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Network slicing is a central feature in 5G and beyond systems to allow operators to customize their networks for different applications and customers. With network slicing, different logical networks, i.e. network slices, with specific functional and performance requirements can be created over the same physical network. A key challenge associated with the exploitation of the network slicing feature is how to efficiently allocate underlying network resources, especially radio resources, to cope with the spatio-temporal traffic variability while ensuring that network slices can be provisioned and assured within the boundaries of Service Level Agreements / Service Level Specifications (SLAs/SLSs) with customers. In this field, the use of artificial intelligence, and, specifically, Machine Learning (ML) techniques, has arisen as a promising approach to cater for the complexity of resource allocation optimization among network slices. This paper tackles the description of a feasible implementation framework for deploying ML-assisted solutions for cross-slice radio resource optimization that builds upon the work conducted by 3GPP and O-RAN Alliance. On this basis, the paper also describes and evaluates an ML-assisted solution that uses a Multi-Agent Reinforcement Learning (MARL) approach based on the Deep Q-Network (DQN) technique and fits within the presented implementation framework.

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Network slicing is a fundamental capability for future Fifth Generation (5G) networks to facilitate the cost-effective deployment and operation of multiple logical networks over a common physical network infrastructure in a way that each logical network (i.e. network slice) can be customized and dimensioned to best serve the needs of specific applications (e.g. mobile broadband, smart city, connected car, public safety, fixed wireless access) and users (e.g. general public, enterprise customers, virtual operators, content providers). The practical realization of such capability still raises numerous technical challenges, both in the Core and RAN parts of the 5G system. Through a comprehensive analysis of the impact that the realization of RAN slicing has on the different layers of the radio interface protocol architecture, this article proposes a framework for the support and specification of RAN slices based on the definition of a set of configuration descriptors that characterize the features, policies and resources to be put in place across the radio protocol layers of a next-generation RAN node.

The Radio Access Network (RAN) is envisaged to undergo a significant transformation in the context of 5G and beyond mobile communications systems. One of the driving forces behind this transformation is the applicability of Machine Learning (ML) techniques. Taking as a reference the high-level architecture for a next generation RAN proposed by the Open RAN Alliance, this paper identifies the applicability domains where ML techniques can play a relevant role. For each domain, namely radio physical layer processing, Medium Access Control (MAC) scheduling, near-real time Radio Resource Management (RRM), RAN data analytics and RAN operational automation, the paper discusses the specific functionalities that can benefit from the application of ML and analyses the key considerations and challenges that need to be addressed when developing ML-based solutions, given the particular characteristics of the mobile radio environment.

Network slicing is a fundamental feature of 5G systems that allows the partitioning of a single network into a number of segregated logical networks, each optimized for a particular type of service, or dedicated to a particular customer or application. While support for network slicing (e.g. identifiers, functions, signalling) is already defined in the latest 3GPP Release 15 specifications, solutions for efficient automated management of network slicing (e.g. automatic provisioning of slices) are still at a much more incipient stage, especially for what concerns the next-generation Radio Access Network (NG-RAN). In this context, and consistently with the new service-based management architecture defined by 3GPP for 5G systems, this paper presents a functional framework for the management of network slicing in a NG-RAN infrastructure, delineating the interfaces and information models necessary to support the dynamic and automatic deployment of RAN slices. A discussion on the complexity of such automation follows together with an illustrative description of the applicability of the overall framework and information models in the context of a neutral host provider scenario that offers RAN slices to third party service providers.

Network slicing is a key capability of 5G networks that facilitates the provision of multi-tenancy by allocating different slices to the tenants that share a certain infrastructure according to specific Service Level Agreements (SLAs). In this context, this paper focuses on radio admission control as the function that controls the amount of radio resources assigned to the different tenants in a 5G radio access network. Specifically, a novel approach is proposed that includes profit-related metrics in the decision-making process, accounting for the additional extra incomes that can be obtained by sporadically granting additional capacity beyond the SLA level and for the penalties incurred due to potential SLA breaches. The proposed approach is evaluated by means of simulations to assess its benefits in terms of achieved profit and throughput.

