Abstract: This paper explores the implementation of adaptive
coding and modulation schemes for Multiple-Input Multiple-Output
Orthogonal Frequency Division Multiplexing (MIMO-OFDM) feedback
systems. Adaptive coding and modulation enables robust and
spectrally-efficient transmission over time-varying channels. The basic
premise is to estimate the channel at the receiver and feed this estimate
back to the transmitter, so that the transmission scheme can be
adapted relative to the channel characteristics. Two types of codebook
based channel feedback techniques are used in this work. The longterm
and short-term CSI at the transmitter is used for efficient channel
utilization. OFDM is a powerful technique employed in communication
systems suffering from frequency selectivity. Combined with
multiple antennas at the transmitter and receiver, OFDM proves to be
robust against delay spread. Moreover, it leads to significant data rates
with improved bit error performance over links having only a single
antenna at both the transmitter and receiver. The coded modulation
increases the effective transmit power relative to uncoded variablerate
variable-power MQAM performance for MIMO-OFDM feedback
system. Hence proposed arrangement becomes an attractive approach
to achieve enhanced spectral efficiency and improved error rate
performance for next generation high speed wireless communication
systems.
Abstract: Most of the commonly used blind equalization algorithms are based on the minimization of a nonconvex and nonlinear cost function and a neural network gives smaller residual error as compared to a linear structure. The efficacy of complex valued feedforward neural networks for blind equalization of linear and nonlinear communication channels has been confirmed by many studies. In this paper we present two neural network models for blind equalization of time-varying channels, for M-ary QAM and PSK signals. The complex valued activation functions, suitable for these signal constellations in time-varying environment, are introduced and the learning algorithms based on the CMA cost function are derived. The improved performance of the proposed models is confirmed through computer simulations.