## 4 Simulation Results
For the considered case, the scheme with 3 GPP HNB PC seems to result in performance close to that of the QoS-SP-IM algorithm with 1 Mbps downlink target. Looking at the  
50 th percentile or 90 the percentile throughput, it is observed that HUEs often experience much higher end-user throughputs as compared to their minimum guaranteed throughput in the service level agreement. In order to demonstrate the scalability of the proposed QoS-SP-IM algorithm to self-adjust for different environments, similar results as in Figure 4 are pictured in Figure 5 for the suburban scenario. Again it is observed that the QoS-SP-IM algorithm efficiently self-adjusts so the outage probability for the desired throughput targets of 256 kbps and 1 Mbps equals only on the order of 1%. Despite the many differences between the dense and suburban environments, it is interesting to note that the experienced HUE throughput in Figures 4 and 5 is actually fairly close. Next, we look in more detail towards outage defined inside the home or the apartment. We extend our outage definition considering that any user in an HNB home/apartment would be interested to connect to the HNB (e.g., for the case with different charging policies) or that the operator wants to offload the user from the macro network. We therefore look across the whole area of the apartment or  
Figure 4: Cumulative distribution function of experienced HUE downlink throughput in dense urban for different HNB Tx power configuration schemes. Figure 5: Cumulative distribution function of experienced HUE downlink throughput in suburban for different HNB Tx power configuration schemes. home where an HNB is installed and we report the outage defined as where the user is unable to get a minimum throughput of 256 kbps from neither the MNB nor the HNB. This outage probability for the dense urban scenario is reported in Figure 6. Also the average HNB Tx power resulting from each scheme is reported in Figure 6. The QoS-SP-IM algorithm displays very high performance, with only \(\sim\)1% probability of not connecting to the HNB or experiencing too low a throughput. The former is achieved with less than 4 dBm HNB Tx power. The higher performance of the QoS-SP-IM algorithm is obtained by adjusting CPICH power to desired coverage, while afterwards setting the HS-DSCH power to meet the minimum throughput requirement. The NOPC case naturally uses 15 dBm Tx power, but despite the relative high power, there is 6% probability of experiencing too low a throughput. The latter comes from not having all UEs connecting to their own HNB, but instead connecting to the outside macro-layer not being able to offer the desired throughput. This behavior is observed because the HNB CPICH Tx power is too low (\(-10\) dB compared to the total HNB Tx power), thus leading to less coverage but higher cell throughput performance. The 3 GPP HNB PC solution shows comparable performance to the QoS-SP-IM algorithm in terms of outage probability, but at the expense of using 4 dB higher transmit power. As commented earlier, one of the merits of the derived QoS-SP-IM algorithm is its ability to dynamically adjust the CPICH and data channel HNB Tx power to achieve the desired objectives in terms of coverage and minimum target data rate. Thus, while the CPICH to total HNB Tx power is fixed at \(-10\) dB for cases with NOPC and 3 GPP PC, the QoS-SP-IM algorithm computes these power settings for each HNB. The cdf of the CPICH to total HNB TX power for the QoS-SP-IM algorithms is reported in Figure 7 for the dense urban scenario. Here it is, among others, visible how the HNB decreases the data channel power as the minimum target bit rate is decreased from 1 Mbps to 256 kbps. The impact on MUE performance is shown in Figure 8. Here, it is clearly demonstrated that the use of NOPC for CSG HNBs have significant influence on MUE performance, showing that 8% of the MUE gets no service (zero throughput).