With the rapid development of 5G technology and the increasing demand for autonomous mobile robots, there is a trend to leverage the ultra-low latency, high data rates, and reliable wireless connectivity offered by 5G to improve the perception and navigation of robots in unknown environments. This paper presents a novel approach for creating and exploiting radio-aware semantic maps to empower 5G-enabled mobile robots operating within an unknown environment. The proposed solution allows for smart offloading of robotic applications and task processing onto the edge systems while facilitating real-time data exchange, and enables robots to gather environment data from both onboard sensors and the mobile network for more efficient robot operation and resource orchestration decisions. A radio-aware semantic mapping framework is introduced, which combines radio signal quality information with semantic mapping techniques to create a comprehensive understanding of the environment, which may evolve over time. The semantic map, enriched with radio quality measurement data, enables mobile robots to make timely informed decisions by considering real-time radio quality variations. Our experimental evaluation demonstrates the effectiveness of adopting radio semantic maps to enhance real-time robot operations on navigation and task offloading in unstructured environments.

The maturity and commercial roll-out of 5 G networks and its deployment for private networks makes 5G a key enabler for various vertical industries and applications, including robotics. Providing ultra-low latency, high data rates, and ubiquitous coverage and wireless connectivity, 5G fully unlocks the potential of robot autonomy and boosts emerging robotic applications, particularly in the domain of autonomous mobile robots. Ensuring seamless, efficient, and reliable navigation and operation of robots within a 5 G network requires a clear understanding of the expected network quality in the deployment environment. However, obtaining real-time insights into network conditions, particularly in highly dynamic environments, presents a significant and practical challenge. In this paper, we present a novel framework for building a Network Digital Twin (NDT) using real-time data collected by robots. This framework provides a comprehensive solution for monitoring, controlling, and optimizing robotic operations in dynamic network environments. We develop a pipeline integrating robotic data into the NDT, demonstrating its evolution with real-world robotic traces. We evaluate its performances in radio-aware navigation use case, highlighting its potential to enhance energy efficiency and reliability for 5Genabled robotic operations.

5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world exploration scenarios. This paper explores, via field experiments, the potential energy-saving gains of OROS, a joint orchestration of 5G and Robot Operating System (ROS) that coordinates multiple 5G-connected robots both in terms of navigation and sensing, as well as optimizes their cloud-native service resource utilization while minimizing total resource and energy consumption on the robots based on real-time feedback. We designed, implemented and evaluated our proposed OROS in an experimental testbed composed of commercial off-the-shelf robots and a local 5G infrastructure deployed on a campus. The experimental results demonstrated that OROS significantly outperforms state-of-the-art approaches in terms of energy savings by offloading demanding computational tasks to the 5G edge infrastructure and dynamic energy management of on-board sensors (e.g., switching them off when they are not needed). This strategy achieves approximately ~15% energy savings on the robots, thereby extending battery life, which in turn allows for longer operating times and better resource utilization.

Cellular Vehicle-to-Everything (C-V2X) is currently at the forefront of the digital transformation of our society. By enabling vehicles to communicate with each other and with the traffic environment using cellular networks, we redefine transportation, improving road safety and transportation services, increasing the efficiency of vehicular traffic flows, and reducing environmental impact. To effectively facilitate the provisioning of Cellular Vehicular-to-Network (C-V2N) services, we tackle the interdependent problems of service task placement and scaling of edge resources. Specifically, we formulate the joint problem and prove that it is not computationally tractable. To address its complexity we propose dhpg, a new Deep Reinforcement Learning (DRL) approach that operates in hybrid action spaces, enabling holistic decision-making and enhancing overall performance. We evaluated the performance of DHPG using simulations with a real-world C-V2N traffic dataset, comparing it to several state-of-the-art (SoA) solutions. DHPG outperforms these solutions, guaranteeing the 99th percentile of C-V2N service delay target, while simultaneously optimizing the utilization of computing resources. Finally, time complexity analysis is conducted to verify that the proposed approach can support real-time C-V2N services.

