The detection and mitigation of coverage problems in cellular networks have traditionally followed a cell-level perspective, based on statistics of aggregated user equipment measurements, e.g. to ensure that a required received power level is achieved in a percentage of time and locations within the cell. However, this overall cell-level performance may not accurately capture the user-level perspective of individual users, which can experience too low received signal power during a large percentage of their connection time. This paper proposes an enhanced coverage hole detection solution that combines different types of measurements collected from multiple complementary sources to capture both cell-level and user-level perspectives. In addition, in order to mitigate the identified coverage problems, this paper proposes a solution that combines the adjustment of the base station transmitted power and the deployment of fixed relays. The results have been obtained using real measurements of the space/time user location distribution in a university campus. The presented results demonstrate that the inclusion of the individual user perspective allows the identification of coverage holes which cannot be identified with a traditional cell-level approach. Moreover, the proposed Coverage Hole mitigation solution solves the Coverage Holes in the considered scenario with a reduction around 60% in Capital and Operational Expenditures with respect to a solution based only on the deployment of fixed relays.

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

Rapid and accurate localization of individuals during search-and-rescue (SAR) missions is essential for reducing casualties in emergencies. Traditional methods often struggle in disaster scenarios, where obstacles like debris or dense foliage hinder performance, and reliance on user-side actions proves impractical for mission-critical operations. This paper presents a novel approach that leverages a 5G-new radio (NR)-based unmanned aerial vehicle (UAV) localization framework, integrating hybrid techniques with adaptive clustering strategies to localize user equipments (UEs). Unlike traditional methods, our system dynamically adjusts its trajectory to balance latency and accuracy, achieving UEs positioning accuracy within tens of centimeters during simulation tests. By integrating 5G-NR technology into UAV-based localization, our approach provides a robust and scalable solution for mission-critical SAR operations, significantly enhancing the latency and reliability of locating individuals in emergency situations.

Relevant services envisaged for beyond 5G (B5G) systems, such as extended reality and holographic communications, have extremely demanding user experience requirements with significant computational and communication demands. While edge computing aims to address the computation requirements by offloading the computational tasks to edge servers near the user, the communication will take advantage of the technologies developed for 5G New Radio jointly with an never-before-seen degree of network densification. This paper proposes the use of relays with edge computing capabilities. The approach's potential for B5G are identified, and a system model is defined to characterize both computational and communications viewpoints. Based on this, results are provided to highlight the gains and limitations of the proposed approach from a system-level perspective. Finally, the main challenges for enabling relays with computing capabilities in B5G deployments are discussed.

Radio Resource Management (RRM) strategies are essential components in mobile wireless networks, such as Long Term Evolution (LTE) or 5G New Radio (NR)[1]. The design of RRM algorithmic solutions is an area of research that has received a lot of attention for decades (see e.g. surveys [2–4] and references therein). Given that RRM strategies need to handle traffic dynamicity in the Radio Access Network (RAN), the availability of realistic user distributions in space and time can be very useful to support a more realistic performance assessment of RRM solutions. For this purpose, this article introduces a dataset containing real measurements of the number of users connected to the different Wifi Access Points (APs) at the Campus Nord facilities of the Universitat Politècnica de Catalunya (UPC) in Barcelona. The Wifi network is composed of 247 APs. To characterize the temporal variations, the data was collected for each AP every 1000 s (approximately) during 62 days. Besides the number of users connected to each AP (including users connected to both 2.4 GHz and 5 GHz bands), the dataset also contains information about the theoretical coverage area of each AP, so that the number of users connected to each AP can be associated to a specific geographical area. In this way, the dataset captures the spatio-temporal variations of users in the Campus at different times of the day, different days of the week and different periods of the academic year.

Relevant services envisaged for beyond 5G(B5G) systems, such as extended reality and holographic communications, present stringent user experience requirements with high computational and communication demands. While edge computing aims to address the computation requirements by offloading the computational tasks to edge servers close to the user, the communication will leverage the technologies developed for 5G New Radio together with an unprecedented level of network densification. This paper advocates for deploying relays equipped with edge computing capabilities. The potentials of this approach for B5G are identified and a system model is presented to characterize both computational and communications perspectives. Based on this, results are provided to show the benefits and limitations of the proposed approach from a system-level perspective.

This paper proposes a model to detect and solve coverage holes in 5G Radio Access Network (RAN) deployments operating with millimeter waves. The proposed model utilizes a DBSCAN-based detector to identify coverage constrained areas and then proposes the use of Relay User Equipment (RUE) capabilities to extend the RAN coverage. To optimize the activation and deactivation of RUEs, a Deep-Q-Network-based algorithm is proposed, aiming to improve spectral efficiency and decrease the outage probability experienced by network users. The obtained results demonstrate the effectiveness of the model in accurately detecting coverage constrained areas and efficiently solving these issues by means of an effective RUE activation, leading to significant improvements in network performance while minimizing the time that RUEs remain in active mode, which implies potential benefits for MNOs and UE holders and significant energy savings.

Deep Reinforcement Learning (DRL) has recently emerged as a promising technique to deal with different problems in the 5G and beyond Radio Access Network (RAN). The practical implementation of DRL solutions in the real network embraces a training process that is fundamental to materialize the expected benefits associated to these techniques. However, little effort has been devoted in the literature to analyze this training process when applying DRL in the RAN. In an effort to contribute to fill this gap, this paper presents an impact analysis of the training process on the obtained performance by a DRL solution for RAN slicing. To this end, a methodology to specify the training dataset is introduced together with the definition of relevant metrics. Then, the paper presents different simulation results to determine the features of the training dataset that allow a satisfactory training and a high performance of the obtained policy when applied during the inference stage.

Reinforcement Learning (RL)-based algorithmic solutions have been profusely proposed in recent years for addressing multiple problems in the Radio Access Network (RAN). However, how RL algorithms have to be trained for a successful exploitation has not received sufficient attention. To address this limitation, which is particularly relevant given the peculiarities of wireless communications, this paper proposes a functional framework for training RL strategies in the RAN. The framework is aligned with the O-RAN Alliance machine learning workflow and introduces specific functionalities for RL, such as the way of specifying the training datasets, the mechanisms to monitor the performance of the trained policies during inference in the real network, and the capability to conduct a retraining if necessary. The proposed framework is illustrated with a relevant use case in 5G, namely RAN slicing, by considering a Deep Q-Network algorithm for capacity sharing. Finally, insights on other possible applicability examples of the proposed framework are provided.

Multi-connectivity emerges as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill the demanding requirements in terms of data rate and reliability. It allows a device to be simultaneously connected to multiple cells belonging to different radio access network nodes from a single or multiple radio access technologies. This paper addresses the problem of optimally splitting the traffic among cells when multi-connectivity is used. For this purpose, it proposes the use of deep learning to determine the optimum amount of traffic of a device that needs to be sent through one or another cell depending on the current traffic and radio conditions. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits in the considered scenario.

Vehicular communications are called upon to become one of the services that will benefit the most from 5G networks. The low latency and unprecedented high data rates 5G is expected to deliver are a key enabler for new vehicular services. A booster for this to happen is network slicing, which enables to tailor radio resources to satisfy the different services demand. However, frequent traffic jams in urban scenarios can lead to network overloading, compromising the required Quality of Service by running out of available radio resources. In this paper, we firstly conduct a microscopic analysis of vehicles mobility using realistic vehicular traces to detect traffic jams. Secondly, the network overload introduced by this congestion is studied considering a network slicing environment. Finally, a load balancing approach to mitigate the radio resources demand is proposed. Results show a significant mitigation of the overload situation.

