Krishnamurthy Giridhar

Improving channel estimation in OFDM systems for sparse multipath channels

We describe an algorithm for sparse channel estimation applicable to orthogonal frequency division multiplexing systems. The proposed algorithm uses a least squares (LS) technique for channel estimation and a generalized Akaike information criterion to estimate the channel length and tap positions. This effectively reduces the signal space of the LS estimator, and hence improves the estimation performance as demonstrated using computer simulations. For example, the proposed modified LS with sparse channel-estimation algorithm has a 5-dB lower mean square error in channel estimation when compared to the conventional approach , which translates to approximately 0.5 dB improvement in signal-to-noise ratio at the receiver.

Nonlinear techniques for the joint estimation of cochannel signals

Cochannel interference occurs when two or more signals overlap in frequency and are present concurrently. Unlike in spread-spectrum multiple-access systems where the different users necessarily share the same channel, cochannel interference is a severe hindrance to frequency- and time-division multiple-access communications, and is typically minimized by interference rejection/suppression techniques. Rather than using interference suppression, we are interested in the joint estimation of the information-bearing narrow-band cochannel signals. Novel joint estimators are proposed that employ a single-input demodulator with oversampling to compensate for timing uncertainties. Assuming finite impulse-response channel characteristics, maximum likelihood (ML) and maximum a posteriori (MAP) criteria are used to derive cochannel detectors of varying complexities and degrees of performance. In particular, a (suboptimal) two-stage joint MAP symbol detector (JMAPSD) is introduced that has a lower complexity than the single-stage estimators while accruing only a marginal loss in error-rate performance at high signal-to-interference ratios. Assuming only reliable estimates of the primary and secondary signal powers, a blind adaptive JMAPSD algorithm for a priori unknown channels is also derived. The performance of these nonlinear joint estimation algorithms is studied through example computer simulations for two cochannel sources.

Bayesian algorithms for blind equalization using parallel adaptive filtering

A new blind equalization algorithm based on a suboptimum Bayesian symbol-by-symbol detector is presented. It is first shown that the maximum a posteriori (MAP) sequence probabilities can be approximated using the innovations likelihoods generated by a parallel bank of Kalman filters. These filters generate a set of channel estimates conditioned on the possible symbol subsequences contributing to the intersymbol interference. The conditional estimates and MAP symbol metrics are then combined using a suboptimum Bayesian formula. Two methods are considered to reduce the computational complexity of the algorithm. First, the technique of reduced-state sequence estimation is adopted to reduce the number of symbol subsequences considered in the channel estimation process and hence the number of parallel filters required. Second, it is shown that the Kalman filters can be replaced by simpler least-mean-square (LMS) adaptive filters. A computational complexity analysis of the LMS Bayesian equalizer demonstrates that its implementation in parallel programmable digital signal processing devices is feasible at 16 kbps. The performance of the resulting algorithms is evaluated through bit-error-rate simulations, which are compared to the performance bounds of the maximum-likelihood sequence estimator. It is shown that the Kalman filter and LMS-based algorithms achieve blind start-up and rapid convergence (typically within 200 iterations) for both BPSK and QPSK modulation formats. >

Joint estimation algorithms for cochannel signal demodulation

Nonlinear algorithms for the joint recovery of cochannel narrowband signals are proposed. For finite impulse response channel characteristics, maximum likelihood and maximum a posteriori criteria are employed to derive cochannel demodulators of varying complexities and degrees of performance. The error rate performance of these joint estimation algorithms is examined through computer simulations.<<ETX>>

Joint demodulation of cochannel signals using MLSE and MAPSD algorithms

Sequence estimation and symbol detection algorithms for the demodulation of cochannel narrowband signals in additive noise are proposed. These algorithms are based on the maximum likelihood (ML) and maximum a posteriori (MAP) criteria for the joint recovery of both cochannel signals. The error rate performance characteristics of these nonlinear algorithms were investigated through computer simulations. The results are presented.<<ETX>>

Robust timing synchronization for OFDM based wireless LAN system

In this paper, a robust a and efficient frame detection and symbol timing synchronization technique suitable for IEEE 802.11a wireless LAN system is proposed. The proposed method does frame detection using a threshold comparison mechanism and performs orthogonal frequency division multiplexing (OFDM) symbol boundary detection using correlation techniques. This algorithm is a novel combination of self and cross correlation information to achieve symbol timing synchronization. The proposed algorithm can robustly detect the symbol boundary even under low SNRs, high frequency offset, and multipath.

