Blind identification and equalization based on second-orderstatistics: a time domain approach

A new blind channel identification and equalization method is proposed that exploits the cyclostationarity of oversampled communication signals to achieve identification and equalization of possibly nonminimum phase (multipath) channels without using training signals. Unlike most adaptive blind equalization methods for which the convergence properties are often problematic, the channel estimation algorithm proposed here is asymptotically ex-set. Moreover, since it is based on second-order statistics, the new approach may achieve equalization with fewer symbols than most techniques based only on higher-order statistics. Simulations have demonstrated promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel     

Blind adaptive MIMO receivers for space-time block-coded DS-CDMA systems in multipath channels using the constant modulus criterion

We propose blind adaptive multi-input multi-output (MIMO) linear receivers for DS-CDMA systems using multiple transmit antennas and space-time block codes (STBC) in multipath channels. A space-time code-constrained constant modulus (CCM) design criterion based on constrained optimization techniques is considered and recursive least squares (RLS) adaptive algorithms are developed for estimating the parameters of the linear receivers. A blind space-time channel estimation method for MIMO DS-CDMA systems with STBC based on a subspace approach is also proposed along with an efficient RLS algorithm. Simulations for a downlink scenario assess the proposed algorithms in several situations against existing methods.     

Cumulant-based inverse filter criteria for MIMO blinddeconvolution: properties, algorithms, and application to DS/CDMAsystems in multipath

Tugnait (1995) and Chi and Chen proposed multi-input multi-output inverse filter criteria (MIMO-IFC) using higher order statistics for blind deconvolution of MIMO linear time-invariant systems. This paper proposes three properties on the performance of the MIMO linear equalizer associated with MIMO-IFC for any signal-to-noise ratio, including (P1) perfect phase equalization property, (P2) a relation to MIMO minimum mean square error (MIMO-MMSE) equalizer, and (P3) a connection with the one obtained by MIMO super-exponential algorithm (MIMO-SEA) that usually converges fast but does not guarantee convergence for finite data. Based on (P2), a fast algorithm for computing the theoretically optimum MIMO equalizer is proposed. Moreover, based on (P3), a fast MIMO-IFC based algorithm with performance similar to that of the MIMO-SEA and with guaranteed convergence is proposed as well as its application to suppression of multiple access interference and intersymbol interference (ISI) for multiuser asynchronous DS/CDMA systems in multipath. Finally, some simulation results are presented to support the analytic results and the proposed algorithms     

A multistep linear prediction approach to blind asynchronous CDMAchannel estimation and equalization

A multistep linear prediction approach is presented for blind channel estimation, multiuser interference (MUI) suppression, and detection of asynchronous short-code direct sequence code division multiple access signals in multipath channels. Only the spreading code of the desired user is assumed to be known; its transmission delay may be unknown. We exploit the previously proposed multistep linear prediction approach for blind multiple-input multiple-output channel estimation in conjunction with the structure imposed by the desired user's spreading code sequence. With the knowledge of the desired user's code sequence, only the second-order statistics of the data are needed under certain sufficient conditions on the underlying multiuser MIMO transfer function. Based on the desired user's channel estimate, a linear minimum mean square error filter is designed for simultaneous equalization and MUI suppression. Three illustrative simulation examples are presented     

Blind MAI and ISI suppression for DS/CDMA systems using HOS-basedinverse filter criteria

Cumulant-based inverse filter criteria (IFC) using second-and higher order statistics (HOS) proposed by Tugnait et al. (1993) have been widely used for blind deconvolution of discrete-time multi-input multi-output (MIMO) linear time-invariant systems with non-Gaussian measurements through a multistage successive cancellation procedure, but the deconvolved signals turn out to be an unknown permutation of the driving inputs. A multistage blind equalization algorithm (MBEA) is proposed for multiple access interference (MAI) and intersymbol interference (ISI) suppression of multiuser direct sequence/code division multiple access (DS/CDMA) systems in the presence of multipath. The proposed MBEA, which processes the chip waveform matched filter output signal without requiring any path delay information, includes blind deconvolution processing using IFC followed by identification of the estimated symbol sequence with the associated user through a user identification algorithm (UIA). Then, some simulation results are presented to support the proposed MBEA and UIA. Finally, some conclusions are drawn     

Closed-form blind channel identification and source separation inSDMA systems through correlative coding