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.

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 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. While significant progress has already been achieved at specification level by 3GPP with regard to the functional support of network slicing, management solutions for the exploitation of these capabilities in the NG-RAN are still at a very incipient stage. In this context, this paper firstly presents a functional framework for the management of network slicing for a NG-RAN infrastructure, identifying the necessary information models and interfaces to support the dynamic provisioning of RAN slices. On this basis, the feasibility to automate the provisioning of RAN slices is discussed. Furthermore, a self-planning solution is presented to illustrate how a traditional network management process such as planning is expected to evolve to cope with the new challenges associated with RAN slicing management.

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 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.

This paper addresses the implementation of Radio Resource Management (RRM) and Self-Organizing Networks (SON) functions in virtualized multi-tenant small cell networks. In this context, the deployment and operation of RRM/SON functions as virtualized services facilitates the customization of the Radio Access Network (RAN) on a per-tenant basis. Specifically, this paper develops the architectural and functional framework for virtualizing RRM/SON functions, together with the illustration of some examples assessing the orders of magnitude of the requirements of the involved Virtual Network Functions (VNF)

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 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.

The sharing of Radio Access Networks is gaining momentum in small cell scenarios, due to the associated reduction in capital and operational costs. In this scenario, the split of radio resources among tenants sharing the network becomes a fundamental problem to provide each one with its required capacity. In this respect, this paper proposes a multi-tenant admission control scheme for performing this split with the target of ensuring an efficient use of the radio resources by exploiting traffic multiplexing principles at both intra-cell and multi-cell level in order to cope with heterogeneities in the spatial traffic distribution of the different tenants. A simulation-based analysis is presented to assess the flexibility of the proposed approach under different traffic mixes of the different tenants in each cell. Substantial bit rate improvements (e.g. up to 106%) and blocking probability reductions are obtained with respect to a baseline scheme.

Network slicing is a fundamental capability for future 5G networks to properly support current and envisioned future application scenarios. Network slicing facilitates a cost-effective deployment and operation of multiple logical networks over a common physical network infrastructure such that each network is customized to best serve the needs of specific applications (e.g., mobile broadband, Internet of Things applications) and/or communications service providers (e.g., special purpose service providers for different sectors such as public safety, utilities, smart city, and automobiles). Slicing a RAN becomes particularly challenging due to the inherently shared nature of the radio channel and the potential influence that any transmitter may have on any receiver. In this respect, this article analyzes the RAN slicing problem in a multi-cell network in relation to the RRM functionalities that can be used as a support for splitting the radio resources among the RAN slices. Four different RAN slicing approaches are presented and compared from different perspectives, such as the granularity in the assignment of radio resources and the degrees of isolation and customization.

Self-organising networks (SON) are seen as one of the hottest topics in telecommunication network research and development, eagerly awaited by network operators to achieve a reduction in operational expenditures. However, there are still many challenges and dif?culties when moving from the SON concept to practical implementation. In this context, this paper ?rst provides a general formulation of the automated optimisation problem and a detailed description of the main challenges and dif?culties ahead. Then, a generic multi-cell multi-objective self-optimisation methodology based on genetic algorithms is proposed. The proposed framework is formulated in detail for a joint coverage and overlap optimisation problem in a multi-cell scenario. A case study using real measurements of a Universal Mobile Telecommunications System network deployed in a medium-size European city is presented to illustrate the proposed methodology. In the presented case study, the pilot power, antenna tilt and antenna azimuth of the different cells are optimised according to certain cell coverage and cell overlap targets. Results reveal that the genetic-based approach is able to provide optimised solutions that ef?ciently achieve the desired targets accounting for inter-cell coupling effects.

