To simplify the analysis and gain some useful insights, we consider the case of high‐resolution quantization as in [12], where the quantization interval, y(j+1)−y(j), is very small.
For additional insights, we considered Euclidian and Kullback-Leibler (K L) distance metrics, as previously described (Goedert et al., 2014a).
This might not sound like much of a new insight until we consider that, by indicating that she freely accepted this great responsibility, we realize that she was free to refuse.
To obtain a closed-form solution and gain useful insight, we first consider a two-cell MISO network consisting of one macro-cell and one pico-cell in the optimization and then extend the designed beamforming to multi-cell networks.
In order to gain insight into this we consider again the results of six evolutionary runs each consisting of 10 iterations.
However, we found it to be a pragmatic and transparent approach for providing insights into what we consider the "right" or "wrong" courses of action, and it gives an approximate assessment of whether guidelines are followed.
To highlight insight, in this section, we consider some special cases, i.e., asymmetric relay channels and symmetric relay channels.
To gain further insight into this comparison, we consider an AWGN channel,b where |h ab |2 is normalized as 1, the distance from the RN to nodes A and B are, respectively, d and 1 - d. Then h a r 2 = 1 d α and h b r 2 = 1 1 - d α, where α is the path loss attenuation factor.
To gain useful insight into the EE comparison, we consider the following two approximations.
