The rise of quantum computing poses a major threat to traditional ciphers, specifically making the Radio Access Network (RAN) vulnerable to future quantum-based attacks. To address this challenge, this paper proposes quantum-inspired RAN (Q-RAN), a next-generation framework designed to future-proof telecom networks against quantum threats. As a foundation, to evaluate, we integrate quantum network simulators with the Open-Source OpenAirInterface (OAI) stack to deploy a 5G standalone network capable of supporting quantum operations. We analyzed these, and the best fit is taken for quantum proofing. This paper considered four different quantum simulators — QuNetSim, SimQN, SeQuence, and NetSquid — for integrating with the 5G Open source stack. The framework generated by these simulators is called Quantum Gateways (namely Qtunnels) enables seamless message exchange between quantum and classical RAN components. This integrated architecture showcases how quantum simulators can be harmonized on existing 5G infrastructure, establishing a resilient and adaptable telco ecosystem catered to the quantum era or post-quantum era. In addition, this paper quantifies the Qtunnel impact — F1 Interface — between the Central Unit (CU) and Distribution Unit (DU) of RAN. The results show that the Post Quantum Cryptography (PQC)-enabled F1 interfaces provide a two-fold overhead delay when compared to the benchmark delay, however, the PQC-enabled RAN interfaces are resistant to quantum threats.

As 5G networks evolve to meet the growing demands for high data rates, low latency, and superior quality of service, hardware acceleration becomes essential for managing computationally intensive tasks. This paper demonstrates an implementation of Orthogonal Frequency Division Multiplexing (OFDM) symbols (both downlink and uplink transmission) using Field Programmable Gate Arrays (FPGAs), offloading the execution of these critical Low-Physical Layer (Low-PHY) functions away from the general-purpose CPU approach found in the open-source mobile software platform OpenAirInterface (OAI). The full integration of FPGA-generated OFDM symbols with an Open Computing Language (OpenCL) framework and the OAI protocol stack yields significant performance improvements, including a a 90% processing time reduction compared to the traditional CPU-based implementation. This demonstration highlights the effectiveness of FPGA acceleration in enhancing the efficiency of 5G Radio Access Network (RAN) functions implemented by open-source efforts, thus offering a path forward to implementing additional Low-PHY functions on accelerated hardware.

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-real-time 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. • 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.

Next-Generation Radio Access Networks (NG-RANs) aim to facilitate high data rates, low-latency applications, and dense mobile connectivity — benefit from the integration of Artificial Intelligence and Machine Learning (AI/ML) to enhance performance and efficiency. Nevertheless, the dynamic service demands within NG-RAN (namely Open RAN) lead to AI/ML performance degradation known as drift, resulting in violations of Service Level Agreements (SLA) and issues like over-or under-provisioning of resources. Detecting and adapting to drift becomes crucial to meet the diverse requirements of intelligent networks. Due to frequent retraining, the existing threshold and classifier-based approaches have potential disadvantages such as SLA violations and resource inefficiency. This paper introduces a novel approach that exploits the Generative Adversarial Network (GAN) architecture to determine the drift and anomaly. The proposed approach is evaluated for a throughput prediction use case over a real-time dataset and compared to the threshold and classifier-based approaches. The results show that the proposed approach outperforms the threshold and classifier-based approaches.

The adoption of Time Division Duplexing (TDD) in 5G not only facilitates efficient spectrum utilization but also paves the way for the implementation of innovative schemes that leverage the unique characteristics of the TDD patterns. For example, Cooperative Dynamic Bandwidth Allocation (CO DBA) exploits information about the TDD pattern to reduce latency in Time Division Multiplexing Passive Optical Networks (TDM-PON)-based fronthaul. In this paper, we harness the information about 5G TDD patterns alongside the programmability offered by Open Radio Access Network (O-RAN) and Software Defined Optical Access Networks (SDOANs) for a different purpose: enhancing x-haul energy efficiency. The scheme we propose seeks to minimize the energy consumption of PON-based x-haul networks by dynamically adjusting the operational states of selected subsystems within the Optical Network Terminal (ONT) in coordination with the TDD patterns. Preliminary simulation results show that up to 80% energy savings can be achieved with the proposed cooperative technique.

