s the system. The network can identify potential victim users via their reported RRM measurements such as RSRP. Note that given the suggested criteria for classifying a user as a victim, its signal-to-interference noise ratio (SINR) will be improved by at least 3 dB by muting its aggressor. The basic principle of the algorithm is pictured with an example in Figure 8.16. Here the small cell no. 1 detects that UE no. 1 fulfills the criteria for a victim user. Small cell no. 1 therefore takes the action of requesting the victim user's dominant interferer (in this example, small cell no. 3) to increase its muting, followed by small cell no. 3 muting more of its preconfigured 'bad' subframes. Furthermore, UE no. is configured with time-domain restricted CSI measurements in coherence with the pattern of 'bad' and 'good' preconfigured subframes of small cell no. 3. Similarly, when UE no. 1 ends its session, small cell no. 1 would inform small cell no. 3 via the backhaul that it can change the earlier muted subframe(s) for protecting UE no. 1 back to normal transmission. Assuming that UEs nos. 3 and 4 in Figure 8.16 are not detected at victim users, no muting is triggered at their neighbouring cells. Figure 8.17 shows the performance gain from using the proposed proactive time-domain ICIC mechanism. The gains in 5 percentile and 50 percentile are reported versus the average Small Cell Optimization Small UE no. 2 cell no. 3 (1) Cell no. 1 identifies UE no. 1 as victim UE no. 1 (3) Cell no. 3 mutes some subframes with cell no. 3 as the dominant interferer Small Backhaul link UE no. 4 cell no. 1 (2) Cell no. 1 requests cell no. 3 UE no. 3 to mute some subframes Small cell no. 2 Figure 8.16 Sketch of the basic principle of the proactive time-domain ICIC scheme offered load per dense cluster of 12 small cells. These results are obtained by the Release 12 small cell simulation assumptions for dense outdoor small cell clusters at 3.5 Hz, using 10 MHz bandwidth and without macro cell interference (i.e. macros are assumed operati