J.J. Shynk

Bio: J.J. Shynk is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Adaptive filter & Signal processing. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

Fast dual-mode adaptive algorithms for the equalization and timing recovery of TDMA mobile radio signals

Abstract: Adaptive algorithms with rapid convergence properties for the equalization of time division multiple access (TDMA) mobile radio signals are presented. When the symbol timing is known, these algorithms approximate maximum a posteriori (MAP) sequence estimators that generate reliable estimates of the transmitted signal. For channels with timing jitter (from random Doppler shifts), joint estimation of the channel parameters and the symbol timing using an extended Kalman filter (EKF) algorithm is proposed. Various methods to reduce the computational complexity of the MAP sequence estimator are also described. >

Bayesian algorithms for blind equalization using parallel adaptive filtering

Abstract: 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. >

Bayesian decision feedback techniques for deconvolution

Abstract: There has been great interest in reduced complexity suboptimal MAP symbol-by-symbol estimation for digital communications. We propose a new suboptimal estimator suitable for both known and unknown channels. In the known channel case, the MAP estimator is simplified using a form of conditional decision feedback, resulting in a family of Bayesian conditional decision feedback estimators (BCDFEs); in the unknown channel case, recursive channel estimation is combined with the BCDFE. The BCDFEs are indexed by two parameters: a "chip" length and an estimation lag. These algorithms can be used with estimation lags greater than the equivalent channel length and have a complexity exponential in the chip length but only linear in the estimation lags. The BCDFEs are derived from simple assumptions in a model-based setting that takes into account discrete signalling and channel noise. Extensive simulations characterize the performance of the BCDFE and BCDPE for uncoded linear modulations over both known and unknown (nonminimum phase) channels with severe ISI. The results clearly demonstrate the significant advantages of the proposed BCDFE over the BCDFE in achieving a desirable performance/complexity tradeoff. Also, a simple adaptive complexity reduction scheme can be combined with the BCDFE resulting in further substantial reductions in complexity, especially for large constellations. Using this scheme, we demonstrate the feasibility of blind 16QAM demodulation with 10/sup -4/ bit error probability at E/sub b//N/sub 0//spl ap/ 18.5 dB on a channel with a deep spectral null. >

Adaptive MAPSD algorithms for symbol and timing recovery of mobile radio TDMA signals

Abstract: Dual-mode adaptive algorithms with rapid convergence properties are presented for the equalization of frequency selective fading channels and the recovery of time-division multiple access (TDMA) mobile radio signals. The dual-mode structure consists of an auxiliary adaptive filter that estimates the channel during the training cycle. The converged filter weights are used to initialize a parallel bank of filters that are adapted blindly during the data cycle. When the symbol timing is known, this filter bank generates error residuals that are used to perform approximate maximum a posteriori symbol detection (MAPSD) and provide reliable decisions of the transmitted signal. For channels with timing jitter, joint estimation of the channel parameters and the symbol timing using an extended Kalman filter algorithm is proposed. Various methods are described to reduce the computational complexity of the MAP detector, usually at the cost of some performance degradation. Also, a blind MAPSD algorithm for combining signals from spatially diverse receivers is derived. This diversity MAPSD (DMAPSD) algorithm, which can be easily modified for the dual-mode TDMA application, maintains a global set of MAP metrics even while blindly tracking the individual spatial channels using local error estimates. The performance of these single-channel and diversity MAPSD dual-mode algorithms are studied via computer simulations for various channel models, including a mobile radio channel simulator for the IS-54 digital cellular TDMA standard.

Reduced complexity MAPSD algorithms with spatial diversity for TDMA mobile radio signal recovery

Abstract: The optimal blind maximum a posteriori symbol detection (MAPSD) algorithm for spatial diversity combining is derived. This diversity MAPSD (DMAPSD) algorithm is applied to a time division multiple access (TDMA) system for the recovery of mobile radio signals. Lower complexity DMAPSD algorithms are described, and their error rate performance for fast fading channels is studied via computer simulations. The results indicate that the proposed MAP detectors provide SNR gains of 6-13 dB over a zero-forcing decision-feedback equalizer (DFE). >

Bayesian techniques for equalization of rapidly fading frequency selective channels

Abstract: This paper applies the Bayesian conditional decision feedback estimator (BCDFE) to rapidly fading frequency selective channels. The BCDFE is a model-based deconvolution algorithm which jointly estimates the transmitted data and channel parameters. The BCDFE smoothly transitions between trained and blind operation and consequently provides robust performance in rapidly fading channels. We provide a brief derivation of the BCDFE and characterize the performance on the land mobile radio channel. We assess the BCDFE's principle design characteristics and the resulting performance in both transient and steady-state operation. The effects of delay spread, Doppler spread, and cochannel interference on the bit error probability performance are also presented. The BCDFE demonstrates many of the desirable characteristics of an equalizer for mobile radio.