Radio Access Networks virtualization (vRAN) is on its way becoming a reality driven by the new requirements in mobile networks, such as scalability and cost reduction. Unfortunately, there is no free lunch but a high price to be paid in terms of computing overhead introduced by noisy neighbors problem when multiple virtualized base station instances share computing platforms. In this paper, first, we thoroughly dissect the multiple sources of computing overhead in a vRAN, quantifying their different contributions to the overall performance degradation. Second, we design an AI-driven Radio Intelligent Controller (AIRIC) to orchestrate vRAN computing resources. AIRIC relies upon a hybrid neural network architecture combining a relation network (RN) and a deep Q-Network (DQN) such that: ($i$) the demand of concurrent virtual base stations is satisfied considering the overhead posed by the noisy neighbors problem while the operating costs of the vRAN infrastructure is minimized; and ($ii$) dynamically changing contexts in terms of network demand, signal-to-noise ratio (SNR) and the number of base station instances are efficiently supported. Our results show that AIRIC performs very closely to an offline optimal oracle, attaining up to 30% resource savings, and substantially outperforms existing benchmarks in service guarantees.

Future robotics systems aiming for true autonomy must be robust against dynamic and unstructured environments. The 5th generation (5G) mobile network is expected to provide ubiquitous, reliable and low-latency wireless communications to ground robots, especially in outdoor scenarios. Empowered by 5G, the digital transformation of robotics is emerging, enabled by the cloud-native paradigm and the adoption of edge-computing principles for heavy computational task offloading. However, wireless link quality fluctuates due to multiple aspects such as the topography of the deployment area, the presence of obstacles, robots' movement and the configuration of the serving base stations. This directly impacts not only the connectivity to the robots but also the performance of robot operations, resulting in severe challenges when targeting full robot autonomy. To address such challenges, in this paper, we propose a framework to build a semantic map based on radio quality. By means of our proposed approach, mobile robots can gain knowledge on up-to-date radio context map information of the surrounding environment, hence enabling reliable and efficient robotics operations.

The 5G mobile networks extend the capability for supporting collaborative robot operations in outdoor scenarios. However, the restricted battery life of robots still poses a major obstacle to their effective implementation and utilization in real scenarios. One of the most challenging situations is the execution of mission-critical tasks that require the use of various on-board sensors to perform simultaneous localization and mapping (SLAM) of unexplored environments. Given the time-sensitive nature of these tasks, completing them in the shortest possible time is of the highest importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the intelligence of the robot operation through joint orchestration of Robot Operating System (ROS) and 5G resources for energy-saving goals, addressing the problem from both offline and online manners. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times as well as overall energy consumption of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. We validate our 5G-enabled collaborative framework by means of MATLAB/Simulink, ROS software and Gazebo simulator. Our results show an improvement between 3.65% and 11.98% in exploration task by exploiting 5G orchestration features for battery savings when using 3 robots.

Battery life for collaborative robotics scenarios is a key challenge limiting operational uses and deployment in real life. Mission-Critical tasks are among the most relevant and challenging scenarios. As multiple and heterogeneous on-board sensors are required to explore unknown environments in simultaneous localization and mapping (SLAM) tasks, battery life problems are further exacerbated. Given the time-sensitivity of mission-critical operations, the successful completion of specific tasks in the minimum amount of time is of paramount importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the Robot Operating System (ROS) capabilities with network orchestration features for energy-saving purposes. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. Our results show that OROS significantly outperforms state-of-the-art solutions in exploration tasks completion times by exploiting 5G orchestration features for battery life extension.