We address the problem of blind identification of multiuser multiple-input multiple-output (MIMO) finite-impulse response (FIR) digital systems. This problem arises in spatial division multiple access (SDMA) architectures for wireless communications. We present a closed-form, i.e., noniterative, consistent estimator for the MIMO channel based only on second-order statistics. To obtain this closed form we introduce spectral/correlation asymmetry between the sources by filtering each source output with adequate correlative filters. Our algorithm uses the closed form MIMO channel estimate to cancel the intersymbol interference (ISI) due to multipath propagation and to discriminate between the sources at the wireless base station receiver. Simulation results show that, for single-user channels, this technique yields better channel estimates in terms of mean-square error (MSE) and better probability of error than a well-known alternative method. Finally, we illustrate its performance for MIMO channels in the context of the global system for mobile communications (GSM) system     

采用时域联合稀疏恢复的多输入多输出水声信道压缩感知估计

多输入多输出(MIMO)水声通信技术可以在极其有限的水声信道频带资源内提高信道容量,但多径和同道干扰的同时存在,使传统信道估计算法如最小二乘算法、压缩感知估计算法的性能急剧下降。考虑到通信数据块间水声信道多径结构存在一定的相关性,该文利用这种数据块间多径结构的时间域相关性建立水声MIMO信道的时域联合稀疏模型,并利用同步正交匹配追踪算法进行多个数据块联合稀疏恢复信道估计,提高MIMO信道多径稀疏位置的检测增益并抑制同道干扰,提高水声MIMO信道的估计性能。仿真和MIMO水声通信海试实验表明了所提方法的有效性。     

Blind MIMO system identification based on cumulant subspace decomposition

Blind identification of multiple-input multiple-output (MIMO) linear systems can be achieved by utilizing higher order statistics of the output signals. We study the blind identification of MIMO systems whose inputs are mutually independent, temporally white, non-Gaussian source signals. Based on sub-space analysis, we develop a new linear batch algorithm to identify MIMO systems from the common space of a set of fourth-order cumulant matrices of the channel outputs. Given knowledge of the channel orders, the identifiability conditions required by the proposed algorithm are properly established. Like most subspace-based approaches, this new algorithm remains sensitive to channel order overestimation. Simulation results illustrate its performance for various channel models.     

多径衰落信道下的一种盲多用户检测方法

多径衰落信道下的盲自适应多用户检测算法的运算复杂度通常都比较大,提出了一种基于MSWF和CMA的盲自适应多用户检测方法,该方法避免了矩阵求逆和信道估计运算,具有相对较小的运算复杂度。通过模拟实验比较了几种算法的误码率性能,结果表明,所提方法具有明显的优势。     

Blind identification of multipath channels: a parametric subspaceapproach

In this paper, blind identification of single-input multiple-output (SIMO) systems using second-order statistics (SOS) only is considered. Using the assumption of a specular multipath channel, we investigate a parametric variant of the so-called subspace method. Nonparametric subspace-based methods require precise estimation of the model order; overestimation of the model order leads to inconsistent channel estimates. We show that the parametric subspace method gives consistent channel estimates when only an upper bound of the channel order is known. A new algorithm, which exploits parametric information on the channel structure, is presented. A statistical performance analysis of the proposed parametric subspace criterion is presented; limited Monte Carlo experiments show that the proposed algorithm is second-order optimal for a large class of channels     

一种压缩感知信道估计中降低信道稀疏度的算法

针对基于压缩感知的稀疏多径信道估计,提出了一种利用智能天线波束赋形改造多径信道,从而改选信道稀疏度的方法。与MIMO系统压缩感知相比,在利用OMP重构算法、获得同样估计性能的前提下,需要的导频数大大减少,这样就可以节省更大的空间来传送用户数据,提高了系统的吞吐量。仿真结果也证实了本文提出方法的优越性。     

Iterative decoding and channel estimation for space-time BICM over MIMO block fading multipath AWGN channel

In this paper, we consider a generic model of space-time bit-interleaved coded modulation (ST-BICM) on a multiple-input multiple-output (MIMO) Rayleigh fading multipath channel. A practical low-complexity receiver structure performing iteratively MIMO data detection, channel decoding and channel estimation, is presented. The MIMO data detection, employing a reduced-state list-type soft output Viterbi algorithm enables to cope with severe channel intersymbol interference (ISI) without MIMO prefiltering. Among other results, simulations show that our approach can dramatically improve the downlink performance of time-division multiple access (TDMA) systems with high order modulation, keeping a reasonable complexity at the receiver side.     

A transmit MIMO scheme with frequency domain pre-equalization for wireless frequency selective channels

In this paper, we introduce a transmit multiple-input multiple-output (MIMO) scheme with frequency domain pre-equalization for a multipath or frequency selective channel. In this scheme, MIMO processing in the frequency domain is performed at the transmitter or base station so that the receiver or mobile station only requires limited processing. This scheme provides high data rates and also inherits from the frequency domain equalization the property of relatively low complexity in severe multipath environments. The MIMO transmit processing is derived by minimizing the minimum mean square errors (MMSE), and expressions for the signal-to-interference-plus-noise ratio and error probability based on the Gaussian approximation of the interference term are provided. Some important associated issues, such as channel errors and computational complexity, are also investigated. Numerical simulations are also provided and these demonstrate the improved performance of our proposed scheme compared to other transmit MIMO schemes. In particular, they show that the proposed system can attain multipath or frequency diversity of the channel.     

