, printed then the receive power is insufficient SO set the transmission power as (again using the worst value of n.(m)( and compare with Pmax (v) If it is <Pmax then use this value as Pn,i for the next loop. Go back to Step 2(i) for the next user. (vi) If it is >Pmax then use Pmax as Pn,i for the next loop. Go back to Step 2(i) for the next user. Step 3: When all users have been considered in Step 2, calculate the new interference map using the new transmit powers for the next loop. Loop back to Step 2 until convergence is reached. Because a user may have been allocated several RBs and the channel conditions for will be different there is another inherent optimization problem on how to reduce the power to each RB separately but here that is simplified by assuming (i) that the transmit power for each RB used by a particular user is the same and (ii) that the worst channel condition used for all RBs. This will not lead to the lowest possible power but is conservative in that the sSINR will actually be a bit higher than the minimum and the last condition in Step 2(iv) prevents the worst channel condition leading to transmit power values higher than The example results in Figure 8.14 show the effectiveness in terms of (a) reducing the overall power through (b) removing excess sSINR from the system: there is no point allocating excess to any user since the requirement is to allocate just sufficient (plus a safety margin) SO that each user is qualified. Other objective functions could, of course, be added and that demonstrates the flexibility of spectrum sharing: any form of suitable optimization (not just game theory) can be used to tailor the allocation of spectrum to what is required. 70 users 518 users - After RB allocation After power reduction Target sSINR with margin Minimum sSINR Number of power reduction loops Number of users Figure 8.14 Power reduction applied as a multi-objective optimization. (a) Convergence of total system power. (b) Reduction of excess overall sSINR. Source: Reprinted from Yang et al. 20