Daljit K. Mehra

Tracking of time-varying channels using two-step LMS-type adaptive algorithm

This paper presents a modified version of the two-step least-mean-square (LMS)-type adaptive algorithm motivated by the work of Gazor. We describe the nonstationary adaptation characteristics of this modified two-step LMS (MG-LMS) algorithm for the system identification problem. It ensures stable behavior during convergence as well as improved tracking performance in the smoothly time-varying environments. The estimated weight increment vector is used for the prediction of weight vector for the next iteration. The proposed modification includes the use of a control parameter to scale the estimated weight increment vector in addition to a smoothing parameter used in the two-step LMS (G-LMS) algorithm, which controls the initial oscillatory behavior of the algorithm. The analysis focuses on the effects of these parameters on the lag-misadjustment in the tracking process. The mathematical analysis for a nonstationary case, where the plant coefficients are assumed to follow a first-order Markov process, shows that the MG-LMS algorithm contributes less lag-misadjustment than the conventional LMS and G-LMS algorithms. Further, the stability criterion imposes upper bound on the value of the control parameter. These derived analytical results are verified and demonstrated with simulation examples, which clearly show that the lag-misadjustment reduces with increasing values of the smoothing and control parameters under permissible limits

Adaptive Multiuser Channel Estimation using Reduced Kalman/LMS Algorithm

This paper presents an adaptive multiuser channel estimator using the reduced-Kalman least-mean-square (RK-LMS) algorithm. The frequency-selective fading channel is modeled as a tapped-delay-line filter with smoothly time-varying Rayleigh distributed tap coefficients. The multiuser channel estimator based on minimum-mean-square-error (MMSE) criterion is used to predict the filter coefficients. We also present its convergence characteristics and tracking performance using the RK-LMS algorithm. Unlike the previously available Kalman filtering algorithm based approach (Chen, Chen IEEE Trans Signal Process 49(7): 1523–1532, 2001) the incorporation of RK-LMS algorithm reduces the computational complexity of multiuser channel estimator used in the code division multiple access wireless systems. The computer simulation results are presented to demonstrate the substantial improvement in its tracking performance under the smoothly time-varying environment.

EM-MMSE based Channel Estimation for OFDM Systems

Accurate estimation of time-varying wireless channels is a difficult task in orthogonal frequency division multiplexing (OFDM) systems. Pilot based techniques have normally been employed for channel estimation. However, for time varying channels, pilot symbols need to be transmitted periodically to overcome the effect of time selective fading. Expectation-maximization (EM) based technique may be used in such situations avoiding the need for periodic retransmission of pilot symbols. In order to reduce the complexity of EM algorithm, we propose an EM-MMSE based iterative procedure to estimate the time-varying channel. The proposed algorithm is not only computationally simpler than EM method but also yields performance improvement over conventional pilot based methods.

Adaptive DFE Multiuser Receiver for CDMA Systems using Two-Step LMS-Type Algorithm: An Equalization Approach

This paper presents an adaptive decision feedback equalizer (DFE) based multiuser receiver for code division multiple access (CDMA) systems over smoothly time-varying multipath fading channels using the two-step LMS-type algorithm. The frequency-selective fading channel is modeled as a tapped-delay-line filter with smoothly time-varying Rayleigh-distributed tap coefficients. The receiver uses an adaptive minimum mean square error (MMSE) multiuser channel estimator based on the reduced Kalman least mean square (RK-LMS) algorithm to predict these tap coefficients (Kohli and Mehra, Wireless Personal Communication 46:507–521, 2008). We propose the design of adaptive MMSE feedforward and feedback filters by using the estimated channel response. Unlike the previously available Kalman filtering algorithm based approach (Chen and Chen, IEEE Transactions on Signal Processing 49:1523–1532, 2001), the incorporation of RK-LMS algorithm reduces the computational complexity of multiuser receiver. The computer simulation results are presented to show the substantial improvement in its bit error rate performance over the conventional LMS algorithm based receiver. It can be inferred that the proposed multiuser receiver proves to be robust against the nonstationarity introduced due to channel variations, and it is also beneficial for the multiuser interference cancellation and data detection in CDMA systems.