Empowered bynetwork softwarization, 5G systems have become the key enabler to foster the digital transformation of the vertical industries by expanding the scope of traditional mobile networks and enriching the network service offerings. To make this a reality, we propose anautomationsolution for vertical services provisioning and hierarchical Service Level Agreement (SLA) management.Service scalingis one of the most essential operations to adapt the service deployments and resource allocations to ensure SLA fulfilment. Three different scaling levels are addressed in this work: application-, service- and resource-level. We have implemented our solution in a proof-of-concept of a virtualized mobile network platform, spanning over three geographically-distributed sites. To evaluate our solution, we leverage field tests, focusing onautomotive vertical servicescomprising a mission-critical application (collision-avoidance) and an entertainment one (video streaming). The results demonstrate the excellent performance of our solution, and its ability to automatically deploy vertical services and ensure their SLAs through different levels of service scaling.

This paper proposes a dynamic pricing and revenue-driven service federation strategy based on a Deep Q-Network (DQN) to instantly and automatically decide federation across different service provider domains, each introduces dynamic service prices offering to its customers and towards other domains. A dynamic pricing model is considered in this work based on the analysis of real pricing data collected from public cloud provider, and upon this a dynamic arrival process as a result of the price changes is proposed for formulating the service federation problem as a Markov Decision Problem (MDP). In this work, several reinforcement learning algorithms are developed to solve the problem, and the presented results show that the DQN method reached 90% of the optimal revenue and outperformed existing state-of-the-art strategies, and it can learn the federation pricing dynamics to make optimum federation decisions according to price changes.

5G networks aim to provide orchestration of services across multiple administrative domains through the concept of federation. In this paper, we are exploring the federation feature of a platform for 5G transport network of vertical services. Then we formulate the decision problem that directly impacts the revenue of 5G administrative domains, and we propose as solution a Q-learning algorithm. The simulation results show near optimum profit maximization and a well-trained Q-learning algorithm can outperform the intuitive “greedy” approach in a realistic scenario.

5G networks require flexibility, automation and programmability to satisfy the requirements of verticals industries. 5G-TRANSFORMER project proposes an SDN/NFV based network platform to enable this vision. Among its features, this platform allows the end-to-end deployment of parts of a network service (NS) in multiple administrative domains, which is known as network service federation (NSF). This feature increases network flexibility, opening the door to new business models. This paper complements our previous work by providing a detailed description of the 5G-TRANSFORMER NSF workflow, its interface and a profiling of the operations involved in the deployment of an NS between multiple administrative domains in a real experimental setup. Experimental results reveal i) that a federated NS can be deployed in the order of few minutes (less than 5 minutes), in line with the 5G target of reducing service setup to minutes, and ii) the impact of the NSF procedure in the deployment time is reduced when compared with the deployment of the same NS in a single administrative domain.

Network densification over space and spectrum is expected to be key to enabling the requirements of next generation mobile systems. The pitfall is that radio resource allocation becomes substantially more complex. In this paper, we propose LaSR, a practical multi-connectivity scheduler for OFDMA-based multi-RAT systems. LaSR makes optimal discrete control actions by solving a sequence of simple optimization problems that do not require prior information of traffic patterns. In marked contrast to previous work, the flexibility of our approach allows us to construct scheduling policies that achieve a good balance between system cost and utility satisfaction, while jointly operate across heterogeneous RATs, accommodate real-system requirements, and guarantee system stability. Examples of system requirements considered in this paper include (but are not limited to): constraints on how scheduling data can be encoded onto signaling protocols (e.g., LTE's DCI), delays when turning on/off radio units, or on/off cycles when using unlicensed spectrum. We evaluate our scheduler via a thorough simulation campaign in a variety of scenarios with e.g., mobile users, RATs using unlicensed spectrum (using a duty cycle access mechanism), imperfect queue state information, and constrained signaling protocol.

The future 5G transport networks are envisioned to support a variety of vertical services through network slicing and efficient orchestration over multiple administrative domains. In this paper, we propose an orchestrator architecture to support vertical services to meet their diverse resource and service requirements. We then present a system model for resource orchestration of transport networks as well as low-complexity algorithms that aim at minimizing service deployment cost and/or service latency. Importantly, the proposed model can work with any level of abstractions exposed by the underlying network or the federated domains depending on their representation of resources.

