Blind widely linear multiuser detection

Widely linear structures for multiuser detection of code division multiple-access signals are proposed, which jointly elaborate the received signal and its complex conjugate. Computer simulations show that the new structures significantly outperform the conventional linear ones, in terms of suppression capability of both wideband multiple-access and narrowband interference     

Linear filtering and piecewise linear correlation functions

For a class of piecewise linear correlation functions, it is shown that optimal linear mean-square filtering is achieved with a finite number of samples of the process for any finite observation interval. The class of correlation functions is defined by a particular property of the points at which they change slope. Conditions are discussed under which an arbitrary piecewise linear function is a correlation function. An example demonstrating various aspects of the theory is given, and applications of the theory are considered.     

Efficient SIC-MMSE MIMO detection with three iterative loops

In this paper, we propose an efficient joint iterative detection and decoding (JIDD) scheme with a soft interference cancellation minimum mean squared error (SIC-MMSE) based method for a turbo coded multiple-input multiple-output (MIMO) system. In the proposed method, we activate a loop inside the SIC-MMSE based MIMO detection process in addition to the iterative loop between the MIMO detector and turbo decoder, so that the iteration inside the SIC-MMSE detection can be performed in parallel to the iterations inside the turbo decoder. Subsequently, soft outputs from each loop is exchanged for next further iteration, and this makes three iterative loops in total. In comparison with the conventional JIDD schemes, employing additional loop inside the MIMO detection process largely contributes to enhance the performance. In addition, the additional loop speeds up the performance convergence and eventually requires smaller overall computational complexity at the same performance.     

Blind iterative decision-feedback multiuser detection of FEC-codedCDMA signals

This letter proposes a decision-feedback multiuser detector for coded CDMA in which training symbols are not used and hence the detector may be said to be blind. The algorithm proposed builds on the concept of the adaptive decision feedback detectors described in some earlier papers     

Superposition coded modulation and iterative linear MMSE detection

We study superposition coded modulation (SCM) with iterative linear minimum-mean-square-error (LMMSE) detection. We show that SCM offers an attractive solution for highly complicated transmission environments with severe interference. We analyze the impact of signaling schemes on the performance of iterative LMMSE detection. We prove that among all possible signaling methods, SCM maximizes the output signal-tonoise/ interference ratio (SNIR) in the LMMSE estimates during iterative detection. Numerical examples are used to demonstrate that SCM outperforms other signaling methods when iterative LMMSE detection is applied to multi-user/multi-antenna/multipath channels.     

Interference cancellation enhancement through generalized widely linear equalization in QAM systems

In this paper, we introduce a generalized widely linear (WL) equalizer for quadrature amplitude modulation (QAM) systems with single/multiple antennas. In our proposed implementation, the WL receiver first separates the in-phase (I) and quadrature (Q) parts of the complex-valued baseband received signal and jointly filters the two branches for signal detection. Infinite length WL minimum mean-square error (WL-MMSE) linear, and WL decision-feedback-equalizer (WL-DFE) settings are derived and performance is analyzed in co-channel interference limited channels. It is shown that, in frequency selective Rayleigh fading channels, the interference cancellation (IC) gain depends mainly on the rank (r) of the interference correlation matrix (ICM) which is defined as the covariance of the vector-valued signal which consists of the real and imaginary parts of the noise-plus-interference signal collected at multiple antenna branches. Assuming that the DFE feedback path is error free, we show that a WL QAM receiver with N antennas exhibits full IC capability (that is complete interference removal) when the ICM is rank deficient i.e., when: r ≪ 2N. This condition implies that a WL-DFE receiver can reject any combination of M1 pulse-amplitude-modulation (PAM) and M2 QAM interferers satisfying the constraint: M1 + 2M2 ≪ 2N. Simulation results show that, in the presence of PAM-type interference, the gain of WL-DFE is reduced by decision feedback errors while the IC benefit of WL-MMSE is limited by the noise enhancement problem. Nevertheless, the proposed receivers are shown to be useful in cellular systems that employ a combination of PAM and QAM schemes.     

On optimal linear filtering for edge detection

In this paper, we revisit the analytical expressions of the three Canny's (1983) criteria for edge detection quality: good detection, good localization, and low multiplicity of false detections. Our work differs from Canny's work on two essential points. Here, the criteria are given for discrete sampled signals, i.e., for the real, implemented filters. Instead of a single-step edge as input signal, we use pulses of various width. The proximity of other edges affects the quality of the detection process. This is taken into account in the new expressions of these criteria. We derive optimal filters for each of the criteria and for any combination of them. In particular, we define an original filter which maximizes detection and localization and a simple approximation of the optimal filter for the simultaneous maximization of the three criteria. The upper bounds of the criteria are computed which allow users to measure the absolute and relative performance of any filter (exponential, Deriche (1987), and first derivative of Gaussian filters are evaluated). Our criteria can also be used to compute the optimal value of the scale parameter of a given filter when the resolution of the detection is fixed.     

