• Introduce a new agentic paradigm- symbiotic agents -that pairs large language models with optimizers and examine it through the lens of trustworthy AI. • Implement two concrete agent designs for dynamic RAN control and multi-tenant SLA negotiations. • Leveraged open-source real-world platforms, including OpenAirInterface (OAI) for the 5G user equipments (UEs), RAN and Core Network, and FlexRIC for the Radio Intelligent Controller (RIC). • Utilized real-world datasets with RAN channel quality fluctuations from moving vehicles to emulate realistic mobility scenarios. • Proved that optimization algorithms maximize the decision accuracy of LLM agents with up to 5 times lower error. • Evaluated both larger and smaller models (SLMs) proving that SLMs retain sufficient near-RT performance (82 ms loop) with substantially smaller GPU overhead (99.9 % less). • Introduced and validated a novel LLM-driven architecture towards AGI networks leveraging the developed agents. Large Language Model (LLM)-based autonomous agents are expected to play a vital role in the evolution of 6G networks, by empowering real-time decision-making related to management and service provisioning to end-users. This shift facilitates the transition from a specialized intelligence approach, where artificial intelligence (AI) algorithms handle isolated tasks, to artificial general intelligence (AGI)-driven networks, where agents possess broader reasoning capabilities and can manage diverse network functions. In this paper, we introduce a novel agentic paradigm that combines LLMs with real-time optimization algorithms towards Trustworthy AI, defined as symbiotic agents . Optimizers at the LLM’s input-level provide bounded uncertainty steering for numerically precise tasks, whereas output-level optimizers supervised by the LLM enable adaptive real-time control. We design and implement two novel agent types including: (i) Radio Access Network (RAN) optimizers, and (ii) multi-agent negotiators for Service-Level Agreements (SLAs). We further propose an end-to-end architecture for AGI-driven networks and evaluate it on a 5G testbed capturing channel fluctuations from moving vehicles. Results show that symbiotic agents reduce decision errors fivefold compared to standalone LLM-based agents, while smaller language models (SLM) achieve similar accuracy with a 99.9 % reduction in Graphical Processing Unit (GPU) resource overhead and in near-real-time (near-RT) loops of 82 m s . A multi-agent demonstration for collaborative RAN on the real-world testbed highlights significant flexibility in service-level agreement and resource allocation, reducing RAN over-utilization by approximately 44 %. Drawing on our findings and open-source implementations, we introduce the symbiotic paradigm as the foundation for next-generation, AGI-driven networks-systems designed to remain adaptable, efficient, and trustworthy even as LLMs advance. A live demo is presented here https://www.youtube.com/watch?v=WQv61z1deXs&ab\_channel=BubbleRAN

The development of 6G wireless technologies is rapidly advancing, with the 3rd Generation Partnership Project (3GPP) entering the pre-standardization phase and aiming to deliver the first specifications by 2028. This paper explores the OpenAirInterface (OAI) project, an open-source initiative that plays a crucial role in the evolution of 5G and future 6G networks. OAI provides a comprehensive implementation of 3GPP and O-RAN compliant networks, including Radio Access Network (RAN), Core Network (CN), and software-defined User Equipment (UE) components. This paper details the history and evolution of OAI, its licensing model, and the various projects under its umbrella, such as RAN, the CN, and the Operations, Administration and Maintenance (OAM) projects. It also highlights the development methodology, Continuous Integration/Continuous Delivery (CI/CD) processes, and end-to-end systems powered by OAI. Furthermore, the paper discusses the potential of OAI for 6G research, focusing on spectrum, reflective intelligent surfaces, and Artificial Intelligence (AI)/Machine Learning (ML) integration. The open-source approach of OAI is emphasized as essential for tackling the challenges of 6G, fostering community collaboration, and driving innovation in next-generation wireless technologies.

Driven by the key principles of open interfaces, virtualization and programmability, Open RAN has emerged as a new paradigm to evolve contemporary Radio Access Networks (RANs) into a more vendor-agnostic, softwarized, and intelligent ecosystem. To this end, Software Defined RAN (SD-RAN) initiatives (e.g., O-RAN) are drafting specifications to provide the means to embrace it. However, even though O-RAN following the Open RAN paradigm specifies Service Models (SMs) to monitor and control the RAN, it does not go beyond the Quality of Service (QoS) mechanisms provided by 3GPP. Therefore, the QoS degradation that occurs mostly due to data flow’s nature at the slowest data path link (e.g., high L2 sublayers), is not addressed by contemporary O-RAN SMs. In this paper, we present a traffic control system for SD-RAN, denoted as TC-RAN, that consists of an E2 service Model (E2SM) and a RAN Function (RF) that adheres to the Open RAN principles and promotes data flows to first-class citizens in cellular networks, upgrading contemporary 5G QoS mechanism. TC-RAN introduces a 6 programmable, extendable, and customizable pipeline composed of a classifier, a policer, a queue, a scheduler, a shaper and a pacer. Additionally, TC-RAN addresses QoS degradation scenarios unsolvable through Resource Block (RB) allocation or 3GPP slicing mechanisms, unleashing the true potential for deploying extremely demanding applications and creating a green field for AI/ML cross-optimization algorithms on the road to 6G. We prototype and validate TC-RAN in a real 5G Stand Alone (SA) RAN stack using an O-RAN compatible near Real-Time Radio Intelligent Controller (nearRT-RIC), xApps, and Commercial off-the-shelf (COTS) User Equipments (UEs). The results show that intelligently composing a TC-RAN pipeline in cellular networks can considerably reduce the latency, notably enhancing the Quality of Experience (QoE) in a real multiplayer online game.

This paper presents ATHENA, a novel design and a new generation of MANO/OAM that fully adheres to the cloud native principles, while fostering innovation and sustainable deployment for 4G, 5G, and beyond. It elicits an agile and intelligent, dynamic control over a variety of vendors and radio stacks (multi-x) coexisting on the same network with built-in observability and at the scale. With an intent-based, declarative, and distributed constitution, authentic to the cloud native pillars of isolation, scalability, and observability, we have established a scalable and efficient design and implemented its concrete proof-of-concept platform that is able to simplify the adaptation of cloud native for telecommunication. ATHENA automates both the semantics and synthetics of the lifecycle of telco workloads while attending to the performance and sustainability requirements. Accompanied by intensive evaluation on a concrete implementation, we show how several uses cases including private networking, Open RAN, and green computing would be facilitated and sustained with a low footprint and green management and operation. In particular, we improve the agility by 75% on Day-1 and 60% on Day-2 in comparison to OSM, while reducing over 93% overhead in Operation, 70% in Management, and 90% in Orchestration. ATHENA shows less than 2% performance loss for high throughput, with less than 50μs jitter. It demonstrates 99.9995% availability for immutable Day-2 upgrades and zero down-time for mutable reconfigurations. And for energy efficiency, we show improvements of maximum 17.4% per UE and 78.3% per gNB using the proposed decision-making framework.

Radio Access Network (RAN) slicing aims to roll out various services in the same network deployment while ensuring performance guarantee and resource isolation, it is identified as one work item of 3GPP Release 18 and as a specific O-RAN use case. However, the current O- RAN architecture lacks control flexibility for real-time programmability of less than 10ms. In this work, we propose the FlexSlice framework, which entails the realization of flexible control logic topologies (i.e., centralized, decentralized, and distributed) by evolving the O-RAN architecture to achieve lower control loop latency. In addition, the radio resource scheduler at the Medium Access Control (MAC) layer is redesigned for recursive operations to facilitate virtualization for multi-level resource allocation. Finally, a concrete prototype is developed to demonstrate its efficiency, real-time programmability and control flexibility.