The rapid evolution of next-generation mobile networks introduces challenges and opportunities in achieving various use cases with extremely low latency, high data rates, and dense user connectivity. However, these objectives can lead to the higher energy consumption of mobile networks, particularly within the Radio Access Network (RAN), which generally consumes 75% of the mobile networks total energy consumption. The current studies available focus mainly on the energy consumption of the next-generation Core Network (5GC), while this demonstration provides a better understanding of the energy consumption of the RAN components. The demonstration provides energy observability leveraging various open-source software tools to measure and monitor RAN energy consumption trends deployed on the Kubernetes platform — a widely adopted container orchestration platform. Moreover, this demonstration shows energy consumption on different RAN architectures — Monolithic, Disaggregated, and Control Plane and User Plane Separation (CUPS) — utilizing tools such as Kepler and Scaphandre for comprehensive energy measurement.

Recently, container-based solutions have become de facto compute units of modern cloud-native applications. However, the exponential growth in data traffic and the power consumption of these technologies to handle high data traffic alarm the strong need for energy evaluation approaches in containerized clouds. Furthermore, the proliferation of highly distributed edge clouds raises additional concerns regarding the power consumption of future cloud architectures. This article presents a detailed overview of methods and techniques for monitoring power consumption within popular cloud platforms. The study offers an in-depth evaluation of these approaches, demonstrating variations in measured power consumption based on the chosen technique. A well-known container orchestration platform named Kubernetes (K8s) has been applied in our extensive measurements. This work argues that energy-efficient container clouds will play a vital role in building a more sustainable and eco-friendly digital infrastructure by optimizing power consumption and reducing carbon footprint, paving the way for a greener future. The paper also discusses open challenges and future research directions on energy sustainability, leading to the conclusion, offering lessons learned and prospects on potential solutions to foster sustainable practices within the container ecosystem.

Smart living applications represent a significant group of 5G vertical use cases. For the most part, these use cases require mobile network connectivity and service high-availability. Simply put, two essential requirements for these applications are short connection time and uninterrupted user data service. Coupled with the expectation of supporting billions of connected devices, these requirements can be achieved by exploiting New Generation Radio Access Network (NG-RAN) architectures. One such emerging architecture is Cloud Radio Access Network (C-RAN), whose Next Generation NodeB (gNB) functions are physically decoupled into distinct entities, such as Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU). The CUs are connected to 5G Core Network (CN) and are likely to be virtualized and distributed across multiple (micro and macro) data centers. The virtualized CUs (vCUs) are further decoupled into virtualized CU Control-Plane (vCU-CP) and virtualized CU User-Plane (vCU-UP) to increase flexibility and scalability. As these 5G RAN entities are virtualized, various resiliency schemes, such as container migration, must be considered to overcome possible congestion in or malfunction of the hosting server.In this paper, two distinct cloud-native RAN resiliency scenarios are evaluated while live-migrating gNB-vCU-CP with the objective of maintaining User Equipment (UE) connection time short and user data service uninterrupted in support of smart living and other advanced applications.

Beyond fifth-generation (B5G) networks (namely 6G) aim to support high data rates, low-latency applications, and massive machine communications. Integrating Artificial Intelligence (AI) and Machine Learning (ML) models are essential for addressing the network’s increasing complexity and dynamic nature. However, dynamic service demands of B5G cause the AI/ML models performance degradation, resulting in violations of Service Level Agreements (SLA), over-or under-provisioning of resources, etc. To address the performance degradation of the AI/ML models, retraining is essential. Existing threshold and periodic retraining approaches have potential disadvantages such as SLA violations and inefficient resource utilization for setting a threshold parameter in a dynamic environment. This paper presents a novel algorithm that predicts when to retrain AI/ML models using an unsupervised classifier. The proposed predictive approach is evaluated for a Quality of Service (QoS) prediction use case on the Open RAN Software Community (OSC) platform and compared to the threshold approach. The results show that the proposed predictive approach outperforms the threshold approach.