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.

This paper envisions to augment the Radio Access Network (RAN) infrastructure in Beyond 5G(B5G) systems by exploiting relaying capabilities of user equipment (UE) as a way to improve the coverage, capacity and robustness. Despite the concept and enabling technologies have been in place for some time, their efficient realization requires the conception and development of new features in B5G systems. Among them, this paper focuses on the Relay UE (RUE) activation decision making, in charge of deciding where and when a UE is suitable to be activated to relay traffic from other UEs. Specifically, the paper analyses seven RUE activation strategies that differ on the criteria and the type of context information considered for this decision-making problem. The considered strategies are evaluated through system level simulations in a realistic urban scenario with the objective of assessing the value of each type of context information. Results reveal that the most efficient strategies from the perspective of outage probability reduction are those that account for the number of UEs that would be served by a RUE based on the experienced spectral efficiency.

This paper envisages a Beyond 5G (B5G) Radio Access Network (RAN) in which the relaying capabilities offered by user equipment (UE) are used as a way to improve the coverage and robustness of the network. The paper proposes and develops the functional framework for supporting the activation of the suitable relay UEs (RUEs) in coverage constrained scenarios. It is based on characterizing each potential RUE through a utility metric that measures the coverage enhancements brought to the network when the RUE is activated. To derive this metric for all the candidate RUEs, the framework considers the use of a Network Digital Twin that allows the offline analysis of different configurations in a fast and safe way. Using the proposed framework, a RUE activation algorithm is proposed and evaluated. The obtained results reflect that significant outage probability reductions can be obtained in the scenario under different traffic distributions thanks to the activation of the RUEs with the highest utility.

One of the key features of the 5G architecture is network slicing, which allows the simultaneous support of diverse service types with heterogeneous requirements over a common network infrastructure. In order to support this feature in the Radio Access Network (RAN), it is required to have capacity sharing mechanisms that distribute the available capacity in each cell among the existing RAN slices while satisfying their requirements and efficiently using the available resources. Deep Reinforcement Learning (DRL) techniques are good candidates to deal with the complexity of capacity sharing in multi-cell scenarios where the traffic in the different cells can be heterogeneously distributed in the time and space domains. In this paper, a multi-agent reinforcement learning-based solution for capacity sharing in multi-cell scenarios is discussed and assessed under heterogeneous traffic conditions. Results show the capability of the solution to satisfy the requirements of the RAN slices while using the resources in the different cells efficiently.

Network slicing is included as a key feature of the 5G architecture in order to simultaneously support diverse service types with heterogeneous requirements. The deployment of network slicing in the Radio Access Network (RAN) needs mechanisms that allow the distribution of the available capacity in the system in an efficient manner while satisfying the requirements of the different services. In this paper, a capacity sharing function is proposed, which is approached as a multi-agent reinforcement learning based on the Deep Reinforcement Learning (DRL) algorithm Deep Q-Network (DQN). The proposed algorithm provides the capacity to be assigned to each RAN slice. Performance assessment reveals the promising behaviour of the proposed solution.

This paper discusses the exploitation of data analytics for supporting the operation of Radio Resource Management (RRM) in the Next Generation Radio Access Network (NG-RAN), analysing the need for standardised and open frameworks and the specific characteristics of the NG-RAN that impact on the design of data analytics solutions. As a practical example of this concept, a simple but illustrative use case is presented, which intends to enhance the Radio Admission Control (RAC) function with data analytics information in scenarios with both Fixed Wireless Access (FWA) users and mobile users. A functional framework for the realization of the proposed solution is described and the performance of the RAC algorithm is evaluated under different scenario conditions to better delimitate its potential.

The network slicing capability introduced in 5G systems facilitates the realisation of flexible multi-tenant networks. Network slicing enables the partition of a common shared network in several logical networks, each configured to fulfil specific service requirements. In scenarios where the lifecycle of network slices has to be managed dynamically (e.g. allocation, modification and deallocation of network slices in response to changing tenants’ needs), slice admission control becomes a central function to assure that the set of slices concurrently activated count with the sufficient resources to fulfil their service requirements. Slice admission control is particularly challenging for the Radio Access Network (RAN) part of a slice, because the amount of required radio resources is highly dependent on the characteristics of the deployment environment and type of cells. In this context, this paper presents a functional data-analytics framework along with a new analytical methodology for estimating the radio resource requirements for RAN slice admission control. Specifically, in order to characterise the propagation and interference conditions in each cell, the proposed resource estimation method leverages statistical information extracted via data analytics from the cell performance measurements collected at the management plane. Results show the benefits of the proposed estimation method under different types of cell deployments.

Network slicing is a fundamental feature of 5G systems that facilitates the provision of particular system behaviours adapted to specific service/application domains on top of a common network infrastructure. A network slice is in general composed by a core network slice and a Radio Access Network (RAN) slice. The realization of RAN slices is particularly challenging because it requires configuring and operating traffic differentiation and protection mechanisms to simultaneously deliver multiple and diverse RAN behaviors over a given pool of radio resources. In this context, this paper proposes to characterize the behavior of a RAN slice through the specification of a set of control parameters that are used to dictate the operation of the packet scheduling function at Layer 2 and the radio admission control function at Layer 3. An evaluation of the suitability of these parameters for achieving efficient radio resource sharing and isolation between RAN slices is presented when configuring a network for supporting a slice with multiple enhanced Mobile BroadBand services and another slice for providing Mission Critical services. The analysis reveals the different impact of the Layer 3 and Layer 2 parameters for isolating services of different slices depending on whether they require guaranteed or non-guaranteed bit rates.

The introduction of multi-tenancy in the Radio Access Network (RAN) is seen as a relevant capability of future 5G systems to support the challenging capacity requirements in a cost-effective way. Multi-tenancy brings in new challenges in the way how the RAN has to be managed and operated in order to provide the agreed service levels to each tenant and at the same time achieve an efficient utilization of the radio resources. This paper proposes a new solution for flexible capacity sharing among tenants based on a hybrid centralized/distributed Self-Organizing Network (SON) function to automatically adjust the Admission Control (AC) settings of the different cells. The proposed solution makes use of fuzzy neural controllers that provide intrinsic benefits in terms of dealing with the uncertainties of complex cellular scenarios and introducing the formulation of preferences and policies in the decision process. Together with the detailed description of the different components of the proposed solution, the paper presents an initial evaluation that provides sufficient insight into the potentials of the fuzzy neural hybrid SON to stimulate further and subsequent analysis.

Multi-tenancy allows diverse agents sharing the infrastructure in the 5thgeneration of mobile networks. Such a feature calls for more automated and faster planning procedures in order to adapt the network capacity to the varying traffic demand. To achieve these goals, Small Cells offer network providers more flexible, scalable, and cost-effective solutions compared to macrocell deployments. This paper proposes a novel framework for cell planning in multi-tenant Small Cell networks. In this framework, the tenant's contracted capacity is translated to a set of detailed planning specifications over time and space domains in order to efficiently update the network infrastructure and configuration. Based on this, an algorithm is proposed that considers different actions such as adding/removing channels and adding or relocating small cells. The proposed approach is evaluated considering the deployment of a new tenant, where different sets of planning specifications are tested.

This paper aims to improve the user Quality of Service (QoS) in 5G networks by introducing a user-centric view that exploits the predictability of the user's daily motifs. An agglomerative clustering is used to identify these motifs according to the cells in which the user is camping during the day. Then, a technique to extract the personalised QoS observed by the user is proposed. The methodology is illustrated with an example that makes use of real measurements obtained from a specific customer of a 3G/4G operator. The presented results illustrate that the proposed user-centric approach is able to identify situations with poor user perceived QoS which could not be identified by a classical network-centric approach.

