Projection minimization algorithm for blind channel equalizer
Signal Processing
Deterministic blind subspace MIMO equalization
EURASIP Journal on Applied Signal Processing
Blind adaptive channel equalization with performance analysis
EURASIP Journal on Applied Signal Processing
Joint power control and blind beamforming over wireless networks: a cross layer approach
EURASIP Journal on Applied Signal Processing
A bigradient adaptive algorithm for blind equalization of digital communication systems
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Single channel 2-D and 3-D blind image deconvolution for circularly symmetric fir blurs
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Blind adaptive equalization of MIMO systems: new recursive algorithms and convergence analysis
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing
Mathematical and Computer Modelling: An International Journal
| Hi-index | 35.68 |
We study the blind symbol estimation problem in digital communications and propose a novel algorithm by exploiting a special data structure of an oversampled system output. Unlike most equalization schemes that involve two stages-channel identification and channel equalization/symbol estimation-the proposed approach accomplishes direct symbol estimation without determining the channel characteristics. Based on a deterministic model, the new method can provide a closed-form solution to the symbol estimation using a small set of data samples, which makes it particularly suitable for wireless applications with fast changing environments. Moreover, if the symbols belong to a finite alphabet, e.g., BPSK or QPSK, our approach can be extended to handle the symbol estimation for multiple sources. Computer simulations and field RF experiments were conducted to demonstrate the performance of the proposed method. The results are compared to the Cramer-Rao lower bound of the symbol estimates derived in this paper