4,194 research outputs found
Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels
The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE
Blind Receiver Design for OFDM Systems Over Doubly Selective Channels
We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch
Performance of adaptive bayesian equalizers in outdoor environments
Outdoor communications are affected by multipath propagation that imposes an upper limit on the system data rate and restricts possible applications. In order to overcome the degrading effect introduced by the channel, conventional equalizers implemented with digital filters have been traditionally used. A new approach based on neural networks is considered. In particular, the behavior of the adaptive Bayesian equalizer implemented by means of radial basis functions applied to the channel equalization of radio outdoor environments has been analyzed. The method used to train the equalizer coefficients is based on a channel response estimation. We compare the results obtained with three channel estimation methods: the least sum of square errors (LSSE) channel estimation algorithm, recursive least square (RLS) algorithm employed only to obtain one channel estimation and, finally, the RLS algorithm used to estimate the channel every decided symbol for the whole frame.Peer ReviewedPostprint (published version
Soft-Decision-Driven Channel Estimation for Pipelined Turbo Receivers
We consider channel estimation specific to turbo equalization for
multiple-input multiple-output (MIMO) wireless communication. We develop a
soft-decision-driven sequential algorithm geared to the pipelined turbo
equalizer architecture operating on orthogonal frequency division multiplexing
(OFDM) symbols. One interesting feature of the pipelined turbo equalizer is
that multiple soft-decisions become available at various processing stages. A
tricky issue is that these multiple decisions from different pipeline stages
have varying levels of reliability. This paper establishes an effective
strategy for the channel estimator to track the target channel, while dealing
with observation sets with different qualities. The resulting algorithm is
basically a linear sequential estimation algorithm and, as such, is
Kalman-based in nature. The main difference here, however, is that the proposed
algorithm employs puncturing on observation samples to effectively deal with
the inherent correlation among the multiple demapper/decoder module outputs
that cannot easily be removed by the traditional innovations approach. The
proposed algorithm continuously monitors the quality of the feedback decisions
and incorporates it in the channel estimation process. The proposed channel
estimation scheme shows clear performance advantages relative to existing
channel estimation techniques.Comment: 11 pages; IEEE Transactions on Communications 201
- âŠ