The use of Long Term Evolution (LTE) in the unlicensed 5 GHz band, referred to as LTE-U or Licensed Assisted Access (LAA), is a promising enhancement to increase the capacity of LTE networks and meet the requirements of future systems. This paper analyses the use of fully distributed channel selection mechanisms for facilitating the coexistence among different LTE-U and/or Wi-Fi systems operating in the same band. Specifically, the paper focuses on a Q-learning and a Game Theory based approach. The implementation considerations of both approaches are discussed in relation to current 3GPP specifications and a comparison in terms of performance is presented to analyze the convergence time, the signaling requirements and the impact of errors in the throughput estimation.

Resilience and high availability are being considered as essential requirements in 5G networks. To fullfil these requirements, the integration of a satellite component within mobile backhaul networks is regarded as a compelling proposition to provide backup connectivity to critical cell sites and divert traffic from congested areas so that a limited capacity in their terrestrial links could be supplemented during peak-time or even replaced in case of total/partial failure or maintenance. Sustained in an architectural framework that enables the integration and management of the satellite capacity as a constituent part of a SDN-based traffic engineered mobile backhaul network, this paper develops and assesses a traffic distribution strategy that exploits the dynamically steerable satellite capacity provisioned for resilience purposes to maximize a network utility function under both failure and non-failure conditions in the terrestrial links.

Multi-tenant Radio Access Networks (RANs) are envisaged to play a key role in highly dense scenarios for fulfilling the challenging capacity requirements of future enhanced Mobile BroadBand services while reducing the capital and operational costs per tenant. In this context, this paper proposes a self-optimized Admission Control (AC) strategy for automatically adjusting the share of resources per tenant in each cell of the shared RAN in order to account for uneven time/space distributions of tenants' traffic demand across cells. The proposed approach is evaluated by means of simulations to analyze the operation of the self-optimization process under different traffic distributions. Results reveal that the proposed self-optimized AC can provide substantial throughput gains of up to 65% with respect to the case without self-optimization.

Key features of satellite communications such as wide-scale coverage, broadcast/multicast support and high availability, together with significant amounts of new satellite capacity coming online, anticipate new opportunities for satellite communications services as an integral part within upcoming 5G systems. To materialize these opportunities, satellite communications services have to be provisioned and operated in a more flexible, agile and cost-effective manner than done today. In this context, this paper describes a solution for the virtualization and dynamic orchestration of satellite communication services that builds on the introduction of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies within the satellite ground segment systems. Along with the description of the main system architecture traits, the flowchart of a general procedure for the dynamic instantiation of virtualized satellite networks on top of a SDN/NFV-enabled satellite ground segment system is provided. The paper also presents experimental results for the dynamic customization of satellite network services through the implementation of a set of virtualized satellite network functions that can be orchestrated over general purpose open virtual platforms.

This paper addresses the channel selection problem for Long Term Evolution Unlicensed (LTE-U). Channel selection is a frequency-domain mechanism that facilitates the coexistence of multiple networks sharing the unlicensed band. In particular, the paper considers a fully distributed approach where each small cell autonomously selects the channel to set-up an LTE-U carrier. The problem is modeled using a non-cooperative repeated game and the Iterative Trial and Error Learning - Best Action (ITEL-BA) learning algorithm is used to drive convergence towards a Nash Equilibrium. The proposed approach is evaluated by means of simulations in different situations analyzing both the throughput performance and the convergence behavior.

Public Protection and Disaster Relief (PPDR) services can benefit greatly from the availability of mobile broadband communications in disaster and emergency scenarios. While undoubtedly offering full control and reliability, dedicated networks for PPDR have resulted in high operating costs and a lack of innovation in comparison to the commercial domain. Driven by the many benefits of broadband communications, PPDR operators are increasingly interested in adopting mainstream commercial technologies such as Long Term Evolution (LTE) in favour of expensive, dedicated narrow-band networks. In addition, the emergence of virtualization for wireless networks offers a new model for sharing infrastructure between several operators in a flexible and customizable manner. In this context, we propose a virtual Public Safety (PS) operator that relies on shared infrastructure of commercial PPDR networks to deliver services to its users. We compare several methods of allocating spectrum resources between virtual operators at peak times and examine how this influences differing traffic services. We show that it is possible to provide services to the PS users reliably during both normal and emergency operation, and examine the impact on the commercial operators.

The use of Long Term Evolution (LTE) in the unlicensed 5 GHz band, referred to as LTE-U, is a promising enhancement to increase the capacity of LTE networks and meet the requirements of future systems. This paper considers a Q-learning based Channel Selection strategy to decide the most appropriate channel to use for downlink traffic offloading in the unlicensed band as a mechanism to greatly facilitate the coexistence among several LTE-U and/or Wi-Fi systems in the same band. The focus is placed on analyzing the robustness of the proposed approach in front of non-stationary conditions in the wireless environment. Simulation results allow assessing quantitatively the capability of the proposed strategy to relearn proper solutions when changes in the environment occur. Furthermore, the analysis evaluates quantitatively how fast the learning process has to be compared to the variations in the environment in order to retain an LTE-U throughput performance very close to the optimum one.

To increase cognitive radio operation efficiency, this paper proposes a new knowledge management functional architecture, based on the fittingness factor concept, for supporting spectrum management in non-stationary environments. It includes a reliability tester module that detects, based on hypothesis testing, relevant changes in suitability levels of spectrum resources to support a set of heterogeneous applications. These changes are captured through a set of advanced statistics stored in a knowledge database and exploited by a proactive spectrum management strategy to assist both spectrum selection and spectrum mobility functionalities. The results reveal that the proposed reliability tester is able to disregard the changes due to the intrinsic randomness of the radio environment and to efficiently detect actual changes in interference conditions of spectrum pools. Thanks to this support, the proposed spectrum management strategy exhibits substantial robustness when the environment becomes non-stationary, obtaining performance improvements of up to 75% with respect to the reference case that does not make use of the reliability tester functionality.

Dynamic Spectrum Access, based on the Cognitive Radio (CR) paradigm, is a promising solution to improve the currently inefficient spectrum utilization. In this respect, this paper deals with the spectrum selection problem when a number of radio links has to be established in a CR network. A novel strategy based on a Partially Observable Markov Decision Process (POMDP) is proposed combining partial observations of the interference state together with a statistical characterization of the interference dynamics. In this context, the use of an efficient observation strategy is a key element to account for the trade-off between achieved performance and measurement requirements. For that purpose, the aim of the paper is to propose and evaluate different observation policies for the POMDP-based spectrum selection framework. Results show that the proposed framework is able to achieve very similar performance than a strategy operating under full knowledge of the interference state requiring much less associated signaling.

The demand for higher data rates, capacity and better Quality of Service (QoS) is constantly growing; therefore, there is a pressing need for efficient use of wireless network resources. In this context, the application of Cognitive Radio (CR) principle has emerged as a solution to the problem of spectrum scarcity. In this respect, this paper presents an algorithm that enables multi-band spectrum selection for wireless applications under interference variations in the available spectrum blocks. The algorithm is formulated as a Partially Observable Markov Decision Process (POMDP) targeting the maximization of a reward function that captures how suitable each allocated spectrum block is for the data transmission in each radio link depending on the application bit rate requirements.

