Exploiting O-RAN neutral hosting for first responders connectivity in emergency scenarios

Neutral Host Service Management and Orchestration Framework Emergency Service OSS Non Real Time RIC Near Real Time RIC A1 E2 E2 E2 Operator A Emergency Operator E AMF UPF SMF NRF Operator B Edge Emergency support O-RAN rApp Emergency connectivity O-RAN xApp O1 O1 O1 R1 E B A E B A E B A E B A E B A E B A E B A E B A Graphical Abstract Exploiting O-RAN Neutral Hosting for First Responders Connectivity in Emergency Scenarios A. Piccioni, A. Marotta, C. Rinaldi, C. Centofanti, K. Kondepu, D. Cassioli, F. Graziosi Highlights Exploiting O-RAN Neutral Hosting for First Responders Connectivity in Emergency Scenarios A. Piccioni, A. Marotta, C. Rinaldi, C. Centofanti, K. Kondepu, D. Cassioli, F. Graziosi •O-RAN Neutral Hosting is a feasible solution for emergency operator connectivity. •First responders seamlessly communicate through efficient radio resource allocation. •O-RAN Neutral Hosting outperforms traditional RAN before and after the disaster. •Slice isolation strategy further amplifies the advantages of O-RAN Neutral Hosting. Exploiting O-RAN Neutral Hosting for First Responders Connectivity in Emergency Scenarios A. Piccionia,b, A. Marottaa,b,c, C. Rinaldib, C. Centofantia,b,c, K. Kondepud, D. Cassiolia,b, F. Graziosia,b aDepartment of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy bNational, Inter-University Consortium for Telecommunications (CNIT), University of L’Aquila Research Unit, Italy cWEST Aquila S.r.l., L’Aquila, Italy dIndian Institute of Technology Dharwad, Dharwad, India Abstract Ensuring seamless connectivity for first responders represents a critical issue in emergency scenarios, where disrupted infrastructures and network congestion can significantly hinder the communication. Open Radio Access Network (O-RAN) promises a level of openness in Radio Access Networks (RANs) that could be advantageous under such critical conditions, allowing for enhanced integration across different operators’ networks to mitigate resource scarcity. This paper introduces an architectural solution based on O-RAN that leverages neutral hosting to provide a unified gateway to RAN infrastructures. Our approach facilitates seamless access for emergency responders and optimizes the allocation of resources at both the RAN and Core levels within a specified area by exploiting the deployment of near-realtime Applications (xApps) and non-real-time Applications (rApps) managed by the neutral host. The results demonstrate the effectiveness of O-RAN neutral hosting in enhancing first responders’ connectivity, showcasing significant improvements in throughput and latency, especially under disaster scenarios. Keywords: 5G, 6G, Radio Access Network (RAN), Open Radio Access Network (O-RAN), Reliability Preprint submitted to Computer Communications December 11, 2025 1. Introduction In the contemporary landscape, the advent of Information and Communication Technologies (ICTs) has significantly altered various facets of daily life, including the domain of public safety solutions. The assimilation of ICT resources and methodologies has profoundly reshaped the strategies employed by governments, organizations, and individuals in addressing and managing issues related to public safety [1]. The progression of public safety mechanisms via ICT has resulted in increased efficiency, effectiveness, and the capacity to respond promptly in protecting communities. This is primarily attributed to the utilization of (i) high-speed and reliable communication technologies; (ii) the analysis of data in real-time; (iii) anticipatory and preventive stance, achievable for example by implementing Wireless Sensor Networks (WSNs) in critical locations [2]. With a specific emphasis on cutting-edge communication systems and data analytics, the widespread adoption of mobile technology has further enabled citizens to play a significant and sometimes unwitting role in contributing to public safety initiatives [3]. In situations where the mobile network is either utilized by citizens during emergency scenarios or by rescue teams after a disaster, it becomes imperative to sustain connectivity with minimum or none disruption [4]. From