The EU-funded Horizon 2020 5GMED project aims to promote Cooperative Intelligent Transportation Systems (CITS) in Europe by deploying Cooperative, Connected, and Automated Mobility (CCAM) services across international borders. In this context, this paper presents a 5G-based road traffic safety & management service based on the development of multiple actors and components capable of assessing the traffic status and providing the proper information to Connected Vehicles (CVs) through 5G and Vehicle-to-Everything (V2X) communication technologies. Performance evaluation has been carried out through real-world experiments and trials conducted along the 5GMED cross-border corridor (CBC) between Spain and France, leading to interesting findings and lessons learned presented in the conclusion of this paper.

Providing 5G seamless and secure services for train passengers is very challenging. In addition to the challenges related to high-speed, many others, such as low signal quality due to 5G coverage holes, tunnels, and complex orography, may compromise the Quality of Service (QoS) provided to train passengers, especially in trains crossing international borders. To overcome these challenges, the EU-funded Horizon 2020 5GMED project has devised a cross-border 5G network architecture that allows train passengers to have the same QoS they have at home in whichever country they are. The network architecture is based on the concept of train neutral host, which operates as a service provider for other mobile network operators. The communication between the train and the ground is facilitated by a heterogeneous network infrastructure comprised of 5G StandAlone (SA), IEEE 802.11ad, and satellite networks to minimize the probability of having low quality signals along the rail track. The seamless switching between these network technologies is performed using Adaptive Communication System-Gateways (ACS-GW) developed in the project. In this paper, we propose the train neutral host architecture, and present the performance evaluation results obtained in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. Experimental results show satisfying results in terms of service throughput, latency and train inter-cell handover.

Cooperative, Connected and Automated Mobility (CCAM) and Future Railway Mobile Communications Systems (FRMCS) services usually require uninterrupted seamless connectivity. However, in cross-border scenarios, legacy roaming techniques lead to interruption times in the range of one to two minutes, which results unsuitable for the provisioning of demanding CAM and FRMCS services. This paper presents the implementation of state-of-the-art roaming optimization techniques in both the 5G Core and radio access network, including a novel radio optimization handover mechanism for home-routed roaming (HRR) that enables the completion of roaming procedures in 5G Standalone (SA) networks with short interruption times. In addition, a network key performance indicator (KPI) tool is presented to measure the network performance in terms of latency, throughput, and interruption time during roaming. Unlike previous works, static and dynamic performance evaluations are performed in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. The proposed optimization mechanism yields average interruption time measurements in the range between 135ms and 155ms, enabling seamless service continuity in crossborder scenarios.

The communication services of future trains require hyperconnectivity between trains and track. Future Railway Mobile Communication System (FRMCS) reserved bands will not be able to cover all digital train requirements including passengers’ services. The 5GMED project adopts the idea where Infrastructure Managers (IMs) will collaborate with Railway Undertaking (RU) to facilitate the connectivity to Mobile Network Operator (MNO) communication services and vice versa (complemented with the use of satellite networks), with an architecture open to build new business models and relationships between stakeholders. This approach encompasses an adaptative Gigabit train-to-track connectivity solution able to fit Digital Train requirements. The 5GMED project will demonstrate these capabilities through four representative services deployed on a TGV of SNCF (French Train Operator) in a challenging scenario: a cross-border section of the Mediterranean corridor between Figueres (Spain) and Perpignan (France).

One of the applications where fifth generation (5G) networks are expected to have a greatest impact is vehicular-to-everything (V2X) communications. The exchange of data among different vehicles on the network will make roads a safer environment. However, the massive usage of V2X communications leads to an increase of data traffic and resources consumption. Moreover, the mobility associated to vehicles may drain the available resources in particular cells, compromising the required quality of service (QoS) of connected users. To avoid these situations, the introduction of machine learning algorithms to perform mobility load balancing between neighboring cells arises as a promising tool to ensure to all connected vehicles their demanded resources. In this paper, we address the cell overload problem by proposing an O-RAN compliant Q-learning algorithm that dynamically adapts the handover offset between two neighboring cells to mitigate a network overload situation. The algorithm performance is assessed using realistic vehicular traces. Results show that the network overload appearing during rush hour can be successfully mitigated and the load is fairly distributed between cells.