RAN openness is a vision for 5G and beyond to avoid unnecessary lock-in effects. In this regard, the O-RAN alliance provides a new architecture with RAN intelligent controller (RIC) for both non-real-time and near-real-time cases, together with running applications, i.e., rApps and xApps. However, two key challenges remain in the current xApp framework: platform lock-in and xApp reusability. Therefore, we introduce a novel FlexApp framework to address both issues, as well as a new E2* interface that consumes less latency and CPU utilization. This new interface speeds up the xApp development process. Our performance evaluation of the FlexApp prototype shows that it can realize scalability and ultra-low latency operations (<10 ms), as well as the capability of two-level abstraction for xApp development.

As modern manufacturing lines embrace greater modularity and flexibility, the need to transition factory networks from wired to wireless grows. Yet the mission-critical nature of factory networks poses a key challenge - connecting numerous diverse machines with high QoS predictability. After formulating this challenge as predictable RAN optimization via Reinforcement Learning (RL), we highlight a major-yet-overlooked modeling issue: matching the packet handling mechanics of a production/real RAN software. In this paper, we show that these mismatches inside RAN simulators can cause non-trivial QoS gaps in production. Then, we present Twin5G, a novel training solution that brings scalable and near-discrete-time emulations to real RAN software, removing the need for RAN simulators. In a RAN Slicing example, Twin5G-trained policy outperforms simulator-trained and standard RL-trained policies in both QoS achieved (+16%) and predictability (+19%) during tests.

3GPP standards organization is performing an impressive effort trying to reach sub-millisecond latencies for 5G. However, such efforts may become fruitless if exogenously generated delays at transport layer are not considered. Nowadays, Radio Access Networks (RANs) are deployed with large buffers to achieve full utilization and avoid squandering wireless resources. Unfortunately, and since the data path’s bottleneck resides on the radio link, RAN’s buffers are bloated by TCP’s congestion control algorithm. Thus, a flow with low-latency requirements that encounters a bloated buffer, suffers from inevitable large sojourn times associated with the buffer depletion time, severely downgrading its Quality of Service (QoS). This paper presents different solutions for efficiently multiplexing distinct traffic patterns that share buffers on the 5G stack. Bufferbloat is extensively studied within the actual 5G QoS scenario, which presents multiple challenges inherited from the dynamic radio link nature and the presence of multiple queues at different entities. We propose and extensively emulate different algorithms in order to avoid the exogenous delay caused by the bufferbloat phenomena. We use real cellular network traces with realistic delay-sensitive and background traffic patterns in different scenarios. The outcome presents valuable insights in the algorithms that will enable low-latency services to be delivered through the 5G network stack satisfying restrictive envisioned constraints.

Unlike previous mobile networks, 5G New Radio (5G-NR) provides unprecedented flexibility in the radio access network (RAN) to support diverse use cases in a multi-tenant environment. In this context, the need for programmability and control through software-defined radio access networking (SD-RAN) is well established. While the underlying RAN is designed to be ultra flexible and lean, existing SD-RAN controllers are either not flexible to address all use cases or use a one-size-fits-all approach. In this paper, we present FlexRIC, a flexible and efficient software development kit (SDK) that enables to build specialized service-oriented controllers. FlexRIC has a modular architecture with minimal footprint and is designed with extensibility in mind. We validate the SDK building concrete implementations of two specialized controllers for state-of-the-art 5G use cases: (1) a recursive RAN controller that virtualizes the network to allow multiple tenants to concurrently control and operate their services in a shared infrastructure over the heterogeneous landscape of 5G networks, and (2) an SD-RAN controller providing programmability for multi-radio access technology (RAT) RAN slicing, and flow-based traffic control targeting low-latency communications. The results reveal that FlexRIC reduces the round-trip time by two while incurring 83 % less CPU compared with O-RAN's reference implementation, and uses 10x less CPU and one third of the memory when compared to FlexRAN. Such performance is required to unleash the potential of emerging 5G use cases.

Network slicing is considered to be the enabler for a coexistence of a multitude of services with heterogeneous requirements on a multi-tenant 5G infrastructure. In the core network, it has shown its potential in customizing and extending service-specific functionality beyond a mere configuration. In the radio access network (RAN) however, service customization and functionality extension remain a challenge due to the rigid and complex nature of the RAN and the fact that all services have to be mapped onto the scarce radio resources. In this article, we present the RAN service engine that allows services to customize and extend RAN functionality using containerized micro-services. This is achieved through micro-SDKs that abstract key RAN control endpoints, and which can then be used by the services to flexibly customize and extend the RAN in order to steer control plane behavior. Through these micro-SDKs, the engine can enforce isolation between services while multiplexing them efficiently onto the infrastructure. We also present a concrete implementation of the engine with its key micro-SDKs, and demonstrate the feasibility through a prototype for the MAC scheduling control endpoint, showing the versatility of the RAN engine.

With the proliferation of use-cases envisioned to be supported by 5G networks, the focus is not only on the performance, but also on the service agility and elasticity. Cloud native principal is a methodology of designing lightweight, isolated-context, and deployable at scale applications that natively exploit the features of cloud. However, supporting telco applications (e.g., 4G/5G services) in the cloud brings up many challenges, such as the coexistence of physical and virtual functions, (near) realtime resource provisioning, service continuity, and strict latency and data rates. In this paper, we propose Kube5G, as a realizable agile service platform in support of 4G/5G cloud native applications. Kube5G introduces a novel approach in building and packaging a cloud-native compliant telco network function (NF) in a form of nested well-defined layers. In addition, we present a workflow for continuous integration and development operations in support of multi-version network functions (physical, virtual and cloud functions). We also present a concrete prototype implementation of Kube5G with experimental results indicating its efficiency and highlighting user and network perceived performance.

Narrowband Internet-of-Things (NB-IoT) is one of the major access technologies proposed to support massive machine type communications (mMTC) services for the 5thgeneration (5G) mobile networks. Many emerging services and networking paradigms are expected to be developed on top of NB-IoT networks. This paper summarizes the steps required to build up an open source narrowband Internet-of-Things (NB-IoT) network. This work is a joint research and development (R&D) result from industry and academic collaboration. The open source NB-IoT enhanced Node B (eNB) is jointly developed by EURECOM, B-COM and NTUST based on the well-known OpenAirInterface TM(OAI) open source Long-Term Evolution (LTE) eNB. The NB-IoT eNB is successfully connected to an evolved packet core (EPC) developed by Nokia Bell Lab. We demonstrate how to use commercial off-the-shelf (COTS) NB-IoT module to forward its sensing data to the Internet via the open source NB-IoT network.

In 5G radio access networks, meeting the performance requirements of the fronthaul network is quite challenging. Recent standardization and research activities are focusing on exploiting the IEEE Time Sensitive Networking (TSN) technology for fronthaul networks. In this work we evaluate the performance of Ethernet TSN networks based on IEEE 802.1Qbv and IEEE 802.1Qbu for carrying real fronthaul traffic and benchmark it against Ethernet with Strict priority and Round Robin scheduling. We demonstrate that both 802.1Qbv and 802.1Qbu can be well used to protect high-priority traffic flows even in overload conditions.

Network slicing is considered to be the enabler for a coexistence of a multitude of services on a multi-tenant 5G infrastructure. It is supported through software-defined radio access networking (SD-RAN), bringing programmability to the network in order to enhance performance according to the needs of slice owners. However, SD-RAN so far remained limited to a mere reconfiguration of the base station. In this work, we demonstrate a prototype of a service-oriented RAN on top of the OpenAirInterface and Mosaic5G platforms that brings programmability and extensibility to the RAN with a range of network applications for the purpose of intelligent slicing. We implemented a slice control and management framework, and plug a traffic analysis application that significantly improves the performance of slice users. We observe an improvement of 30% in application round-trip time with negligible variability for the considered traffic. Further, we demonstrate how to extend control plane functionality from a network store to improve slice performance.