the standpoint of network infrastructure, the emergence of either natural or human-inducted hazards could lead to the impairment of antenna towers, which in turn deactivates radio access points and simultaneously causes congestion in the remaining accessible ones, all while maintaining connectivity for first responders is deemed a critical task. To address the need for quick and efficient connectivity solutions in the context of Public Protection and Disaster Relief (PPDR) [5], a variety of studies have introduced different concepts of Rapidly Deployable Networks (RDNs). The demand for fast and efficient establishment of connectivity solutions to aid rescue operations necessitates the exploration of novel technological strategies. This study investigates the employment of two pivotal technological enablers to assist first responders: Neutral Hosting and the innovative framework of Open Radio Access Network (O-RAN) to provide seamless connectivity. A significant challenge in providing unified and seamless connectivity to first responders arises from the diversity among mobile network providers, necessitating complex interactions by public authorities and emergency ser2 vices management systems. Neutral hosting proposes a solution by allowing a third-party to construct the infrastructure for the public mobile access network, which is then accessible to Mobile Network Operators (MNOs). Economically, neutral hosting offers a cost-reduction advantage by relieving telecommunications companies from the necessity to establish the network’s foundational infrastructure, thereby reducing the risks tied to its deployment. Technologically, it maintains network visibility and control, serving as a cohesive layer across various operators to augment flexibility [6]. From the standpoint of emergency services connectivity, this approach opens up a promising avenue for achieving a standardized interface with communication infrastructures, thus efficiently supporting emergency connectivity. While neutral hosting streamlines the complexity of network infrastructure, O-RAN introduces a technological framework designed to make mobile Radio Access Networks (RANs) more open, adaptable, and programmable. As a novel architectural concept, O-RAN seeks to transform the conventional methodologies prevalent within current RAN frameworks. It advocates for an open and modular architecture that supports interoperability and cooperation across different network components and suppliers [7]. By embracing virtualization and cloud-native strategies, O-RAN enhances scalability, optimizes resource use, and facilitates the flexible distribution of network functions. Adhering to the principles of Software Defined Networking (SDN), O-RAN separates hardware from software functions, enabling the integration of varied technologies. This separation not only fosters competition but also accelerates innovation within the sector. The use of open interfaces and standardized protocols ensures smooth integration with pre-existing network systems and encourages the implementation of machine learning-based and data-centric methods to improve network efficacy and service quality. Owing to its inherent flexibility and the ability for software-defined modifications of the RAN, O-RAN emerges as a vital asset in achieving the envisioned seamless support for emergency services connectivity. In this manuscript, we introduce an architectural framework that harnesses the concept of neutral hosting to capitalize on a unified infrastructure layer, alongside the programmability of O-RAN, to dynamically implement an emergency connectivity service. This service is designed to optimally adjust radio resource allocation to efficiently support the connected devices of first responders. Our approach exploits the flexibility and scalability of O-RAN to ensure that critical communication services remain operative during disaster scenarios. 