This paper proposes a new knowledge management framework for spectrum selection under non-stationary conditions supporting a set of applications with heterogeneous requirements. In this respect, an optimization problem is formulated to maximize an aggregate utility function that captures the suitability of spectrum portions with respect to the various application requirements and a set of preferences for using the different spectrum bands. Motivated by the practical limitations when solving the considered problem directly, an alternative solution is proposed. It exploits a statistical characterization of the environment retained in the knowledge database of the proposed framework. To cope with the non-stationarity of the environment, a reliability tester is proposed to detect relevant changes in radio conditions, and update database statistics accordingly. Then, a knowledge manager exploiting these statistics is developed to monitor the time-varying suitability of spectrum resources. Based on this, a novel spectrum management is proposed to approximate the optimal solution of the considered problem. The results obtained in a realistic digital home reveal that, under stationary conditions, the proposed strategy performs very close to the optimal solution with much less requirements in terms of spectrum reconfiguration and measurement reporting. Furthermore, thanks to the reliability tester support, substantial robustness is shown when interference conditions become non-stationary, thus proving the practicality of the proposed solution.

In order to increase spectrum utilization efficiency, CRs (Cognitive Radios) have been introduced to reuse white spaces left unused by legacy services under the strict constraint of not interfering them. In this context, this paper proposes to exploit a statistical characterisation of Primary User (PU) activity to be retained in Radio Environment Maps (REMs) for spectrum selection purposes. The objective is to match multi-service secondary traffic to heterogeneous primary spectrum opportunities minimizing the SpHO (Spectrum handOver) rate. Specifically focusing on dependence structures potentially exhibited by primary ON/OFF periods, two spectrum selection criteria have been first proposed to benchmark the utility of the embedded statistical patterns in the REM. Results have shown that the one or the other criterion can introduce significant gains with respect to a random selection depending on the secondary configuration and characteristics of PUs. Therefore, a novel pro-active spectrum selection strategy combining the proposed criteria has been developed and proven to achieve in most of the cases the best performance for a given secondary service mix and the dependence level between primary ON/OFF periods.

This paper addresses the estimation of different context parameters of a primary user network based on signal strength measurements obtained by a sensor network and without any prior knowledge of the antenna types used by primary transmitter or the propagation model. The obtained context information can be stored in a database like a Radio Environment Map (REM) and be used to decide the appropriate configuration of a secondary cognitive radio network. Proposed methodology is based on applying image processing over the set of measurements to identify the existing transmitters in the scenario and their parameters such as position, antenna pattern and propagation model.

In this letter a Discrete-Time Markov Chain (DTMC) framework is adopted to capture the effect of flexible spectrum channelization for the opportunistic access of secondary users (SUs) in a primary-secondary shared spectrum scenario. Two implementation alternatives are proposed: a fixed channelization scheme (FCS) and an adaptive channelization scheme (ACS). Moreover, service-type characterization of SUs is also addressed by defining time vs. volume based services. Results indicate the suitability of the ACS over the FCS.

This paper presents a decentralized framework for dynamic spectrum assignment in multicell orthogonal frequency division multiple access (OFDMA) networks. The proposed framework allows each cell to autonomously decide the frequency resources it should use through a procedure that incorporates concepts from self-organization and machine learning in multiagent systems (MASs). Simulation results have been obtained for several scenarios, including both macrocells (MCs) and femtocells (FCs), revealing important improvements in terms of spectral efficiency and intercell interference mitigation over reference approaches, and close performance with the one obtained by a centralized strategy. Results also suggest that the framework would be practical for future FC cellular deployments where a high degree of independence of the network nodes is expected to reduce operational costs.

This paper proposes reinforcement learning as a foundational stone of a framework for efficient spectrum usage in the context of next-generation mobile cellular networks. The objective of the framework is to efficiently use the spectrum in a cellular orthogonal frequency-division multiple access network while unnecessary spectrum is released for secondary spectrum usage within a private commons spectrum access model. Numerical results show that the proposed framework obtains the best performance compared with other approaches for spectrum assignment. Moreover, the framework is relatively simple to implement in terms of computational requirements and signaling overhead.

This paper proposes a self-organized spectrum assignment strategy in the context of next generation multicell orthogonal frequency division multiple access networks. The proposed strategy is able to dynamically find spectrum assignments per cell depending on the spatial distribution of the users over the scenario, opening new spectrum access opportunities for secondary spectrum usage. Reinforcement learning methodology has been employed to implement the strategy, which compared with other fixed and dynamic spectrum assignment strategies shows the best tradeoff between spectral efficiency and quality-of-service while releases spectrum in large geographical areas.

Spectrum sensing is one key enabler towards opportunistic spectrum access in cognitive radio networks. Such scenarios allow cognitive users (a.k.a. secondary users) to access some licensed spectrum band as long as they do not interfere with the licensed (or primary) users. The main goal is to achieve an efficient and utmost access to the otherwise underutilized spectrum resources while still guaranteeing primary users a non-harmful operation. Spectrum sensing can be then used by secondary users to detect spectrum holes that may be accessed in a non-interfering manner. However, spectrum sensing may be subject to errors in the form of false-alarm and misdetection. False-alarm causes spectrum under-use while misdetection leads to spectrum interference between primary and secondary users. Unfortunately, these two magnitudes pose a trade-off on the sensing mechanism: low misdetection is achieved at the expense of high false alarm and vice versa. Consequently, an adequate operating point of the sensing mechanism should be determined. In this work we evaluate the impact of false-alarm and misdetection errors on the performance of a spectrum sensing scenario. We use a discrete time Markov chain (DTMC) model and we determine the suitable operating point for the sensing mechanism under different traffic load conditions such that some quality of service is attained by both primary and secondary users. Performance results reveal that by effectively choosing the operation point bearing in mind the traffic load levels will lead to enhanced perceived quality of service of both primary and secondary users.

This paper details the current work status of the ETSI Reconfigurable Radio Systems (RRS) Technical Committee (TC) and gives an outlook on the future evolution. In particular, Software Defined Radio (SDR) related study results are presented with a focus on SDR architectures for Mobile Devices (MD), such as mobile phones, etc., as well as for Reconfigurable Base Stations (RBS). For MDs, a novel architecture is presented enabling the usage of SDR principles in a mass market context. Cognitive Radio (CR) principles within ETSI RRS are concentrated on two topics, a Cognitive Pilot Channel (CPC) proposal and a Functional Architecture (FA) for Management and Control of Reconfigurable Radio Systems, including Dynamic Self-Organising Planning and Management, Dynamic Spectrum Management, Joint Radio Resource Management, etc. Finally, study results are indicated which are targeting a SDR/CR security framework

Constantly increasing demand for throughput and quality in wireless communication systems leads to continuous research of wise radio resource management, because of the scarce availability of frequency bands and the consequent capacity limitations. In addition, technology evolution is addressed towards spectral efficient techniques that can offer higher data rates. This is the case of OFDMA (Orthogonal Frequency-Division Multiple Access), introduced by 3GPP as the technology for future Long Term Evolution (LTE). However, given the current penetration of legacy technologies such as UMTS (Universal Mobile Telecommunications System), operators will have to deal with the coexistence of multiple Radio Access Technologies (RATs), so that the exploitation of the complementarities between technologies through Joint Radio Resource Management (JRRM) mechanisms will be needed. In this paper we propose a novel dynamic JRRM algorithm for LTE-UMTS coexistence scenarios. The proposed mechanism is based on Reinforcement Learning (RL) which is considered to be a good candidate to achieve cognition in future reconfigurable networks. The proposed solution implements autonomous RL agents in each base station which decide on the allocation of the most suitable RAT to each user. We give a detailed description of the solution and analyze the behavior under various load conditions. We also demonstrate the capability of the algorithm to adjust in dynamic scenarios.