Fifth Generation (5G) networks will support countless new applications and new business models. One of the 5G paradigms is network slicing, which enables the integration of multiple logical networks each one tailored to the requirements of the different services that can be provided by both network operators and vertical industries. One of the services where 5G is expected to have a greatest impact is vehicular-to-everything (V2X) communications, which will have their stringent latency requirements now met. However, the mobility associated to vehicles can lead to cell overload compromising the required quality of service (QoS). To address this problem, in this paper we propose and evaluate the performance of three network overload alleviation techniques to control network congestion provoked by traffic jams using realistic vehicular traces in a network slicing environment. Firstly, we describe the architecture supporting V2X communications. Secondly, the network congestion control approaches are explained. Finally, after providing a complete description of the considered scenario, results will be detailed, showing that the network overload appearing during rush hour can be significantly reduced.

The target of this paper is to propose a practical low-complexity power allocation algorithm that strikes a good balance between Spectral Efficiency (SE) and power saving for the downlink of interference-limited cellular networks. Because abundant interference usually results from dense frequency reuse and high power transmission, power optimization schemes are critical to interference management in wireless systems. Powerful power optimization schemes can be efficiently implemented in the framework of Self-Organizing Network (SON). In this context, we resort to non-cooperative game theory to devise two distributed power allocation schemes. By only considering SE, our first Power Control Game (PCG) algorithm, deemed SE-PCG, provides high SE but push autonomous eNBs into consuming all available power. To address this shortcoming and enhance Energy Efficiency (EE), we put forward another PCG algorithm, deemed EE-PCG, which inflicts a penalty on power consumption. The originality of our scheme lies in deriving the power penalty through a signaling-free heuristic. We have analyzed the proposed algorithms through extensive numerical simulations and compared them with the state-of-the-art approaches. The results have shown that our algorithms outperform the latter.

The focus of this paper is targeted towards multi-cell 5G networks composed of High Power Node (HPNs) and of simplified Low Power Node (LPNs) co-existing in the same operating area and sharing the scare radio resources. Consequently, high emphasis is given to Inter-Cell Interference Coordination (ICIC) based on multi-resource management techniques that take into account user association to cells. Beside user association, this paper takes also power control and scheduling into consideration. This complex problem is remained largely unsolved, mainly due to its non- convex nature, which makes the global optimal solution difficult to obtain. We address the user association challenge according to the two broadly adopted approaches in wireless networks: the network-centric approach where user association is allocated efficiently in a centralized fashion; and the user-centric approach where distributed allocation is used for reduced complexity. The scheduling and ICIC power control are solved in a centralized fashion, in order to reach an optimal solution of the joint optimization problem.

Traditional design of wireless networks mainly focuses on system capacity and spectral efficiency. As green networking is an inevitable trend, energy-efficient design for future wireless networks becomes paramount. In this paper, we address energy-efficient resource management in downlink orthogonal frequency division multiple access networks. The focus is targeted toward multi-cell networks, which are composed of multiple base stations (BSs) sharing the available radio resources. Consequently, greater emphasis is given to techniques that take inter-cell interference into account. Resource management in our context refers to the task of allocating the radio resources in order to maximize energy efficiency. We devise resource management techniques that jointly tackle the problems of scheduling and power control. Accordingly, we adopt two different approaches: a centralized approach, where BSs coordinate in order to reach a globally optimal energy-efficient solution, and a distributed approach, where BSs selfishly strive to maximize their own energy efficiency. We portray the centralized approach as a convex optimization problem, whereas we have recourse to non-cooperative game theory to model the distributed approach. In particular, we show that the non-cooperative game converges to a unique Nash equilibrium in low- and high-interference scenarios. We perform thorough numerical simulations to quantify the discrepancy between the centralized and distributed approaches, and identify the conditions where they have precedence over the state of the art. Moreover, the simulation results highlight the fast convergence of our algorithms, which is a precious asset for realistic deployments.

