Ronald A. Iltis

A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems

The use of multiple transmit/receive antennas forming a multiple-input multiple-output (MIMO) system can significantly enhance channel capacity. This paper considers a V-BLAST-type combination of orthogonal frequency-division multiplexing (OFDM) with MIMO (MIMO-OFDM) for enhanced spectral efficiency and multiuser downlink throughput. A new joint data detection and channel estimation algorithm for MIMO-OFDM is proposed which combines the QRD-M algorithm and Kalman filter. The individual channels between antenna elements are tracked using a Kalman filter, and the QRD-M algorithm uses a limited tree search to approximate the maximum-likelihood detector. A closed-form symbol-error rate, conditioned on a static channel realization, is presented for the M=1 case with QPSK modulation. An adaptive complexity QRD-M algorithm (AC-QRD-M) is also considered which assigns different values of M to each subcarrier according to its estimated received power. A rule for choosing M using subcarrier powers is obtained using a kernel density estimate combined with the Lloyd-Max algorithm.

Joint estimation of PN code delay and multipath using the extended Kalman filter

The problem of delay estimation in the presence of multipath is considered. It is shown that the extended Kalman filter (EKF) can be used to obtain joint estimates of time-of-arrival and multipath coefficients for deterministic signals when the channel can be modeled as a tapped-delay line. Simulation results are presented for the EKF joint estimator used for synchronization in a direct-sequence spread-spectrum system operating over a frequency-selective fading channel. A simplified model of the EKF joint estimator is considered for analysis purposes. The evolution in time of the tracking error probability density function and the nonlinear tracking error variance are examined through numerical solution of the Chapman-Kolmogorov equation. The nonlinear tracking error variance is compared to both the linear error variance estimate directly provided by the EKF and the Cramer-Rao lower bound. >

Joint detection and channel estimation algorithms for QS-CDMA signals over time-varying channels

We consider a quasi-synchronous code-division multiple access (QS-CDMA) cellular system, where the code delay uncertainty at the base station is limited to a small number of chips. For such QS-CDMA systems, the need for code acquisition is eliminated, however, the residual code tracking and channel estimation problems still have to be solved. An extended Kalman filter (EKF) is employed to track the user delays and channel coefficients. By separating data detection, based on the QR decomposition combined with the M-algorithm (QRD-M) from the delay/channel estimation process, the computational complexity can be significantly reduced as the number of users increases. Simulations show that the EKF channel estimator performance is improved when the QRD-M algorithm is used instead of the MMSE detector or decorrelator for data decisions.

A digital DS spread-spectrum receiver with joint channel and Doppler shift estimation

A digital spread-spectrum receiver design is presented for communication over multipath channels with severe Doppler shifts. The characteristics of the underwater channel relevant to spread-spectrum system design are discussed, and a channel model for short-range communications (less than 10 km) is defined. The receiver considered uses a digital coherent RAKE combiner, coupled with an extended Kalman filter (EKF)-based estimator for channel parameters and pseudonoise code delay. Receiver performance is evaluated by computing average bit-error rate (BER) versus iterations of the EKF joint estimator, using both fixed and time-varying channels. It is shown that the BER obtained using the EKF joint estimator closely tracks the optimum BER obtained when the channel, delay, and Doppler parameters are known exactly. Finally, the Cramer-Rao lower bound for time-invariant joint channel, delay, and Doppler estimation is derived, and compared with the ensemble averaged mean-squared error of the EKF estimator. >

Neural solution to the multitarget tracking data association problem

The problem of tracking multiple targets in the presence of clutter is addressed. The joint probabilistic data association (JPDA) algorithm has been previously reported to be suitable for this problem in that it makes few assumptions and can handle many targets as long as the clutter density is not very high. However, the complexity of this algorithm increases rapidly with the number of targets and returns. An approximation of the JPDA that uses an analog computational network to solve the data association problem is suggested. The problem is viewed as that of optimizing a suitably chosen energy function. Simple neural-network structures for the approximate minimization of such functions have been proposed by other researchers. The analog network used offers a significant degree of parallelism and thus can compute the association probabilities more rapidly. Computer simulations indicate the ability of the algorithm to track many targets simultaneously in the presence of moderately dense clutter. >