A PDF estimation-based iterative MIMO signal detection with unknown interference

The equivalent diversity order of iterative minimum mean squared error (MMSE) multiuser detectors for multiple input multiple output (MIMO) channels is decreased if they aim at suppressing unknown cochannel interference (UCCI) while detecting multiple users' signals. In this letter, we propose a new MIMO signal detection scheme with the aim to preserve the detector's diversity order by estimating the probability density function of the UCCI plus background noise. It is shown that the proposed detector significantly outperforms the conventional detector based on UCCI's covariance matrix estimation.     

Intensity spatial filtering applied to defect detection in integrated circuit photomasks

An intensity spatial filter has been devised and used for the semiautomatic photoelectric detection of 2.5-µ defects in 5-cm²photolithography masks for silicon integrated circuits. The filter is based on a simple geometric approximation to the form factor or envelope function for the intensity in the Fourier-transform plane, and permits small-area diffraction-limited illumination. This approach complements the Watkins' method.     

联合迭代滤波与压扩参数优化的OFDM信号峰平比抑制

针对现有基于压扩变换处理的信号峰平比抑制方法性能单一且参数固定等缺陷,提出一种联合迭代滤波与自适应压扩参数优化的OFDM信号峰平比抑制方案。该方案能够同时对信号的峰平比PAPR和接收端误码率BER性能进行联合优化,并在迭代过程中有效消除因信号幅度畸变所引起的带外频谱再生;所提信号压扩及解压扩函数形式简单,计算复杂度较小;推导并给出了该方案可获得的PAPR抑制增益和BER理论性能界。仿真结果表明,该方案可同时获得较好的信号PAPR抑制、误码率以及带外功率谱性能,并在迭代过程中对压扩参数进行自适应调整,能够有效提高算法的适用灵活性。     

Estimation of channel statistics for iterative detection of OFDM signals

Maximum likelihood sequence estimation for orthogonal frequency division multiplexing (OFDM) transmissions over unknown multipath fading channels is analytically infeasible for lack of efficient methods to maximize the likelihood function. A practical solution to this problem has been recently proposed in the context of space-time block-coded OFDM by resorting to the expectation-maximization (EM) algorithm. The resulting detector operates iteratively, exploiting knowledge of the channel statistics and the operating signal-to-noise ratio (SNR). In this work, we address the problem of estimating the above quantities and propose a recursive solution based on ad hoc reasoning. Simulations indicate that the EM detector employing the estimated SNR and channel statistics has better performance than other schemes operating in a mismatched mode. Also, the performance loss with respect to a system with perfect channel knowledge is negligible at SNR values of practical interest.     

On the performance of iterative noncoherent detection of codedM-PSK signals

Differential encoding is often used in conjunction with noncoherent demodulation to overcome carrier phase synchronization problems in communication systems employing M-ary phase-shift keying (M-PSK). It is generally acknowledged that differential encoding leads to a degradation in performance over absolutely encoded M-PSK systems with perfect carrier synchronization. In this paper, we show that when differential encoding is combined with convolutional encoding and interleaving, this degradation does not necessarily occur. We propose a novel noncoherent receiver for differentially encoded M-PSK signals that is capable of significantly outperforming optimal coherent receivers for absolutely encoded M-PSK using the same convolutional code. This receiver uses an iterative decoding technique and is based on a multiple differential detector structure to overcome the effect of the carrier phase error. In addition, to better illustrate the benefits of the powerful combination of convolutional encoding, interleaving, and differential encoding, we also present an iterative coherent receiver for differentially encoded M-PSK     

DORT method as applied to ultrawideband signals for detection of buried objects

The decomposition of the time reversal operator (DORT) method, originally developed in acoustics, allows detection of scatterers embedded in the probed domain and provides some very robust means for focusing an incident wave onto a given scatterer. Hence, this method is very helpful for clutter reduction. Here, it is applied to the detection of buried cylindrical objects with the help of electromagnetic ultrawideband signals. It is shown that when the set of antennas is located on a piece of line above an interface, the use of the DORT method remains simple, whatever the polarization, provided the contribution from the target can be separated from that of the interface. Using wideband signals also permits one to excite natural resonances of the buried scatterer, which can easily be extracted from the eigenvalues of the time reversal operator. Numerical examples based on a finite-difference time-domain algorithm are given.     

An improved Markov Chain Monte Carlo method for MIMO iterative detection and decoding

Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo （MCMC） simulation technique for Multiple-Input Multiple-Output （MIMO） systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability （APP） estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.     

An approximate MAP-based iterative receiver for MIMO channels using modified sphere detection

For coded multiple-input multiple-output spatial multiplexing (MIMO-SM) systems, the iterative receiver consisting of the MIMO detector and decoding can provide near optimal performance. While the sphere detection (SD) technique can be employed to implement the MIMO maximum likelihood (ML) detection with a lower complexity, some modifications of the SD have been proposed to provide a soft-decision for iterative receivers. In the paper, we propose an alternative approach that is based on a quadratic cost function to find the maximum a-posteriori (MAP) solution for the MIMO detection. Using the proposed approach, the MAP detection with the soft-decision can be straightforwardly implemented by the SD technique. Compared to the existing approach, in the new scheme, the soft-decision is well defined and it avoids a numerical instability in computing a soft-decision (which is an approximation of the log likelihood ratio (LLR)). Through simulation results, it is shown that the performance of the proposed scheme is comparable to that of the existing approach.     