3 The proposed solution addresses key limitations of traditional singleoperator networks, which often fail to provide the necessary resilience and robustness during emergencies. By enabling multiple operators to share dedicated RAN infrastructures, our architecture not only enhances the overall network performance but also provides a reliable fallback mechanism in the event of infrastructure damage. This is particularly crucial in emergency scenarios where rapid response and uninterrupted communication are essential. Moreover, the integration of slice isolation within the O-RAN framework ensures that first responders receive prioritized access to network resources leveraging on dedicated resources, thereby reducing latency and improving throughput under high traffic conditions. This capability is essential for supporting bandwidth-intensive applications such as real-time video streaming, which are increasingly used in emergency response operations. Our simulation results demonstrate significant improvements in network performance and service robustness, highlighting the practical viability and advantages of the proposed O-RAN neutral hosting solution in operational networks. This paper is organized as follows: Section 2 describes a literature review about first responders connectivity and O-RAN, while Section 3 presents the system model. The first responders connectivity through O-RAN neutral hosting is presented in Section 4, while Section 5 describes the radio scheduling model and Section 6 exhibits the simulation results. Finally, some conclusions are reported in Section 7. 2. Related Works In the context of emergency situations, having reliable and efficient connectivity is crucial for effective response and coordination efforts [4, 8]. By leveraging O-RAN neutral hosting, an architectural framework can be established to enhance emergency connectivity in such situations. This framework would involve the deployment of shared and open infrastructure that can support multiple network operators, ensuring robust connectivity coverage. Existing research has identified the need for resilient disaster recovery access networks that can provide connectivity in harsh environments such as after disasters. In this sense, [9] presents an exhaustive analysis to provide seamless and reliable connectivity after a disaster exploiting a latency-aware hybrid architecture. In this proposal the authors split the architecture into four different layers, relying on heterogeneous communication based on Bluetooth, WiFi, and long-range WiFi. Approaching the issues related to con4 nectivity recovery in disaster scenarios, in [10] the authors present a solution to rapidly supply a Movable and Deployable Resource Unit (MDRU) to configure an ad hoc recovery network and provide network services to victims, in particular exploiting vehicles and movable units. A similar solution has been proposed in [11], where a novel approach for emergency communications in disaster scenarios and rescue operations is described, in particular for situations where connectivity cannot be guaranteed due to Base Station (BS) failures. Unmanned Aerial Vehicle (UAV)-assisted solutions, such as flying ad-hoc networks, represent an effective and fast solution to replace the damaged BSs exploiting multi-hop UAV relay nodes. [12] presents a similar approach where flying real-time networks act as communication gateways to provide reliable communications between the disaster command and the disaster areas. However, these solutions could not be efficient due to the requirement for dedicated equipment and specialized first responders. In this sense, O-RAN represents a promising solution for seamless and reliable first responders connectivity, as it allows for flexibility, scalability, and resilience in network deployment [13, 14, 15]. In fact, the intelligent scheduling and allocation of radio resources will play a crucial role in effectively managing radio resources. This will ensure optimal connectivity for both first responders and civilian users in disaster scenarios, enhancing overall communication efficiency and effectiveness. O-RAN is a suitable solution for this task, as described in [16] with the dynamic computing and network resource allocation, showing that the integration of reinforcement learning and AI-based techniques will further improve the Radio Intelligent Controller (RIC) latency performance, representing a critical requirement for first responders in disaster scenarios. In [17] the advantages of O-RAN neutral