# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
## 4 Discussion  
This section discusses the results of the proposed UAV-based olive irrigation system. We start by emphasizing the significance and reasonability of the results. Then, we analyze the limitations and areas for improvement of the study. Firstly, as mentioned earlier, to the best of the authors' knowledge, and in light of the previous description of related works, researchers have not considered the combination of unmanned aerial vehicles and massive MIMO technology in smart irrigation systems. Furthermore, this study is general, and the obtained simulation results can be easily extended for any other parameters. Moreover, unlike previous studies in the literature that analyzed the spectral and energy efficiency trade-off (for example, [2,16]), our analysis takes into account not only the transmit power and throughput but also the circuit power (CP). Indeed, in Figure 3 (respectively, Figure 4), we plot the CP of the proposed UAV-based irrigation system as a function of the number of IoT devices \(K_{I}\) (respectively, the number of GBS antennas \(M\)). The simulation results showed that the M-MMSE combining technique consumes a higher CP than the MR combining technique. Clearly, as aforementioned, the MR scheme does not require matrix inversions, whereas the M-MMSE scheme requires the inversion of an \(MxM\)-dimensional matrix. In addition, in Figure 7, we plot the energy efficiency of the proposed system as a function of the throughput. We noticed that the M-MMSE combining technique performs better and maximizes the energy efficiency. Thus, the M-MMSE combining technique finds a non-trivial trade-off between a high signal power and low interference and noise, which maximizes the instantaneous SINR. This can be explained by the fact that in massive MIMO communication, the M-MMSE scheme exploits the spatial degrees of freedom (offered by multiple antennas) to focus signals on intended receivers, reduce interference, and consequently increase the system throughput [32]. Hence, M-MMSE improves the energy efficiency since the higher the throughput, the higher the circuit power (CP), due to the larger number of antennas deployed in massive MIMO communication. In this way, the corresponding energy efficiency gain comes from suppressing intra-cell interference from the massive antennas and by sharing the per-cell CP costs among multiple IoT devices. Moreover, the results given in Figures 8 and 9 show how to determine the optimal number M of GBS antennas as well as the optimal number \(K_{I}\) of IoT devices (UAVs and actuators) that can be served. Additionally, Figures 8 and 9 illustrate that the proposed UAV-based irrigation system outperforms conventional ones regarding the energy efficiency, while conserving network spectral efficiency. In fact, when the number of GBS antennas \(M\) increases proportionally to \(K_{I}\) (\(M/K_{I}\gg 1\)), inter-user interference (that increases with  
Figure 9: Energy Efficiency of the proposed UAV-based olive irrigation system with reference to the number of IoT devices \(K_{I}\) and the number of GBS antennas \(M\), for multi-cell MMSE combiner. \(K_{I}\)) is suppressed. Note that the case when \(MK_{I}=1\) represents the traditional scheme adopted in the literature. We can clearly deduce that our proposed UAV-based method (\(M/K_{I}\gg 1\)) outperforms the traditional one (\(MK_{I}=1\)). Hence, through qualitative and quantitative analyses of the simulation results, it was verified in this work that the proposed irrigation system is effective and optimal in terms of energy efficiency and conserving network spectral efficiency. However, this research still has some limitations, mainly regarding the following two aspects:  
* Although M-MMSE combining is optimal, it is not frequently used in the research literature. There are several reasons for this. One is the high computational complexity of computing the inverse MxM matrix, especially when M is large.