## 4 Research Opportunities in O-RAN
### Massive MIMO  
In the last two decades, the idea of using multiple antennas for transmitting and receiving information over the air has evolved from the classic single-cell MIMO technology, to the distributed cooperative massive MIMO with no cell boundaries technology, also known as cell-free technology. With mMIMO and cell-free becoming ubiquitous in 5G and 6G, O-RAN will need to accommodate these technologies and provide the right interfacing for making the most of their large spectral efficiency (SE) potential. In this regard, the work of [75] looks at how mMIMO and cell-free can be integrated in O-RAN. They provide several options of integrating the multi-antenna processing (mMIMO precoding/beamforming) of cell-free MIMO in the O-RAN architecture, by adjusting the level of coordination/cooperation between the open-distributed units (ODUs) and open-radio units for performing this processing. It turns out that increasing the centralization (exchange of information between the various units) increases significantly the SE. Even though the current O-RAN architecture can, in their view, already support cell-free networks, there are opportunities for achieving higher SE in the future by specifying the inter-O-DU interface in O-RAN and performing the multi-antenna processing at the O-DU. Finally, they also point out the importance of RU clustering (which is, itself, tied to user grouping) for achieving better SE performance; this could be efficiently implemented at the Near-RT RIC in O-RAN. We can foresee that AI and ML will help to dynamically adjust the level of coordination/cooperation between the ODUs, as well as efficiently perform the RU clustering.  
As pointed out by [75], the integration of the multi-antenna processing for the mMIMO/cell-free technology is clearly an important issue in O-RAN, given the split of baseband functionalities, and the work [76] investigates this issue in a more practical manner. More specifically, it investigates how to effectively distribute the channel state information (CSI) between the split baseband functions to minimize the performance degradation it incurs when performing multi-antenna processing. This work also identifies further research opportunities on the same topic, as for instance, the optimization of the fronthaul bandwidth allocated to different users according to their mobility, priority, or channel conditions, and reduce the exchange of information over the fronthaul interface between O-DU and O-RU. This optimization process can obviously made more generic and efficient by using ML.  
Another important technology that will deploy in 6G is the intelligent reflective surface (RIS) technology. The idea behind RIS is to increase the SE/EE of wireless communications systems by dynamically improving the propagation environment, without the need of deploying extra costly access points (APs). The work in [77] mentions the possible integration of RISs in the O-RAN architecture, where RISs are managed via a dedicated controller. This controller is then linked to O-RAN via a new interface at the Near-RT RIC. Accordingly, it is clear that the design of a practical and effective signaling interface will be the main challenge for integrating RISs into the O-RAN architecture. This work sees the deployment of RISs as an the opportunity to create fully inter-operable so-called'smart-radio environments' which, in turn, can provides more openness and flexibility for the network operators. The management of such'smart-radio environments' will require intelligence provided by AI/ML.