and seek the linear estimator that minimizes the worst-case mean-squared error (MSE) across all possible values of x. We show that for an arbitrary choice of weighting, the optimal minimax MSE estimator can be formulated as a solution to a semidefinite programming problem (SDP), which can be solved very efficiently. We then develop a closed form expression for the minimax MSE estimator for a broad class of weighting matrices and show that it coincides with the shrunken estimator of Mayer and Willke, with a specific choice of shrinkage factor that explicitly takes the prior information into account. Next, we consider the case in which the model matrix is subject to uncertainties and seek the robust linear estimator that minimizes the worst-case MSE across all possible values of x and all possible values of the model matrix. As we show, the robust minimax MSE estimator can also be formulated as a solution to an SDP. Finally, we demonstrate through several examples that the minimax MSE estimator can significantly increase the performance over the conventional least-squares estimator, and when the model matrix is subject to uncertainties, the robust minimax MSE estimator can lead to a considerable improvement in performance over the minimax MSE estimator.     

Robust joint optimization for MIMO AF multiple-relay systems with correlated channel uncertainties

In the paper, robust joint optimization of the source/relays precoders and destination equalizer is proposed for non-regenerative dual-hop multiple-input multiple-output (MIMO) amplify-and-forward (AF) multiple-relay systems with correlated channel uncertainties. By taking the imperfect channel state information (CSI) into consideration, the robust transceiver/relays joint optimization is developed based on the minimum mean-squared error (MMSE) criterion under individual power constraints at the source and relays. The optimization problem of precoding and amplifying matrices under power constraints belongs to neither concave nor convex so that a nonlinear matrix-form conjugate gradient (MCG) algorithm is applied to explore local optimal solutions. Simulation results illustrate that the robust transceiver/relays joint architecture for an AF-MIMO multiple-relay system outperforms the non-robust transceiver/relays design.     

Robust hybrid beamforming design for millimeter-wave massive MIMO systems

Hybrid analog-digital beamforming is recognized as a promising solution for a practical implementation of massive multiple-input multiple-output(MIMO) systems based on millimeter-wave(mmWave) technology. In view of the overwhelming hardware cost and excessive power consumption and the imperfection of the channel state information(CSI), a robust hybrid beamforming design is proposed for the mmWave massive MIMO systems, where the robustness is defined with respect to imperfect knowledge or error of the CSI at the transmitter due to limited feedback and/or imperfect channel estimation. Assuming the errors of the CSI are bounded, the optimal hybrid beamforming design with robustness is formulated to a mean squared error(MSE) minimization problem. An iterative semidefinite programming(SDP) based algorithm is proposed to obtain the beamforming matrices. Simulation results show that the proposed robust design can provide more than 4 dB performance gain compared to that of non-robust design.     

Linear equalizer in MIMO radio communications systems

A linear equalizer for MIMO radio communications systems has been considered. The weight coefficients of the MIMO equalizer were obtained in accordance with the criterion of minimum mean squared error. Using the computer simulation technique and analyzing the case study of HSDPA (High Speed Downlink Packet Access) channel it was shown that the application of equalizer made it possible to significantly improve the reception characteristics as compared with those of a conventional rake receiver.     

面向Massive MIMO低轨卫星通信系统的鲁棒高效波束成形设计

以低轨卫星通信系统能量效率最大化为优化目标,重点研究鲁棒波束成形设计。假设卫星侧配备大规模多输入多输出技术,综合考虑传播延迟和多普勒频移造成的信道状态信息误差影响,在发射功率受限和用户服务质量约束下,提出了一种鲁棒高效的波束成形方案。考虑到遍历用户速率和遍历信干噪比都没有闭合表达式,采用具备闭合表达式的近似值代替,同时,采用半正定规划算法将非凸二次约束二次规划形式问题进行等价转换,并提出了一种联合二次变换分式规划和凹凸过程的内外两层嵌套迭代算法。最后,采用一种惩罚函数算法解决半正定规划算法中存在的秩1约束问题。仿真结果表明,所提算法性能优于传统算法,且鲁棒性较高。     

Bitrate-maximizing time-domain equalizer design for DMT-based systems

A time-domain equalizer (TEQ) is inserted in discrete multitone (DMT) receivers to impose channel shortening. Many algorithms have been developed to initialize this TEQ, but none of them really optimizes the bitrate. We present a truly bitrate-maximizing TEQ (BM-TEQ) cost function that is based on an exact formulation of the subchannel signal-to-noise ratio as a function of the TEQ taps. The performance of this BM-TEQ comes close to the performance of the per-tone equalizer.     