Cloud Radio Access Network (C-RAN) will become a main building block for 5G. However, the stringent requirements of current fronthaul solutions hinder its large-scale deployment. In order to introduce C-RAN widely in 5G, the next generation fronthaul interface (NGFI) will be based on a cost-efficient packet-based network with higher path diversity. In addition, NGFI shall support a flexible functional split of the RAN to adapt the amount of centralization to the capabilities of the transport network. In this paper we question the ability of standard techniques to route NGFI traffic while maximizing the centralization degree-the goal of C-RAN. We propose two solutions jointly addressing both challenges: (i) a nearly-optimal backtracking scheme, and (ii) a low-complex greedy approach. We first validate the feasibility of our approach in an experimental proof-of-concept, and then evaluate both algorithms via simulations in large-scale (real and synthetic) topologies. Our results show that state-of-the-art techniques fail at maximizing the centralization degree and that the achievable C-RAN centralization highly depends on the underlying topology structure.

This paper proposes an efficient optimization algorithm to dynamically control the Evolved Packet System (EPS) bearer rates to transport various services between the UE and Evolved Packet Core (EPC) in a LTE Femtocell network scenario. The algorithm is focused on improving the accumulated QoE in the networks and takes joint consideration of limited radio and transport resource so as to leverage the resource management in LTE Radio Access Network (RAN) and transport network. In case of link congestion applications running on TCP tend to share the capacity equally. However, different traffic types have different QoE behaviors and hence sharing the resource equally will lead to a non-optimal aggregated QoE. The proposed algorithm will solve this problem by considering the QoE of individual application flows. We formulate the problem as a convex optimization problem, which maximizes the aggregated QoE, represented by Mean Opinion Score (MOS) value, of all users using different applications, and then solve it using Lagrangian relaxation method. The performance of our algorithms is investigated and evaluated by simulations. The simulations show that our proposed QoE-driven rate shaping algorithm results in a significantly better aggregated QoE compared to the legacy scheme that with fixed rate shaping, especially in heavily congested scenarios. Moreover, a discussion on how often to adjust the shaping rates is given based on the complexity and performance investigations.

Modern mobile devices comprise multiple interfaces for heterogeneous network technologies. However, currently implemented mechanisms to decide which one(s) to use and distribute application flows accordingly (i.e., solving the multihomed flow management problem, MFM) are rather coarse and do not leverage the opportunities. A user-centric quality-aware (QA) MFM has been proposed which optimises network use based on user-perceivable metrics such as application quality as well as energy and monetary costs. This paper refines this approach by providing a single method for both real-time and elastic (i.e., TCP-based) traffic, and uses realistic available capacity estimation. We evaluate this method in OPNET-simulated LTE and WLAN mobile networks. We study the impact of methods used to trigger the decision algorithm, and investigate the influence of an increasing number of users employing the QA-MFM technique on both the user-perceivable metrics and the global network performance. We find that on-demand triggering performs better than a static periodic method. We also demonstrate that the proposed approach out-performs classical network selection techniques in terms of application quality. We also show that the QA-MFM is not too greedy as to not scale with a number of users, and has a positive effect on the network loads, by preemptively adapting applications parameters to match network conditions.

Mobile Network Virtualization (NV) is an emerging technique which has drawn increasingly research attention. Network Virtualization enables multiple network operators to share a common infrastructure (including core network, transport network and access network) so as to reduce the investment capital while improving the overall performance at the same time. This is achieved by exploring the multiplexing gain. Similarly, Load Balancing (LB) is a well-known mechanism used in mobile networks to offload excessive traffic from high-load cells (hot spots) to low-load ones within one network operator. This paper aims at investigating the potential gain of applying NV in LTE (Long Term Evolution) networks and compares it with the LB scheme gain. In this paper, we propose an LTE virtualization framework (that enables spectrum sharing) and a dynamic load balancing scheme for multi-eNB and multi-VO (Virtual Operator) systems. We compare the performance gain of both schemes for different applications, e.g. VoIP, video, HTTP and FTP. We also investigate the parameterization of both schemes, e.g. sharing intervals, LB intervals and safety margins, in order to find the optimal parameter settings. The presented results show that the LTE networks can benefit from both NV and LB techniques.