In this demo, we present 5G network automation in cloud-native environment. Our proposition is to demonstrate the network automation using Kubernets as container orchestration and automating application deployment, while using Openshift Operator as a tool to manage complex services, such as 5G services. For this purpose, we use the containerized OpenAirInterface (OAI) to deploy the network and demonstrate the automatability, such as dynamic switch of RAN between monolithic base station and disaggregated RAN (i.e. Distributed Unit-DU and Centralized Unit-CU), and auto-configuration.

The 5G mobile network is supposed to handle a variety of services with different requirements. By means of virtualization, network slices form customized virtual networks transporting services with associated service guarantees. Especially the radio access network (RAN) requires an efficient multiplexing of multiple services onto the sparse radio resources. In this demo, we show how a RAN can be dynamically customized without service interruptions for different slices. In particular, our solution considers the slice requirements and adapts the slicing algorithm without interrupting other slices in the network. This allows an efficient resource usage while respecting isolation and performance requirements, in particular latency. Furthermore, dynamic end-to-end slicing is enabled by automatically adding core networks as required by the slice owner. Finally, this solution allows to compare different slice algorithm implementations.

5G networks generate massive (quasi-) real-time data streams that different network apps can exploit to implement sophisticated single- or cross-domain control and management logic. This paper presents ElasticSDK, a Software Development Kit specially designed to abstract the development and chaining of such agile 5G monitoring apps for the control, management, and coordination of the underlying 5G network heterogeneous modules. Custom apps can collect, incrementally process and further expose flows in a flexible Pub/Sub fashion via appropriate SDK API calls, thus sharing both raw and complex data flows among themselves. Furthermore, the design of ElasticSDK allows respecting typical 5G data ownership and privacy models, as desired by the different 5G stakeholders ranging from physical infrastructure providers up to service providers over slicing. Finally, we provide two important contributions to the 5G open-source research community: (i) a RAN monitoring prototype implementation over the ElasticSearch and FlexRAN platforms that allows to demonstrate ElasticSDK app development and capturing hierarchical control features of typical SDN-enabled 5G architectures, and (ii) a first-ever publicly available dataset of realistic 5G RAN monitoring traces.

Future mobile networks are supposed to handle a variety of services with different requirements. Network slicing is considered to be a key enabler to cope with the increasing complexity of these networks. This includes slicing of the radio resources in order to use them efficiently. In this paper, we propose a radio resource slicing system for three types of slices with specific radio resource/quality of service (QoS) requirements. It enables co-existence of (1) rate-based/efficiency-oriented and (2) low-latency slices, as well as (3) slices with fixed allocations. Slice scheduling is based on utility functions with a priority-based resource allocation. Using simulations, we validate the applicability of the proposed system, and demonstrate that both a guaranteed throughput and low delay for different slices at the same time is possible. Our system outperforms existing slicing solutions in terms of delay requirement satisfaction and efficient resource utilization.

Alongside the mobile network evolution toward the fifth generation (5G) era, it is expected that the radio access network (RAN) will be the most challenging technology domain to serve multiple service requirements. Specifically, three critical aspects are particularly emphasized: (i) heterogeneous RAN deployments, (ii) RAN functional splits between disaggregated entities, and (iii) sliced RAN for multiple services. To synthesize these three different aspects, a unified and customizable control framework is needed to serve both needs of infrastructure provider and slice owner. To this end, we propose the FlexVRAN control framework as an extension to our previous work to provide a two-level abstraction scheme between the underlying physical infrastructures, logical base stations (BSs), and slice-specific virtual BSs. We present a proof-of-concept prototype of the proposed FlexVRAN over the OpenAirInterface (OAI) and FlexRAN platforms, and the evaluation results show the applicability and feasibility of software-defined RAN control over heterogeneous deployments in support of network slicing.

Future networks including the Fifth Generation (5G) and beyond mobile networks shall manage, control and orchestrate the new services for users especially vertical sectors, thereby they shall maximize the potential of 5G infrastructures and their services. Network slicing has emerged as a major new networking paradigm for meeting the diverse requirements of various vertical businesses in virtualized and softwarised 5G networks. SliceNet is a project of the EU 5G Infrastructure Public Private Partnership (5G PPP) and focuses on network slicing as a cornerstone technology in 5G networks. This article describes how the SliceNet Control Plane shall evolve to meet the end-to-end needs of many different vertical businesses. SliceNet Control Plane shall span across multiple administrative domains, by integrating different technologies in each involved segments (RAN, MEC, CN, inter-connectivity). Moreover, SliceNet Control Plane is able to allow verticals to plug their own control logic on top of provisioned slices and specialize their services characteristics while optimizing the use of shared resources, providing dynamic configuration, dynamic management, resource isolation and scalability.

We propose a smart offloading policy that dynamically assigns data flows to the WiFi and cellular interfaces, so as to minimize a given cost function (related to energy consumption and cellular plan usage), while keeping the average per-flow delay bounded. The basic insight of the proposed Threshold Policy is to assign larger flows to the network that provides the best rate (often WiFi), and smaller flows to the other, since energy is generally related to the time needed to send/receive data. However, choosing the size cutoff optimally must also consider load-balancing and queueing aspects, WiFi availability, flow size statistics, and user/application preferences. We validate our model against simulations, and show that our policy outperforms other standard or smart policies, achieving considerably better energy-delay trade-offs, while only offloading a small percentage of (large)flows. Initial measurements performed on an Android-based offloading prototype further support our findings.

Dynamic spectrum access and management is one key enabler to constitute the foundation for a multi- service architecture with a high level of flexibility. In this work, we design and implement the spectrum management application (SMA) as an efficient tool to manage and process different policies and rules defined by various stakeholders such as national regulatory authorities, operators and licensed shared access. The SMA is an open- source and clean-slate replacement for legacy platform-dependent spectrum management solutions and can provide custom control programmability and agile resource utilization. We also elaborate on the design details of the SMA and show how it can dynamically select the optimal spectrum offers based on different applied rules in time-series. Finally, we demonstrate two specific use cases via integrating the implemented SMA prototype on top of the Mosaic5G and OpenAirInterface platforms.

In this paper, we propose a network planning and provisioning framework that optimizes deployment cost in C-RAN based 5G networks. Our framework is based on a Mixed Integer Quadratically Constrained Programming (MIQCP) model which optimizes ``virtualized'' 5G service chain deployment cost while performing adequate provisioning to address user demand and performance requirements. We use two realistic scenarios to showcase that our framework can be applied to different types of deployments and discuss the computational cost and scalability of our solution.

Cloud-RAN based architectures are widely considered a fundamental part of 5G networks. As a consequence, in the upcoming standards for 5G RAN, disaggregating the RAN functionality between a Central Unit (CU) and multiple Distributed Units (DUs) is considered, addressing the splitting of the 5G protocol stack at the PDCP/RLC point. This split is expected to bring numerous advantages to mobile network operators, as through the isolation of the stack from the PDCP layer and upwards, the CU will be able to act as the Cloud-based convergence point among multiple heterogeneous technologies in the provisioned networks and hence able to serve multiple heterogeneous DUs. Moreover, data rate requirements for this type of split are not very demanding, thus allowing the IP-based transferring of data from the DU to CU and vice-versa. In this work, we propose, implement and evaluate a protocol for a Cloud-RAN based architecture allowing the selection and dynamic switching of different heterogeneous networks in the RAN. We rely on the open source OpenAirInterface platform and extend it to support data plane splitting of the LTE functionality, and the subsequent data injection to WiFi networks. We evaluate the platform using a real network setup, under several scenarios of network selection and different delay settings.

