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.

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.

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.

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.

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 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.

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%.

In this paper, the problem of resource allocation optimization is studied for a single-cell multiuser cognitive radio network in the presence of primary user networks. The spectral access of the cognitive radio network is based on Orthogonal Frequency Division Multiple Access (OFDMA). A joint bandwidth and power allocation is performed so that users' rate requirements are satisfied, and the integrity of primary user communication is preserved. In this work, two unique challenges are addressed. The first is the incorporation of primary user activity in the design of resource allocation technique, and the second is the limited hardware capabilities of cognitive terminals compared to those available at the cognitive base station. To address these problems, a novel resource allocation framework is proposed based on the bandwidth-power product minimization, which is an effective metric in evaluating the spectral resource consumption in a cognitive radio environment. The framework takes into consideration the challenges aforementioned. The results show significant enhancement in spectral efficiency by using our framework compared to classical power adaptive optimization using iterative waterfilling scheme.

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 this paper, a novel Cognitive Radio Resource Management (RRM) is proposed to improve the spectrum utilization efficiency. In this system, heterogeneous Primary Users (PUs) with multiple features are considered where these PU features are exploited to improve the adaptability in Cognitive Radio (CR) networks and, thus, to design an efficient Cognitive RRM. An optimization framework is developed by considering heterogeneous PUs and variable CR demands while assuring interference protection towards PUs. A suboptimal solution is proposed after showing that an optimal solution is computationally infeasible. Simulation results are conducted in terms of total achieved data rate and satisfaction of CRs requirements.

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.

The ability of adapting to the environment in the most efficient way is a crucial issue in Cognitive Radio (CR) networks. For this purpose, an accurate estimation of the characteristics and activity of the Primary Users (PUs) is required. A system that takes into account heterogeneous PUs with several features is developed. A new scheme is integrated in the system to exploit these motleys and to improve the adaptability in CR networks. Through the proposed PU signal type recognition, the PU signal is detected and classified. The features of each PU type: the allowed interference levels, the bandwidth and the idle time, are extracted and exploited for CR adaptability effects. For this, a new CR throughput/interference adapter is proposed. The CR throughput is efficiently increased depending on the specific characteristics of PU types. Simulation results show that the proposed PU type recognition detects, distinguishes and classifies PU signals in Additive White Gaussian Noise (AWGN). It is shown that CR throughput varies with PU features for the improvement of CR adaptability.

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.

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.

This paper analyses the possibility to exploit opportunistic spectrum access (OSA) for short-range radio communication systems within indoor locations in dense urban areas. In particular, considering the service area of a primary system devoted to providing outdoor coverage in a dense urban scenario, the percentage of indoor locations where the secondary users can reuse the primary frequency band without disturbing the primary system or being disturbed is estimated. The analysis considers heterogeneous path loss models for the primary and secondary systems encompassing the characterization of outdoor, indoor and building penetration losses. Obtained results quantify how aspects like the location of the primary network elements and the considered interference margins to protect primary transmissions impact on the spatial availability of the primary band within the interior of the buildings.

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.

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.

This paper introduces a novel joint channel-aware and queuing-aware OFDMA scheduler for delay sensitive traffic based on a Hopfield Neural Network scheme. It allows providing an optimum OFDMA performance by solving a complex combinational problem. Some illustrative results on the dropping rate and delay have been obtained for a delay- sensitive service modelled as www traffic. These results, shown for comparison purposes, outperform significantly those obtained for a conventional heuristic scheduling.

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.

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 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 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.

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.

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 a novel dynamic resource allocation algorithm that makes use of the Hopfield neural network (HNN) methodology, which provides a fast way of finding the optimum resource allocation that minimises a given energy function reflecting specific service and system constraints. The proposed algorithm is applied to schedule the downlink transmissions in a CDMA scenario with delay-oriented services, although by a proper modification of the constraints imposed in the energy function, it could be easily extended to other services or access technologies. The algorithm is evaluated by means of simulations and compared with a reference scheme, revealing its ability to adapt to the specific service and traffic conditions

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 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.

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.

A simple algorithm is proposed for the optimal distribution of a number of base stations in a microcellular environment. Criteria are defined in order to decide the combinations of antennas that should be tested, and a quality factor is proposed to compare them. Results are presented for a specific environment.

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.