In order to increase cognitive radios (CRs) operation efficiency, there has been an increasing interest in strengthening awareness level about spectrum utilisation. In this respect, this paper proposes to exploit the fittingness factor concept to capture the suitability of spectral resources exhibiting time-varying characteristics to support a set of heterogeneous CR applications. First, a new knowledge management functional architecture for optimizing spectrum management has been constructed. It integrates a set of advanced statistics capturing the influence of the dynamic radio environment on the fittingness factor. Then, a knowledge manager (KM) exploiting these statistics to monitor time-varying suitability of spectrum resources has been proposed to support the spectrum selection (SS) decision-making process. In particular, a new Fittingness Factor-based strategy combining two SS and spectrum mobility (SM) functionalities has been proposed, following either a greedy or a proactive approach. Results have shown that, with a proper fittingness factor function, the greedy approach efficiently exploits the KM support at low loads and the SM functionality at high loads to introduce significant gains in terms of the user dissatisfaction probability. The proactive approach has been shown to maintain the introduced performance gain while minimizing the signalling requirements in terms of spectrum handover rate.

Self-Organizing Network (SON) functionalities in forthcoming 5G systems stand as a promising field for the fertilization of Artificial Intelligence (AI) mechanisms. In this respect, this paper analyzes how SON functionalities for radio access network planning and optimization in future 5G systems can be built upon AI concepts. A framework is presented that processes input data from multiple sources and extracts, through learning-based classification, prediction and clustering models, relevant knowledge to drive the decisions made by 5G SON functionalities. Different candidate AI-based tools for knowledge discovery are identified together with the associated knowledge models that can be extracted. On this basis, the applicability of these models to a comprehensive range of 5G SON functions across the categories of self-planning, self-optimization and self-healing is analyzed. Finally, the paper identifies the research directions deriving from the proposed framework.

Small Cell as a Service (SCaaS) is envisaged as a solution to facilitate the provisioning of shared radio access capacity to mobile operators in areas where dedicated per-operator deployments may be impractical, typically highly densified scenarios such as stadiums, malls, office buildings, etc. In this context, this paper firstly establishes a reference framework for SCaaS provisioning from the Radio Access Network (RAN) perspective, including management architecture and functionalities as well as Service Level Agreement aspects impacting on the way that the shared RAN is deployed and operated. Then, the implications of multi-tenancy on mobility control are analysed in order to delineate the impact on Radio Resource Management (RRM) and Self-Organizing Network (SON) functionalities as a result of the emergence of this novel business model.

Part 12: 5G – Putting Intelligence to the Network Edge (5G-PINE); International audience; This paper considers the use of clustering techniques to learn the mobility patterns existing in a cellular network. These patterns are materialized in a database of prototype trajectories obtained after having observed multiple trajectories of mobile users. Both K-means and Self-Organizing Maps (SOM) techniques are assessed. Different applicability areas in the context of Self-Organizing Networks (SON) for 5G are discussed and, in particular, a methodology is proposed for predicting the trajectory of a mobile user.

This paper presents a vision of how the different management procedures of future Fifth Generation (5G) wireless networks can be built upon the pillar of artificial intelligence concepts. After a general description of a cellular network and its management functionalities, highlighting the trends towards automatization, the paper focuses on the particular case of extracting knowledge about the time domain traffic pattern of the cells deployed by an operator. A general methodology for supervised classification of this traffic pattern is presented and it is particularized in two applicability use cases. The first use case addresses the reduction of energy consumption in the cellular network by automatically identifying cells that are candidates to be switched-off when they serve low traffic. The second use case focuses on the spectrum planning and identifies the cells whose capacity can be boosted through additional unlicensed spectrum. In both cases the outcomes of different classification tools are assessed. This capability to automatically classify cells according to some expert guidance is fundamental in future networks, where an operator deploys tenths of thousands of cells, so manual intervention of the expert is unfeasible.

The fundamental features of cognitive radio (CR) systems are their ability to adapt to the wireless environment where they operate and their opportunistic occupation of the licensed spectrum bands assigned to the primary network. CR users in CR systems should not cause any interference to primary users (PUs) of the primary network. For this purpose, CR users need to accurately estimate the features and activities of the primary users. In this paper, a novel characterization of heterogeneous PUs and a novel reconfigurability solution in CR networks are introduced. The characterization of PUs consists of a detector and classifier that distinguishes between heterogenous PUs. The PU characteristics stored in radio environmental maps are utilized by an interference/throughput adapter for the optimization of CR parameters. The performance of the proposed solutions is evaluated by showing false alarm and missed detection probabilities of the detector/classifier in a multipath fading channel with additive white Gaussian noise. Moreover, the impact of the PU characteristics on the CR throughput is analyzed.