Ultra-dense small cell networks will require sophisticated user association algorithms that consider (i) channel characteristics, (ii) base station load, and (iii) uplink/downlink (UL/DL) traffic profiles. They will also be characterized by high spatio-temporal variability in UL/DL traffic demand, due to the fewer users per BS. In this direction, Dynamic TDD is a promising new technique to match BS resources to actual demand. While plenty of literature exists on the problem of user association, and some recent on dynamic TDD, most works consider these separately. In this paper, we argue that user association policies are strongly coupled with the allocation of resources between UL and DL. We propose an algorithm that decomposes the problem into separate subproblems that can each be solved efficiently and in a distributed manner, and prove convergence to the global optimum. Simulation results suggest that our approach can improve UL and DL performance at the same time, with an aggregate improvement of more than 2×, compared to user association under static TDD allocation.

RAN slicing is one of the key enabler to enable virtualization of a BS and its delivery as a service with different levels of network isolation and sharing so as to accommodate the needs of mobile network operators and verticals. In this demonstration, we show a prototype of a RAN slicing runtime system to enable flexible slice customization on the top of a disaggregated RAN infrastructure [1] with different levels of isolation and sharing in terms of resources and network functions, while retaining the quality of service (QoS) for different slice instances. Furthermore, a novel plug & play network application chaining framework empowered by a network software development kit (SDK) is demonstrated to show how the multi-service programmability on per-slice basis can be achieved. Our demonstration is based on the OpenAirlnterface [3], Mosaic-5G FlexRAN [4] and LL-MEC [2] platforms. Finally, we highlight how the the proposed approach can be extended to an end-to-end network slicing scenario.

Network slicing is expected to be the main pillar around which virtualization technologies together with SDN control and NFV, will provide on-demand network and cloud infrastructures and facilitate rapid service deployment. In this paper we present JOX, an event-driven orchestrator for the virtu- alized network, operating on top of the Juju management system, that inherently supports network slicing. JOX is a python-based generic network slicing orchestrator, with a plugins architecture that is able to support different segments of a modern mobile edge network. We present a concrete prototype implementation of JOX for LTE, with experimental results considering footprint analysis, performance metrics, and implementation experience for slicing and orchestrating of an operational LTE network.

Low latency communication with end-user and knowledge of real-time network information, such as radio conditions and network statistics, are among two key advantages of Multi-access Edge Computing (MEC) technology. In this paper, we propose a hierarchical MEC architecture and present a proof-of-concept (PoC) implementation of a Radio Aware Video optimization in a fully Virtualized Network (RAVEN) use case. The PoC has been assembled in a small-scale LTE network based on OpenAirInterface and commercial terminals, representing a scenario where a real-time adaptive video streaming service is provided at a Mobile Edge platform serving multiple eNBs. Real-time radio information is provided by eNB agents which act as local controllers co-located with eNB, collecting and providing realtime access to the requested RAN data. Agents can cooperate with each other to control the network in a distributed manner or can be delegated and/or controlled by a master controller entity. Information gathered by eNB agents is thus provided to the MEC system, allowing other MEC apps to consume it. The results of the RAVEN experimental activity demonstrate the benefit of recently ETSI MEC specified RNI (Radio Network Information) service in improving the perceived user quality of experience.

Multi-connectivity is considered as a 5G key technique to improve both the user performance and the overall resource utilization. In this paper, we examine a resource allocation problem under multi-connectivity in evolved LTE and propose a utility proportional fair (UPF) resource allocation that preserves users quality-of-service (QoS) considering backhaul capacity limitations. The proposed policy is compared with proportional fair (PF) resource allocation through extensive simulations. Presented results show that multi-connectivity outperforms single-connectivity in terms of network aggregated rate and users QoS satisfaction in different network case studies, i.e., empty and loaded cell scenarios with fixed and variable backhaul capacity.

LTE is deployed in most countries and continuously evolving to match new use cases as well as requirements. While it is expected to evolve toward 5G deployments for outdoor and long range communications, it will also be used for Public Safety services in major countries. Among that, new scenarios call for wider networks relying on dynamic meshing of base stations. Leveraging the LTE air interface for base station meshing is an appealing idea but is not straightforward to guarantee quality of service (QoS). In this article, we study and evaluate scheduling strategies for multi-hop LTE mesh networks relying on the LTE relay channel. We firstly present the LTE relay channel and the scheduling problem for in-band LTE self-backhauling. We then propose a practical cross-layer method in order to fulfill the QoS requirements of real-time flows in such network. We finally evaluate the proposed method through extensive simulations. We show the effectiveness of the proposed approach compared to aegacy method in terms of both QoS requirement satisfaction of real-time flows and throughput enhancement of elastic flows.

Thorough investigation of the Cloud-RAN (C-RAN) architecture has recently shown that C-RAN can bring advanced cooperated and coordinated processing capabilities as well as the multiplexing gains toward future radio access networks. The baseband processing of each base station instance can now be flexibly split into smaller functional components, that can be placed either at remote radio units (RRUs) or baseband units (BBUs), depending on the available fronthaul (FH) performance. Additionally, with the wide adoption of Ethernet in data centers and core networks, the Radio over Ethernet (RoE) approach is now considered as an off-the-shelf candidate for the FH link. To this end, we propose a unified RRU/BBU architectural framework for C-RAN that can support both a flexible functional split and a FH transport protocol over Ethernet. Furthermore, we experimentally evaluate the main key performance indicators (KPIs) of an operational C-RAN network built based on OpenAirInterface (OAI), a software implementation of LTE/LTE-A systems, under two functional splits and different deployment scenarios.

Mobile Edge Computing (MEC) consists of deploying computing resources (CPU, storage) at the edge of mobile networks; typically near or with eNodeBs. Besides easing the deployment of applications and services requiring low access to the remote server, such as Virtual Reality and Vehicular IoT, MEC will enable the development of context-aware and context-optimized applications, thanks to the Radio API (e.g. information on user channel quality) exposed by eNodeBs. Although ETSI is defining the architecture specifications, solutions to integrate MEC to the current 3GPP architecture are still open. In this paper, we fill this gap by proposing and implementing a Software Defined Networking (SDN)-based MEC framework, compliant with both ETSI and 3GPP architectures. It provides the required data-plane flexibility and programmability, which can on-the-fly improve the latency as a function of the network deployment and conditions. To illustrate the benefit of using SDN concept for the MEC framework, we present the details of software architecture as well as performance evaluations.

Centralized RAN processing has been identified as one of the major enablers for 5G mobile network access. By moving the baseband units (BBU) to the Cloud, multiple instances can be instantiated on the fly, serving several Remote Radio Head (RRH) units. The goal is to satisfy the existing demand of particular geographical areas, whereas drastically reducing the overall CAPEX and OPEX costs of the mobile operators. In this work, we present an experimental study of real Cloud-RAN deployments, with respect to different functional splits. We use as a reference architecture the 3GPP LTE stack, and argue about the functional split applicability in contemporary networks. We evaluate Layer 2 functional splits, that can be used for the convergence of multiple heterogeneous wireless technologies in an all-in-one unit. By deploying our approach in a real testbed setup, we extract the backhaul network transfer requirements for the different splits and present our experimental findings, compared with the respective simulation results.

Although SDN is considered as one of the key technologies behind the impending 5G evolution of mobile networks, the opportunity of reaping its benefits is largely still untapped on the Radio Access Network (RAN) side due to the lack of a software-defined RAN (SD-RAN) platform. In this work we demonstrate the capabilities of FlexRAN, an open-source SD-RAN platform developed to fill this void. FlexRAN separates the RAN control and data planes with a custom-tailored southbound API. Besides it features a hierarchical control plane architecture that enables programmability, flexible and dynamic control function placement (allowing different degrees of coordination within and among base stations) and real-time control. Virtualized control functions and control delegation are two key features in FlexRAN that makes these capabilities possible. This demo illustrates the capabilities and the performance of FlexRAN based on a prototype implementation, while its applicability is highlighted through a Mobile Edge Computing use case, where it acts as an enabler of a video bitrate adaptation application based on the radio conditions at the network edge.

