## 6 Conclusions, Future Works and Limitation
### Limitations and Open Research Issues  
Although there has been extensive research in the area of IRS-enabled wireless communication, there are key areas such as energy efficient channel estimation, practical protocols for information exchange, reflection as a source of IRS-assisted HetNets and flexible lightweight phase reconfiguration that require further investigation. These aspects are discussed below. * Energy efficient channel estimation: The ability for an IRS to accurately direct the incident EM wave to the receiver while maintaining the passive attribute is vital in the design of an IRS. The signal processing for IRS-aided wireless communication is expected to be performed at the BS, thus requiring the IRS to sense the channel and steer the EM waves accordingly [24]. To implement the abovementioned future wireless communication systems, the core requirement is to have a complete CSI. However, exact CSI is difficult to obtain, therefore, long-term and/or near-instantaneous CSI could be considered. However, due to the large number of reflecting elements, the channel matrix is going to be too large and will require many pilots for training. Therefore, designing an efficient training sequence which could reduce the overhead would be a future challenge when employing an IRS for near-instantaneous CSI estimation. Furthermore, for long-term estimation we might require the angle or location information which might be a key challenge for indoor communications especially in a NLOS scenario. This will further lead to mobility challenges, especially when we are trying to track the movement of a fast-moving individual or an object. Both the near-instantaneous and long-term CSI estimation require several key parameters to be estimated as well as many iterations to find an optimal solution if the problem is convex or a model-based optimization methods for nonconvex problems. ML/AI-driven solutions may be sought as for this issue. One option is to employ ML/AL-based data driven solutions where feature extractions without mathematical models can be carried out. It has been found to have quite robust solutions in imperfect CSI and hardware implementation in other fields. Therefore, it might be another important and timely approach for IRS-based systems in the future. * Beamforming design: Considering various degrees of CSI availability, namely imperfect CSI, partial CSI and statistical CSI, the beamforming design must be varied to keep the interference to a minimum level and enable robust communication [105; 106]. Codebook-based beamforming may also be utilized to reduce the training overhead, reduce the interference and improve reliable/robust communication. These codebook-based methods require the codebook to be filled up overtime with relevant CSI data. The varying channel state information will make the codebook outdated and will require better prediction methods to forecast the future CSI based on existing codebook data. * Practical protocols for information exchange: In designing information exchange protocols, factors such as low power consumption and latency should be considered. Particularly, MAC and joint layer optimization solutions should be addressed. Most of the proposed work carried out in the IRS domain is along the physical layer. However, MAC layer and joint optimization of physical and MAC layer still are open questions. Furthermore, the problem is exacerbated by the limited number of available datasets that can be employed for joint optimization of these layers. * Reflection as a source of IRS-assisted HetNets: An IRS offers a green technology alternative to existing systems and is expected to significantly improve future wireless systems. Another challenge is that the massive deployment of IRS to form HetNets would require optimized techniques to serve multiple data streams. A centralized coordination for the IRS-assisted HetNets and transceivers need continuous channel training for learning the CSI [44; 109].