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.

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.

The limited availability of frequency bands and their capacity limitations, together with the constantly increasing demand for high-bit-rate services in wireless communication systems, require the use of smart radio resource management strategies to ensure that different services are provided with the required quality of service (QoS) and that the available radio resources are used efficiently. In addition, the evolution of technology toward higher spectral efficiency has led to the introduction of Orthogonal Frequency-Division Multiple Access (OFDMA) by 3GPP for use in future long-term evolution (LTE) systems. However, given the current penetration of legacy technologies such as Universal Mobile Telecommunications System (UMTS), operators will face some periods in which both Radio Access Technologies (RATs) coexist. In this context, Joint Radio Resource Management (JRRM) mechanisms are helpful because they enable complementarities between different RATs to be exploited and thus facilitate more efficient use of available radio resources. This paper proposes a novel dynamic JRRM algorithm for LTE-UMTS coexistence scenarios based on Reinforcement Learning (RL), which is considered to be a good candidate for achieving the desired degree of flexibility and adaptability in future reconfigurable networks. The proposed algorithm is evaluated in dynamic environments under different load conditions and is compared with various baseline solutions.

This work addresses the problem of radio access congestion control and resource allocation in scenarios where multiple available radio access technologies (RATs) support a wide range of services over a given coverage area. A key issue in these networks is selecting the most appropriate RAT at a call/session establishment according to some specified user/operator criteria. In this sense, a wide range of high-level policies can be defined, providing the most favorable resource allocation. Regardless of having efficient RAT selection policies, which may ensure some initial quality of service (QoS) requirements, intrinsic network dynamics (e.g., mobility, user activity, and interference rise) can cause potential QoS failures, leading to a degraded network performance and, hence, radio access congestion. This work is devoted to the study of the impact of radio access congestion on a number of RAT-selection policies. Consequently, a congestion probability (CP) model is developed to capture the statistical behavior of radio access congestion events. In addition, a general Markovian framework is adopted to evaluate the allocation of multiple services into multiple RATs by means of high-level policy definitions. Specific RAT-selection policies are defined according to several criteria, and their performances are evaluated in a time-division multiple-access (TDMA)/wideband code-division multiple-access (WCDMA) multi-RAT scenario supporting voice and data services. Moreover, the use of CP information as a possible allocation principle for RAT selection is also evaluated, which, in the assumed scenario, results in the most favorable allocation policy.

This paper addresses the problem of opportunistic access of secondary users to licensed spectrum in cognitive radio networks. In order to avoid interference to the licensed primary users, efficient spectrum detection methods need to be developed. For this purpose, in recent years several sensing techniques have been proposed to monitor and regulate the spectrum access to the shared spectrum resources. However, spectrum sensing may be affected by errors in the form of missed-detections (i.e., an occupied spectrum is erroneously detected as free) or false-alarms (i.e., a free spectrum is erroneously detected as occupied). These two magnitudes pose a tradeoff on the design of the spectrum sensing mechanisms meaning that low missed-detection can only be achieved at the expense of high false-alarm and vice versa. Thus, the network designers should adaptively tune the sensing techniques such that the highest perceived Quality of Service (QoS) is achieved by both primary and secondary users. In this paper, a framework is introduced for determining the sensing operating points. Also the definition of Grade-of-Service (GoS) metrics is adopted to the case of primary/secondary users spectrum sharing. It is shown that the operating points of the sensing mechanisms can be easily adjusted according to the current traffic load of both primary and secondary users so that the perceived GoS is maximized. In addition, the Erlang Capacity of the spectrum sharing system for both primary and secondary users is also evaluated considering the effects of erroneous sensing.

