# Open RAN--Radio Access Network Evolution, Benefits and Market Trends
## 6 Research Activities  
Parallel to Open RAN standardization works, academic and other research and development (R&D) centres are conducting research mainly based on the O-RAN Alliance reference architecture proposed that is expected to be the first commercial deployed solution in the field. In this section, an overview of the latest addressed research topics is presented. In Table 4, the research topic are assigned to proposed features area groups together with indication whether those research activities are related to theoretical studies or laboratory/PoC (proof-of-concept) experiments. The authors of [58] focus on exploiting the capabilities of Non-RT and RT RIC in optimization of 5G FH and BH services in a dynamic optical transport network. The solution proposed consists of an off-line optimization scheme based on integer linear programming that is run at the Non-RT RIC and a machine learning scheme that is executed at the RT RIC. In [59], the authors present a novel machine learning solution to resource orchestration in energy constrained vRANs that addresses a challenging problem of predicting the power consumption and performance of software stack processing. The authors of [60] propose a reinforcement learning-based scheme for improving overall transmission throughput and minimizing the number of handovers (HOs). In [61], the authors perform a simulation study based on a reinforcement learning technique applied in dynamic functional split selection (among DUs and CU) for minimization of energy consumption. The author of [22] proposes a method for the placement of CNF in different network segments, i.e., local, regional, and core, for scalable service provisioning. In [62], the authors develop a machine learning workflow based on the O-RAN specification and open-source software implementation (O-RAN SC, Acumos, ONAP). The authors of [63] and [64] discuss a possible path for future 5G network slicing integration with multi-access edge computing (MEC). In [65], a new approach based on automatic neighbour relation (ANR) is proposed for minimization of handover failures in an O-RAN based on open interfaces. The authors of [66] make use of a convolutional neural network and propose a physical layer authentication method in 5G Open RAN based on a specific emitter detection (RF fingerprint), which is evaluated on the Software Defined Radio (SDR) platform. In [20], the authors review the FH compression \begin{table}
\begin{tabular}{c c} \hline \hline
**Overall (Potential) Benefits** & **Source** \\ \hline Multivendor ecosystem & [25; 37; 55; 56] \\ Reduce cost & [25; 55; 56; 57] \\ Interoperability & [25; 37; 41; 57] \\ Open Interfaces & [25; 54; 56; 57] \\ Hardware and Software Disaggregation & [25; 37; 41; 55; 56] \\ Open Software & [25; 41; 56] \\ Open Hardware (e.g., x86, ARM CPUs) & [25; 41; 56] \\ COTS Hardware & [25; 41; 55] \\ Programmable interfaces for SMO & [25] \\ Support compute-heavy scenarios on COTS HW & [25] \\ Native AI/ML support & [25; 41; 43; 56; 57] \\ Support Virtualization of RAN & [41; 55; 56; 57] \\ Additional types of network deployments & [45; 57] \\ Operational simplification & [37; 55] \\ More flexible scaling & [25; 37; 45; 56] \\
3 rd RAN programmability & [25; 44] \\ Improving user performance & [43; 44] \\ Time to deploy & [55] \\ Enabling or speed up innovation & [25; 43; 45; 56; 57] \\ Gain for specialized company to deliver products & [57] \\ Consolidation of various radio generation & [55] \\ More energy efficient & [55] \\ Uses standardized 19” racks & [25] \\ Security enhanced & [55] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Review of expected Open RAN benefits. techniques proposed in 3 GPP and O-RAN and assess the impact of capacity reduction in fronthaul on air interface performance. The authors of [67] and [68] propose a reference design of the M-Plane for 5G open FH and a network management system for 5G Open RAN according to O-RAN Alliance specifications.