Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigate prediction algorithms that exploit both temporal and spatial correlations in MIMO correlated narrowband fading wireless channels.  first derive the optimal two dimensional minimum mean square error (2D-MMSE) prediction filter that maximally exploits the available temporal and spatial correlations.  then propose a lor complexity 2-step prediction algorithm, which first exploits temporal correlations using a classical single-input single-output (SISO) MMSE time- domain prediction filter for each entry in the MIMO channel, follod by an MMSE spatial smoothing step to exploit the spatial correlations. Compared to the SISO and specular prediction approaches, this approach either achieves lor MSE with a slight increase in complexity, or comparable MSE with lor complexity, in a wide range of wireless channel conditions. The same advantage holds for the average mutual information.
['Ian C. Wong', 'Brian L. Evans']
I am writing a research paper on a idea SPC03-4: Exploiting Spatio-Temporal Correlations in MIMO Wireless Channel Prediction
[{"from": "human", "value": "I am writing a research paper on a idea SPC03-4: Exploiting Spatio-Temporal Correlations in MIMO Wireless Channel Prediction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigate prediction algorithms that exploit both temporal and spatial correlations in MIMO correlated narrowband fading wireless channels.  first derive the optimal two dimensional minimum mean square error (2D-MMSE) prediction filter that maximally exploits the available temporal and spatial correlations.  then propose a lor complexity 2-step prediction algorithm, which first exploits temporal correlations using a classical single-input single-output (SISO) MMSE time- domain prediction filter for each entry in the MIMO channel, follod by an MMSE spatial smoothing step to exploit the spatial correlations. Compared to the SISO and specular prediction approaches, this approach either achieves lor MSE with a slight increase in complexity, or comparable MSE with lor complexity, in a wide range of wireless channel conditions. The same advantage holds for the average mutual information."}]