1,232 research outputs found
MIMO decision feedback equalization from an H∞ perspective
We approach the multiple input multiple output (MIMO) decision feedback equalization (DFE) problem in digital communications from an H∞ estimation point of view. Using the standard (and simplifying) assumption that all previous decisions are correct, we obtain an explicit parameterization of all H∞ optimal DFEs. In particular, we show that, under the above assumption, minimum mean square error (MMSE) DFEs are H∞ optimal. The H∞ approach also suggests a method for dealing with errors in previous decisions
Cyclic-Coded Integer-Forcing Equalization
A discrete-time intersymbol interference channel with additive Gaussian noise
is considered, where only the receiver has knowledge of the channel impulse
response. An approach for combining decision-feedback equalization with channel
coding is proposed, where decoding precedes the removal of intersymbol
interference. This is accomplished by combining the recently proposed
integer-forcing equalization approach with cyclic block codes. The channel
impulse response is linearly equalized to an integer-valued response. This is
then utilized by leveraging the property that a cyclic code is closed under
(cyclic) integer-valued convolution. Explicit bounds on the performance of the
proposed scheme are also derived
Radial Basis Function Aided Space-Time Equalization in Dispersive Fading Uplink Environments
A novel Radial Basis Function Network (RBFN) assisted Decision-Feedback aided Space-Time Equalizer (DF-STE) designed for receivers employing multiple antennas is proposed. The Bit Error Rate (BER) performance of the RBFN aided DF-STE is evaluated when communicating over correlated Rayleigh fading channels, whose Channel Impulse Response (CIR) is estimated using a Kalman filtering based channel estimator. The proposed receiver structure outperforms the linear Minimum Mean-Squared Error benchmarker and it is less sensitive to both error propagation and channel estimation errors
Successive Integer-Forcing and its Sum-Rate Optimality
Integer-forcing receivers generalize traditional linear receivers for the
multiple-input multiple-output channel by decoding integer-linear combinations
of the transmitted streams, rather then the streams themselves. Previous works
have shown that the additional degree of freedom in choosing the integer
coefficients enables this receiver to approach the performance of
maximum-likelihood decoding in various scenarios. Nonetheless, even for the
optimal choice of integer coefficients, the additive noise at the equalizer's
output is still correlated. In this work we study a variant of integer-forcing,
termed successive integer-forcing, that exploits these noise correlations to
improve performance. This scheme is the integer-forcing counterpart of
successive interference cancellation for traditional linear receivers.
Similarly to the latter, we show that successive integer-forcing is capacity
achieving when it is possible to optimize the rate allocation to the different
streams. In comparison to standard successive interference cancellation
receivers, the successive integer-forcing receiver offers more possibilities
for capacity achieving rate tuples, and in particular, ones that are more
balanced.Comment: A shorter version was submitted to the 51st Allerton Conferenc
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