In this paper, the bit error rate (BER) performance of predetection diversity trellis coded multilevel phase shift keying (TCM-MPSK) and postdetection diversity trellis coded multilevel differential phase shift keying (TCM-MDPSK), transmitted over a Rayleigh fading channel is presented. Novel analytical upper bounds using the transfer function bounding technique are obtained and illustrated by several numerical examples. A new asymptotically tight upper bound for the maximal ratio combining (MRC) diversity schemes is also derived. In order to analyze practical TCM schemes (four or more states), only trellis codes holding uniform error property (UEP) and uniform distance property (UDP) are considered, enabling the encoder transfer function to be obtained from a modified state transition diagram, having no more states than the encoder itself. Monte-Carlo simulation results, which are more indicative of the exact system performance, are also given.>

This paper gives an overview of the studies accomplished by COST 231 members on the subject of trellis coded modulation (TCM). We establish first the interest of TCM for mobile systems by examining its bandwidth efficiency and potential performance. Then, we give simulation results concerning co-channel interference, the interleaving delay constraint, post detection diversity and frequency hopping.

In a code division multiple access (CDMA) mobile radio communication system, interference from other users is one of the most critical drawbacks due to the lack of perfect orthogonality between user access codes. So far, practical systems have used power control to overcome fading and near/far effects. Another approach which also results in an important improvement of the capacity is the use of interference cancellers. A simple linear interference cancellation scheme for coherent BPSK modulation is proposed and analyzed under Rayleigh multipath fading, and in particular, the influence of the channel impulsive response estimation on the performance of the cancellation scheme has been assessed.

The performance of a linear interference canceller for a Direct-Sequence Code-Division Multiple-Access (DS/CDMA) synchronous system in a multipath environment is considered in this paper. The scheme proposed for intra-cell interference cancellation is analyzed by means of computer simulations under two different power control techniques: fast (FPC) and average power control (APC). The obtained results show a significant improvement in terms of capacity with regard to the conventional receiver.>

A propagation model to estimate the average received power in an urban microcell environment has been developed. This model can be used both in line of sight (LOS) and non line of sight (NLOS) conditions in any regular or irregular microcell structure.>

A method for calculating the asynchronous frequency-hopping (FH) spread-spectrum multiple-access performance is presented. The method is based on a Gauss quadrature rule, and for this purpose a moment-generation algorithm of the random variables involved is introduced. False alarm and miss probabilities in a basic FH search acquisition configuration and bit error rate in both slow and fast FH binary frequency-shift keying are obtained. Different power levels of the multiuser interference, user activity modeled as a Poisson point process, and nonfading as well as nonselective Rayleigh fading channels are considered.>

This paper presents a comparative study of 16-QAM, 32QAM, 64-QAM, and 128-QAM 140 Mbit/s digital radio in the presence of multipath fading. ConventionalT-spaced and fractional tapspaced equalizers have been considered to compensate for the ISI-induced symbols. The performance measure used is the bit error ratio (BER); thus, upper and lower bounds on BER have been developed. Based on this calculation procedure, results showing the net fade margin versus the gross fade margin have been obtained in a variety of cases that include the presence of both nonlinear and linear equalizer structures.

This paper presents the performance of a pseudoerror monitoring technique for a 16 QAM 140 Mbit/s digital radio in presence of multipath dispersive fading. The so-called pseudoerrors, generated by means of a threshold modification of two, namely, secondary receivers are entered into an extrapolating function to obtain a fast bit error ratio (BER) calculation. A counting time of 10 ms was retained in order to follow fading depth changes up to 100 dB/s and fading notch speeds up to 300 MHz/s approximately. We have considered three structures for the receiver: without equalization, with IF amplitude equalization, and decision feedback equalization (DFE). The results obtained show the estimated and real BER within a margin that includes the two recommended CCIR values: 10-3and10-6. In particular, the estimated signature obtained in the case of using IF equalization reveals that this fast BER calculation could be an effective choice to control a frequency diversity switch, even in the presence of a fading activity with rapid variations.

This paper considers a frequency-hopped multilevel frequency shift keying (FH-MFSK) spread-spectrum communication system applied to cellular mobile radiotelephony. We present a mobile-to-base transmission model that allows us to study system impairments, such as interference from nonsynchronous users and adjacent frequency channels in the presence of matched tuned receiver filters. For interceli interference, the usual Gaussian approximation is used, but the variance calculation takes into account shadow fading. Power control in the mobiles, a mean path loss exponent of -3.5 and fast Rayleigh and slow Iognormal fading have been assumed. We have obtained results with mobile-to-base communication of 32 kbits/s per user in a 20 MHz (one-way) bandwidth. In an isolated cell system a bit error probability less than 10-3can be maintained with up to 110 simultaneous users for practical average SNR ratio of 25 dB. The presence of intercell interference degrades the bit error probability enormously, and clustering of cells is required for controlling interference.