hosting for optimal radio resource management leveraging on different scheduling algorithms are described. In this case, O-RAN solution clearly outperforms traditional RANs in terms of performance and resource utilization efficiency. Regarding O-RAN neutral hosting, [18] offers significant insights by presenting an end-to-end solution for zero-touch automated allocation and deployment of micro-services in a neutral host architecture, optimizing RAN and spectrum sharing based on tenant intents. Additionally, [19] introduces a multi-tenant O-RAN ecosystem using a min-max fairness approach and Vickrey-Clarke-Groves auctionbased resource allocation to ensure fair opportunities for OPEX minimization across different MNOs. 5 Neutral Host Service Management and Orchestration Framework Emergency Service OSS Non Real Time RIC Near Real Time RIC A1 E2 E2 E2 Operator A Emergency Operator E AMF UPF SMF NRF Operator B Edge Emergency support O-RAN rApp Emergency connectivity O-RAN xApp O1 O1 O1 R1 E B A E B A E B A E B A E B A E B A E B A E B A Figure 1: Reference Architecture. Despite these advancements, our work addresses a critical gap by focusing specifically on emergency scenarios and the performance of first responders. The proposed scheduling policy within the O-RAN neutral hosting framework is designed to meet the stringent requirements of emergency communications, ensuring prioritized and reliable service for first responders during network faults due to disaster events. 3. System Model Fig. 1 depicts the reference scenario under consideration, where, following a critical incident, first responders including firefighters, rescue teams, and similar emergency operators, are extensively deployed in an urban setting. In this scenario, a RAN, overseen by a neutral host, exists to ensure that connectivity is maintained to facilitate the operations of these emergency services. Conventional radio resource allocation methods, such as best-Channel Quality Indicator (CQI) [20] or proportional fair [21], can lead to significant delays, which, in the wake of severe incidents leading to network congestion caused by infrastructural failures and increased traffic from users, might be perceived as network outages by the application layer. This degree of latency 6 is untenable for emergency services connectivity, as the devices used by first responders are critical and require guaranteed access to resources. Therefore, it is essential to adopt a versatile approach to dynamically allocate resources, ensuring low latency and the necessary bandwidth for such crucial services under specific conditions. It is important to note that reserving resources in advance for these services could lead to resource wastage, making it an inefficient strategy both technically and economically for the neutral host. To address these challenges, the proposed architecture incorporates a Service Management and Orchestration (SMO) framework, as delineated by O-RAN specifications. The SMO is tasked with overseeing network management and the lifecycle of virtualized functions across cloud platforms. This framework enables the coexistence of multiple operators (denoted as Operator A and Operator B in Fig.1) on the same physical infrastructure. In this scenario, the SMO provides an interface to an emergency service Operational Support System (OSS), facilitating the activation of a connectivity service for first responders’ User Equipments (UEs) on demand. This setup entails the inclusion of an additional virtual operator, such as a citizen protection agency or a public authority (Emergency Operator (EO), defined as E in Fig.1), whose Core Network components and radio resources are orchestrated by the SMO. Notably, the components dynamically deployed for the EO are illustrated in red in Fig. 1. The integration with the external OSS is facilitated through a specifically designed non-real-time Application (rApp), also known as Emergency Connectivity rApp. As defined by O-RAN, rApps are responsible for conducting management activities, operations with a timescale greater than 1 second, and optimization tasks. They engage with a Non-Real Time (Non-RT) RIC located at the SMO through the R1 interface. The SMO, critical in orchestrating