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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.

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.

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 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.

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

This paper proposes a Dynamic Spectrum Assign- ment strategy in the context of next generation multicell Orthog- onal 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. Index Terms—Dynamic Spectrum Assignment, Multicell OFDMA, Reinforcement Learning.

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.

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

This paper 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.

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.

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.

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.

Inter-working and convergence of heterogeneous wireless networks are paving the way to scenarios in which end users will be capable of using simultaneously services through different Radio Access Technologies (RATs), by means of reconfigurable mobile terminals and different network elements. In order to exploit the potential of these heterogeneous networks scenarios, optimal RAT selection and resource utilization mechanisms are required. As a result, the heterogeneous networks are introducing a new dimension to the Radio Resource Management (RRM) problem, so that new algorithms dealing with the dissimilarities and complementarities of the multiple RATs from a joint perspective have to be considered. In this sense, this paper proposes a Joint Radio Resource Management (JRRM) strategy in a multi-RAT, multicellular and multiservice scenario. An approach based on Fuzzy Neural methodology is presented. Firstly, the way how the proposed Fuzzy Neural framework deals with the multiservice allocation in a heterogeneous scenario is presented. A reinforcement learning algorithm based on neural networks allows guaranteeing a multidimensional QoS focusing on those QoS requirements which mainly affect the user perception of the service. In addition to this, the performances obtained by the Fuzzy Neural JRRM for both real-time and non real-time services, are compared to the ones offered by alternative JRRM strategies. Secondly, special attention is paid to real-time services and to mechanisms to improve their performances. An approach based on predicting future JRRM decisions and on accordingly reserving radio resources for handoff calls is presented. Simulation results will show improvements in terms of both new connection blocking and handoff call dropping probabilities. Finally, the full set of results provides the sufficient insight into the problem to allow stating that the present Fuzzy Neural framework can be a firm candidate for JRRM.

This paper deals with the problem of the downlink admission control for UTRA-FBD systems. In W-CDMA systems, elements such as traffic variability or user mobility cause that the resource requirements for each user in order to guarantee its QoS (quality of service) vary along time, making very difficult to estimate whether there will be or not available resources to satisfy the user demands. Admission control and other RRM (radio resource management) strategies play an important role in order to achieve an efficient use of the radio interface. In this context, information of certain user characteristics (such as user location or user mobility) would be prime important to obtain a more accurate estimation of the required resources for each user during its connection lifetime. This paper proposes an admission control algorithm which takes into account user mobility and service characteristics in order to obtain better decisions of admission or rejection of connection requests. The obtained results show the improvement of this new admission control proposal in heterogeneous scenarios when low-speed indoor users and medium/high-speed outdoor users have to coexist.

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.

This paper focuses on the analysis of 3G mobile communications systems in scenarios with non-uniformly distributed traffic. Special attention is paid to the impact of hotspots on radio resource management (RRM) strategies such as uplink admission control. Users path loss is examined for different hotspot configurations, and degradation suffered by users in terms of BLER is observed by varying hotspot density and distance from base station. Results reveal that the performance of the system in the uplink depends on traffic distribution and that some parameters of RRM strategies should be adapted to these varying situations.

In this paper an innovative mechanism to perform joint radio resource management (JRRM), based on fuzzy-neural methodology, is introduced. A multicellular and multi-RAT scenario including UMTS, GERAN and WLAN radio access technologies (RATs) is considered. It is shown that the proposed framework is able to efficiently manage a multiservice architecture. The proposed JRRM algorithm is capable of dealing with different operator policies as well as controlling the most representative QoS objectives for each class of service, retaining them to a predefined target value by means of smart reinforcement learning mechanisms based on neural networks.

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.

The definition and assessment of suitable radio resource management (RRM) strategies able to provide 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 a scenario with a mixture of interactive and conversational services. In particular the effect of prioritization of conversational users in the admission control has been analyzed in terms of admission, dropping probabilities and packet delay. Furthermore, the paper also studies the importance of suitable congestion control mechanisms that cope with load fluctuations in order to guarantee the negotiated QoS to already connected users. These fluctuations are mainly due to the randomness in the traffic generation of interactive users, that can seriously degrade performance of both conversational and even interactive users if no congestion control is carried out.