Software-defined Networking (SDN) is seen as a promising solution that allows for a more distributed, flexible, and scalable network. Multi-access Edge Computing (MEC), initiated as an Industry Specification Group (ISG) within ETSI, is also emerging as a low-latency and high-throughput cloud environment at the edge of network. The noticeable success that aforementioned technologies made attracts massive research interests and the interplay between them on programmable network requires an open source platform to evaluate. In this work, we present a low-latency MEC platform (LL-MEC) providing the required flexibility and programmability to meet the expected performance gain following SDN and MEC principles. We also demonstrate an use case of real-time content caching application using LL-MEC platform and OpenAirInterface LTE implementation on commodity hardware.

In the current 4G era, the dual connectivity technique utilizes radio resources scheduled by two distinct base stations for a single user equipment to enhance the data throughput. Multi- connectivity, as a natural evolution of dual connectivity, is one of the key 5G techniques to improve both the user performance and overall resource utilization, allowing dynamic user traffic steering across multiple connections of one or more radio access technologies (RATs). However, one of the main challenge in multi-connectivity is to efficiently allocate resources across multiple connections under heterogeneous quality of service (QoS) requirements. In this paper, we examine a resource allocation problem under multi- connectivity in an evolved LTE network and propose a utility proportional fairness (UPF) resource allocation that supports QoS in terms of requested rates. We evaluate the proposed policy with the proportional fairness (PF) resource allocation through extensive simulations and characterize performance gain from both the user and network perspectives under different conditions.

LTE is deployed in most countries and continuously evolving to match new user requirements. While it is expected to support 5G deployments for outdoor and long range communications, it will also be used for Public Safety services in major countries. Among those, new scenarios call for wider networks on the move relying on self-backhauling. In this article we argue that the LTE relay interface (Un) is an efficient candidate to enable multi-hop LTE mesh networks. We first study the Un interface and highlight its main properties. We then analyze and compare out-band LTE-Uu, in-band LTE-Uu with full duplex radios and in-band Un interface to mesh LTE base stations. We perform link-level emulations to assess the performance of the Un interface subject to different conditions. Finally, we compare the achievable throughput of self-backhauled LTE mesh network using either Un or LTE-Uu through a system-level simulation.

Reliable service provisioning is crucial to the public safety (PS) communications especially when network outage happens. Isolated E-UTRAN operation, introduced in LTE Release 13, is able to host separate core network functions at the base stations (BSs) to provide limited set of services to the users. However, a significant issue remains to be solved is to coordinate among BSs to create an autonomous network and enhance service availability and reliability. In this paper, an in-band LTE self-backhauling operation leveraging the relay interface is proposed to create an autonomous mesh network of BSs. This calls for an efficient resource allocation for multiple unplanned backhaul links between BSs. To this end, we present a cross-layer scheduling problem for in-band self-backhauled LTE network, and provide a interference-aware hierarchical resource allocation algorithm that is able to meet specific quality of service (QoS) requirements for real-time traffic while adapting to the workload of other types of traffic, through efficient leverage of FDD capabilities and network frequency re-use. Finally, a thorough evaluation on our proposed approach is realized via extensive simulations on different network topologies and diverse traffic flows, and the results demonstrate our work effectiveness utilization of available resources to satisfy QoS requirements.

In this paper we address the support of wearable mHealth applications in LTE and future 5G networks following a holistic approach that spans across the elements of a mobile network. The communication requirements change from one application to another so we propose a measurement methodology to facilitate the selection of the user equipment to fulfil these requirements. We also discuss a new network architecture to support traffic prioritization, RAN programmability, low latency and group communications to over-the-top applications. Our proposal is validated using several realistic experimentation platforms and the results show that mHealth systems can benefit from our approach.

Operators, struggling to continuously add capacity and upgrade their architecture to keep up with data traffic increase, are turning their attention to denser deployments that improve spectral efficiency. Denser deployments make the problem of user association challenging, and much work has been devoted to finding algorithms that strike a tradeoff between user quality of service, and network-wide performance (load-balancing). Nevertheless, the majority of these algorithms typically consider simple setups with a single type of traffic, usually elastic non-guaranteed bit rate (GBR). They also focus on the radio access part, ignoring the backhaul topology and potential capacity limitations. Backhaul constraints are emerging as a key performance bottleneck in future networks, partly due to the continuous improvement of the radio interface, and partly due to the need for inexpensive backhaul links to reduce capital and operational expenditures. To this end, we propose an analytical framework for user association that jointly considers radio access and backhaul network performance. Specifically, we derive an algorithm that takes into account spectral efficiency, base station load, backhaul link capacities and topology, and two traffic classes (GBR and non-GBR) in both the uplink and downlink directions. We prove analytically an optimal user association rule that ends up maximizing either an arithmetic or a weighted harmonic mean of the achieved performance along different dimensions (e.g., uplink and downlink performances or GBR and non-GBR performances). We then use extensive simulations to study the impact of: 1) traffic differentiation; and 2) backhaul capacity limitations and topology on key performance metrics.

Mobile-Edge Computing (MEC) is about offering application developers and service providers cloud-computing capabilities and an IT service environment at the edge of the mobile network. However, although cloud computing can be used to meet traditional challenges, like scalability concerns and provide for fast resource provisioning times, a multifaceted analysis is required when it comes in multi-operator environments with time-critical applications and services. In this work, we claim that the service importance must be at the epicenter when it comes to the scheduling and placement decision of whether to deploy the service at the edge network or not. Virtual machine (VM) scheduling decisions should avoid SLA violations for popular or time-critical services, and be fair between the service providers. A Lyapunov optimization framework is derived to solve this stochastic optimization problem that aims to maximize the revenue of the physical infrastructure owner in a multi-network operator-sharing environment with time-critical SLAs. A series of simulation experiments validate the high effectiveness of the proposed approach over benchmarking ones.

Although the radio access network (RAN) part of mobile networks offers a significant opportunity for benefiting from the use of SDN ideas, this opportunity is largely untapped due to the lack of a software-defined RAN (SD-RAN) platform. We fill this void with FlexRAN, a flexible and programmable SD-RAN platform that separates the RAN control and data planes through a new, custom-tailored southbound API. Aided by virtualized control functions and control delegation features, FlexRAN provides a flexible control plane designed with support for real-time RAN control applications, flexibility to realize various degrees of coordination among RAN infrastructure entities, and programmability to adapt control over time and easier evolution to the future following SDN/NFV principles. We implement FlexRAN as an extension to a modified version of the OpenAirInterface LTE platform, with evaluation results indicating the feasibility of using FlexRAN under the stringent time constraints posed by the RAN. To demonstrate the effectiveness of FlexRAN as an SD-RAN platform and highlight its applicability for a diverse set of use cases, we present three network services deployed over FlexRAN focusing on interference management, mobile edge computing and RAN sharing.

Being considered as a key enabler for beyond 4G networks, Cloud-RAN (CRAN) offers advanced cooperation and coordinated processing capabilities and brings multiplexing gains. The high capacity and low latency fronthaul (FH) links requirement in the CRAN architecture can be reduced by a flexible functional split of baseband processing between remote radio units (RRUs) and Baseband units (BBUs). Under the wide adoption of Ethernet in data centers and the core network, we consider the Radio over Ethernet (RoE) as an off-the-shelf alternative for FH link in this work. Moreover, the packetization process that packs each sample into Ethernet packets transported over the FH link will impact the CRAN performance. To this end, we investigate the impact of packetization on the proposed CRAN network and provide a packetization algorithm over the FH links. Furthermore, we also survey and analyze various packet scheduling policies applied at the aggregated RRU gateway in order to increase the multiplexing gain. Finally, the simulation results provide more in-depth insights on the potential multiplexing gains in terms of the maximum number of RRUs that can be supported over the Ethernet-based FH network.