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 multi-operator 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.

Wireless technologies are rapidly evolving to allow operators to deliver more advanced multimedia services. High-speed packet access (HSPA) for uplink and downlink is seen as an intermediate evolutionary step since the first wave of wideband code division multiple access (WCDMA)-based networks rollout, while evolved universal mobile telecommunications system (UMTS) terrestrial radio access networks (E-UTRAN) are the long term perspective for the Third Generation Partnership Project (3GPP) technology family. Similar paths are drawn from the 3GPP2 around the evolution of code division multiple access 2000 (CDMA2000). Moreover, the IEEE 802 working groups are producing an evolving family of standards, such as 802.11 local, 802.15 personal, 802.16 and 802.20 metropolitan and 802.22 regional area networks.

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.

In this paper, we introduce a framework encompassing the creation and the exploitation of secondary spectrum usage opportunities. The paper develops a complete positioning-based framework to assess the feasibility of supporting secondary communications on frequencies which are released by primary spectrum management methodologies. In particular, the paper analyzes four possible combinations, depending on known/unknown positions of primary/secondary transceivers. Afterwards, the paper focuses on a specific applicability case, where the dynamic spectrum management mechanism of a WCDMA-based network operator aims at releasing certain frequencies in a large area when possible and thus facilitating secondary exploitation of the released spectrum. Moreover, some practical examples are introduced to show the different procedures when secondary networks with infrastructure are sharing the same frequency with a mobile network. In this context, results have been obtained to assess the practical usability of the released spectrum under different conditions as well as the efficiency of different dynamic spectrum management methodologies.

In this paper, an innovative mechanism to perform joint radio resource management (JRRM) in the context of heterogeneous radio access networks is introduced. In particular, a fuzzy neural algorithm that is able to ensure certain quality-of-service (QoS) constraints in a multicell scenario deployment with three different radio access technologies (RATs), namely, the wireless local area network (WLAN), the universal mobile telecommunication system (UMTS), and the global system for mobile communications (GSM)/Enhanced Data rates for GSM Evolution (EDGE) radio access network (GERAN), is discussed. The proposed fuzzy neural JRRM algorithm is able to jointly manage the common available radio resources operating in two steps. The first step selects a suitable combination of cells built around the three available RATs, while the second step chooses the most appropriate RAT to which a user should be attached. A proper granted bit rate is also selected for each user in the second step. Different implementations are presented and compared, showing that the envisaged fuzzy neural methodology framework, which is able to cope with the complexities and uncertainties of heterogeneous scenarios, could be a promising choice. Furthermore, simulation results show that the reinforcement learning mechanisms introduced in the proposed JRRM methodology allow guaranteeing the QoS requirement in terms of the so-called user dissatisfaction probability in the presence of different traffic loads and under different dynamic situations. Also, the proposed framework is able to take into consideration different operator policies as well as different subjective criteria by means of a multiple decision-making mechanism, such as balancing the traffic among the RATs or giving more priority to the selection of one RAT in front of another one.

This paper presents a framework to achieve an efficient dynamic and decentralized spectrum and radio resource usage in heterogeneous wireless network scenarios. The envisaged technical solution follows a layered approach, where joint radio resource management (JRRM) and advanced spectrum management mechanisms are identified at both the intra- and inter-operator levels. The importance of cognitive network functionalities is highlighted. An on-demand Cognitive Pilot Channel (CPC) is proposed as radio enabler solution for decentralized operation with decision making processes executed at the mobile terminal side. The suitability of the proposed solution is shown by comparison with a broadcast CPC approach. Finally, the paper presents and evaluates decentralized JRRM algorithms both at the intra- and inter-operator levels using the proposed framework.

This paper presents a novel methodology for capturing the coupling between the different cells in both the uplink and downlink directions in a Wideband Code Division Multiple Access (WCDMA) scenario. It is based on the definition and computation of the gradient of the uplink cell load factor and the downlink transmitted power, which are the two main parameters that reflect the actual cell load in the two link directions. The paper shows that the gradient is able to capture the relevant information about the spatial distribution of traffic, which has an impact on cell performance. The proposed methodology is also used as the basis for defining and evaluating new Radio Resource Management (RRM) strategies that operate at a multi-cell level.

