nlink transmission power is very high, RS power boosting is used or uplink noise rise is high, the problems may start already at a higher RSRP. The problem with directly measuring the dependence of RSRP and throughput is that it cannot predict the impact of network load on the throughput. If the network load changes the measurement would have to be repeated. If a high-quality scanner is available a more powerful result can be obtained by predicting SINR from RSRP measurement of strongest cells. This allows factoring network RF load into the SINR prediction, and furthermore gives a good indi- cation of the impact of cell overlap on throughput performance. If accurate RSRP measurement of top-N strongest cells is available, the SINR at a measurement point can be calculated as predicted RSRP + thermal noise + EVM power where n = 0, , N-1 are the measured RSRPs of the N cells, ordered from strongest to weakest. Subcarrier activity factors of the neighbours are denoted with Yn .Thermal noise power depends on the UE noise figure which can be assumed to be in the range of 7-9 dB. The final term in the denominator is the error vector magnitude (EVM) power in the analogue parts of the eNodeB transmitter which limits the SINR to values of less than 30 dB even at very high 13 The maximum UE power amplifier power of 23 dBm need not be supported with 16QAM modulation due to the required back-off to maintain PA linearity [10]. LTE Small Cell Optimization RSRP versus SINR prediction for different network loads 800 MHz, 15% load 800 MHz, 100% load 2.6 GHz, 15% load 2.6 GHz, 100% load RSRP (dBm) Figure 15.10 RSRP versus predicted downlink SINR for different downlink subcarrier utilization, based on RSRP drive test measurement RSRP. Also other system imperfections such as subcarrier leakage due to carrier frequency offset could be included in the EVM term, but on the other hand these have impact mainly in the less interesting high-SINR regime, SO accurate modelling of EVM is not typically required. Figure 15.10 shows RSRP versus pr