Cloud-RAN (CRAN) is considered as one key enabler for beyond 4G networks, offering multiplexing gains, and advanced cooperation and coordinated signal processing. However, a key obstacle in the adoption of the CRAN architecture is that it requires very high capacity and low latency fronthaul (FH) links to carry raw I/Q samples between remote radio heads (RRH) and the baseband units (BBUs). These capacity requirements could be reduced by a more flexible split of baseband processing between BBUs and RRHs. Nevertheless, while moving some of the processing back into the RRH is expected to reduce FH rates, the amount of reduction mainly depends on the split, cell load, scenario and it might also introduce some delays. To this end, this paper studies the impact of different functional splits on the FH capacity for representative scenarios. Furthermore, we propose the use of a packet-based fronthaul network and study the joint impact of different packetization methods and RRH-BBU functional splits on the FH rate and latency. Based on this study, we provide some insights on the feasibility and optimality of different combinations, and the potential multiplexing benefits in terms of numbers of RRHs one could support over a single Ethernet-based FH network.

Operators, struggling to continuously add capacity and upgrade their architecture to keep up with data traffic increase, are turning their attention to denser deployments that improve spectral efficiency. Denser deployments make the problem of user association challenging, and much work has been devoted to finding algorithms that strike a tradeoff between user quality of service (QoS), and network-wide performance (load-balancing). Nevertheless, the majority of these algorithms typically consider only the radio access part, and ignore the backhaul topology and potential capacity limitations. Backhaul constraints are emerging as a key performance bottleneck in future heterogeneous networks, partly due to the continuous improvement of the radio interface, and partly due to the need for inexpensive backhaul links to reduce CAPEX/OPEX. To this end, we propose an analytical framework for user association that jointly considers radio access and backhaul performance. We derive an algorithm that takes into account spectral efficiency, base station load, backhaul link capacities and topology, and uplink and downlink traffic demand, and prove it converges to an optimal solution. We then use extensive simulations to study the impact of (i) backhaul capacity limitations and (ii) backhaul topology on key performance metrics.

In this work we propose a novel handover (HO) algorithm, that considers system performance from both user and network perspective, in the context of heterogeneous networks (HetNets), i.e., networks composed of BSs with asymmetrical transmission power. In such an environment, conventional HO algorithms that consider only the user perspective, e.g., received signal strength (RSS)-based, might offer suboptimal performance, since they mainly push users to cells with high transmission powers. Thus, new algorithms that take into account also the network perspective, e.g., cell load, are needed. In this work, a load-aware algorithm is proposed considering the service delay that a user experiences from the network. In addition, an implementable framework based on Software Defined Networking (SDN) architecture is sketched to support the algorithm. The proposed algorithm is compared with the traditional one we meet in long-term evolution (LTE) systems and a distance-based one. Extracted cell assignment probability and user service delay performance results show that the load-aware approach outperforms both of them.

Handover procedure in LTE/LTE-A has been radically evolved when compared to the previous 3GPP standards. In particular, X2 handover is introduced to allow neighboring eNBs to handle the user mobility without the involvement of the core network. While most of the application could considerably benefit from the X2 handover performance improvement, delay breakdown and impact of parameters from the UE perspective are not well investigated. This paper analyzes the performance of the X2 handover from the UE perspective. Furthermore, the impact of the different parameters on the handover decision algorithm is investigated. Preliminary results, obtained from the OpenAirInterface LTE/LTE-A emulation platform, demonstrate that main delay bottleneck resides in the uplink synchronization of the UE to the target eNB.

Cloud computing enables the on-demand delivery of resources for a multitude of services and gives the opportunity for small agile companies to compete with large industries. In the telco world, cloud computing is currently mostly used by mobile network operators (MNO) for hosting non-critical support services and selling cloud services such as applications and data storage. MNOs are investigating the use of cloud computing to deliver key telecommunication services in the access and core networks. Without this, MNOs lose the opportunities of both combining this with over-the-top (OTT) and value-added services to their fundamental service offerings and leveraging cost-effective commodity hardware. Being able to leverage cloud computing technology effectively for the telco world is the focus of mobile cloud networking (MCN). This paper presents the key results of MCN integrated project that includes its architecture advancements, prototype implementation, and evaluation. Results show the efficiency and the simplicity that a MNO can deploy and manage the complete service lifecycle of fully cloudified, composed services that combine OTT/IT- and mobile-network-based services running on commodity hardware. The extensive performance evaluation of MCN using two key proof-of-concept scenarios that compose together many services to deliver novel converged elastic, on-demand mobile-based but innovative OTT services proves the feasibility of such fully virtualized deployments. Results show that it is beneficial to extend cloud computing to telco usage and run fully cloudified mobile-network-based systems with clear advantages and new service opportunities for MNOs and end-users.

The widespread adoption of tablets and smartphones, and an abundance of data-hungry mobile applications, are overwhelming wireless networks with increased demand and introduce considerable traffic diversity. Operators struggling to continuously add capacity and upgrade their architecture have resorted instead to building denser deployments to improve spectral efficiency. By increasing the number of cells a user can associate with, (i) user quality of service (QoS) can be improved, and (ii) traffic can be offloaded from congested base stations, to achieve better load balancing. However, these two goals are not always aligned. To this end, we develop an analytical framework for optimal user association in future HetNets that investigates the potential tradeoffs between user- and network-related performance, in a more realistic setup encompassing additional key features: (i) different types of user flows, and (ii) uplink and downlink performance. We believe this better reflects the diversity of the services offered to users and their impact on system performance. We evaluate our proposed framework through extensive simulations, and provide some qualitative and quantitative insights on the related tradeoffs.

Cloudification of the Centralized-Radio Access Network (C-RAN) in which signal processing runs on general purpose processors inside virtual machines has lately received significant attention. Due to short deadlines in the LTE frequency division duplex access method, processing time fluctuations introduced by the virtualization process have a deep impact on C-RAN performance. This paper evaluates bottlenecks of the OpenAirInterface (OAI is an open-source software-based implementation of LTE) cloud performance, provides feasibility studies on C-RAN execution, and introduces recommendations for cloud architecture that significantly reduces the encountered execution problems. In typical cloud environments, the OAI processing time deadlines cannot be guaranteed. Our proposed cloud architecture shows good characteristics for OAI cloud execution. As an example, in our setup more than 99.5% processed LTE subframes reach reasonable processing deadlines close to performance of a dedicated machine of a single core CPU.

To reduce the uplink channel access latency in LTE/LTE-A, we propose a Transmission Time Interval (TTI) bundling scheme for the random access procedure. With the proposed method, a UE sends multiple preambles in consecutive subframes in order to increase the success rate of random access and hence to reduce the latency. We introduce a Semi-Markov model to accurately model and analyze the random access mechanism with TTI bundling. With this model, we formulate the access latency as a function of the number of TTI bundles and select the optimal value which minimizes the channel access latency. The proposed Semi-Markov model is validated against simulation and the performance of the TTI bundling method is also evaluated. We find that channel access latency can be significantly reduced when the preamble collision rate is not high.

Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers (e.g., MNO, MVNO) move from proprietary and bespoke hardware and software platforms toward an open, cost-effective, and flexible cellular ecosystem. In addition, rich and innovative local services can be efficiently created through cloudification by leveraging the existing infrastructure. In this work, we present RANaaS, which is a cloudified radio access network delivered as a service. RANaaS provides the service life-cycle of an on-demand, elastic, and pay as you go 3GPP RAN instantiated on top of the cloud infrastructure. We demonstrate an example of real-time cloudified LTE network deployment using the OpenAirInterface LTE implementation and OpenStack running on commodity hardware as well as the flexibility and performance of the platform developed.