Single Snapshot Spatial Smoothing With Improved Effective Array Aperture

Spatial smoothing is a widely used preprocessing scheme for direction-of-arrival (DOA) estimation of more than one source from a single snapshot, although the effective array aperture gets reduced by this process. In this paper we propose a preprocessing scheme applicable for DOA estimation algorithms that exploit the shift invariance property of the array steering matrix and call it spatial smoothing with improved aperture (SSIA). SSIA, when applied to a noise corrupted data vector, improves the effective array aperture significantly as opposed to conventional spatial smoothing. Simulations confirm the significant performance gain provided by SSIA in conjunction with Unitary ESPRIT.

Bayesian/decision-feedback algorithm for blind adaptive equalization

A new blind equalization algorithm is presented that incorporates a Bayesian channel estimator and a decision-feedback (DF) adaptive filter. The Bayesian algorithm operates as a preprocessor on the received signal to provide an initial estimate of the channel coefficients. It is an approximate maximum a posteriori (MAP) sequence estimator that generates reliable estimates of the transmitted symbols. These decisions are then filtered by an adaptive decision-feedback algorithm to further reduce the intersymbol interference. The new algorithm is more robustto catastrophic error propagation thanthe standard decision-feedback equalizer (DFE), with only a modest increase in the computational complexity.

Parametric Channel Estimation for Pseudo-Random Tile-Allocation in Uplink OFDMA

We consider the uplink channel estimation of a multipath wireless channel used for orthogonal frequency division multiple access (OFDMA) transmission, where the uplink uses a pseudo-random ldquotilerdquo allocation pattern. A tile is made of small number of physically adjacent data subcarriers along with a few embedded pilot subcarriers and an uplink sub-channel allocated to an user in OFDMA systems such as IEEE 802.16d/e wireless MAN consists of several such pseudo-randomly chosen tiles. While the embedded pilots enable intra-tile channel interpolation, such an estimation will have an error floor which degrades performance substantially for highly frequency selective channels. We propose a parametric channel estimation method applicable to such irregular and sparsely spaced pilots, that does not exhibit an error-floor over the nominal operating range of signal to noise ratios, even for highly selective channels. The proposed algorithm exploits the pilot structure in each tile in estimating the delay subspace corresponding to the parametric channel description. Although this algorithm is more computationally complex when compared to the intra-tile linear interpolator, it offers a greatly enhanced bit-error probability (BEP) performance with a significantly lower pilot overhead. The uncoded BEP expression for the proposed estimator are analytically derived. Simulation results provided compares the mean squared error performance of this parametric channel estimator with the Cramer-Rao bound and also illustrates the significantly improved BEP performance over the existing methods.

Closed-loop transmit diversity schemes for five and six transmit antennas

Closed-loop, rate-one, channel orthogonalized space-time block codes (CO-STBCs) for three and four transmit antennas using a single (real) phase feedback term have been proposed . These codes achieve full diversity and result in maximum likelihood (ML) decoding with only linear processing at the receiver similar to OSTBCs. In this paper, we propose a closed-loop STBC for five and six transmit antennas with rate 3/4, where the one parameter feedback angle can be evaluated in closed form. These codes achieve full diversity and are delay optimal. Simulation results comparing the error-rate performance of the CO-STBC with open-loop OSTBC as well as some quasiorthogonal space-time block codes are also provided. While the 1- to 5-dB gain accrued by the closed-loop scheme (over the open-loop methods) is not surprising, the novel contribution of this work is in the approach taken to derive the feedback parameter as a single phase term, which is purely a function of the channel gains.