functions, plays a key role in deploying 5G Core network functions for the EO, such as Access and Mobility Management Function (AMF), Session Management Function (SMF), Network Repository Function (NRF), and User Plane Function (UPF). To ensure low latency for first responders, these functions may be situated within an edge computing node. It is important to note that the edge computing infrastructure could be compromised by critical events. In such instances, the SMO is equipped to make alternative orchestration decisions, reallocating functions across different computing nodes within the O-RAN Cloud (O-Cloud). Furthermore, within the EO network, multiple slices could be set up to support heterogeneous services aiding first responders and characterized by heterogeneous requirements, e.g., aug7 vides significant advantages during emergencies. Furthermore, we conduct a performance evaluation of the two strategies presented in Sec. 5, i.e. with and without SI. We analyze the performance before and after the occurrence of disasters with varying sizes causing BS failures, expressed as a percentage of NBS. The simulation has been performed with the parameters listed in Table 1 assuming a squared urban area of length L. The RAN layout is assumed with hexagonal macro cells design, and Inter-Site Distance (ISD)= 500 m, corresponding to the ISD of the reference Urban Macro scenario adopted in [29]. We consider the 3GPP Urban Macro scenario with NLOS channel model in [29]. Furthermore, [29] proposes a set of standard values among which of shadow fading standard deviation σSF = 6 dB, hBS = 25 m, and hUE within the range [1.5−22.5] m. The UEs are uniformly distributed according to a Poisson Point Process (PPP). The target BLER is set to 10−3, which reflects the high reliability required for communications in emergency scenarios [30]. A PHY numerology with 15kHz subcarrier spacing and 12 subcarriers per PRB has been considered, with 14 Orthogonal Frequency-Division Multiplexing (OFDM) symbols per TTIs, where each snapshot simulates a single TTI. Different percentages of BS failure, ranging from [10 −60] %, have been considered to evaluate various disaster magnitudes and their impact on network performance. Additionally, different percentages of First Responders (FRs) with respect to the total number of UEs have been investigated to assess network load evolution, particularly in scenarios with dedicated resources (i.e., with SI). We consider FTP model 3 downlink traffic [27] with λ= 30 pkt/ms and q= 1 kb/pkt, which allow for the analysis of network saturation condition. Typical values adopted in literature [31, 32, 33] are considered for transmitted power, carrier frequency, channel bandwidth, and noise spectral density, as reported in Table 1. Fig. 4a and Fig. 4b exhibit the Cumulative Distribution Functions (CDFs) of throughput and latency, respectively, for the addressed scenarios considering 20% BS failure after the disaster and 20% population of first responders with respect to NUEs (FR = 20%). Fig. 4a illustrates the advantages of O-NH before and after the disaster, highlighting higher throughput for the whole UE set and for first responders as depicted in dash-dotted plots. Instead, Fig. 4b reports the advantages of O-NH in terms of latency, showing that it outperforms MNO approach, in particular for first responders. It also highlights that O-NH after the disaster is able to achieve similar performance of standard MNO scenario for latencies greater than 6ms. 14 (a) Throughput CDFs (b) Latency CDFs Figure 4: Comparison between O-NH and MNO before (pre) and after (post) the disaster. The average throughput and latency of first responders’ UEs in MNO and O-NH scenarios have been compared in Fig. 5a and Fig. 5b for FR = 20% and different disaster impact sizes in terms of percentage of BS failures. Fig. 5a illustrates a throughput decrease for higher failure proportions, with performance reductions up to −61% and −57% for MNO and O-NH, respectively. Importantly, O-NH outperforms MNO with an average 7% higher throughput. Similarly, Fig. 5b shows the average first responder latency, which increases with BS failure percentage with a significantly higher growth rate for MNO compared