In this work, we demonstrate and validate a novel architecture for next generation cellular networks that enables collaborative forwarding at Layer 2 among adjacent eNBs with the aid of enhanced user equipment (UE) devices, that act voluntarily as packet forwarders. We introduce an evolved-UE (eUE) which is capable of operating simultaneously over multiples eNBs in order to enable reliable multi-hop operation through relaying and to achieve low-latency communication through efficient L2/MAC forwarding. For the demonstration and the evaluation of this architecture, we used the OpenAirInterface emulation platform to implement it, and also to evaluate its performance. The obtained results show that, the proposed architecture achieves significant reduction in latency (up to 16.94%) and improvement on packet loss rate (up to 59.25%), as the number of the employed eUEs increases with increasing BLER up to 20%. Moreover, the proposed architecture enables eUEs to increase the aggregated data rate in downlink by exploiting data connection to multiple eNBs.

In this paper, we propose a novel architecture for next generation cellular networks that enables collaborative forwarding at Layer 2 among adjacent eNBs with the aid of enhanced user equipment (UE) devices, that act voluntarily as packet forwarders. Therefore, legacy UEs are leveraged as active network elements being capable of operating simultaneously over multiple base stations (eNBs). To this end, we introduce an evolved-UE (eUE) in order to enable reliable multi-hop operation through relaying and to achieve low-latency communication through efficient L2/MAC forwarding. Through extensive experimentation with OpenAirInterface emulation platform, we evaluated the performance and also validated the feasibility of the proposed architecture. Our results show that, in certain use cases corresponding to public safety and moving/small cell scenarios, the proposed architecture achieves significant reduction in latency (up to 16.94%) and improvement on packet loss rate (up to 59.25%), as the number of the employed eUEs increases with increasing BLER up to 20%. Moreover, the proposed architecture enables eUEs to increase the aggregated data rate in downlink by exploiting data connection to multiple eNBs at the expense of extra power consumption, which calls for the appropriate incentives to enable such a cooperation.

Small cell networks (SCNs) are widely considered as a promising solution for future cellular deployments. Lately, the benefits of small cells to improve spectrum utilization and the user quality of experience (QoE) have been well documented. In addition, the power consumption of current deployments, for instance due to idle power and cooling equipment, is a major concern for operators. Small cells offer the opportunity for more dynamic power management of base stations, due to coverage overlaps and larger spatio-temporal load fluctuations. Yet, such power management decisions (e.g. turning off a base station) should not lead to excessive performance degradation for users associated with it or additional power consumption. This tradeoff becomes significantly more challenging to evaluate in future networks, due to the diversity of services offered to users beyond the traditional voice calls, as well as the complexity of traffic scheduling algorithms. The goal of this paper is to make a first step towards an analytical investigation of this tradeoff. To this end, we propose a number of QoE constraints that a power management decision should consider, and analytically relate them to key parameters such as user traffic mix, cell load, user density, etc. We then use this framework to perform a preliminary study of the potential energy savings an operator could achieve, while guaranteeing the satisfaction of these constraints. Our results provide some qualitative and quantitative insights on the interesting tradeoff between switch-off duration and number of small cells one can safely switch off.

Overall high power consumption in the mobile applications forces the mobile users to recharge frequently. Most of the Android applications do not implement any self-adaptive strategies that react to the battery level, status and context. Thus the applications continue to consume power even when battery is critically low. Intelligent control of hardware and software optimization based on the battery level is the key to power saving. This paper introduces a self-adaptive application development framework which proposes three profiles with various self-adaptive features for mobile applications. The framework employs an analyzer engine which decides the activation of appropriate profile based on battery and context information. The self-adaption takes place in four levels - hardware & software features adaption, user features adaption and additional optimization. When the battery is critically low, priority is given to maximize the battery life until next charging opportunity. Such implementation is highly desirable for mobile applications with high dependency on display hardware (e.g. games) and/or on network operations (e.g. YouTube, Dropbox). Prototype Android applications are developed and results show up to 40% reduction in application power consumption. Power Tutor has been used to get the power consumption results.

LTE 4G cellular networks are gradually being adopted by all major operators in the world and are expected to rule the cellular landscape at least for the current decade. They will also form the starting point for further progress beyond the current generation of mobile cellular networks to chalk a path towards fifth generation mobile networks. The lack of open cellular ecosystem has limited applied research in this field within the boundaries of vendor and operator R&D groups. Furthermore, several new approaches and technologies are being considered as potential elements making up such a future mobile network, including cloudification of radio network, radio network programability and APIs following SDN principles, native support of machine-type communication, and massive MIMO. Research on these technologies requires realistic and flexible experimentation platforms that offer a wide range of experimentation modes from real-world experimentation to controlled and scalable evaluations while at the same time retaining backward compatibility with current generation systems.

Wireless networks are evolving continuously and expected to provide seamless experience for multiple real-time internet applications. Quality-of-service is one of the major component associated with user experience. In this paper, we have considered the provisioning of desired QoS to mobile users that are capable of running multiple internet applications in parallel. For this purpose, a three-step iterative downlink scheduler is proposed for resource management at per-userper-service level. The scheduler performs sorting in multiple iterations on the basis of three weights. In the first iteration, the scheduler performs sorting based on the throughput weight. The second iteration latency weight and followed by buffer weight in the third iteration. The allocation of resources is done to satisfy the promised QoS to all the services of every user. A comparison is carried out with traditional scheduling algorithms in terms of system throughput, fairness index and percentage of satisfied guaranteed bit-rate users. Results show that the proposed algorithm outperforms existing schemes and the performance is more closer to theoretical system throughput.

Mobile devices have evolved remarkably over the last decade and are now being utilized to access much broader range of internet applications. Moreover, their capability to simultaneously run many applications has significantly transformed the traffic characteristics of mobile networks. Quality of service (QoS) is a fundamental component associated with these applications and network should be able to support multiple QoS requests from the same user at same time. This requires complex buffer management and simultaneous scheduling of resources to multiple users with multiple services. In this paper, we propose a framework with pre-processor for MAC-layer scheduler including two-dimensional buffer management (users × services) that enable more efficient allocation of resources to users running multiple internet applications in parallel. The framework will enhance the performance of existing scheduling algorithms by increasing the resolution of scheduling. A comparative analysis of traditional scheduling algorithms is provided to show the gains of proposed framework.

Mobile application development is becoming more challenging with diverse platforms and their software development kits. In order to reduce the cost of development and reach out to maximum users across several platforms, developers are migrating to cross platform application development tools. In this paper, we provide several decision criteria beyond the portability concerns for choosing suitable cross platform tool for application development. The desirable requirements in a cross platform framework are identified. A general architecture for cross platform application development is discussed. Then a survey of several write once run anywhere tools (PhoneGap, Titanium, Sencha Touch) are provided along with a classification and comparison among the tools. To examine the performance in terms of CPU, memory usage, power consumption, Android test applications are developed using such tools. It is found that PhoneGap consumes less memory, CPU and power since it does not included dedicated UI components. Finally the paper summarizes the contributions and concludes with some future directions.

In this work we propose a new scheduling approach designed from scratch to maximize heterogeneous computers usage and the event processing flow at the same time. The scheduler is built based on three fundamental concepts which introduces a new vision of discrete event simulation: 1) events are clustered according to their potential time parallelism on one hand and to their potential process and data similarity on the other hand. 2) events meta-data is enhanced with additional descriptor which simplifies and accelerates the scheduling decision. 3) the simulation is hybrid time-event driven rather than time- or event-driven. The concretization of our approach is denoted the H-scheduler which uses several processes to manage the event flow. Furthermore we propose a dynamic scheduling optimization which aims to further maximize the event flow. The combination of those features allows the H-scheduler to provide the highest efficiency rate compared to the majority of GPU and CPU schedulers. In particular it goes beyond the default Cunetsim Scheduler by 90% in average while it keeps a significant lead on existing simulators.