The focus of this paper is targeted towards multi-cell dense LTE and LTE-Advanced networks, which are composed of multiple evolved Node B (eNodeB) co-existing in the same operating area and sharing the available radio resources. In such scenarios, momentous emphasis is given towards the techniques that take Inter-Cell Interference (ICI) into account while allocating the scarce radio resources. In this context, we propose solutions for the problem of joint power control and scheduling in the framework of Inter-Cell Interference Coordination (ICIC) in the downlink of LTE OFDMA-based multi-cell systems. Two approaches are adopted to allocate system resources in order to achieve high performance: a centralized approach based on convex optimization and a semi-distributed approach based on non-cooperative game theory. The centralized approach needs a central controller to optimally allocate resources like in LTE CoMP (Coordinated Multipoint). In the semi-distributed approach, eNodeBs coordinate among each other for efficient resource allocation based on local knowledge conveyed by the X2 interface. It turns out that despite the lower complexity of the semi-distributed approach and its inherent adaptability, there is only a slight discrepancy of results among both approaches, which makes the distributed approach much more promising, in particular as a procedure of SON (Self Organized Network).

Femtocells are a promising approach to provide high data rates through autonomous configuration in indoor environments. However, due to the random and uncontrolled deployment of femtocells within users premises, interference between femtocells themselves and with macrocell base stations is a major issue. In this work, we look into the interference management problem and work towards the development of an interference mitigation algorithm based on the localization of randomly positioned femtocells using radio environmental information. In particular, we show that based on building floorplans and basic information on the urban landscape, femtocells can accurately localize themselves using macrocellular base stations as anchor nodes. Based on the localized femtocell positions, various channel allocation schemes are employed to mitigate interference.

Wireless network deployments are becoming increasingly complex in order to cope with the exponential growth in mobile data traffic. Multi-tier technologies capable of supporting spatially dense networks enable high data rates, but require timely and accurate models of the radio environment, particularly for scenarios that incorporate dynamic access to spectrum. However, network planners, algorithm designers, and policy makers are often forced to rely on a small set of radio propagation scenarios, often assuming fixed receiver antenna heights (RAHs). In this paper we show that the RAH in fact plays a key role not only in terms of reception quality but also in determining the interference originating from other transmitters. Our extensive simulation studies show a large variability in key performance metrics of realistic wireless network topologies as RAH is varied, which are not captured through the current practice of selecting few effectively two-dimensional scenarios and drawing general conclusions from them. These results underline the importance of adopting more realistic assumptions on RAH for design, assessment, and policy making for wireless communication systems, and the need for new propagation models valid for a wider range of RAH values than models currently in use.

In this paper we investigate the possibility of using measurement-based propagation models in the framework of Dynamic Spectrum Access (DSA), where secondary users can opportunistically access the licensed spectrum of a primary system. In principle this will provide a precise representation of the radio environment since it takes into account all environmental details at a given period of time. However the accuracy of the estimated models will depend on sensor density, propagation factors, and any external error. We study the impact of these factors on the accuracy of the estimated model and the satisfaction of primary and secondary networks.We also propose a guideline to overcome these problems in DSA scenarios by using a power margin.

We study the performance characteristics of cognitive wireless networks under different dynamic spectrum access scenarios. Our focus is especially on the influence of spatial structures of the primary and secondary user networks on the achievable performance of the secondary network. We adopt techniques from spatial statistics to develop stochastic models for the structure and interaction of these networks. The chosen models are based on Gaussian random fields and Gibbs point processes, and are firmly grounded on empirical data. We then apply extensive Monte Carlo simulations to study the behavior of these models and their related performance properties. The models and the applied techniques are applicable also to a wider variety of networking problems. Our results provide first quantitative assessments on the influence of the spatial structure, and especially correlation properties, of the involved networks on the expected performance and thus on the utility of dynamic spectrum access based systems.