to O-NH. In this case, the O-NH scenario provides higher performance, with up to −35% latency reduction with respect to MNO. Fig. 6a and Fig. 6b illustrate the performance reduction of first responder and civilian UEs for different values of BS failures in O-NH scenario with and without SI, considering FR = 20%. Specifically, Fig. 6a shows that the experienced throughput of first responder and civilian UEs suffers similar degradation without SI, due the lack of dedicated resources for first responders. Conversely, SI guarantees dedicated resources to first responders, resulting in higher performance than civilian UEs (up to +7%). This is further evidenced in Fig. 6b, which exhibits the latency variation for different disaster impact sizes. In this case, SI provides a lower latency increase for first responders compared to civilian UEs (up to −50%) and the UEs without SI (up to −33%). Furthermore, the different performance reduction experienced by first responders and civilians with and without SI is evidenced in Fig. 7a and Fig. 7b, showing the average throughput and 15 (a) Average throughput (b) Average latency Figure 5: Average performance comparison of O-NH and MNO for different BS failures. (a) Throughput reduction (b) Latency increase Figure 6: Throughput reduction (a) and latency increase (b) for O-NH first responders (Fr) and civilian (Civ) UE with and without SI for different BS failures. latency per UE, respectively. In fact, Fig. 7a and Fig. 7b show the performance loss of civilian UEs, which suffer the resources dedicated to first responders, with respect to the scenario without SI where the performance degradation is more evenly distributed among all UEs. In Fig. 8 we compare the performance of first responders’ UEs with and without SI in relation to the considered scenarios for different values of BS failure and FR percentage. Considering SI, we assumed a portion of reserved PRBs equal to the percentage of first responders. This approach allows us 16 (a) Average throughput (b) Average latency Figure 7: Comparison of throughput and latency average performance for first responders (Fr) and civilian (Civ) UE with and without SI for different BS failures. (a) MNO scenario (b) O-NH scenario (c) MNO scenario including SI (d) O-NH scenario including SI Figure 8: Average throughput comparison between O-NH and MNO scenarios before (0% BS failure) and after the disaster without and with SI. to consider a slice dedicated only to first responders where the slice absorbs all the available PRBs for FR = 100% (i.e. NFR =Nand NC= 0). Fig. 8 highlights a throughput improvement of the SI compared to the unsliced case up to 54% as exhibited in Fig. 8a and Fig. 8c. The throughput increase reaches 62% improvement by enforcing SI in the O-NH case (Fig. 8b and 17 (a) MNO scenario (b) O-NH scenario (c) MNO scenario including SI (d) O-NH scenario including SI Figure 9: Average latency comparison between O-NH and MNO scenarios before (0% BS failure) and after the disaster without and with SI. Fig. 8d). Instead, Fig. 9 presents the latency experienced by first responders, where SI enforces a latency reduction up to 26% for MNO (Fig. 9a and Fig. 9c) and 32% for O-NH (Fig. 9b and Fig. 9d). It must be noticed that in the unsliced case the FR percentage does not affect first responders’ performance in terms of both throughput (Figs. 8a and 8b) and latency (Figs. 9a and 9b), being the performance comparable to civilian UEs from RAN’s point of view. Finally, in Fig. 10a and Fig. 10b the throughput and latency CDFs of MNO and O-NH scenario before and after the disaster for FR = 20% and 20% BS failure are reported. In this case, the advantage of SI strategy in terms of throughput and latency is evident, showing generally higher performance in O-NH compared to MNO. However, Fig. 11a and Fig. 11b exhibit the corresponding side effect with throughput and latency CDFs of civilian UEs, showing a clear performance decrease. This results from the reduction of available resources for civilian UE to guarantee the priority for first responders UEs. 18 (a) Throughput CDFs (b) Latency CDFs Figure 10: First responders UE performance comparison without and with SI in MNO and O-NH scenarios. (a) Throughput CDFs (b) Latency CDFs Figure 11: Civilian UE performance comparison without and with SI in MNO and O-NH scenarios. 