A packet aggregation method is proposed in this paper to lower the packet collision rate when the random access channel is used for machine type communications (MTC) uplink channel access in LTE. With the proposed packet aggregation method, a UE triggers random access when the aggregated packets in the buffer reaches the given threshold. However, this method reduces the packet collision rate at the expense of an extra latency which is used to aggregate certain amount of packets. Therefore, the tradeoff should carefully be selected between packet loss rate reduction and extra channel access latency. In this paper, we derive the packet loss rate and channel access latency as functions of amount of aggregated packets using a Semi-Markov chain model. With the derived results, the optimal amount of aggregated packets which satisfies the packet loss requirement and keeps the latency as small as possible can be found, which is verified through simulations.

In this paper, we propose a dynamic resource allocation method to enable efficient and low-latency machine type communications (MTC) in LTE/LTE-A with the contention based random access (CBA) scheme [9]. In the proposed method, we firstly estimate the probabilities of events caused by a CBA transmission and then calculate the latency with the measured resource unit. We increase the amount of CBA resources until the estimated latency satisfies the application QoS requirement. The simulation results demonstrate that with the proposed resource allocation method for CBA, the uplink channel access latency has been drastically reduced and that it always guarantees the latency requirements. Furthermore, the achievable latency is significantly reduced when compared to the regular scheduling and the standard random access scheme.

Body area networks (BANs), referring to embedded wireless systems in, on, and around bodies, are expected to take an important role for health, leisure, sports, and all the facets of our daily life. In many cases, several BANs coexist in a small area, resulting in very strong inter-BAN interference, which seriously disturbs intra-BAN communications. The goal of this paper is to decrease inter-BAN interference by cooperative scheduling, hence increasing packet reception rate (PRR) of intra-BAN communications. Cooperative scheduling here is divided into two sub-problems: single-BAN scheduling as an assignment problem and multi-BAN concurrent scheduling as a game. For the first sub-problem, a low complexity algorithm, horse racing scheduling, is proposed, which achieves near-optimal PRR for the BAN performing scheduling. For the second sub-problem, we prove the existence of a set of mixed strategy Nash equilibria (MSNE). Then, we propose a distributed cooperative scheduling scheme, which efficiently achieves higher PRR than the MSNE without degrading fairness.

Most of the existing packet-level simulation tools are designed to perform experiments modeling a small to medium scale networks. The main reason of this limitation is the amount of available computation power and memory in quasi mono-process simulation environment. To enable efficient packet-level simulation for large scale scenario, we introduce a new CPU-GPU co-simulation framework where synchronization and experiment design are performed on CPU and node's processes are executed in parallel on GPU according to the master/worker model [13]. The framework is developed using Compute-Unified Device Architecture (CUDA) and denoted as Cunetsim, CUDA network simulator. To study the performance gain when GPU is used, we also introduce the CPU-legacy version of Cunetsim optimized for multi-core architecture. In this work, we present Cunetsim architecture, design concept, and features. We evaluate the performance of Cunetsim (both versions) compared to Sinalgo and NS-3 using benchmark scenarios. Evaluation results show that Cunetsim execution time remains stable and that it achieves significantly lower computation time than CPU-based simulators for both static and mobile networks with no degradation in the accuracy of the results. We also study the impact of the hardware configuration on the performance gain and the simulation correctness. Cunetsim presents a proof of concept, demonstrating the feasibility of a fully GPU-based simulation rather than GPUoffloading or partial acceleration, through adequate architecture.

Traffic generation represents one of the main challenge in modeling and simulating the application and network load. In this work, we present a tool, called OpenAirInterface Traffic Generator (OTG), for the generation of realistic application traffic that can be used for testing and evaluating the performance of emerging networking architectures. In addition to the traffic of conventional applications, OTG is capable of accurately emulating the traffic of new application scenarios such as online gaming and machine-type communication. To highlight the capability and new features of the tool, the one-way delay of OpenArena online gaming application in the presence of the background traffic is analyzed over the LTE network using OpenAirInterface emulation platform.

To enable the efficient and low latency machine-type communications (MTC) over long term evolution (LTE), a contention based access (CBA) method is proposed. With CBA, UEs transmit packets on the randomly selected resource without having any UE specific scheduled resources. To address the problem of collision caused by CBA in high traffic load, eNB exploits the MU-MIMO detection technique to decode radio network temporary identifier (RNTI) of the collided UEs and use this information to perform a regular scheduling in subsequent subframe. Detailed low layer signaling enhancement to implement CBA technique in current LTE specification (Rel. 10) is also presented. Simulation results demonstrate that the CBA significanlty outperforms the existing uplink channel access methods.

This paper presents a method to disseminate alert messages in the context of new emerging communication standards, such as LTE and Wave. The applications involving the broadcast of periodic messages, can be described using the MBMS (Multicast/Broadcast Multimedia Service). Public Safety alert systems perform one important task in the context of Public Safety Networks (PSNs). The method proposed here is responsible for delivering alert messages to the greatest number of people in a specified area. To accomplish this task a new method, Virtual Road Side Unit (vRSU) is proposed to help the authorities to reach isolated people. The system works even if the deployed structure is severed damaged, i.e. most part of the regular Road Side Units (RSU) are out of order. In our method nodes work cooperatively to propagate the message to other nodes, when re-propagating messages nodes, vRSUs, behave as regular RSUs.

This paper proposes a topology management mechanism for hierarchical group oriented networks. The key innovative aspect of this paper is to consider different interest groups in the clustering formation process. For administrative or performance reasons sometimes it is required to organize nodes, located in the same geographical area, into different groups. This group-oriented topology management provides optimal routing for group members as well as for the last miles communication, where you only need to broadcast the message once to reach the entire interest group. Furthermore, it significantly improves the service provided to upper layers by creating a stable network topology. For example, when establishing a Public Safety Networks (PSN) to handle a disaster scenario the communication from the rescue teams may have no interest, or relation, with the communication hold by the law enforcement teams. In this case it makes sense to divide the network in two subgroups to improve routing and simplifying the medium access control. Here we propose a hierarchical network architecture to enable the organization of nodes into multiple interest groups in the same geographical area. Through simulations, we will show that how this architecture could be adapted to different PSN requirements and what are the key parameters.

While most topology control protocols only address limited network mobility, we propose in this paper a quasi-localized topology control algorithm that considers mobility predictions in order to construct and maintain a power efficient topology without relying on periodic beacons. Indeed, a node is capable of extracting linear trajectories of its neighboring nodes based on their positions and velocities. Based on such information, a node obtains a local prediction of neighborhood evolution and can thereafter proactively adapt the topology without relying on periodic beacons. Maintenance is driven on a per-event basis. It is therefore only when a node changes course that messages are exchanged in order to adapt the structure. Our approach is able to create and keep a stable kinetic backbone at a linear message and time complexity. It also improves concurrent communications by providing a significant reduction on local power assignments, therefore reducing interferences, increasing battery life and improving the overall network lifespan.

This paper presents an alternative simple loop-free bandwidth-efficient distributed routing algorithm for mobile ad hoc networks, denoted as distributed dynamic routing (DDR). Although DDR benefits from classical concepts like zone and forest, unlike previous solutions it achieves several goals at the same time. Firstly, it provides different mechanisms to drastically reduce routing complexity and improve delay performance. Secondly, it is infrastructureless in a strong sense: it does not even require a physical location information. Finally, zone naming is performed dynamically and broadcasting is reduced noticeably.