The evolution toward 6G networks envisions a heterogeneous Radio Access Network (RAN) comprising diverse access technologies, such as private 5G, public 4G/5G, and Wi-Fi, managed by multiple stakeholders. While considerable research effort has been devoted to O-RAN-based frameworks enabling rApp and xApp implementation and validation, few works provide integrated support for cross-technology RAN orchestration, end-to-end multi-tenancy, and a unified subset of SMO functionalities, including Non-RT RIC components. This paper introduces COSMO, a novel RAN Service Management and Orchestration platform designed to support heterogeneous 3GPP (5G NR, LTE) and non-3GPP (Wi-Fi) access networks. COSMO enables cross-technology multi-tenancy, defined as the capability to allow multiple tenants to dynamically share heterogeneous RAN resources with explicit resource allocation guarantees based on Service Level Agreements (SLAs). This is achieved through management primitives that support flexible and on-demand resource allocation. Additionally, the platform includes a cross-technology Non-Real-Time RAN Intelligent Controller (Non-RT RIC) that enables the development of intelligent rApps for closed-loop control and network orchestration. Beyond its architectural design, COSMO improves resource utilization and operational flexibility through unified orchestration of heterogeneous multi-tenant RAN resources. Through prototyping and benchmarking, we demonstrate the effectiveness of COSMO in resource allocation, SLA enforcement, and scalability. In our prototype, the SLA-based rApp reduces SLA violation from approximately 21% to below 10% under dynamic traffic conditions in a heterogeneous RAN deployment including 5G, 4G, and Wi-Fi access networks. Our results confirm that COSMO offers an efficient solution for managing and orchestrating future multi-tenant cross-technology RAN environments.

Energy efficiency is a critical concern in the deployment and operation of 5G networks, particularly due to the low utilization of 4G and 5G carriers during off-peak hours. While considerable research has focused on designing energy-efficient cell on/off switching strategies that avoid disrupting user connectivity, the integration of operator-specific policies to guarantee particular Quality of Service (QoS) levels has received limited attention. This paper presents a machine learning (ML)-based energy saving strategy, trained using a real-world dataset from a European mobile operator, that enforces operator-defined policies that jointly consider strong throughput requirements and maximum outage tolerance constraints. By tuning the model’s class ratios during training, the proposed solution enables operators to manage the trade-off between energy savings and QoS policy compliance prior to deployment in live networks. Evaluation results show that the method provides substantial energy savings while maintaining policy-compliant service levels under realistic 5G operating conditions.

The proper orchestration of end-to-end network slices demands dynamic and meticulous resource management while addressing the complexities of multi-tenancy and multiservice scenarios. In this context, integrating network orchestrators with distributed ledger technologies has gained significant attention for its potential to implement decentralized finance marketplaces and service-level agreements using smart contracts. This approach can support the evolution of business models beyond traditional network-sharing agreements, such as crowdfunding. To this end, we propose the 5G-as-a-Service (5GaaS) system architecture, which leverages distributed ledger technologies and smart contracts to orchestrate and optimize network slicing, enabling ubiquitous computing and connectivity in 5G networks. We evaluated the feasibility of this system across various Ethereum testnets, demonstrating the cost-effectiveness, scalability, and minimal latency of the 5GaaS system, making it suitable for seamless integration into existing telecommunications frameworks.

Next-generation networks have become key enablers for distributed eXtended Reality (XR) services, which pose stringent demands in terms of bandwidth, compute and reliability. In this context, the Network as a Service (NaaS) paradigm, where the network offers APIs tailored to specific vertical domains, is a promising approach to enhance delivery protocols and communications for XR services. In this paper, we present NetXRate which is a new NaaS API tailored to interactive XR services. NetXRate leverages Open RAN (O-RAN) and the network exposure capabilities of the 5GCore, to deliver rate recommendations to XR applications based on the instantaneous capacity available in the cellular network. Using a simulative approach, we demonstrate how NetXRate can be integrated with two state-of-the-art Over-the-Top (OTT) XR rate recommendation strategies, achieving a reduction of up to 72% in outage probability in scenarios with severe network congestion where multiple XR users compete for higher share of bandwidth in the same cell.