This paper proposes a dynamic spectrum assignment strategy in the context of next generation multicell orthogonal frequency division multiple access networks. The proposed strategy is able to dynamically find spectrum assignments per cell depending on the spatial and temporal distribution of the users over the scenario. Reinforcement learning methodology has been employed to implement the strategy, which compared with other fixed and dynamic spectrum assignment strategies shows the best tradeoff between spectral efficiency and quality-of-service.

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.

This paper presents a novel framework based on image processing techniques for the radio environment characterisation. Specifically, after digitalising a given scenario in accordance with the frequencies that are detected in each point, an image can be built whose pixel intensities capture the radio-electrical conditions. Through non-linear filtering and object detection operations the proposed framework allows identifying the homogeneous regions where certain frequencies can be detected. This methodology can be used in different contexts, such as the construction of databases to provide the terminals with spectrum awareness in flexible spectrum scenarios to facilitate the switch-on procedure or the secondary usage of spectrum, the support to devise proper strategies for cell and RAT selection, etc.

Cognitive networks are envisaged to provide optimized resource usage in future. While heterogeneity and resource scarcity draw research attention to the wireless part, the rest of the network (mobile backhaul) is rarely considered for these improvements. The future of next generation wireless networks is probable to be all-IP, where a common flexible infrastructure is looking for dynamic autonomous solutions that cognition may provide. This work proposes a novel solution, where the introduction of reinforcement learning over multiprotocol label switching (MPLS) in a differentiated services (DiffServ) mobile backhaul should provide autonomous network adaptation aiming at enhanced QoS capabilities. The proposed solution enables intelligent traffic routing by means of distributed reinforcement learning agents that base decisions on edge-gained experience.

In this work the feasibility of Reinforcement Learning (RL) for Dynamic Spectrum Management (DSM) in the context of next generation multicell Orthogonal Frequency Division Multiple Access (OFDMA) networks is studied. An RL-based algorithm is proposed and it is shown that the proposed scheme is able to dynamically find spectrum assignments per cell depending on the spatial distribution of the users over the scenario. In addition the proposed scheme is compared with other fixed and dynamic spectrum strategies showing the best tradeoff between spectral efficiency and Quality-of-Service (QoS).

Joint radio resource management (JRRM) is the envisaged process aimed at optimizing the radio resource usage of wireless systems to satisfy the requirements of both the network operators and the users in the context of future generation wireless networks. In particular, this paper proposes a two-layered JRRM framework to improve the efficiency of multiradio and multioperator cellular scenarios. On the one hand, the intraoperator JRRM relies on fuzzy neural mechanisms with economic-driven reinforcement learning techniques to exploit radio resources within a single-operator domain. Microeconomic concepts are included in the proposed approach so that user profile differentiation can be considered when making a JRRM decision. On the other hand, interoperator JRRM enables subscribers to obtain service through other operators, if the home operator network is blocked. Simulation results in a number of different scenarios show that interoperator agreements established in a cooperative scenario benefit both the operators and users, which enables efficient load management and increased operator revenue.

Abstract The aim of this paper is to propose a set of novel Dynamic Spectrum Assignment (DSA) algorithms for multicell Orthogonal Frequency Division Multiple Access (OFDMA) scenarios. Thanks to the proposed algorithms, licensed spectrum holders can release spectrum bands in large geographical areas to be leased to other secondary markets in a cognitive radio environment, while preserving the quality of service (QoS) of the licensed users in the system. Results are obtained comparing the proposed schemes against other conventional frequency reuse strategies, revealing significant improvements in terms of both spectral efficiency and opportunities for secondary usage. Copyright © 2009 John Wiley & Sons, Ltd.

This work takes as a reference the scenario of heterogeneous radio access networks where several radio access technologies (RATs) coexist and cooperate providing a range of services over a specific coverage area. In this context, an appropriate RAT must be selected upon each user request in order to provide the specified quality of service (QoS) level for the requested service. Despite of having an efficient RAT selection mechanism, the intrinsic network dynamics may pose some risk on the QoS guarantees in each RAT which may not be met thus leading to a degraded network performance. In such situations, the network undergoes so-called radio access congestion situations. In this work, a statistical characterization of radio access congestion is provided for TDMA and WCDMA based systems. Furthermore, the impact of several representative RAT selection policies is evaluated in terms of their obtained congestion probability. Numerical results conclude that a proper knowledge of congestion information can be used as a relevant input for RAT selection specification.

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 optimization of third-generation mobile communication systems has become an important aspect for network operators. This paper proposes a new methodology for RF failure detection in UMTS networks based on Key Performance Indicators (KPI) which are obtained by measuring certain parameters at different network entities. With the proposed methodology possible uplink problems are identified. The proposed method has been validated with data extracted from a real UMTS network.

This paper concentrates on the dynamic tuning of radio resource management parameters in the context of HSDPA systems coexisting with UMTS Rel’99. In particular, an automatic tuning system is proposed taking into account the close interdependence between the cell throughput and the users channel quality indicators. From these, appropriate performance indicators are derived and a mid-term allocation mechanism is designed to maximize cell throughput while guaranteeing blocking and dropping criteria.

An efficient and utmost utilization of radio spectrum resources has stimulated the introduction of what has been termed dynamic spectrum access methodologies and implementations. While the traditional approach has been based on licensed (or primary) spectrum access, this new communication paradigm enables an opportunistic secondary access to shared spectrum resources provided mutual interference is kept below predefined margins. In this paper we propose a novel and flexible framework so as to account for primary-secondary spectrum sharing scenarios. In this sense, the use of a discrete time Markov chain (DTMC) model is suggested and further justified. Some illustrative results are provided and validated against a system-level simulator thus confirming the suitability of the proposed approach.

In this work, an approach to an advanced spectrum management (ASM) framework in a multicell OFDMA network is given. The aim of this paper is to illustrate that an ASM operation over a multicell OFDMA scenario enables the efficient use of the available spectrum. Then, some spectrum bands could be released by primary or licensed spectrum holders in a geographical area to be shared with or rented to other secondary markets in a cognitive radio environment.

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.

This paper addresses the problem of Radio Access Technology (RAT) selection in heterogeneous multi-access/multi-service scenarios. For such purpose, a Markov model is proposed to compare the performance of various RAT selection policies within these scenarios. The novelty of the approach resides in the embedded definition of the aforementioned RAT selection policies within the Markov chain. In addition, the model also considers the constraints imposed by those users with terminals that only support a subset of all the available RATs (i.e. multi-mode terminal capabilities). Furthermore, several performance metrics may be measured to evaluate the behaviour of the proposed RAT selection policies under varying offered traffic conditions. In order to illustrate the validation and suitability of the proposed model, some examples of operative radio access networks are provided, including the GSM/EDGE Radio Access Network (GERAN) and the UMTS Radio Access Network (UTRAN), as well as several service-based, load-balancing and terminal-driven RAT selection strategies. The flexibility exhibited by the presented model enables to extend these RAT selection policies to others responding to diverse criteria. The model is successfully validated by means of comparing the Markov model results with those of system-level simulations.

This paper proposes a two-layer joint radio resource management (JRRM) framework to improve the efficiency in multi-radio and multi-operator cellular scenarios. On the one hand, the intra-operator JRRM relies on fuzzy-neural mechanisms with economic-driven reinforcement learning techniques to exploit radio resources within a single operator domain. On the other hand, inter-operator JRRM allows subscribers to get service through other operators in case the home operator network is blocked. Simulation results in a number of different scenarios show that inter-operator agreements established in a cooperative scenario bring benefits to both operators and users, enabling an efficient load management and increasing the operators' revenue.