The paper investigates the use of Radio Environment Maps (REMs) as a tool for Interference Management (IM) in two-tier cellular networks comprising macro- and femto-cells. The REMs are databases that provide, through different instances distributed over network elements, a variety of network- and user-related context information for improving IM and Radio Resource Management (RRM) procedures. In this context, the focus in this paper is to present the benefit of using REM information on practical power control schemes for the Femtocell downlink transmission in co-channel two-tier deployment.

The radio interface is the bottleneck of most wireless communication systems. All techniques used to enhance this interface require certain knowledge of the radio environment. This knowledge usually concerns either short-term, or long-term models and statistical characteristics of the environment, depending on the technique. Short-term models are suitable especially for signal processing applications, whereas long-term models are mostly used by radio resource management techniques. Most of the existing work is either focusing on outdoor propagation or short-term models. In this paper we present first results on developing long-term indoor propagation models based on extensive measurements. The results show that fast fading in indoor propagation models cannot be modeled with one distribution alone and thus dynamic models are required. Moreover, slow fading and its characteristics change very often and cannot be modeled with fixed distribution over time.

The demand for higher data rates, capacity and better quality-of-service is constantly growing for home networks. Therefore, there is a pressing need for efficient use of wireless network resources. In this context, the application of cognitive radio principles that enable network nodes to characterize their environment and control their resources based on the acquired knowledge, is the prominent solution for next generation home networks. In this paper we present an architecture and a prototype implementation based on these principles. The proposed system is able to autonomously optimize the performance of network nodes in a dynamic environment according to the goals, restrictions and policy regulations formulated by network stakeholders. The obtained results show the momentous and suitability of the cognitive framework for home networking.

In this paper we propose a general model to estimate the maximum allowed transmit power of a secondary node based on the measured signal from the primary transmitter that is willing to share its downlink spectrum. The transmit power should satisfy primary constraints related to the minimum required Signal to Interference and Noise Ratio (SINR) of its users. The model can be used by secondary nodes whether or not they have full knowledge about the correlation between the different path losses. Simulation results have shown the high impact of the level of correlation on the performance of both primary and secondary users.

In this paper we introduce a utility-based optimization approach for wireless communication based on developed Common Application Requirement Interface (CAPRI). The proposed system allows applications to register their utility functions to a radio resource controller at run-time and in automatic way. This enables the implementation of utility-based optimization and run-time reconfiguration of applications. Specifically, in contrast to traditional utility-based optimization approaches that usually aim at optimizing a single application deployed for the entire system, our approach aims to generalize and express the requirements of multiple applications. To validate our proposed system, we have implemented a prototype of CAPRI using Windows platform. The implementation has been made publicly available under an open source license. We show that the overhead induced by the adoption of the CAPRI framework in terms of memory and processing requirements is small, and additional latencies induced by the framework are negligible. Further, a practical case study of multi-application utility-based optimization has been addressed and the results have been analyzed.

In this contribution a layered radio environment map (REM) architecture is designed and applied in the framework of radio-access network optimization for heterogeneous LTE systems that comprise both macrocells and femtocells. We define layer as the hierarchical representation of a geographical area. In each layer, different instances of the same architectural block will have different spatial/temporal granularities, associated with network functionalities. The effectiveness of the proposed architecture to support LTE functions like automatic neighbor relation (ANR) and minimization of drive tests (MDT) is discussed. In addition, we present the benefits of using such architecture in the implementation of these functions along with its potential to bring performance gains.