3GPP Integrated Access and Backhaul (IAB) allows operators to deploy outdoor mm-wave access networks in a cost-efficient manner, by reusing the same spectrum in access and backhaul. In IAB networks the performance bottleneck is the wireless backhaul segment, where efficient forwarding strategies are needed to effectively use the available capacity. In addition, the performance of the mm-wave IAB backhaul segment is contingent on the availability of line of sight (LoS) conditions in the selected deployment sites. To mitigate LoS dependence, in this paper, we propose to complement the mm-wave backhaul segment of IAB networks with additional Sub6 backhaul links, which contribute to the capacity and robustness of the backhaul network. We refer to IAB networks combining Sub6 and mm-wave links in the backhaul as Sub6 enhanced IAB networks. In this context, the main contribution of this paper is PHaul, a forwarding engine for Sub6 enhanced IAB networks that accomodates different traffic engineering criteria, and combines an offline path selection heuristic with an online Deep Reinforcement Learning (DRL) agent based on Proximal Policy Optimization (PPO). By leveraging a network digital twin of the IAB wireless backhaul, PHaul periodically samples the input traffic of the backhaul network and updates flow to path mappings, with execution times below 10 seconds in realistic backhaul topologies. We present an exhaustive performance evaluation, where we demonstrate that PHaul can achieve gains of up to 36% in throughput efficiency and of up to 20% in fairness, when compared against two alternative heuristics in a wide range of network configurations. We also demonstrate that PHaul is robust to differences between the network topologies considered in the training and inference phases, which can occur in practice due to link failures.

As part of 3GPP releases 15 and 16 Wi-Fi networks have been integrated with the 5G Core, which is a key feature in private network scenarios. Another important feature for private networks is multi-tenancy, whereby an infrastructure provider shares a common radio access network among several tenants subject to service level agreements (SLAs). 3GPP has defined network slicing on the radio access segment supporting multi-tenancy for 5GNR, but a similar feature is lacking in Wi-Fi networks. In this paper we present G-ADRR, which, to be best of our knowledge, is the first global slicing policy for Wi-Fi that delivers per-tenant radio level SLAs over a given geographical area. We extensively evaluate the performance of G-ADRR by means of an experimental prototype and packet level simulations, and demonstrate its advantages as compared to two alternative global scheduling strategies and a static slice configuration policy commonly used in the state-of-the-art.

Software Defined Wireless Networks offer an opportunity to enhance the performance of specific services by applying centralized mechanisms which make use of a global view of the network resources. This paper presents FALCON, a novel solution that jointly optimizes fair airtime allocation and rate recommendations for Server and Network Assisted DASH video streaming, providing proportional fairness among the clients. Since this problem is NP-hard, FALCON introduces a novel heuristic algorithm that is proved to achieve almost optimal results in a practical amount of time. The performance of FALCON is evaluated when used in conjunction with three referent Adaptive Bit Rate strategies (PANDA, BOLA and RobustMPC) in a simulated ultra-dense In-flight Entertainment System scenario. The obtained results show that FALCON provides significant benefits by minimizing instability and buffer underruns, while obtaining a fair video rate and airtime allocation among clients, thus contributing to an enhanced Quality of Experience.

In this paper we present SWAM, a system that builds on commodity Wi-Fi routers with multiple wireless interfaces to provide a wireless access infrastructure supporting multi-tenancy, mobility, and integrated wireless access and backhaul. An infrastructure provider can deploy inexpensive SWAM nodes to cover a given geographical area providing on-demand connectivity for Mobile Network Operators. Our main contribution is the design of the SWAM datapath and control plane, which are inspired by the overlay techniques used to enable multi-tenancy in data-center networks. We prototype SWAM in an office wireless testbed and validate experimentally its functionality.