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.

This work presents a novel metric, named fittingness factor, for developing common radio resource management (CRRM) algorithms in heterogeneous wireless scenarios. It reflects the suitability of allocating a given radio access technology (RAT) and cell to a user depending on the specific context in terms of requested service and on the terminal and network capabilities at each instant. Thus, the fittingness factor allows synthesizing a number of considerations supporting CRRM decisions into a single and generalized parameter. Simulation results reveal that the proposed approach is able to capture the different terminal and service heterogeneities and obtains better performance than other approaches existing in the literature.

In future all-IP based wireless networks, like the envisaged in the Long Term Evolution (LTE) architectures for future systems, network providers will have to deal with large traffic volumes with different QoS requirements. In order to increase exploitation of network resources wisely, intelligent adaptive solutions for class based traffic regulation are needed. In particular, Active Queue Management (AQM) is regarded as one of these solutions to provide low queuing delay and high throughput to flows by smart packet discarding. In this paper, we propose a novel AQM solution for future all-IP networks based on a reinforcement learning scheme that allows controlling both the queuing delay and the packet loss of the different service classes. The proposed approach is evaluated through simulations and compared against other algorithms used in the literature, like the Random Early Detection (RED) and the Drop From Tail (DFT), confirming the benefits of the proposed algorithm.

Dynamic optimization of UMTS systems has been gaining a growing interest by the research community. In this context, the current paper concentrates on dynamic automated tuning of RRM parameters to detect whether the uplink or the downlink is the limiting link and force a reconfiguration to balance both and maximize capacity. Modifications of the soft handover algorithm are revealed as a feasible solution to achieve this objective. A three blocks based functional architecture is described to adapt parameters to service mix dynamics and overcome capacity problems. Conducted tests show the feasibility of the approach. Effective adaptation to changes in traffic patterns and significant capacity gains are obtained.

Among the common radio resource management (CRRM) functions that are responsible for the proper allocation of resources in a multi-access network, congestion control is the one devoted to overcome potential QoS failures due to the inherent dynamics of the network. In this paper we address the problem of congestion control in a scenario considering the GSM/EDGE radio access network (GERAN) and the UMTS terrestrial radio access network (UTRAN). In particular, we face the problem where the two available radio access technologies (RATs) undergo simultaneous congestion situations. For this case, a congestion resolution scheme based on vertical (inter-system) handover (VHO) jointly with a bit-rate reduction (BRR) scheme is proposed and evaluated for a mixed services scenario considering voice and data users.

This paper proposes a novel approach for developing common radio resource management (CRRM) algorithms in heterogeneous wireless scenarios. It is based on a new indicator, denoted as fittingness factor, which reflects the suitability of allocating a given RAT and cell to a user depending on the service and on the terminal and network capabilities. Simulation results reveal that the proposed approach is able to capture the different terminal and service heterogeneities and obtains better performance than other approaches existing in the literature.

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.

This paper presents a novel end-to-end quality of service (QoS) architecture for wireless networks. The focus is placed in the coordination of QoS management between common radio resource management (CRRM), for heterogeneous radio access technologies, and the QoS enabled IP-based network segments. A functional reference QoS framework is introduced and the envisaged QoS management functions are described. The innovations proposed in the presented QoS framework arise mainly from the study of cross effects between the radio and fixed transport resources management. The proposed QoS framework has been designed considering the current 3GPP R6 networks as well as their long-term evolution (LTE) scenario. Some illustrative results for a coordinated admission control algorithm are given as a proof of concept, to show the advantages of CRRM and IP QoS management coordination.

This paper develops common radio resource management (CRRM) concepts, which allow exploiting trunking gains of heterogeneous cellular 3GPP-based networks through smart radio access technology selection mechanisms. The presented framework covers the complete development cycle: firstly the theoretical conception of the algorithmic solution also supported by means of an analytical approach, secondly the performance assessment and finally, the algorithm implementation definition on real systems.

This paper presents the optimum user allocation in heterogeneous scenarios with CDMA and TDMA technologies in order to minimize the total outage probability in the uplink. An analytical model reflecting the different nature of the two access technologies is presented in order to formulate the optimization procedure. It is shown how the optimum allocation depends on the specific parameters of the two technologies, as illustrated with some representative results. The proposed optimization methodology is claimed to have applicability in the field of common radio resource management strategies for beyond 3G networks.

In this paper it will be shown how the fuzzy neural methodology can be used to develop an innovative mechanism to perform joint radio resource management (JRRM) in the context of heterogeneous radio access networks (RANs). In particular, an algorithm able to ensure certain quality of service (QoS) constraints in a multi-cell scenario deployment with three different radio access technologies (RATs), namely WLAN (wireless local area network), UMTS (universal mobile telecommunications system) and GERAN (GSM EDGE radio access network), is discussed. In addition, particular attention is paid to the design of an approach capable of managing handoff calls in a heterogeneous network. It is based on predicting future JRRM decisions. A RLS (recursive least square) predictor has been selected to provide reliable and accurate estimations of handoff calls and handoff call droppings. Performance improvements in terms of new connection blocking and handoff call dropping probabilities are presented.

This paper proposes a new joint radio resource management (JRRM) strategy which, as a difference from previous works in the literature, introduces economic and user differentiation concepts. This economic-driven algorithm is based on guaranteeing a certain user acceptance of a given service, combining both the economic and technical dimensions, while at the same time increasing the operator revenue. Two user profiles with different needs are considered, namely consumer and business users, and it is shown that the needs of both profiles can be met by means of the proposed framework. Three implementations of the proposed JRRM are compared. Two of them are based on a user-centric approach, whereas the third implementation falls into the joint user-centric and network-centric category aiming at maximizing the operator revenue while maintaining the user satisfaction at a target contracted value. In this case, particular attention is paid to business users and to strategies based on resource reservation to improve their utility as well as the operator revenue.

This paper describes and models a multi-cellular multiservice CDMA system with repeaters in an environment with traffic and spatial non-uniformities. An analytical model is presented and expressions for the required transmitted power and outage probability are derived for the reverse link. Results are focused on determining the maximum load factor that could be acceptable in order to limit the outage probability

This paper proposes the enhancement of different Radio Access Technology (RAT) allocation strategies in a heterogeneous scenario with CDMA and TDMA access technologies by including path loss information. Results obtained from an analytical modelling reveal that, by making use of path loss information, either by allocating low path loss users in CDMA or in TDMA, depending on the specific characteristics of each technology, significant outage reductions can be achieved.

This paper addresses the inclusion of dynamic pricing concepts to provide CRRM solutions in heterogeneous scenarios. A RAT selection algorithm based on a dynamic pricing strategy aimed at controlling the load in the considered RATs in a decentralised way is proposed and evaluated through system level simulations. Results reveal that, by a proper variation of the RAT price, which at the end becomes transparent to the actual price paid by the users, the algorithm is able to provide better performance than when the pricing strategy is not considered.