7. Conclusion and Future Directions O-RAN neutral hosting is identified as a potential breakthrough for fulfilling the seamless connectivity demands of first responders during emergency scenarios. This study has explored the essential attributes, advantages, and obstacles linked with O-RAN neutral hosting. Offering an open and adaptable infrastructure for the operation and governance of RANs, O-RAN neutral hosting presents numerous technical ben19 efits for emergency service operators. The employment of virtualization and cloud-native methodologies enhances network availability, enabling effective resource management and the agile distribution of network functionalities. The scalability and adaptability of O-RAN neutral hosting facilitate the real-time allocation of resources, tailoring to the particular requirements of first responders across various emergency contexts. This ensures that critical communications for emergency services are prioritized, securing the essential bandwidth and minimal latency indispensable for their operations. Results validate the potential of O-RAN neutral hosting as a promising approach for maintaining uninterrupted connectivity for first responders during emergencies. The simulation results highlight that O-RAN not only outperforms traditional mobile networks in throughput and latency metrics but also maintains superior performance in disaster scenarios with varying degrees of base station failures. Importantly, the slice isolation strategy further amplifies this advantage, providing a robust framework for prioritizing emergency services without compromising the communication quality. Acknowledgment This work has been partially supported by the European Union through SNS Joint Undertaken SEASON project (GA: 101096120), by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, through National Innovation Ecosystem (ECS00000041 - VITALITY - CUP E13C22001060006); and Mission 4, Component 2, Investment 1.3, CUP D93C22000910001, CUP E83C22004640001 partnership on “Telecommunications of the Future” (Program ”Research and Innovation on Future Telecommunications Systems and Networks (RESTART)”- PE00000001); and Project SEcurity and RIghts In the CyberSpace (SERICS) under Grant PE00000014, CUP D33C22001300002, and by the Italian Government program ”Programma di supporto tecnologie emergenti - Asse I (Casa delle Tecnologie Emergenti) Progetto SICURA” - CUP C19C20000520004. This work also has been partially supported by TTDF “SMART-RIC6G: Smart Drift-Handling Enabler for RAN Intelligent Controllers in 6G Networks (TTDF/6G/422)” project. References [1] G. Baldini, S. Karanasios, D. Allen, F. Vergari, Survey of Wireless Communication Technologies for Public Safety, IEEE 20 Communications Surveys & Tutorials 16 (2) (2014) 619–641. doi:10.1109/SURV.2013.082713.00034. [2] F. Franchi, A. Marotta, C. Rinaldi, F. Graziosi, L. Fratocchi, M. Parisse, What Can 5G Do for Public Safety? Structural Health Monitoring and Earthquake Early Warning Scenarios, Sensors 22 (8) (2022). doi:10.3390/s22083020. URL https://www.mdpi.com/1424-8220/22/8/3020 [3] L. S. Snyder, Y.-S. Lin, M. Karimzadeh, D. Goldwasser, D. S. Ebert, Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness, IEEE Transactions on Visualization and Computer Graphics 26 (1) (2020) 558–568. doi:10.1109/TVCG.2019.2934614. [4] K. Miranda, A. Molinaro, T. Razafindralambo, A survey on rapidly deployable solutions for post-disaster networks, IEEE Communications Magazine 54 (4) (2016) 117–123. doi:10.1109/MCOM.2016.7452275. [5] R. Ferr´us, O. Sallent, Public Protection and Disaster Relief Communications, Wiley, 2015, pp. 1–48. doi:10.1002/9781118831243.ch1. [6] R. Bajracharya, R. Shrestha, H. Jung, H. Shin, Neutral host technology: The future of mobile network operators, IEEE Access 10 (2022) 99221– 99234. [7] A. Garcia-Saavedra, X. Costa-Perez, O-RAN: Disrupting the virtualized RAN ecosystem, IEEE Communications Standards Magazine 5 (4) (2021) 96–103. [8] S. Sawyer, A. Tapia, L. Pesheck, J. Davenport, Mobility and the first responder, Commun. ACM 47 (3) (2004) 62–65. doi:10.1145/971617.971647. URL