The 5G Radio Access Network (RAN) architecture provides a split option, whereby a gNodeB Central Unit (gNB-CU) is connected to one or more gNB-Distributed Units (gNB- DUs). The CU is in turn connected to the 5G Core Network (CN) and its functions can be virtualized through software containers. This demonstration showcases live migration of a containerized Central Unit (CU) component in a Cloud-native 5G network without loss of service. In terms of resiliency, virtual function live migration can circumvent the failure of the server hosting the gNB-virtualized CU (gNB-vCU) that would otherwise cause an interruption of user-plane (UP) traffic and disconnection of User Equipment (UE). The proposed gNB-vCU container live migration technique reduces the end-user service temporary downtime by 50% when compared to the traditional backup/restore option.

The advent of Open Radio Access Network (O-RAN) technology enables intelligent edge solutions for base stations in beyond 5G (B5G) networks. O-RAN Working Group 2 (WG2) focuses on the architecture and specifications of AI/ML workflows, allowing AI/ML applications in O-RAN environments to meet different QoS requirements for different use cases over varying time periods. This study shows the technical challenges in mapping AI/ML functionalities at Near-Real Time (RT) RAN Intelligence Controller (RIC) and/or Non-RT RIC for closed loop control-based resource adaptation in O-RAN. We also present a drift-based solution to avoid performance violations if there is decay in prediction accuracy. Results show that drift-based solution outperforms offline models.

Disaggregated, virtualized, and open next-generation eNodeB (gNB) could bring several benefits to the Next Generation Radio Access Network (NG-RAN) by enabling more market competition and customer choice, lower equipment costs, and improved network performance. This can be achieved through gNB-central unit (CU)-control plane (CP), gNB-CU-user plane (UP) and gNB-distributed unit (DU) separation, CU and DU function virtualization, and zero touch RAN management and control. However, to achieve the performance required by specific foreseen 5G usage scenarios (e.g., Ultra Reliable Low Latency Communications — URLLC), offloading selected disaggregated gNB functions into an accelerated hardware becomes a necessity. To this aim, this study proposes the implementation of 5G DU Low-PHY layer functions into an FPGA-based SmartNIC exploiting the Open Computing Language (OpenCL) framework to facilitate the integration of accelerated 5G functions within the mobile protocol stack. The proposed implementation is compared against (i) a CPU-based OpenAirInterface implementation, and (ii) a GPU-based implementation of IFFT exploiting clfft and cufft libraries. Experimental results show that the different optimization techniques implemented in the proposed solution reduce the Low-PHY processing time and the use of FPGA resources. Moreover, the GPU-based implementation of the cufft and the proposed FPGA-based implementation have a lower processing time and power consumption compared to a CPU-based implementation for up to two cores. Finally, the implementation in a SmartNIC reduces the delay added by the host-to-device communication through the Peripheral Component Interconnect Express (PCIe) interface, considering both functional split options 2 and 7-1.

The Open Computing Language (OpenCL) is increasingly adopted for programming processors with reconfigurable hardware acceleration. The 5G telecommunication infrastructure, imposing strong latency constraints on the managed communications, may benefit from OpenCL-designed accelerated processing. This paper presents the first study to evaluate OpenCL hardware acceleration in the context of a 5G base station physical layer. The implementation and optimization process to accelerate the Orthogonal Frequency Division Multiplexing (OFDM) part of the 5G downlink is conducted on a high-end Field Programmable Gate Array (FPGA). We show that the proposed OpenCL implementation complies with the 5G processing timing requirements since the computation time is consistent with the present 5G deployment. However, to be suitable for 5G, the OpenCL platform must improve the data latency transfer between hardware and software. Moreover, a further enhancement for the OpenCL implementation is to improve the code by means of OpenCL optimization techniques. In this way, the performance can be further improved with respect to optimized software on vectorized high-end processors.

