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Figures 4 and 5 illustrate the effectiveness of the proposed relay selection algorithm based on minimum residual interference power.
We have also proposed an adaptive scheme and relay selection algorithm based on the minimum residual interference power.
An adaptive mapping scheme based on minimum residual interference power is introduced to reduce the effect of quantization error.
Second, we propose an adaptive mapping scheme based on minimum residual interference power to reduce the effect of quantization error.
Our development is further enhanced by an adaptive STBC-CQ-PNC scheme based on minimum residual interference power.
The proposed STBC-CQ-PNC scheme is introduced in Sect. 3 followed by the relay selection base on minimum residual interference power in Sect. 4.
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In this section, we aim to propose a simple relay selection algorithm based on the minimum residual interference power in order to improve the system performance.
We assume that each BS performs a linear minimum mean-square error (MMSE) detection to suppress the residual interference and detect the desired signal.
On the other hand, if the user u is cognitive, it can perform an additional step of equalizing the residual interference with a linear minimum mean square error (LMMSE) spatial equalizer.
As the congestion measure the minimum residual capacity is considered.
The minimum residual compression is estimated as 6.5 MN.
More suggestions(15)
minimum residual force
minimum residual resampling
minimum residual energy
minimum residual value
minimum residual vibration
minimum governmental interference
minimum possible interference
minimum residual algorithm
minimum residual analysis
minimum residual displacement
minimum human interference
minimum residual turbidity
minimum spectral interference
minimum necessary interference
minimum residual capacity
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