Iterative Carrier Frequency Offset and Channel Estimation for Underwater Acoustic OFDM Systems

This paper presents a practical low-density parity-check (LDPC) coded OFDM system designed for the underwater acoustic channel with its attendant sparse multipath channel and Doppler effects. The carrier frequency offset (CFO) and channel state information (CSI) are assumed unavailable to both to the transmitter and the receiver. Several different receiver structures are considered, all of which perform CFO/channel estimation, detection and decoding in an iterative manner. The convergence behavior of the iterative receivers and their asymptotic performance are evaluated using the extrinsic information transfer (EXIT) chart method. OFDM receiver performance is further evaluated through simulations and field tests in shallow water.

An adaptive multiuser detector with joint amplitude and delay estimation

A multiuser detector is developed in which the delays and amplitudes of the incoming waveforms are estimated recursively. The algorithm is an extension of the symbol-by-symbol detector of Abend and Fritchman, originally derived for intersymbol interference (ISI) channels, to the multiuser application. In order to make the multiuser detector adaptive, the likelihoods in the symbol-by-symbol metric update are approximated using a set of extended Kalman filter (EKF) innovations. The EKFs provide, in addition to the likelihoods, joint estimates of the signal delays and amplitudes. The resulting algorithm is quite complex, due to the large number of possible composite symbols corresponding to the multiple users. However, it is shown that the likelihood computations and EKF updates can be expressed it terms of a set of cross-correlation functions, which need only be computed for a subset of the possible symbols. The cross-correlator outputs are updated at the bit rate, and thus the EKF and metric update computations need be performed only at a function of the spread-spectrum chip rate. A metric pruning technique is proposed that further reduces the number of EKF delay/amplitude estimators required. Finally, an important sampling simulation strategy is used to obtain bit-error rate estimates for the adaptive multiuser detector. >

Channel estimation and data detection for MIMO-OFDM systems

The use of multiple antennas at both the transmitter and receiver can significantly increase the channel capacity. These systems are called the multiple-input multiple-output (MIMO) systems. By using orthogonal frequency division multiplexing (OFDM) transmission techniques, the MIMO-OFDM system can achieve high spectral efficiency, which makes it an attractive candidate for high-data-rate wireless applications. In this paper, we propose a convolutionally coded MIMO-OFDM system with EM-based channel estimation and a QRD-M data detection algorithm. In our systems, one training symbol is transmitted from each transmit antenna for the MIMO channel estimation at the receiver. With the channel estimates available, we apply the QRD-M algorithm on the estimated channel matrix for suboptimal data detection with reasonable computational cost. The bit error rate (BER) and packet error rate (PER) performance of the MIMO-OFDM systems are compared. In the simulations, the bit error rate performance of our systems is 9 (or 5) dB better than that of uncoded (or coded) BLAST systems.

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. >

A Bayesian maximum-likelihood sequence estimation algorithm for a priori unknown channels and symbol timing

It is shown that the optimum demodulator for the case of an a priori unknown channel and symbol timing can be approximated using a modified Viterbi algorithm (VA), in which the branch metrics are obtained from the conditional innovations of a bank of extended Kalman filters (EKFs). Each EKF computes channel and timing estimates conditioned on one of the survivor sequences in the trellis. It is also shown that the minimum-variance channel and timing estimates can be approximated by a sum of conditional EKF estimates, weighted by the VA metrics. Simulated bit error rate (BER) results and averaged-squared channel/timing error trajectories are presented, with estimation errors compared to the Cramer-Rao lower bound. The BER performance of the modified VA is also shown to be superior to that obtained using a decision-directed channel/timing estimation algorithm. >