This paper proposes an analytical model for dimensioning transport bandwidths in the Long Term Evolution (LTE) access network. In this work the criterion for dimensioning is the transport network delay QoS (at the packet level). The presented analytical model takes into considerations the key features of the LTE radio interface and the use of Differentiated Service (DiffServ) QoS scheme in the LTE access transport network. Furthermore, the proposed dimensioning approach can be performed for a single transport link as well as for the entire transport network. For validating the analytical dimensioning models, a developed LTE system simulation model is used. The presented results demonstrate that the proposed analytical models can appropriately estimate the transport network delays for different QoS priorities and hence can be used for bandwidth dimensioning for various traffic and network scenarios.

This paper proposes efficient analytical models to dimension the required transport bandwidths for the Long Term Evolution (LTE) access network for the elastic Internet traffic (which is carried by the TCP protocol). The dimensioning models are based on the use of Processor Sharing queuing theory to guarantee a desired end-to-end application QoS target. For validating the analytical dimensioning models, a developed LTE system simulation model is used. Extensive simulations are performed with various traffic and network scenarios. The analytical results derived from the proposed dimensioning models are compared against the simulation results. The presented results demonstrate that the proposed analytical models can appropriately estimate the application performances of different QoS priorities and thus be used for the link dimensioning for various traffic and network scenarios.

This paper presents dimensioning of an IP-based UMTS Terrestrial Radio Access Network (UTRAN) with multiple Node Bs connected to one RNC, where the applied QoS architecture is based on Differentiated Service (DiffServ) with an integrated Weighted Fair Queue (WFQ) and Strict Priority (SP) scheduling. To provide an appropriate network dimensioning for guaranteeing a desired end-to-end QoS, a general analytical approach is proposed in this paper to dimension the individual links of the Iub interface (between the Node B and the RNC) and furthermore to derive over-booking for the backbone link for elastic traffic. The analytical approach is validated by simulations. Furthermore, based on the proposed analytical approach, important dimensioning rules are summarized.

HSUPA (High Speed Uplink Packet Access) is introduced by the 3GPP Release 6 to enhance the UMTS uplink with higher data rates, reduced latency and increased capacity. This paper discusses the dimensioning of the backhaul resources on the ATM-based Iub interface in UMTS HSUPA networks. The main focuses of this paper is to (1) analyze the important factors that have influence on the transport network dimensioning and further investigate their impacts; (2) estimate the required backhaul bandwidth as a function of user QoS requirements; (3) discuss the impact when including HSDPA traffic on the downlink. In addition, this paper also provides insights of HSUPA air interface given diverse user applications as well as different number of users within one cell. The investigations and dimensioning are based on simulations.

This paper discusses the use of Carrier Ethernet for the transport of UMTS radio access network as a alternative solution for the gradual migration towards pure IP-based RAN. By means of Pseudo-Wire technique, the ATM service is emulated over the underlying Ethernet network. Within this work, the performance of such Carrier Ethernet based UTRAN is evaluated and compared to the ATM-based UTRAN of UMTS Release 99, in particular the transport efficiency, the delay and packet losses of the Iub. Another contribution of this paper is to investigate the parameter settings to provide a guideline for the optimum configurations.

In order to improve the effective utilization of the radio resources, bit rate adaptation (BRA) is applied in the UMTS system, specifically for the best effort and interactive packet traffic. This paper presents investigation results on the optimization of bit rate adaptation (BRA) scheme for an efficient data support in the UMTS radio access network.