Channel Modeling And Estimation For Robust Mc-Ss Systems (original) (raw)
Minimum Mean Square Error channel estimates for DS-SSS in the presence of ISI
R esum e Cet article propose une am elioration de l'estimation de canal lorsque de petits facteurs d' etalements sont utilis es dans les syst emes a etalement de spectre par s equences directes. Nous tenons compte de la structure de l'interf erence entre symboles pour construire une estim ee optimale selon le crit ere de minimisation de l'erreur quadratique moyenne. Une am elioration signi cative des performances est ainsi obtenue pour le r ecepteur en râteau et l' egaliseur lin eaire.
Impact of Robust Channel Estimation for High Mobility Systems
Emerging technologies like L TE (long term evolution) and WiMAX (Worldwide Interoperability for Microwave Access) are advancing in order to respond the needs for future mobile wireless access systems, whereas the demand is continuously increasing the requirement on high data rate on every mobile application. In the third generation Partnership project-Long Term Evolution (3GPP L TE) of Fourth Generation (4G) provides greater speed compare with it is predecessor 3G and 2G. The higher data rate is achieved in the wireless environment by deployed OFDM (Orthogonal Frequency Division Multiple) and MIMO (Multiple Inputs and Multiple Outputs) technique. It employs smart selection and combining techniques in the receiver end to improve the signal quality. The sub-carrier fluctuates in different time slots with in an OFDM symbol as a cause of multipath fading (color noise environment). Here the priority is given to minimize the interference occurred among the symbols in the channels and compress the data stream for high speed digital data transmission. Estimating the channel as accurately as possible is essential. This paper constitutes for different channel estimation technique is implemented with OFDM and MIMO for high mobility. In this work an iterative channel estimation scheme is introduced that makes use of pilot symbols, Doppler spread information and previously estimated data symbols are utilized to estimate the channel accurately. The channel estimation techniques as Maximum Likelihood (ML), Minimum Mean
On the Robustness of MIMO LMMSE Channel Estimation
2010
The robustness of the linear minimum mean square error (LMMSE) channel estimator is studied with respect to the reliability of the estimated channel correlation matrix used for its implementation. The analysis is of interest in practical applications of multiple-input multiple-output (MIMO) systems, where a perfect estimate of the channel correlation matrix is not available. The channel estimation mean square error (MSE) is analytically analyzed assuming a general structure for the estimated channel correlation matrix used to implement the LMMSE channel estimator. The obtained results are successively detailed to the case of channel correlation matrices derived by sample correlation estimation methods. It is observed that the use of a coarse estimate of the channel correlation matrix can lead to a severe degradation on the LMMSE channel estimator performance, whereas the simpler least-square (LS) channel estimator may provide comparatively better results. Nevertheless, it is shown that a robust approach, although suboptimal, relies on implementing the LMMSE channel estimator by assuming transmissions over uncorrelated channels, since, with such an assumption, the resulting estimation MSE is certainly smaller than for the LS channel estimator.
Statistical modeling of the LMS channel
IEEE Transactions on Vehicular Technology, 2001
In this paper, a statistical model for the land mobile satellite (LMS) channel is presented. The model is capable of describing both narrow-and wide-band conditions. Other relevant characteristic is that it can be used to study links with geostationary as well as nongeostationary satellites. The model is of the generative type, i.e., it is capable of producing time series of a large number of signal features: amplitudes, phases, instantaneous power-delay profiles, Doppler spectra, etc. Model parameters extracted from a comprehensive experimental data bank are also provided for a number of environments and elevation angles at L-, S-, and Ka-Bands.
Iet Communications, 2009
A new variable step-size least mean squares (VSS-LMS) algorithm for the estimation of frequencyselective communications channels is herein presented. In contrast to previous works, in which the step-size adaptation is based on the instantaneous samples of the error signal, this algorithm is derived on the basis of analytical minimisation of the ensemble-averaged mean-square weight error. A very simple rule for step-size adaptation is obtained, using a small number of communication system parameters. This is another significant difference from other proposals, in which a large number of control parameters should be tuned for proper use. The algorithm here proposed is shown to be applicable to both time-varying and time-invariant scenarios. While the lack of a termination rule for step-size adaptation is a common characteristic of other schemes, the algorithm here presented adopts a criterion for stopping the step-size adaptation that assures optimal steadystate performance and leads to large computational savings. A simulation-based performance comparison with other VSS-LMS schemes is provided, including their application to maximum likelihood sequence estimation receivers using per survivor processing (MLSE/PSP). The results show that the algorithm proposed in this work has good performance characteristics and a very low computational cost, specially in the application to MLSE/ PSP receivers. Besides, this algorithm is shown to be robust to changes in the signal-to-noise ratio (SNR).
Signal design for LS and MMSE channel estimators
The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
We analyzed the effect of time and frequency domain windowing, or weighting, of the transmitted signal for least squares (LS) and minimum mean-square error (MMSE) estimators when the channel is time-variant. We considered the estimation error for different windows and we found that the windows can be selected independently. We explained the performance of the estimators with the help of the radar ambiguity function of the transmitted signal including the windowing.
A Conditional ${\ell}1$ Regularized MMSE Channel Estimation Technique for IBI Channels
IEEE Transactions on Wireless Communications, 2018
Inter-block-interference (IBI) caused as a result of pursuing the spectral efficiency can deteriorate channel estimation performance. For this problem, previously-proposed chained turbo estimation (CHATES) performs IBI cancelation by using the soft replica of the transmitted signal. The IBI cancelation technique can, however, suffer from a mean squared error (MSE) floor problem, since the soft replica is unavailable at the first turbo iteration. The IBI problem can be avoided by using channel impulse response (CIR) length constraint. Nevertheless, as shown in this paper, the IBI avoidance approach is difficult to be performed independently since it requires unbiased second-order statistics. This paper proposes, therefore, a new conditional ℓ1 regularized minimum mean square error (MMSE) channel estimation algorithm by jointly utilizing the IBI avoidance/cancelation and subspace techniques. Simulation results verify that the proposed algorithm solves the MSE floor problem, and, hence, improves the bit error rate (BER) convergence performance in realistic IBI channels including the effect of pulse shaping filters. Index Terms-Inter-block-interference (IBI), subspace-based channel estimation, ℓ1 norm regularization, compressive sensing, turbo equalization. I. INTRODUCTION T ERMINALS in a mobile Internet-of-Things (IoT) network are expected to work with limited batteries for long duration while being required to achieve high spectral efficiency demanded for most wireless communication systems (e.g., [1]). Uplink multiple-input multiple-output (MIMO) transmission (TX) assuming single-carrier frequency division multiple access (SC-FDMA) is, hence, an attractive option to improve energy-and spectral-efficiencies [2], [3] when we consider massive machine type communication (MTC) systems (e.g., [4]) and/or the physical layer security (e.g., [5]) for future IoT systems. There has been, however, criticism that it centralizes the complexity required for the whole system into