Broadband hybrid model for relay-aided cooperative MIMO power line communication systems

A hybrid model for broadband multiple-input multiple-output (MIMO) relay-aided indoor power line communications (PLC) system was proposed in this paper. The proposed model combines the top-down and bottom-up approaches and extends to a two-hop relay-aided cooperative system with variable gain relay in amplify-and-forward (AF) mode. Based on the proposed PLC model and generated channel, the channel statistical characteristics are further investigated in 2MHz - 100MHz bandwidth. Simulated results show that the proposed model overcomes the difficulties that the existing models need a lot of topological information of the network or measurements information. It provides a practical simulation analysis method for cooperative relay MIMO-PLC system. The results also show that cooperative MIMO relaying communications can improve the indoor PLC performances and communication reliability.     

Robust cerebellar model articulation controller design for unknown nonlinear systems

In this study, a robust cerebellar model articulation controller (RCMAC) is designed for unknown nonlinear systems. The RCMAC is comprised of a cerebellar model articulation controller (CMAC) and a robust controller. The CMAC is utilized to approximate an ideal controller, and the weights of the CMAC are on-line tuned by the derived adaptive law based on the Lyapunov sense. The robust controller is designed to guarantee a specified H/sup /spl infin// robust tracking performance. In the RCMAC design, the sliding-mode control method is utilized to derive the control law, so that the developed control scheme has more robustness against the uncertainty and approximation error. Finally, the proposed RCMAC is applied to control a chaotic circuit. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performance with unknown the controlled system dynamics.     

Multiband-OFDM MIMO coding framework for UWB communication systems

The emerging ultrawideband (UWB) system offers a great potential for the design of high speed short-range wireless communications. In order to satisfy the growing demand for higher data rates, one possible solution is to exploit both spatial and multipath diversities via the use of multiple-input multiple-output (MIMO) and proper coding techniques. In this paper, we propose a general framework to analyze the performance of multiband UWB-MIMO systems regardless of specific coding schemes. A combination of space-time-frequency (STF) coding and hopping multiband OFDM modulation is also proposed to fully exploit all of the available spatial and frequency diversities, richly inherent in UWB environments. We quantify the performance merits of the proposed scheme in case of Nakagami-m frequency-selective fading channels. Different from the conventional STF coded MIMO-OFDM system, the performance of the STF coded hopping multiband UWB does not depend on the temporal correlation of the propagation channel. We show that the maximum achievable diversity of multiband UWB-MIMO system is the product of the number of transmit and receive antennas, the number of multipath components, and the number of jointly encoded OFDM symbols. Interestingly, the diversity gain does not severely depend on the fading parameter m, and the diversity advantage obtained under Nakagami fading with arbitrary m parameter is almost the same as that obtained in Rayleigh fading channels. Finally, simulation results are presented to support the theoretical analysis.     

A combining design of precoder and equalizer based on shared redundancy to improve performance of ISI MIMO systems

Wireless Networks - There have been a number of researches on block transmission systems via MIMO channels due to really high data transmission rate. However, because of the existence of...     

Fading correlations in wireless MIMO communication systems

We investigate the effects of fading correlations on wireless communication systems employing multiple antennas at both the receiver and the transmitter side of the link, so called multiple-input multiple-output (MIMO) systems. It turns out that the amount of transmitter sided channel knowledge plays an important part when dealing with fading correlations. Furthermore, the possible availability of time diversity in a time-selective channel can have essential influence on performance. To study the influence of time-selectivity, the concept of sample-mean outage is introduced and applied to information theoretic measures, like capacity or cutoff rate. It will be shown, that in some cases correlated fading may offer better performance than uncorrelated fading permits, which is due to exploitable antenna gain, that will also be defined in a general form for MIMO systems.     

Robust Kalman filters for linear time-varying systems withstochastic parametric uncertainties

We present a robust recursive Kalman filtering algorithm that addresses estimation problems that arise in linear time-varying systems with stochastic parametric uncertainties. The filter has a one-step predictor-corrector structure and minimizes an upper bound of the mean square estimation error at each step, with the minimization reduced to a convex optimization problem based on linear matrix inequalities. The algorithm is shown to converge when the system is mean square stable and the state space matrices are time invariant. A numerical example consisting of equalizer design for a communication channel demonstrates that our algorithm offers considerable improvement in performance when compared with conventional Kalman filtering techniques     

Robust filtering for 2-D state-delayed systems with NFT uncertainties

This paper is concerned with the robust filtering problem for two-dimensional (2-D) state-delayed systems with uncertainties represented by nonlinear fraction transformation. The authors first establish the stability H/sub /spl infin// performance and generalized H/sub 2/ performance criteria for the system. Based on the results, the authors propose efficient methods to solve the robust H/sub /spl infin// filtering, generalized H/sub 2/ filtering, and mixed generalized H/sub 2//H/sub /spl infin// filtering problems by using a parameter-dependent Lyapunov function approach. The methods involve solving linear matrix inequalities. Two examples are given to show the effectiveness of the proposed approach.     