This paper focuses on the modelling of the reverse link of a WCDMA system in a non-homogeneous environment. Multiple traffic spatial and service non-uniformities are considered in the analytical model, then deriving expressions for required transmitted power and the associated outage probability. Special attention is also paid to the effect caused by different transmission bit rates and the spatial location of the traffic non-uniformities. The performance of the system is determined by QoS parameters, so that QoS may be controlled by setting an appropriate threshold. Such a threshold may be obtained by means of expressions found out throughout the paper

In order to support the conceptual development of common radio resource management (CRRM) algorithms, this paper provides an analytical approach to the performance evaluation of Radio Access Technology (RAT) selection procedures in a multi-RAT/multiservice environment. In particular, a 4-Dimensional (4D) Markovian model is devised so as to consider the allocation of voice and data services in a GERAN/UTRAN system. Through the analytical definition of well-established Key Performance Indicators (KPIs) we provide numerical results on the evaluation of a load balancing RAT allocation policy.

Adaptive networks are envisaged to play a significant part in the future, where the time and space variations in the traffic pattern will necessitate the ability to continuously amend the Radio Access Technologies' (RATs') operating parameters. Reconfiguration of communications systems is a facilitator towards this convergence and enables the dynamic adaptation and optimization of the access characteristics. However, such far ranging optimization concept involves many different mechanisms and work areas. Each of these areas provides an answer to a different optimization problem; Dynamic Network Planning and Management (DNPM) provides a load and demand driven optimization of the radio planning of multiple different networks within a given area. Advanced Spectrum Management (ASM) enables short term use of spectrum for services with higher demand. Finally Joint Radio Resource Management (JRRM) coordinates different access schemes and facilitates a more centralized approach to allocation of radio resource. Each of the schemes optimizes spectrum and radio resource usage on a different time scale. ARRM deals with the rather short term allocation, ASM with more medium term spectrum assignments while DNPM assumes time scales up to the range of weeks or months. Consequently, there is need of combining all working areas in the form of a Functional Architecture (FA), where each module represents a concept, aiming at forming part of the global end-to-end reconfigurability architecture. This paper includes a detailed analysis of the Reconfigurability FA, along with a description of the functionality of each of the modules included therein.

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.

This paper proposes a new radio access technology (RAT) selection algorithm for multi-service scenarios which allows increasing the capacity of heterogeneous CDMA/TDMA systems by controlling the effective cell radius of the CDMA- based system (i.e. a network-controlled cell-breathing), so that the interference level is reduced. The strategy is evaluated in a scenario including voice and interactive traffic and combined with service-based RAT selection strategies.

This paper proposes a novel framework for developing advanced frequency planning algorithms in WCDMA scenarios under changing traffic conditions. It is based on an analytical model that captures the influence among the different cells by means of the derivatives of the corresponding cell load factors. Then, the cells having the largest influence can be considered as candidates to operate with a different frequency. The proposed algorithm is compared by means of simulations with an algorithm based on load measurements, revealing that the proposed approach is able to capture the relevant cells more accurately

This paper proposes a new Radio Access Technology (RAT) selection algorithm which allows increasing the capacity of heterogeneous CDMA/ TDMA systems. This is achieved by controlling the effective cell radius of the CDMA-based system (i.e. a network-controlled cell-breathing), so that the interference level in the CDMA-based RAT is reduced while at the same time the target coverage area is assured by means of the cooperation of the FDMA/TDMA based RATs.

The vision of the wireless communications beyond 3G is characterized by flexibility. Future communication networks should be able to flexibly allocate resources and radio access technologies (RATs) to maintain high quality of communication and efficient use of radio resources. This should be the task of the joint radio resource management (JRRM) algorithms. Furthermore, this flexibility should also cover the pricing mechanism which should be able to react to the instantaneous users' needs and resource availability. This will be tackled by a real time spectrum auction system. This paper draws an architecture which embodies the two mechanisms: JRRM and spectrum auction, in order to create highly efficient wireless systems.

This paper proposes and evaluates a radio resource reservation algorithm that takes advantage of location-aware techniques in order to facilitate handovers in a WCDMA system, specially for users following straight trajectories. The mobility characteristics of these users may provide certain information about the instant of time when they require a handover process. The proposed resource reservation algorithm takes into account the user location information in order to manage the available radio resources in a more efficient way. The proposed algorithm optimizes the existing trade-off between the blocking probability of new connection requests and the dropping probability of users in handover. This optimization is obtained by minimizing the system grade of service (GoS). Moreover, the algorithm includes a prioritization of business users over consumer users. The obtained results are compared to a reference algorithm and the impact on the most important performance indicators has been analyzed, including the influence of call duration and service bit rate

This paper proposes a new common radio resource management (CRRM) methodology which allows increasing the capacity of heterogeneous CDMA/TDMA. This is achieved by controlling the effective cell radius of the CDMA-based system (i.e. a network-controlled cell-breathing) through appropriate initial RAT selection and vertical handover strategies, so that the interference level in the CDMA-based RAT is reduced while at the same time the target coverage area is assured by means of the cooperation of the FDMA/TDMA based RATs. The proposed approach is evaluated by means of system level simulations in a detailed scenario including UTRAN and GERAN as examples of CDMA and FDMA/TDMA-based access technologies. Results reveal that throughput improvements up to 24% can be achieved with respect to other CRRM strategies based on load balancing principles

This paper presents a joint radio resource management algorithm to operate in a heterogeneous network scenario including cellular and wireless local area network radio access technologies. It makes use of a methodology based on fuzzy-neural systems in order to carry out a coordinated management of the radio resources among the different access networks. In order to reveal the potentials of the proposed algorithm, it is compared with other strategies in a multicellular and multi-RAT scenario.

This paper develops a feasible architecture for end-to-end QoS management over B3G networks where multiple heterogeneous radio access networks (RAN) can be coordinated at radio level by means of supporting common radio resource management (CRRM) functionalities. The presented approach is claimed to be innovative as long as it is inspired in the most relevant trends being developed in different fields and forums and come up with a global solution capturing such partial concepts. A key point of the architecture is the introduction of a wireless QoS broker entity that becomes the link between CRRM and E2E QoS management. In the paper, after introducing the proposed QoS framework, the signaling and procedures to support such architecture in the B3G network are addressed. Over such a basis, a connection establishment procedure is discussed to show how the problem of initial RAN selection can be managed by the proposed architecture.

This paper presents a novel JRRM strategy based on reinforcement learning mechanisms that control a fuzzy-neural algorithm to ensure certain QoS constraints. Three RATs (radio access technologies), namely UMTS, GERAN and WLAN are considered as common available technologies to select. The fuzzy logic allows for a very simple handling of the joint radio resource manager simply by activating a set of rules. The membership functions considered by these rules are adaptive so that a desired performance in terms of the probability of user satisfaction can be guaranteed by means of the reinforcement learning algorithm. Some illustrative simulation results to evaluate the behaviour of the proposed JRRM technique are presented.

In the B3G system considered, which is based on the 3GPP UMTS architecture, the initial hypothesis made in this paper, is that the QoS management of the IP access network is taken in charge by a bandwidth broker. A pre-handover signalling for QaS-aware path selection is investigated with the goal of achieving the "always best connected" paradigm. Furthermore, the scalability of the proposed scheme is analysed taking into account the overhead due to IP micromobility, QoS routing, CRRM and bandwidth broker

Common radio resource management strategies are devoted to achieve an efficient usage of the pool of radio resources available in a heterogeneous radio access network context. This paper describes the functionalities associated with the common vision of radio access technologies, the different possibilities in the functional model split and the corresponding implementation considerations.

This paper evaluates the impact of static and dynamic hotspots on the performance of a WCDMA network and provides certain guidelines and mechanisms in order to manage in an appropriate way the use of radio resources under non-homogeneous traffic spatial distributions. First of all, the impact of the geographical location of a static hotspot has been analysed for the forward link. Then, dynamic hotspots (i.e. hotspots that move following a certain mobility pattern) have been studied, indicating how the hotspot movement affects on the network performance. Moreover, a pilot power adjustment algorithm that equalises the base station transmission power of the hotspot cell and its adjacent cells has been proposed and analysed. The improvement provided by this pilot power adjustment algorithm has been evaluated in terms of base station transmission power, transmission power of pilot channel, dropping probability and rate of handovers per user.