EM-based blind LDPC identification in multipath channels

As the advent of cognitive radios,blind encoder identification has attracted increasingly attentions since it plays an important role.The existing works mainly focus on additive white Gaussian noise (AWGN) channel,while the blind identification in multipath scenarios has not been sufficiently investigated.Considering the blind low density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel,a likelihood-based classifier was proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters.Then,an average log-likelihood ratio (LLR) estimator was adopted to classify the unknown encoder.Numerical results show that the proposed algorithm provides promising identification performance in multipath channels,especially in the low signal-to-noise ratio region.     

Closed-form correlative coding (CFC2) blindidentification of MIMO channels: isometry fitting to second orderstatistics

We present a blind closed-form consistent channel estimator for multiple-input multiple-output (MIMO) systems that uses only second order statistics. We spectrally modulate the output of each source by correlative coding it with a distinct filter. The correlative filters are designed to meet the following desirable characteristics: no additional power or bandwidth is required; no synchronization between the sources is needed; the original data rate is maintained. We first prove an identifiability theorem: under a simple spectral condition on the transmitted random processes, the MIMO channel is uniquely determined, up to a phase offset per user, from the second order statistics of the received data. We then develop the closed-form algorithm that attains this identifiability bound. We show that minimum-phase finite impulse response filters with arbitrary memory satisfy our sufficient spectral identifiability condition. This results in a computationally attractive scheme for retrieving the data information sequences after the MIMO channel has been identified. We assess the performance of the proposed algorithms by computer simulations. In particular, the results show that our technique outperforms the previously introduced transmitter-induced conjugate cyclostationarity approach when there are carrier frequency misadjustments     

MIMO-OFDM系统中信道盲估计与检测

针对MIMO-OFDM系统,提出了一种基于子空间的盲信道估计与检测方案,该算法将阵列信号处理的思想应用到MIMO-OFDM系统中,通过发送端信号的冗余编码,利用一种类ESPRIT算法进行盲信号检测和信道估计。仿真结果表明该算法的有效性及其信道盲估计方法的性能。     

Fast Recovery Blind Equalization for Time-Varying Channels Using “Run-and-Go” Approach

In this paper, we propose a blind adaptive equalization scheme for time-varying channels, which combines a blind algorithm based on high order statistics (HOS) and decision-directed (DD) LMS algorithm. In contrast to the ldquostop-and-gordquo algorithm , where DD-LMS adaptation is stopped for unreliable decisions, the proposed algorithm applies a blind algorithm based on HOS for the unreliable decisions. Furthermore, the region of reliable decisions is updated corresponding to the estimated signal quality. Hence, the proposed ldquorun-and-gordquo algorithm inherits MMSE performance of DD-LMS and the (re)acquisition ability of the blind algorithm. Especially, for decision feedback equalizers (DFEs) the proposed algorithm provides robust blind initialization and reacquisition ability under time varying multipath environments.     

Precoder design and blind identification of MIMO channels

In this paper, blind identification problem based on second-order statistics (SOS) of received signals is investigated for multiple-input multiple-output (MIMO) channels. It is shown that precoders introduced in transmitters can provide certain extra information for blind identification problem and commonly used identifiability conditions on MIMO channels can be relaxed. A design criterion for the precoders in frequency domain is proposed. Two blind identification algorithms based on the knowledge of the precoders and SOS of the received signals are then developed. In these algorithms, due to particular structure of the precoders, the blind identification problem for a MIMO channel is decomposed into several independent single-input multiple-output (SIMO) channel identification problems. The presented algorithms are robust to an overestimated channel order and not sensitive to signal-to-noise ratio (SNR). Compared with the existing results, a larger class of MIMO finite-impulse-response (FIR) channels can be identified by the new methods.     

Joint Semi-Blind Channel Estimation and Data Sequence Detection of OFDM Systems

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一种新的抗远近效应的盲DS-CDMA接收机

本文研究多径频率选择性衰落信道下直接序列扩频码分多址(DS-CDMA)信号的处理问题,提出了一种新型的盲接收机.该接收机不需预知多址干扰用户的扩频码,不需预知信道参数,只需已知期望用户的扩频码和粗略的定时,就可以完成用户的盲检测.同时,该接收机通过降维自适应去相关滤波和基于判决指导的自适应多径合并,来获得低成本和良好的抗衰落、抗远近效应性能.仿真结果验证了本文提出的接收机的优良性能.