## 4 Simulation Results  
Extensive quasi-static system level simulations are conducted in order to assess the performance of the proposed scheme. The basic simulation methodology is in coherence with the 3 GPP guidance for HetNet simulations [14], including simulation of both downlink and uplink and the major RRMalgorithms such as packet scheduling, power control (PC), link adaptation, _etc._[13]. Cases with co-channel deployment of OSG and CSG HnBs in line with assumptions outlined in Section 2 are simulated. We primarily focus on dense urban environments with 500 meters macro inter-site distance, and the case with CSG HnBs deployed inside multi-floor buildings. The so-called dual-stripe 3 GPP model is adopted, simulating two adjacent three-floor building blocks, each consisting of 2 \(\times\) 10 apartments of 10 \(\times\) 10 meters per floor [14]. It is assumed that 30 HnBs with different CSGs are randomly placed inside those two building blocks, one HNB per four apartments, with the constraint of a maximum of one CSG HNB per apartment. Each apartment with a CSG HNB also has at least one HUE with matching CSG identity. In addition, MUEs are randomly placed, assuming that 80% of those are located inside the building blocks. If an MUE is dropped inside an apartment with an HNB, the MUE is considered an HUE with CSG identity matching the HNB. Except for the former constraints, serving cell selection corresponds to the cell with strongest RSCP for the UE. Thus, a UE with configured CSG identity may also connect to the macro-layer if it is subject to higher RSCP from that layer as compared to the HNB with matching CSG identity. In order to further evaluate the scalability of the proposed QoS-SP-IM algorithm, we also present example performance results from a suburban residential scenario. The suburban environment is characterized by a larger macro inter-site distance of 1732 meters and CSG HnBs placed inside separate single-floor residential family homes. More information on the simulation assumptions for both the dense urban and suburban scenario is available in [15, 16]. In addition to simulation of the derived QoS-SP-IM algorithm in Section 3, two simpler cases are also simulated in order to have baseline results to compare against. The baseline cases include: (i) no power control (NOPC), where the HnBs simply transmit at their maximum power of 15 dBm; and (ii) an adaptive HNB PC scheme where the HNB Tx power is adjusted depending on RSCP from the strongest macro-cell. The latter method is referred to as \"3 GPP,\" as this solution has recently been adopted by 3 GPP for the LTE femtocells [15]. To be more specific, the 3 GPP solution for the HNB transmission power expressed in dBm equals \(\min\{RSCP_{CPICH}^{macro}+55\text{dB};\)\(P_{max}\}\), where \(RSCP_{CPICH}^{macro}\) is the RSCP from the strongest received macro at the HNB (measured using NLM) and \(P_{max}\) equals the maximum HNB transmission power. For both NOPC and 3 GPP cases, it is assumed that the CPICH to total transmit power equals \(-10\) dB. Recall from Section 3 that with QoS-SP-IM, the CPICH power is adjusted dynamically. For the simulations, we assume that the desired HNB coverage in terms of equivalent path loss equals 56 dB and 62 dB for the dense and suburban environments, respectively. These values can be estimated based on typical size of apartment/homes as, building types, and maximum separation between HUE and HNB. Figure 4 shows the cumulative distribution function (cdf) of the experienced downlink end-user throughput for HUEs. As expected, the best HUE performance is observed for the NOPC case, _i.e._, HNB transmitting at its maximum power. The results for the QoS-SP-IM algorithm with a desired throughput guarantee of 256 kbps and 1 Mbps shows accurate adjustment according to those targets. It is observed that the outage probability only equals 3%-4% for the QoS-SP-IM algorithm, _i.e._, first evidence of the QoS-SP-IM algorithm's ability to self-adjust the HNB transmission power to fulfill the minimum QoS target.