Affine projection adaptive algorithm for multiuser MMSE receivers in frequency-selective fading channels for asynchronous W-CDMA systems

In this paper we propose the use of affine projection adaptive (APA) filtering algorithm for multiuser adaptive minimum mean square error (MMSE) receivers for asynchronous wideband code division multiple access systems (W-CDMA) in multipath fading channels. Performance of APA algorithm is compared with least mean square (LMS), normalized least mean square (NLMS), and recursive least square (RLS) algorithms. It is found that APA algorithm outperforms LMS and NLMS. Though RLS, algorithm offers better convergence characteristics but at much higher computational complexity. Three adaptive multiuser receiver architectures are considered, all employ chip-matched filter (CMF) at their front end. In the first two architectures perfect estimation of time delay of individual user is assumed and one CMF is employed per user. The difference between two architectures is that, in the first the samples collected from CMFs are cross-coupled and in the second architecture samples collected for individual users are not combined. In the third architecture, there is no need to estimate the time delay, but fractional sampling is used. It is shown that first architecture outperforms the other two architectures.

Block adaptive interference suppression in DS-CDMA systems

In this letter, we introduce interference suppression in direct-sequence code-division multiple-access systems using the minimum-mean-square-error criterion based on a block adaptive filtering algorithm. Simulation results show that the proposed implementations of the block algorithm requires much lower computation as compared to recursive least square (RLS) algorithms while maintaining comparable convergence characteristics as that of the RLS algorithm. Also, the block algorithm is shown to be near-far resistant. It is concluded that the proposed implementation is suitable for moderate block sizes, which will have fast convergence while introducing small processing delay.

Simplified Semi-Blind Channel Estimation for Space---Time Coded MIMO-OFDM Systems

The combination of Space–Time Coded Multiple Input Multiple Output systems (STC-MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is recently being investigated as an effective means of providing high-speed data transmission over dispersive wireless channels. The strengths of the two techniques coalesce and render MIMO-OFDM systems robust to ISI and IBI. However, the decoding and demodulation of STC-OFDM needs reliable channel knowledge at the receiver, unless differential modulation techniques are used. Semi-blind methods for channel estimation are seen to provide the best trade-off in terms of bandwidth overhead, computational complexity and latency. The conventional Expectation-Maximization (EM) algorithm for semi-blind channel estimation improves a pilot-based estimate with a two step process; however, it is computationally complex to implement. In this paper, we propose a variant of the EM method, referred to as ML-EM, for semi-blind estimation of doubly dispersive channels in space–time coded MIMO-OFDM systems. Here, the conventional EM algorithm is coupled with the ML decoder for space time block codes (STBCs). The technique shows good performance, even in highly correlated antenna arrays, and is computationally simpler than conventional EM. The method incurs a training overhead of less than 1%, and performs close to exact CSI through iterative processing at the receiver.

Kalman Filter based Channel Estimation and ICI Suppression for High Mobility OFDM Systems

The use of orthogonal frequency division multiplexing (OFDM) in frequency selective fading environments has been well explored. However, OFDM is more prone to time selective fading as compared to single carrier systems. Rapid time variations destroy the subcarrier orthogonality and introduce inter-carrier interference (ICI). Besides this, obtaining reliable channel estimates for receiver equalization is a non-trivial task in rapidly fading systems. In our work, we have addressed the problem of channel estimation and ICI suppression by viewing the system as a state space model. Kalman filter is employed to estimate the channel; this is followed by a time domain ICI mitigation filter which maximizes the SINR (signal to interference plus noise ratio). This method is seen to provide significant SINR gain with low training overhead.

Cyclostationary spectrum sensing for OFDM signals in the presence of cyclic frequency offset

In this paper, we consider the problem of spectrum sensing in cognitive radio for OFDM signals using correlated pilots. The pilot correlation is exploited to detect the presence of a primary signal based on its cyclostationary properties. The performance of such detector relies on the exact value of the cyclostationary frequency of the signal of interest. However, in actual cases an offset may be caused in the cyclic frequency due to various reasons. Therefore there is a need to estimate the cyclic frequency offset in case of cyclostationary spectrum sensing for OFDM signals. In this paper, an iterative method to estimate and compensate the Cyclic Frequency Offset (CFO) is proposed and evaluated using simulations.

New results for probability of error performance in MMSE multiuser detection for CDMA

The minimum-mean-square-error (MMSE) linear multiuser detector considers multiple access interference (MAI) and background Gaussian noise asymptotically Gaussian for a large number of users in asynchronous code division multiple access (CDMA) system. For this asymptotic condition, the MMSE detector outperforms the decorrelating detector only if the value of normalized cross-correlation (NCC) for any pair of code sequence is less than or equal to numerically derived upper bounded value. The available results in literature have been derived for two-user case. In this paper, we have presented a general formula to calculate upper bound on NCC for arbitrary number of users under near-far situation. We also propose Chernoff bound on error probability of MMSE multiuser detector. Its proof based on divergence theorem and study of leakage coefficients for more than two users imposes the stringent condition on signal-to-noise ratio of desired user.