The algorithm starts with some arbitrary (e.g., equal) time slot allocation (widetilde {phi }^{l}(boldsymbol {widetilde {delta }_{(t)}})) and iteratively improves total system utility function in a while loop.
Due to different channel conditions, the achievable rate on each link is not identical as assumed in [18] by c and the fixed equal time slot allocation scheme is neither efficient nor applicable with different achievable rates on different channels in dynamic changing wireless network environment.
This is a simple and well-known consequence of our choice of concave rate functions, as has been proved previously in [12]: when there are no constraints, an even (equal across time) allocation is optimal.
For an equal time and rate allocation, network coding gains more than 2.5 dB for R = 2.0 bits per symbol.
The rate for equal time and rate allocation with network coding changes its pre-log-factor from 1 to 0.5 at γR1 = 9 dB because the rate is limited by the communication to the terminals for γR1 < 9 dB and by the communication to the relay for γR1 > 9 dB.
Note that contrary to the considered transmitted sum-rate, the received sum-rate ((N -1)·R) would increase with growing N. Figure 3 Example 1: Sum-rate for different number of terminals with optimal time allocation and equal rate ratios for symmetrical setup.
If the time allocation is optimized for an equal rate allocation, network coding gains more than 1.3 dB for R = 3.0 bits per symbol.
For example, if a program with 2 activities has a staff time allocation of 50% to each activity, then each activity requires an equal amount of staff time and, therefore, of resources for program support.
In contrast, the min-power policy and equal time policy are done without jointly optimization, leading to fewer degrees of freedom for resource allocation.
Now how about equal time for time?