Robust steady-state filtering for systems with deterministic and stochastic uncertainties

For uncertain systems containing both deterministic and stochastic uncertainties, we consider two problems of optimal filtering. The first is the design of a linear time-invariant filter that minimizes an upper bound on the mean energy gain between the noise affecting the system and the estimation error. The second is the design of a linear time-invariant filter that minimizes an upper bound on the asymptotic mean square estimation error when the plant is driven by a white noise. We present filtering algorithms that solve each of these problems, with the filter parameters determined via convex optimization based on linear matrix inequalities. We demonstrate the performance of these robust algorithms on a numerical example consisting of the design of equalizers for a communication channel.     

Adaptive bit rate maximizing time-domain equalizer design for DMT-based systems

The classical discrete multitone receiver as used in, e.g., digital subscriber line (DSL) modems, combines a channel shortening time-domain equalizer (TEQ) with one-tap frequency-domain equalizers (FEQs). In a previous paper, the authors proposed a nonlinear bit rate maximizing (BM) TEQ design criterion and they have shown that the resulting BM-TEQ and the closely related BM per-group equalizers (PGEQs) approach the performance of the so-called per-tone equalizer (PTEQ). The PTEQ is an attractive alternative that provides a separate complex-valued equalizer for each active tone. In this paper, the authors show that the BM-TEQ and BM-PGEQ, despite their nonlinear cost criterion, can be designed adaptively, based on a recursive Levenberg-Marquardt algorithm. This adaptive BM-TEQ/BM-PGEQ makes use of the same second-order statistics as the earlier presented recursive least-squares (RLS)-based adaptive PTEQ. A complete range of adaptive BM equalizers then opens up: the RLS-based adaptive PTEQ design is computationally efficient but involves a large number of equalizer taps; the adaptive BM-TEQ has a minimal number of equalizer taps at the expense of a larger design complexity; the adaptive BM-PGEQ has a similar design complexity as the BM-TEQ and an intermediate number of equalizer taps between the BM-TEQ and the PTEQ. These adaptive equalizers allow us to track variations of transmission channel and noise, which are typical of a DSL environment.     

多用户MIMO双向中继信道鲁棒预编码

在多用户MIMO双向中继系统中,若使用非理想信道模型,中继处仅能获得部分下行信道状态信息,这样将导致系统性能大幅度下降。由此提出基于最小信干噪比最大化的鲁棒预编码方案。仿真表明,该方案不仅可以使最差信干噪比最大化,也有效地改善了系统的比特误码率性能。     

Robust resource allocation scheme under channel uncertainties for LTE-A systems

Wireless Networks - Nowadays, resource allocation is one of the major problems in the cellular networks. Due to the increasing number of autonomous heterogeneous devices in future mobile networks,...     

Geodesical codebook design for precoded MIMO systems

We propose a numerical method for finding packings of multiple-input and multiple-output (MIMO) semi-unitary precoding matrices in Grassmannian manifolds with different metrics. The proposed expansion-compression algorithm (ECA) is practical, simple and produces efficient packings without the need for a sophisticated initialization. With chordal distance metric, the algorithm tends to converge into a degenerated point constellation, where two points contain identical as well as orthogonal columns and distance between them cannot increase further along geodesic. Therefore, we alternate between max-min and min-max clustering parts of ECA algorithm, where the latter prevents degenerated constellations. With Fubini-Study distance metric, the algorithm converges to best known packings without extra min-max processing.     

A nonlinear electrical equalizer with decision feedback for OOK optical communication systems

In this paper we introduce a nonlinear equalizer using the Radial Basis Function (RBF) network with decision feedback equalizer (DFE) for electronic dispersion compensation in optical communication systems with on-off-keying and a direct detection receiver. The RBF method introduces a non-linear equalization technique suitable for optical communication direct detection systems that include nonlinear transformation at the photodetector. A bit error rate performance comparison shows that the RBF with DFE out performs the RBF without DFE and achieves similar results provided by maximum likelihood sequence estimator.