The concept of network slicing in 5G infrastructures allows to deliver multiple virtual services over shared environments and fully customized based on vertical-driven requirements and target performance indicators. A key feature of 5G networks is the capability to dynamically re-optimize the allocation of computing and network resources, from the core up to the edge, to instantiate and manage different types of concurrent services over a shared infrastructure. In this demonstration, we present a network slicing and orchestration solution for vertical services in the media sector, where enhanced Mobile Broadband (eMBB) network slices are instantiated interconnecting physical and virtual functions, provisioned and configured on-demand using the concept of NFV Network Services. The eMBB slices are automatically dimensioned to match the requirements of the virtual Content Delivery Network (vCDN) service, e.g. in terms of number of target users, video quality and geographical coverage area. Arbitration and resource allocation schemas optimize the sharing of mobile communication services and virtual resources among concurrent media service instances, in compliance with the Service Level Agreement between network operators and vertical service providers.

Softwarization of mobile and optical networks facilitates the inter-working between control planes of the two domains, allowing a more efficient management of available resources. Radio resource utilization benefits from the centralization of mobile network functionalities with the application of high-order functional split options by fronthauling. However, higher-order options require larger bandwidth and lower latency in the fronthaul. Advanced mechanisms for the joint control of the access network represent the sole solution to support such fronthaul requirements. This paper proposes a new cooperation scheme to manage the adaptive flexible functional split in 5G networks conditioned to the resource availability in the optical access network. Results show that the proposed converged approach guarantees the optimal allocation of optical resources through a software defined wavelength and bandwidth allocation. The proposed scheme adapts to current traffic demand and simultaneously allows the mobile network to take advantage of the highest possible centralization of mobile network functions by leveraging flexible functional split adaptively compliant to the current optical traffic demand.

Edge Data Centres (EDC) are often managed by a single administrative entity with logically centralized control. The architectural split of control and data planes and the new control plane abstractions have been touted as Software-Defined Networking (SDN), where the OpenFlow protocol is one common choice for the standardized programmatic interface to data plane devices. However, in the design of an SDN architecture, there is no clear distinction between functional network parts such as core and edge elements. It means that all switches require to support lookups over hundreds of bits with complex actions that have to be specified by multiple tables. In this paper, we propose a new programmable architecture for EDC networks, named Residues Defined Networks (RDN). In RDN, a controller defines a network policy (e.g. connectivity protection) setting flow entries at the edges. Based on these entries, the edge switches assign routeIDs to flows. A route is defined as the remainder of the division (Residue) between a route-ID and a set of switch-IDs within RDN core. In case of failures, emergency routes are compactly encoded as programmable residues forwarding paths written into the packets. RDN scalability is evaluated considering 2-tier Clos topologies which cover mostly EDC deployments supporting up to 2304 servers. A RDN proof-of-concept prototype is implemented in Mininet for network emulation. Also, to increase the accuracy on latency measures, we implement RDN in NetFPGA that is validated in a testbed with 10Gbps Ethernet boards. RDN offers ultra-fast failure recovery (sub-milliseconds carrier grade), achieves low latency with RDN switching time per hop (≈0.6μs) and no jitter within the RDN core.

The utilization of Ethernet in the cellular terrestrial cloud radio access network (C-RAN) fronthaul is considered as a way for improving C-RAN network reconfigurability and efficiency in terms of both capital and operational expenditures. Moreover CPRI line bit rate dynamic reconfiguration may spare further capital expenditures by avoiding a peak traffic capacity allocation. A possible solution for introducing Ethernet in the fronthaul is the encapsulation of Common Packet Radio Interface (CPRI) over Ethernet. However, it must be assured that CPRI strict requirements on delay and jitter are still met. In this work the combined impact of encapsulating CPRI on Ethernet and of the dynamic CPRI line bit rate reconfiguration on delay and jitter is evaluated. The evaluation is both analytical and based on the pre-synthesis emulation of dynamic CPRI line bit rate reconfiguration. Results show that dynamic CPRI line bit rate reconfiguration can be achieved within about one millisecond. However, if a size-based encapsulation of CPRI over Ethernet is utilized, dynamic CPRI line bit rate reconfiguration might cause delay variations (i.e., jitter) up to few microseconds.