We empirically study problems of online estimation and modeling of radio activity (ON/OFF) patterns and their duty cycles. We consider performance of two algorithms that analyze power spectrum samples in time domain as sensed by WARP SDR boards placed at different indoor locations. The first algorithm utilizes density function of the marginal distribution of the received readings to perform clustering of the power samples into the classes that correspond to ON/OFF activity states. The second algorithm uses hidden Semi-Markov processes and the Viterbi algorithm to model and estimate the radio activity patterns of signal sources. Unlike the first algorithm it can also be used for both signal classification and estimation of the state duration that corresponds to the certain power level. We study signals obtained from one or two transmitters. Our results indicate the first technique can often provide accurate estimations. However, if sensor nodes receive low RSSI values or modeling of the temporal structure of the activity patterns is required then the second technique is preferable. This method also has a potential to benefit from cooperation between nodes.

A common concept to mitigate the effects of spectrum scarcity is to allow opportunistic dynamic spectrum access (DSA) to licensed frequency bands. In this context, the license holder may experience a decrease in the performance of its network. For cellular networks this may lead to unwanted coverage holes at locations where the secondary-induced interference is too strong. In this paper we propose a planning tool and a channel assignment mechanism for cellular OFDMA-based networks that takes the coverage requirements of the primary system into account. By keeping a dynamic reserve of channels for those nodes that experience the highest interference, we can maximize spectrum utilization while guaranteeing a predefined service reliability. Our simulations show that only a small fraction of channels will need exclusive reservation, hence large parts of the spectrum may be employed in secondary systems.

In this paper we describe an LTE based demonstrator of the Universal Link Layer API (ULLA) and Cognitive Resource Manager (CRM) modules that are developed in ARAGORN project. The demonstrated LTE system comprises one LTE TDD eNode B and one User Equipment (UE). We first introduce ULLA and CRM framework and then demonstrate their suitability to be implemented with the existing LTE equipments. We show how, through ULLA, CRM is able to obtain PHY/MAC status information of the link between the eNode B and UE, and in turn change system parameters to achieve better resource utilization and transmission efficiency. The control logic can be implemented with simple adaptation or policy-based intelligent methods. The platform clearly shows the feasibility to use ULLA/CRM architecture for radio resource management in a LTE network. It also shows the neutrality of ULLA/CRM mechanisms towards PHY/MAC characteristics of LTE technology platform; hence the platform is viable to flexibly switch between technology platforms (e.g. between LTE access and WiFi access) under the control of ULLA/CRM.

In this paper, we propose an advanced spectrum management approach for the downlink of WCDMA systems using genetic algorithms and the concept of coupling matrix, which is able to capture inter-cell interactions. Simulation results show that the proposed approach increases spectrum efficiency while guaranteeing the required QoS levels. Moreover, it allows releasing carriers in some cells, so that they could be eventually used by e.g. secondary cognitive radio users exploiting the flexible spectrum allocation and opportunistic spectrum access.

In this paper, we propose a new Advanced Spectrum Management (ASM) methodology for WCDMA systems using simulated annealing and the concept of coupling matrix, which is able to capture inter-cell interactions. The proposed methodology takes into account the fact that each cell can be associated to more than one carrier. Simulation results show that the proposed methodology increases spectrum efficiency while guaranteeing the requested QoS levels. Moreover, it allows releasing carriers in some cells, so that they could be eventually used by e.g. secondary cognitive radio users exploiting the flexible spectrum allocation and opportunistic spectrum access.

The authors propose a new advanced spectrum management (ASM) methodology for wideband code division multiple access systems based on the concept of coupling matrix, which is able to capture inter-cell interactions. The proposed methodology takes into account the fact that each cell can be associated to more than one carrier and aims at liberating some carriers in large geographical zones, so that they could eventually be used by, for example, secondary cognitive radio users that exploit the flexible frequency allocation and opportunistic spectrum access. Simulation results show that the proposed methodology increases spectrum efficiency while guaranteeing the requested QoS levels. Moreover, a new metric has been introduced to reflect the capability of the ASM methodology to liberate some carriers in large geographic zones. This metric has been used to compare the different approaches presented here.