https://doi.org/10.1145/971617.971647 [9] S. Saha, S. Nandi, P. S. Paul, V. K. Shah, A. Roy, S. K. Das, Designing delay constrained hybrid ad hoc network infrastructure for post-disaster communication, Ad Hoc Networks 25 (2015) 406–429, New Research Challenges in Mobile, Opportunistic and Delay-Tolerant Networks Energy-Aware Data Centers: Architecture, Infrastructure, and Communication. doi:https://doi.org/10.1016/j.adhoc.2014.08.009. URL https://www.sciencedirect.com/science/article/pii/S1570870514001802 21 [10] T. Sakano, S. Kotabe, T. Komukai, T. Kumagai, Y. Shimizu, A. Takahara, T. Ngo, Z. M. Fadlullah, H. Nishiyama, N. Kato, Bringing movable and deployable networks to disaster areas: development and field test of MDRU, IEEE Network 30 (1) (2016) 86–91. doi:10.1109/MNET.2016.7389836. [11] N. Zhao, W. Lu, M. Sheng, Y. Chen, J. Tang, F. R. Yu, K.-K. Wong, UAV-Assisted Emergency Networks in Disasters, IEEE Wireless Communications 26 (1) (2019) 45–51. doi:10.1109/MWC.2018.1800160. [12] M. Micheletto, V. Petrucci, R. Santos, J. Orozco, D. Mosse, S. F. Ochoa, R. Meseguer, Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas, Sensors 18 (5) (2018). doi:10.3390/s18051662. URL https://www.mdpi.com/1424-8220/18/5/1662 [13] M. Polese, L. Bonati, S. D’Oro, S. Basagni, T. Melodia, Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges, IEEE Communications Surveys & Tutorials 25 (2) (2023) 1376–1411. doi:10.1109/COMST.2023.3239220. [14] J. X. Salvat, J. A. Ayala-Romero, L. Zanzi, A. Garcia-Saavedra, X. Costa-Perez, Open Radio Access Networks (O-RAN) Experimentation Platform: Design and Datasets, IEEE Communications Magazine 61 (9) (2023) 138–144. doi:10.1109/MCOM.003.2200711. [15] A. Arnaz, J. Lipman, M. Abolhasan, M. Hiltunen, Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey, IEEE Access 10 () (2022) 67747–67770. doi:10.1109/ACCESS.2022.3183989. [16] Q. Wang, Y. Liu, Y. Wang, X. Xiong, J. Zong, J. Wang, P. Chen, Resource Allocation Based on Radio Intelligence Controller for Open RAN Toward 6G, IEEE Access 11 () (2023) 97909–97919. doi:10.1109/ACCESS.2023.3311888. [17] H. Navidan, M. Naseri, I. Moerman, A. Shahid, Radio Resource Management for Intelligent Neutral Host (INH) in Multi-Operator Environments, IEEE Open Journal of the Communications Society () (2024) 1–1. doi:10.1109/OJCOMS.2024.3380517. 22 [18] L. Bonati, M. Polese, S. D’Oro, S. Basagni, T. Melodia, NeutRAN: An Open RAN Neutral Host Architecture for Zero-Touch RAN and Spectrum Sharing, IEEE Transactions on Mobile Computing 23 (5) (2024) 5786–5798. doi:10.1109/TMC.2023.3311728. [19] S. Mondal, M. Ruffini, Fairness Guaranteed and Auction-Based x-Haul and Cloud Resource Allocation in Multi-Tenant O-RANs, IEEE Transactions on Communications 71 (6) (2023) 3452–3468. doi:10.1109/TCOMM.2023.3260239. [20] S. Schwarz, C. Mehlf¨uhrer, M. Rupp, Low complexity approximate maximum throughput scheduling for LTE, in: 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010, pp. 1563–1569. doi:10.1109/ACSSC.2010.5757800. [21] T. Girici, C. Zhu, J. R. Agre, A. Ephremides, Proportional fair scheduling algorithm in OFDMA-based wireless systems with QoS constraints, Journal of Communications and Networks 12 (1) (2010) 30–42. doi:10.1109/JCN.2010.6388432. [22] C. Centofanti, A. Marotta, D. Cassioli, F. Graziosi, V. Gudepu, K. Kondepu, End-to-End Slicing via O-RAN and Software Defined Optical Access, in: 2023 Optical Fiber Communications Conference and Exhibition (OFC), IEEE, 2023, pp. 1–3. [23] 3GPP, Physical layer procedures for data (Release 18), Technical Specification (TS) 38.214 V18.1.0, 3rd Generation Partnership Project (3GPP) (12 2023). [24] A. Mamane, M. Fattah, M. E. Ghazi, M. E. Bekkali, Y. Balboul, S. Mazer, Scheduling Algorithms for 5G Networks and Beyond: Classification and Survey, IEEE Access 10 () (2022) 51643–51661. doi:10.1109/ACCESS.2022.3174579. [25] 3GPP, Study on self evaluation towards IMT-2020 submission (Release 17), Technical Report (TR) 37.910 V17.0.0, 3rd Generation Partnership Project (3GPP) (03 2022). [26] G. Pocovi, K. I. Pedersen, P. Mogensen, Joint Link Adaptation and Scheduling for 5G Ultra-Reliable Low-Latency Communications, IEEE Access 6 () (2018) 28912–28922. doi:10.1109/ACCESS.2018.2838585. 23