This paper presents an optimized uplink admission control for W-CDMA. In order to ensure that all the accepted requests achieve their QoS requirements in the planned coverage area and taking into account the power constraints, the algorithm controls the uplink load factor, which is statistically estimated. The key parameters that have influence over this estimation are presented as well as their adequate setting in order to achieve a high admission probability while keeping QoS guarantees. Specifically, some innovative modifications with respect to state of the art admission control approaches are proposed and it is shown that significant performance improvements may follow. In particular, it is shown that a traffic averaging method is adequate to smooth traffic fluctuations in the estimation that would lead to unnecessary rejections. Similarly, it is shown that users in soft handover should be accounted for in the estimation with a reduction factor. Finally, it is also shown that the proper setting of the f factor that accounts for the intercell to intracell interference ratio should not take into account the average factor but another more representative statistic like the 50% percentile.

W-CDMA performance is tightly coupled to the amount of interference in the air interface and eventually depends on many strongly interrelated system parameters that need to be suitably managed through radio resource management (RRM) strategies in order to achieve a high efficiency. Interference characterisation in terms of average and standard deviation, which are suitable for radio network planning, may not suffice for a proper RRM design because of its inherently dynamic nature. The time correlation of the interference and the main system level parameters affecting its dynamics are raised: shadow fading, mobile speed, traffic characteristics and cell load.

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 evaluates specific algorithms for the admission control of a new connection and UE-MAC strategies for the dynamic management of the transmission parameters. Conversational and interactive-like services are studied. Results reveal that the behaviour of the different proposed UE-MAC algorithms in the uplink has an impact on the admission phase and, consequently, they should be taken into account for a proper admission control algorithm design.

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.

Presented is the analysis of inter-operator agreements to exchange radio resources in a multi-operator scenario, to improve radio resource usage and operator revenue. The establishment of these agreements increases user perception of the service and reduces churning rate.

In the framework of 3G systems, the adoption of efficient packet scheduling algorithms is necessary to guarantee QoS as well as to provide high capacity. It is shown that for proper system optimisation, different packet scheduling strategies should be used depending on the deployment scenario (macrocell, microcell, picocell), the link direction (uplink or downlink), as well as the service characteristics (continuous, bursty traffic).

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.

In the context of 3G mobile communication systems, a mix of services with different requirements are expected. Consequently, packet scheduling mechanisms for QoS guarantee 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.

Third-generation mobile communication systems will bring a wide range of new services with different quality of service requirements and will open the ability to exploit radio resource management functions to guarantee a certain target QoS, to maintain the planned coverage area and to offer a high-capacity while using the radio resources in an efficient way. RRM functions impact the overall system efficiency and the operator infrastructure cost, so they will definitively play an important role in a mature 3G scenario. In order to provide some insight into RRM strategies implementation, a range of rep resentative case studies with several innovative algorithms arc presented and supported by simulation results in a realistic UMTS Terrestrial Radio Access Network scenario as devised in the 3GGP standardization forum. In particular, a decentralized uplink transmission ratc selection algorithm in the short term, a congestion control mechanism to cope with overload situations, and downlink scheduling for layered streaming video packets are proposed.

Cognitive networks are envisaged to provide op- timized resource usage in future. While heterogeneity and re- source 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 en- hanced QoS capabilities. The proposed solution enables intel- ligent traffic routing by means of distributed reinforcement learning agents that base decisions on edge-gained experience. Index Terms—all-IP, DiffServ, MPLS, QoS, reinforcement learning.

The design of radio resource management strategies is an important issue in the context of 3G W-CDMA-based systems such as UTRAN. Some of the relevant elements influencing downlink RRM are identified and presented, in particular, the downlink admission threshold and the importance of establishing some limits on the maximum power per connection. The effect of these two parameters is studied for different cell radius scenarios. Moreover, the capacity of the system is determined under certain established quality of service requirements, in terms of admission probability, block error rate and dropping probability.

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.

©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. 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.

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 Qualityof- Service while releases spectrum in large geographical areas.

Radio Resource Management (RRM) entity is responsible for the utilization of the air interface resources in UTRA-FDD and, consequently, the adoption of efficient RRM algorithms is needed to guarantee QoS as well as to provide high capacity. Among the several RRM functions that will help to achieve such objectives, this paper proposes three different MAC strategies applied at UE (User Equipment) level, which are devoted to decide the suitable radio transmission parameters for each connection in the uplink direction. Also, the impact of these algorithms on admission control decisions is studied. Results reveal that for a proper admission control algorithm design, the behaviour of the different UE-MAC algorithms needs to be taken into account.