In TWDM-PON, the utilization of tunable transceivers at the ONUs allows to dynamically reconfigure the OSU-ONU pairings to decrease, for example, the energy consumption at the OLT. However, if the utilized dynamic wavelength and bandwidth allocation (DWBA) does not take into account which ONUs are reallocated, the ONU tuning overhead can severely impact the average frame delay experienced by the tuning ONUs. The paper shows that, by alternating the ONUs that tune at each reconfiguration, the impact of ONU tuning overhead on average frame delay is fairly distributed among the ONUs.

When traffic carried by a Time and Wavelength Division Multiplexed (TWDM) PON is fluctuating, the network performance, both in terms of energy efficiency and delay, is heavily impacted by the decision of when network reconfiguration (i.e., switching OSUs from active mode to sleep mode and vice versa) is triggered. This paper provides hints on when to trigger a network reconfiguration is such scenario by considering also the ONU tuning time. Results show that reconfigurations shall follow as soon as possible traffic variations. However, if traffic variations are fast, the ONU tuning time impairs delay performance. Therefore, when traffic variations are fast, it is better not to reconfigure the network at the expense of a higher energy consumption.

This paper proposes an advanced sleep-aware dynamic bandwidth allocation (ASDBA) scheme for 10G-EPONs that aims to maximize ONU energy efficiency with sleep mode. In the proposed ASDBA scheme, both upstream (US) and downstream (DS) transmissions are scheduled in the same transmission slot whose duration is minimized based on both DS and US bandwidth requests and the ONU transceiver is switched off outside the slot for saving energy. The ASDBA swaps the conventional order of control message exchange utilized in legacy 10G-EPON to convert the ONU idle time between a REPORT message and its replying GATE message into ONU sleep time. This enables the ONU to sleep continuously after sending a REPORT message until the beginning of the next transmission slot, further improving ONU energy-savings. Results show that the proposed scheme significantly saves ONU energy whilst incurring acceptable frame queuing delays.

In sparse wireless sensor networks, data collection is carried out through specialized mobile nodes that visit sensor nodes, gather data, and transport them to the sink node. Since visit times are typically unpredictable, one of the main challenges to be faced in this kind of networks is the energy-efficient discovery of mobile collector nodes by sensor nodes. In this paper, we propose an adaptive discovery algorithm that combines a learning-based approach with a hierarchical scheme. Thanks to its hybrid nature, the proposed algorithm is very flexible, as it can adapt to very different mobility patterns of the mobile collector node(s), ranging from deterministic to completely random mobility. We have investigated the performance of the proposed approach, through simulation, and we have compared it with existing adaptive algorithms that only leverage either a learning-based or a hierarchical approach. Our results show that the proposed hybrid algorithm outperforms the considered adaptive approaches in all the analyzed scenarios.

In sparse wireless sensor networks data collection is typically accomplished through specialized mobile nodes. One of the main challenges to be faced in this kind of networks is the energy-efficient and timely discovery of mobile nodes. In this paper we propose a simple yet effective discovery protocol based on two different Beacon messages emitted by the mobile node (i.e., Long-Range Beacons and Short-Range Beacons). Our simulation results show that, although very simple, the proposed scheme can provide a significant energy reduction with respect to the commonly used scheme based on a single Beacon, especially if the discovery phase is long.

In sparse wireless sensor networks the distance between neighboring nodes is larger than the transmission range of sensor nodes and data collection is typically accomplished through specialized mobile nodes. If visit times of mobile nodes are not known in advance a discovery protocol is required for energy-efficient and timely mobile node's detection. In this work we propose a simple yet effective hierarchical discovery protocol based on two different Beacon messages emitted by the mobile node (i.e., Long-Range Beacons and Short-Range Beacons). Our preliminary results show that, although very simple, the proposed scheme can provide a significant energy reduction with respect to the commonly used scheme based on a single Beacon, especially if the discovery phase is long.

A partially overlapping super location area (POSLA) Scheme is suggested to overcome the varying updation problem of Overlapping Super Location Area (OSLA) Scheme. In this scheme, the LA is partially overlapped by its surrounding LAs and the location updates will be done based on the minimum distance from the center cell. The suggested scheme, POSLA, shows the reduction in the location update cost as compared to OSLA scheme.