Authors :
Roji Y.; Shiva Prasad Avutha; Arun Dakoori; Binith Dandu
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/4b4mjxjp
Scribd :
https://tinyurl.com/mvt6jhzu
DOI :
https://doi.org/10.5281/zenodo.10212388
Abstract :
Enhancing communication efficiency and
dependability in multiple input/multiple output systems
(MIMO) is largely dependent on channel estimation.
Two popular methods for calculating mean square error
(MMSE) and zero forcing (ZF) are obtaining precise
channel estimation. By creating an inverted channel
matrix, the Zero Forcing method seeks to neutralize
interference and essentially eliminate the influence of
inter-symbol interference. Though it may be susceptible
to errors and noise, this approach is computationally
efficient. On the other hand, the aim of lowest mean
square error channel estimating is to minimize the mean
square error between the estimated and actual channels.
keeping in mind both noise and interference. This
technique provides improved robustness compared to
ZF, especially in challenging signal conditions. Both ZF
and MMSE techniques contribute significantly to the
advancements of MIMO systems, with each method
presenting its own advantages and trade-offs. The choice
of channel estimation method depends on specific
application requirements, computational complexity
considerations, and the prevailing signal environment.
Accurate channel estimates are crucial for optimizing
data rates and ensuring reliable communication
in MIMO Systems.
Keywords :
Bit error rate (BER), Rayleigh Channels, Zero Forcing (ZF), Multiple Input Multiple Output Systems (MIMO), Signal to Noise Ratio (SNR), Binary Phase Shift Keying (BPSK), and Minimum Mean Square Error Method (MMSE).
Enhancing communication efficiency and
dependability in multiple input/multiple output systems
(MIMO) is largely dependent on channel estimation.
Two popular methods for calculating mean square error
(MMSE) and zero forcing (ZF) are obtaining precise
channel estimation. By creating an inverted channel
matrix, the Zero Forcing method seeks to neutralize
interference and essentially eliminate the influence of
inter-symbol interference. Though it may be susceptible
to errors and noise, this approach is computationally
efficient. On the other hand, the aim of lowest mean
square error channel estimating is to minimize the mean
square error between the estimated and actual channels.
keeping in mind both noise and interference. This
technique provides improved robustness compared to
ZF, especially in challenging signal conditions. Both ZF
and MMSE techniques contribute significantly to the
advancements of MIMO systems, with each method
presenting its own advantages and trade-offs. The choice
of channel estimation method depends on specific
application requirements, computational complexity
considerations, and the prevailing signal environment.
Accurate channel estimates are crucial for optimizing
data rates and ensuring reliable communication
in MIMO Systems.
Keywords :
Bit error rate (BER), Rayleigh Channels, Zero Forcing (ZF), Multiple Input Multiple Output Systems (MIMO), Signal to Noise Ratio (SNR), Binary Phase Shift Keying (BPSK), and Minimum Mean Square Error Method (MMSE).
