So the ( {leftVert {mathbf{H}}_k^{i,k}rightVert}_F^2 ) norm of channel matrices between base stations and native users can be calculated [34].
So before the hypothesis tests, the users can be sorted by the strength of their channels, i.e., the Frobenius norm of channels (see Eq. (9)) and the users with weaker channels should be tested first.
containing the unit-norm reconstructed channel vectors.
In light of this, the 2-norm of channel coefficients is introduced into the cost function to regulate the power consumption in different channel conditions.
The performance gap of the SINRs estimated with the per‐cell channel norm and the global channel norm is less than the gap of the quantized global channel norm.
It shows that in both scenarios, 3 bits is enough for quantizing the global channel norm to ensure the average rate loss less than 3% of that with perfect global channel norm.
The logic of the Uppsala model asserts that developing market knowledge (e.g., about regulations, norms, distribution channels, business cultures) can reduce uncertainty and perceived risks (c.f., Johanson and Wiedersheim-Paul 1975) and thus lead to engagement in international markets.
To show how many bits are needed for quantizing the global channel norm, in Figure 2b, we simulate the average per‐user rate achieved using the perfect global channel norm (with legend 'Perfect Norm') and the global channel norm quantized with different bits (with legend 'Global Feedback').
Considering that both the global channel norm and the global CDI reconstruction depend on the per‐cell channel norms, we analyzed if these norms need to be fed back by deriving the average quantization performance of the global channel norm and the asymptotic weighting factors in the global CDI with and without the per‐cell channel norms.
The global channel norm is a weighted sum of the per‐cell norms, whose dimension is the same as that of the per‐cell norm.
