# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 5 Performance Evaluation  
We conducted Monte Carlo simulations to validate the proposed resource management framework and to evaluate the system performance of the introduced cognitive HTS system. It was assumed that the bandwidths of satellite and terrestrial system were 0.5 and 2 GHz, respectively. The multi-beam satellite was assumed to be equipped with a uniform rectangular array with \(N_{t}\) antennas, and the terrestrial BS was equipped with a single antenna. Within the satellite coverage with an area of 40,000 \(\text{km}^{2}\), terrestrial BSs and \(K\) users were uniformly distributed, where there were \(\beta\) BSs per 10,000 \(\text{km}^{2}\). A total of \(N_{g}\) gateways were considered; each gateway served one group. The total transmit power of the satellite was 60 dBW. We used the Ricean channel model to fit the forward link containing the line-of-sight (LoS) component, with free space path-loss of 190 dB. Antenna gain at the satellite was 66.5 dBi, while user terminal antenna \(G/T\) was assumed as 16.9 dB/K in clear sky [39]. The received signal-to-noise ratios (SNRs) of terrestrial links ranged from 15 to 25 dB. In the simulations, we first calculated the channel covariance matrices between satellite and users, which were taken as the parameter of the K-means algorithm to conduct user grouping. After user grouping, by regarding all the terrestrial BSs as a new group, we employed the approximated BD algorithm to design the first stage precoder to reduce intergroup interference with threshold \(\alpha=0.8\). Then, the greedy algorithm was utilized to schedule users in order to maximize the throughput of the network. Last, we employed the regularized ZF algorithm to design the second stage precoder to eliminate the intragroup interference. In Figure 4, we firstly assess the performance of the proposed resource management scheme for satellite communications, where the K-means user grouping based on spatial correlations (referred to as SCUG) is compared with the existing geographical user grouping (referred to as GUG) method, combined with two classical user scheduling algorithms. One is the MAX algorithm, the key idea of which is to schedule users with the maximal signal-to-interference plus noise ratio (SINR). The other one is the GREEDY algorithm, which dynamically schedules users with potentially maximum enhancement to the system throughput among all the unscheduled users. From Figure 4, we can observe that as the number of users increases, the satellite throughput increases linearly at the beginning and tends to remain unchanged. This is because when user size grows, the resource becomes crowded and the system performance eventually gets saturated. Figure 4: Satellite throughput versus the number of users with different schemes (under the full frequency reuse (FFR) pattern without cognitive radio (CR), \(N_{t}=128\), \(N_{g}=10\), and \(\beta=10\)). Moreover, the SCUG method outperforms the existing GUG method except for the case where users are sparsely distributed, verifying the benefits of exploiting spatial correlations in user grouping. Furthermore, for a given user grouping scheme, the satellite throughput obtained with the GREEDY algorithm is superior to that with MAX, which coincides with the design principles of algorithms. Figure 5 illustrates the throughput of terrestrial and satellite systems with different numbers of satellite antennas and gateways in the case of FFR without CR. In general, the larger the number of antennas at the satellite is, the more throughput can be obtained. With the same number of antennas, the throughput of satellite increases as the number of gateways decreases. This is because when \(N_{g}\) is small, the residual intergroup interference resulting from the approximated BD algorithm in the first stage precoder is small, and the intragroup interference can be effectively eliminated through the second stage precoder.