Walsh transform applied to digital filtering

The filtering process can also be implemented by other transformations than the well-known Fourier transformation. The paper is devoted to the one-dimensional (1-D) and two-dimensional (2-D) digital filtering process by the use of 1-D and 2-D discrete Walsh transforms (DWTs). Particularly, the optimum Walsh filter problem with one- and two-dimensional discrete data with emphasis on reduction of the computational requirements is considered. The optimum Walsh filters which are determined are based upon some modified minimum-mean-square-error criterion. Two basic classes of signals, which are to be filtered, have been taken into account: periodic signals and stationary random processes. Two computational examples are given.     

Linear time-variant transformations of generalized almost-cyclostationary signals .I. Theory and method

The problem of linear time-variant filtering is addressed in the fraction-of-time (FOT) probability framework. The adopted approach, which is an alternative to the classical stochastic one, provides a statistical characterization of the system in terms of time averages of functions of time rather than ensemble averages of stochastic processes. Thus, it is particularly useful when stochastic systems transform ergodic input signals into nonergodic output signals, as it happens with several channel models encountered in practice. The analysis is carried out with reference to the wide class of the generalized almost-cyclostationary signals, which includes, as,a special case, the class of almost-cyclostationary signals. In this paper, systems are classified as deterministic or random in the FOT probability framework. Moreover, the new concept of expectation in the FOT probability framework of the impulse-response function of a system is introduced. For the linear time-variant systems, the higher order system characterization in the time domain is provided in terms of the system temporal moment function, which is the kernel of the operator that transforms the additive sinewave components contained in the input lag product into the additive sinewave components contained in the output lag product. Moreover, the higher order characterization in the frequency domain is also provided, and input/output relationships are derived in terms of temporal and spectral moment and cumulant functions. Developments and examples of application of the theory introduced here are presented in part II of this two-part paper.     

大规模MIMO系统低复杂度混合迭代信号检测

在大规模MIMO系统上行链路信号检测算法中,最小均方误差(MMSE)算法能获得接近最优的线性检测性能.但是,传统的MMSE检测算法涉及高维矩阵求逆运算,由于复杂度过高而使其在实际应用中难以快速有效地实现.基于最速下降(steepest descent,SD)算法和高斯一赛德尔(Gauss-Seidel,GS)迭代的方法提出了一种低复杂度的混合迭代算法,利用SD算法为复杂度相对较低的GS迭代算法提供有效的搜索方向,以加快算法收敛的速度.同时,给出了一种用于信道译码的比特似然比(LLR)近似计算方法.仿真结果表明,通过几次迭代,给出的算法能够快速收敛并接近MMSE检测性能,并将算法复杂度降低一个数量级,保持在O(K2).     

Linear time-variant transformations of generalized almost-cyclostationary signals.II. Development and applications

For pt. I see ibid., vol.50, no.12, p.2947-61 (2000). In Part I, the problem of the linear time-variant (LTV) filtering is addressed in the fraction-of-time (FOT) probability framework. The adopted approach, which is an alternative to the classical stochastic one, provides a statistical characterization of the systems in terms of functions that can be estimated by a single time-series. The analysis is carried out with reference to the wide class of the generalized almost-cyclostationary (GACS) signals, which includes, as a special case, the class of the almost-cyclostationary (ACS) signals. Examples of applications and developments of the theory introduced in Part I are presented here in Part II. Specifically, the countability of the set of the output cycle frequencies is studied with reference to linear time-variant systems for both ACS and GACS not containing any ACS component input signals. Thus, the linear almost-periodically time-variant filtering and the product modulation are considered in detail. Moreover, several Doppler channel models are analyzed. In all these examples, it is shown that the FOT probability approach allows one to characterize the system and its output in terms of statistical functions that can be measured by a single time-series. Furthermore, the usefulness of considering the linear filtering problem within the class of the GACS signals is clarified, and several pitfalls arising from continuing to adopt for the observed time-series the ACS model when an increase in the data-record length makes the GACS model more appropriate are pointed out.     

接近最优检测性能的低复杂度线性并行MIMO检测算法

在高阶正交幅度调制下,现有用于MIMO系统的并行检测算法复杂度极高,且随着天线数增多复杂度快速增加;而低复杂度的非并行检测算法与最优检测算法相比,其误比特率性能仍存在一定差距。针对上述问题,提出了一种接近最优检测性能的低复杂度并行MIMO检测算法,该算法基于信道分组检测的思想,对通过受噪声干扰严重的子信道信号采用遍历所有空间映射点的方式进行检测,对其余信号则采用新的基于lattice reduction的线性并行检测算法进行检测。仿真结果表明该算法在获得近似最优检测性能以及提高分集增益的同时,仍可保持较低的复杂度,且在高阶QAM调制方式下,复杂度降低尤为明显。