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DSCH transport channel through suitable packet scheduling is the envisaged mechanism to deal with delay tolerant services in the downlink of UTRA-FDD. When operating in a mixed service scenario, where both real time and non real time traffic are present, different issues should be considered in order to guarantee some performance figures for both kinds of users. In particular, this paper shows the impact of the OVSF code tree size devoted to DSCH in terms of non real time services average packet delay. Besides, the consequences for real time traffic in terms of admission and dropping probabilities are also dealt. The completion of this research is the definition of a traffic mix region where some QoS figures are guaranteed and the corresponding DSCH dimensioning is devised.

The role of indoor traffic in 3G mobile communication networks will be even more important than in 2G since many of the expected multimedia high bit rate services will be originated inside buildings. In case of W-CDMA based 3G networks, the importance will be even higher because of the power based management in the radio interface. In this context, This work presents a model to assess the impact of indoor traffic over uplink cell capacity in different scenarios. As a result, it provides enough information to the network operator to decide how important is in every scenario to make the transition from outdoor macrocells to micro and picocells.

This paper presents a comprehensive framework to develop joint RRM (radio resource management) strategies taking full advantage of the reconfigurable equipment capabilities and the diversity offered by available RATs (radio access technologies) in a multi-radio environment. The envisaged JRRM treatment calls for establishing links with all the entities involved, first at functional level, identifying realistic scenarios in terms of deployment, technologies and services, and managing the emerging complexity with proper algorithms. Then, a fuzzy-neural methodology framework able to cope with the complexities and uncertainties these new scenarios rise is presented. In particular both technical and economical aspects are considered when selecting a particular RAT. Finally some significant examples of the algorithm behaviour are shown.

Assuming the use of GSM/UMTS co-sitting for initial 3G network deployment, real data extracted from the GSM network can be used to optimise 3G planning and RRM issues. It has been developed a 3G planning simulation tool which instead of using theoretical models, benefits from the availability of realistic propagation, mobility, as well as spatial and temporal traffic distributions. The tool is used in this paper to study the effects of pilot power and active set parameters in both, uplink and downlink capacity, in a real urban scenario, thus anticipating the expected behaviour of UTRA-FDD in such conditions.

This paper focuses on the interactions between uplink admission and congestion control strategies in UTRA-W-CDMA. The proposed strategies are analysed in the framework of a representative multiservice scenario where conversational and interactive traffic are present and the presented solutions are compliant with 3GPP UTRA FDD specifications. Furthermore, the results consider a complete approach where all the radio resource management strategies are taken into account, including admission, congestion, short term RRM, handover and power control feasible solutions. Therefore, obtained results allow to devise a range of guidelines for the joint design of RRM algorithms.

This paper provides the framework for the design of downlink admission control algorithms in the FDD mode of UMTS. Such algorithms are responsible of accepting or refusing requests of new connections depending on the resource availability in the downlink direction, which is measured in terms of the power and the amount of OVSF codes required In all the existing connections. So a specific algorithm that account for both power and code availability is presented and it is analyzed for different situations, revealing the crucial parameters that should be appropriately set in order to ensure the QoS expected by all the admitted connections.

This paper describes a simulation tool developed for 3G planning suitable also for some RRM strategies evaluation. The innovative issue is that the simulator is based on data extracted from real GSM/UMTS networks. Considering the extensive use of GSM/UMTS co-siting for 3G network deployment, an accurate representation of propagation, mobility as well as traffic distribution is achieved compared to classical statistical and modeling approaches in simulators. Results presented reveal that the availability of real data is highly valuable since it provides a more detailed view of the network behavior and performance.

A type II hybrid automatic repeat request scheme is considered as a retransmission strategy in a direct-sequence code-division multiple-access packet mobile radio network. An analysis based on equilibrium point analysis is presented to model the behavior of the system in a message-based traffic generation model. A simulation approach is introduced to validate the proposed analytical model, obtaining results that closely match those derived theoretically.

This paper presents some packet transmission mechanisms dealing with the provision of quality of service (QoS) over the radio interface in a TDD-TD/CDMA scenario such as the one considered for the UTRA TDD proposal in the UMTS system. To this extent, two different multiple access protocols are proposed for such an environment, based on the ISMA and DSA++ approaches, and they are complemented by a polling mechanism that allows a reduction in the access time. A scheduling algorithm is also considered in order to prioritise transmissions to guarantee the specific QoS requirements for the different services.

The definition and assessment of suitable radio resource management (RRM) strategies able to provide a required QoS in the framework of the UTRA segment of UMTS is a key issue for achieving the expectations created on 3G technology. This paper proposes and evaluates specific algorithms for the different RRM functions involved in the uplink direction. In particular, the admission control of a new connection, the congestion control mechanisms to deal with peak interference situations and the dynamic management of the transmission parameters are studied by considering interactive-like services as a reference.

In the context of third-generation (3G) systems a mix of services with different requirements are expected. Consequently, packet scheduling mechanisms for quality of service (QoS) guarantees will play a key role. This paper proposes a new scheduling strategy that makes consistent the target quality in the radio link with the priority level assigned to each user. The performance of such a strategy is assessed by system level simulations and, in order to gain more insight into the difficulties of this optimization problem, it is compared to other alternatives. This work is part of the Wineglass project, within the Fifth Framework Program of the European Commission (IST), where a real time demonstrator including the radio resource management tasks is being developed. Thus, an implementation approach of the proposed scheduling is also described. The implementation is based on lookup tables and this approach is validated by simulation.

This paper presents a new mechanism that combines the flexibility of an access protocol such as ISMA-DS/CDMA with the ability of a polling mechanism to provide a specific bound for the access delay. This protocol is proposed for a packet transmission mobile communication system together with a scheduling algorithm that arranges the different transmissions depending on the quality of service required by the set of considered services.

This paper presents and analyzes a new near-optimum medium access control (MAC) protocol. The proposed access scheme is suitable for a CDMA mobile communication environment, and keeps under control and upper bounded the number of simultaneous transmissions. It has a delay performance approaching that of an ideal optimum M/M/K system, where K is the number of spreading codes being used (maximum number of simultaneous transmissions). The protocol is a free random access protocol when the traffic load is light, and switches smoothly and automatically to a reservation protocol when traffic load becomes heavier. It is based on distributed queues and a collision resolution algorithm. Moreover, a physical receiver structure is proposed and analyzed in order to preserve the robustness of the protocol in a wireless link. The results obtained show that the protocol outperforms other well known medium access protocols in terms of stability and delay, even when taking into account the loss caused by channel propagation conditions.

This paper presents a novel adaptive DS-CDMA slotted-ALOHA packet random access scheme with transmitter-based spreading codes for mobiles. It is aimed at improving the throughput and message delay delivery when traffic load values below the saturation point of the conventional DS-CDMA slotted-ALOHA system are sensed in the channel. For this purpose a mobile assisted algorithm is envisaged to control the change of the transmission rate according to the traffic load. This algorithm revealed that the optimum behavior, obtained using a Markov chain model, may be almost reached at a low complexity cost. Moreover, priorities between mobiles could be easily established. Finally, a traffic model based on a realistic statistical length distribution of the messages illustrates how the delay delivery can be greatly reduced.