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.

Heterogeneous Networks (HetNets) have been a crucial point in the evolution of mobile networks that boost the network performance with the addition of small cells. The use of Enhanced ICIC (eICIC) techniques such as Almost Blank Subframes (ABS) has been introduced in HetNets to protect the small cells from high interferences. However this is done at the expense of lower macrocell user capacities. In this respect, in this work we propose a solution that starts from the principles of ABS and exploits jointly the dimensions of frequency, power and time in order to balance the trade-off between interference mitigation on the small cells and capacity degradation in the macrocells. Comparisons against the classical ABS and other existing solutions show the efficiency of the proposed scheme with improvements in the user capacity that reach up to 26% in the considered scenarios. This is achieved without significant degradation of the performance observed by small cell users.

The highly demanding requirements envisaged for future 5G networks together with the required support of new customers from vertical industries (e.g. e-health, automotive, energy) pose a big challenge for operators in 5G on how to balance investments, user experience and profitability. There will be the need to revisit the actual methodologies of network planning and operation, fully exploiting cognitive capabilities that embrace knowledge and intelligence to achieve a proper understanding of the network usage in multiple dimensions. In this respect, this paper presents a vision on how these planning and operation processes can rely on the inclusion of Artificial Intelligence (AI) concepts that will allow devising models to characterize the impact of many correlated inputs on specific operator objectives and to drive decisions for different processes.

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 foreseen for future systems. Nevertheless, coexistence among several LTE-U and/or Wi-Fi systems in the same band is a key technical challenge to be resolved. In this context, this paper focuses on the channel selection functionality for LTE-U enabled cells to decide the most appropriate channel to use for downlink traffic offloading in the unlicensed band. A distributed Q-learning mechanism that exploits prior experience is proposed to support this functionality, thus enabling coexistence with other systems in a smart and efficient way. The behavior of the proposed approach is illustrated in an indoor scenario with small cells from two different operators. A fully decentralized approach, where the channel selection decision-making is performed independently by each small cell in the scenario, is considered to initially assess the potentials of the Q-learning solution. Promising results are obtained revealing that the throughput achieved by the proposed approach can be between 96% and 99% of the optimum ideal achievable throughput.

In the last few years, a new design paradigm has arisen in the field of wireless communications research: the so-called cross-layer optimization. In fact, this paradigm implies the redefinition of the overall design strategies for this kind of systems as it breaks the classical OSI model. The endless need for higher and higher bit rates, stringent QoS requirements and anytime-anywhere connections for wireless systems leads to the necessity of squeezing to the utmost the available radio bandwidth. Cross-layer plays a key role to achieve this goal. The amount of literature about this issue is still relatively scarce, but the premier published results show that the potential obtainable gains are worthy to deserve the increasingly attention that cross-layer is getting. This paper revises the different definitions used for such paradigm, describes the possible mechanisms that can be fitted into the definitions, outlines research challenges to meet in the near future, and analyses different strategies proposed by the authors showing some recent novel results for CDMA-based and WLAN systems.

A set of novel PHY-MAC mechanisms based on a cross layer dialogue have been proposed, and their performance has been analyzed. System efficiency improvement is achieved by means of automatic transmission rate adaptation, trading off bit rate for power, with resulting energy saving features in a generic packet-switched CDMA access network. The rate adaptation mechanism improves spectrum efficiency while keeping packet delay minimized. On the other hand, power dependent strategies reduce power consumption and intercell interference. Simulation results show that the benefits obtained are very encouraging, so the proposed schemes could be used in future communication systems.

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.