In this paper, we propose a novel analytical framework that is able to capture inter-cell interactions and relevant variations in WCDMA systems using the so-called coupling matrix. Coupling matrix properties related to its spectral radius are analyzed and used to introduce a pertinent indicator which is able to dynamically optimize spectrum efficiency in the context of cognitive networks. Herein, an advanced spectrum management methodology based on coupling matrices is proposed as an illustrative example. Simulation results have shown that the proposed methodology has the capability to adapt to relevant changes in the network and outperforms the frequency allocation in classical hierarchical cell structure planning.

In this paper a new spectrum management methodology for WCDMA systems is proposed based on the concept of coupling matrix, which is able to capture inter-cell interactions. The proposed methodology takes into account the fact that uplink and downlink frequency carriers are jointly allocated and optimizes simultaneously the two link directions. Simulation results for symmetric and asymmetric services show the efficiency of the methodology in increasing capacity and reducing transmitted power.

The combination of the TDD mode and the TDMA technique in WCDMA systems engenders a new degree of flexibility that can increase system capacity and QoS levels of active channels if efficient slot allocation techniques are used. These techniques allow the system to control the allocation of slots to users, and thus lead to relevant reduction in interference levels. In order to study slot allocation performance, it is important to define a reference performance that gives adequate approximation of system maximum capacity. In this paper, we propose a novel formulation to the computation of system maximum capacity using a set of combinatorial optimization problems. Furthermore, we propose meta-heuristic algorithms that give satisfactory approximations for each combinatorial problem

Although a single carrier frequency is usually considered in wideband code division multiple access (WCDMA) systems, each operator has more than one carrier frequency in practical 3G systems. Moreover, QoS levels and the throughput within a given frequency highly depend on interference patterns, which are mainly related to cell-frequency allocation. Therefore, frequency management plays a key role in WCDMA network planning. However, the frequency management problem has not been in the center of attention for WCDMA systems so far due to the fact that WCDMA has not yet been implemented in wide range with all possible services. Nevertheless, the arrival of multimedia services will emphasize the critical importance of a smart frequency allocation. In this context, the presence of several frequencies for each operator (typically 2 or 3 in Europe) manifests itself as a fertile dimension of flexibility to be exploited. This paper introduces a novel frequency management methodology based on coupling matrices that reflects the interaction between cells for a given interference pattern. The proposed methodology is implemented as an integral task of the planning tool and has lead to better results than the frequency allocation in the classical hierarchical cell structure.

Adaptive power control algorithms has been widely studied for interference reduction, base station radiation decrease and terminal battery saving in 3G cellular systems. We study some of the existent adaptive power control algorithms and we propose an evolution of these algorithms in the forward link. In the proposed algorithm, we try to maintain the intercell interference stable and to reduce intracell interference when it is possible. Therefore, the power control steps of mobiles requesting a power increase may be reduced to limit the total power increase if some constraints are verified. The proposed algorithm uses an adaptive step, which is updated using the instantaneous mobile power control command and the command history. Moreover, a stabilization zone above the SIR target is used to limit oscillations. The proposed algorithm has been compared to the 3GPP standardized power control and to a recently proposed adaptive power control. Simulation results has shown that the proposed algorithm decreases slightly the outage probability as the already proposed adaptive power control and reduces the power consumption.

Power control is a very important feature of CDMA systems and it dramatically impacts the cellular capacity. The UTRA-TDD (Universal Terrestrial Radio Interface with Time Division Duplex) adopted for third generation systems is a hybrid radio interface, which mixes TDMA and CDMA schemes. Therefore, power control and slot allocation must be optimised jointly. In this paper, we introduce two slot allocation methods combined with optimal power control. In the first method, we calculate the mean normalized path loss of mobile stations and try to obtain for each cell an allocation with the same mean normalized path loss on all time slots. In another way, we try to obtain the same signal to interference ratio in all cells. The principle of the second method is to group mobile stations with close path loss values on the same slot for each cell and associate a set of high-gain mobile stations of a cell with sets of low-gain mobile stations from neighbouring cells in the same time slot. The second method performs better than the first one and gives very close results to the optimal ones.