User demand for capacity and high data rate applications imposes the need for new technologies able to cope with these challenges. Fourth Generation cellular networks have set the initiative for a technology evolution that will surpass the constraints and provide better quality of service and improved performance. In this context, Heterogeneous Networks (HetNets) deployments, combining a variety of different cell sizes, are considered in the literature in order to enhance the coverage and capacity of cellular systems. However, they require enhanced techniques especially for the user-to-cell association, resource allocation and interference management processes. On that respect, in this work we present a novel scheme that exploits jointly the frequency, power and time dimensions for interference mitigation in order to balance the trade-off between interference reduction to small cell users and throughput degradation for macrocell users. Simulation results have shown that the proposed solution utilizes more efficiently the available resources compared to a conventional scheme and boosts the capacity up to 45%.

The progressive deployment of cell sites in wireless networks to deal with the massive demand of wireless services experienced in the last years has led to a huge increase in energy consumption and associated operational expenditures for wireless network operators. As a result, significant research efforts are being recently devoted towards energy efficient wireless communications. The use of relay nodes that enhance coverage/capacity in wireless systems has been identified as a means to improve energy efficiency thanks to the associated reduction in transmit power. In this context, this paper presents the assessment of the energy savings that can be achieved by means of relaying in a wireless system. As a difference from prior works where the impact of relaying on energy efficiency is obtained as a result of multiple aggregated effects, the analysis performed in this paper will highlight, separately, the role played by each of the key elements influencing on the performance, such as the propagation and shadowing effects, the energy consumption model parameters and the influence of the bit rate and spectral efficiency.

An inhibit sense multiple access-direct sequence/code division multiple access (ISMA-DS/CDMA) medium access control protocol for a packet transmission mobile radio network is presented. The main feature of this protocol is its ability to retain the inherent flexibility of random access protocols while at the same time reducing to some extent the randomness in the access in order to increase the system capacity. In this framework, the protocol is presented together with some adaptive mechanisms that improve the protocol performance by means of regulating the access and varying the transmission bit rate according to the channel load that is broadcast by the base station. As a result, an adaptive bit rate algorithm is presented that reaches a throughput value close to the theoretical maximum.

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 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.

This paper focuses on the use of Opportunistic Networks as a candidate solution for extending the coverage of cellular networks when providing high bit rate data services. It is based on establishing a device-to-device (D2D) radio link with another mobile that can provide the connectivity to the infrastructure. Specifically, the paper proposes a novel cognitive solution for the joint selection of node and spectrum to be used in the D2D radio link. It makes use of learning-based mechanisms for supporting the decision-making process taking into account the context information and the application requirements. Simulation results reveal that the performance of the proposed approach is very close to the optimum one. At the same time it is capable of adapting to changes in the scenario, such as the appearance/disappearance of candidate nodes as well as the variability in the interference conditions in the different bands.

Spatial diversity is an attractive technology to cope with the fading channel encountered in mobile communications. This paper presents novel closed analytical expressions of the bit-error rate (BER) achievable in a coherent binary phase-shift-keying (CBPSK) direct-sequence code-division multiple-access (DS/CDMA) system for any power delay profile and for either postdetection selection or maximal ratio combining (MRC). In particular, expressions for the cutoff rate R/sub o/ and for its related parameter D are also formulated in order to assess the system performance under the consideration of some channel coding schemes. Finally, an exemplary study is carried out in order to illustrate the behavior of a realistic space-diversity code-division multiple-access (CDMA) system according to the analytical expressions that have been derived.

Space diversity reception, in which several signals received at different antennas are combined, is a well known method that can be used to combat the effects of fading in wireless systems. Also, trellis coded modulation (TCM), when combined with interleaving of sufficient depth, is known to provide some form of time diversity that allows the achievement of good error performance in fading environments. In this paper we consider the analysis of the error performance of reference-based Maximal Ratio Combining (MRC) systems when used in conjunction with trellis-coded MPSK modulation techniques over a Rayleigh fading channel. We also consider the analysis of MRC trellis-coded MPSK systems with conventional differential detection. The results are obtained by using a combination of theoretical analysis and simulation. Exact and near-exact expressions for the pairwise error-event probability in Rayleigh fading are derived. Monte-Carlo simulation results, which are more indicative of the exact system performance, are also given.