Daniel Calabuig

On the way towards fourth-generation mobile: 3GPP LTE and LTE-advanced

Long-Term Evolution (LTE) is the new standard recently specified by the 3GPP on the way towards fourth-generation mobile. This paper presents the main technical features of this standard as well as its performance in terms of peak bit rate and average cell throughput, among others. LTE entails a big technological improvement as compared with the previous 3G standard. However, this paper also demonstrates that LTE performance does not fulfil the technical requirements established by ITU-R to classify one radio access technology as a member of the IMT-Advanced family of standards. Thus, this paper describes the procedure followed by the 3GPP to address these challenging requirements. Through the design and optimization of new radio access techniques and a further evolution of the system, the 3GPP is laying down the foundations of the future LTE-Advanced standard, the 3GPP candidate for 4G. This paper offers a brief insight into these technological trends.

A Delay-Centric Dynamic Resource Allocation Algorithm for Wireless Communication Systems Based on HNN

This paper proposes a dynamic resource-allocation (DRA) algorithm for packet data services in wireless communication systems based on Hopfleld neural networks (HNNs). The resource-allocation algorithm assumes a delay-centric approach in that it maximizes resource utilization of the overall system while minimizing the packet delay. The real-time (RT) working capability of HNN hardware implementation means that a very powerful scheduling DRA algorithm can be obtained. A generic formulation of the algorithm is presented to establish the optimal bit rate allocation. In addition, some illustrative examples of this formulation are given, considering specific wireless communication systems, such as general packet radio service (GPRS) or universal mobile telecommunications system (UMTS). To be more precise, the performance of the proposed DRA algorithm is evaluated in a realistic UMTS scenario, considering both RT and nonreal-time (NRT) services. To obtain the best resource distribution and fulfill the different quality-of-service (QoS) levels required by RT and NRT services, the new HNN-based delay-centric DRA algorithm is performed twice. Initially, only the RT services are considered, and following this, all the NRT services are taken into account. The results reveal that the proposed DRA algorithm outperforms other reference algorithms in terms of not only average packet delay but the allocated total bit rate as well.

HSDPA Link Adaptation Improvement Based on Node-B CQI Processing

In this paper HSDPA link adaptation (LA) based on Channel Quality Indicator (CQI) reports is optimised. A pre-processing of the last received CQI reports is done before the execution of the LA algorithm in the Node-B in order to obtain more profitable channel quality estimations and hence improve the LA performance. Different types of processing techniques are presented and assessed, considering from the simplest sample averaging to some more elaborated predictive algorithms. Results demonstrate that a non negligible enhancement in the LA performance can be obtained if medium and high speed users are considered.

Hopfield Neural Network - Based Approach for Joint Dynamic Resource Allocation in Heterogeneous Wireless Networks

This paper presents a comprehensive approach to solve the problem of joint dynamic resource allocation (JDRA) in heterogeneous wireless networks using a Hopfield neural network (HNN). A generic formulation for packet services with delay constraints is proposed to decide the optimal bit rate and radio access technology (RAT) allocation. Some illustrative simulations results in a basic scenario are presented to evaluate performance of the proposed algorithm.

Cognitive Radio enabling opportunistic spectrum access in LTE-Advanced femtocells

The Cognitive Radio (CR) paradigm provides mechanisms and methodologies for an utmost and efficient use of scarce spectrum resources. Among its implementations, Opportunistic Spectrum Access (OSA) enables the use of otherwise-unutilized licensed spectrum provided no interference is caused to the licensee. This paper focuses on the application of this concept for indoor femtocell deployments where, in addition to own licensed spectrum resources, opportunistic spectrum resources can be aggregated. Provided certain interference among femtocells, coordination mechanisms are required. In this framework, this paper studies the use of Media Independent Handover (MIH) signaling to report interference measurements that allow a central controller to make femtocells share white spaces. Moreover, whenever an excessive femto-to-femto interference would require coordination, the proposed opportunistic spectrum access scheme enhances spectrum efficiency and reduces interferences. Results indicate that the proposed opportunistic system significantly improves capacity without requiring additional intelligence in the femtocell.

Complete Shadowing Modeling and its Effect on System Level Performance Evaluation

Computer simulations are a common procedure for assessing the performance of new algorithms. To conduct a valid and accurate study, the models employed in such simulators need to be carefully selected. Regarding shadowing modeling, one-dimensional models are fairly commonplace in the literature. While simple and with low computational costs, these models can not produce correlated fading values for mobiles that are in nearby positions and, besides, do not include the cross-correlation effect. To overcome these limitations, this paper presents a bi-dimensional shadowing model which introduces both the spatial correlation and the cross-correlation present in real systems. Finally, the impact of considering different aspects of shadowing modeling for system level investigations is evaluated. For that purpose, the UMTS radio access technology has been considered as a case study.

Joint Dynamic Resource Allocation for QoS Provisioning in Multi-Access and Multi-Service Wireless Systems

This paper proposes a Joint Dynamic Resource Allocation (JDRA) algorithm that allocates simultaneously the best-suited Radio Access Technologies (RATs) and amount of resources to all the users active in a multi-access wireless system. Both distributions are performed at the same time so as to make the most of the heterogeneous network. In this scenario users can connect to several RATs but not simultaneously and, therefore, the JDRA algorithm is able to consider the required handover time in the decision making. Moreover, the algorithm guarantees the Quality of Service (QoS) provision in terms of delay and bit rate in a multi-service scenario where different users may have different QoS requirements. Such a complex optimization problem has been tackled using a Hopfield Neural Network (HNN) formulation. These neural networks have fast response times once hardware implemented, which is very significant since current and future wireless networks must rapidly adapt to changing circumstances in wireless environment and traffic. Results prove the benefits achieved by the usage of the HNN-based JDRA algorithm. Firstly, the joint decision outperforms a two-steps procedure in which, after the RAT selection, the same uni-RAT DRA algorithm is applied. Secondly, the proposed algorithm can deal with different levels of congestion and load distribution among RATs in a much better way that other reference algorithms specifically designed for multi-service scenarios.

Multi-Channel Radio Resource Distribution Policies in Heterogeneous Traffic Scenarios

Multi-channel operation in wireless systems has been proposed to increase user throughput and reduce transmission delays. However, multi-channel operation requires adequate resource allocation policies to guarantee user fairness and avoid channel access stagnation. The definition of such policies is particularly challenging in heterogeneous traffic scenarios where each traffic service has different quality of service requirements. In this context, this work proposes and evaluates various multi-channel radio resource distribution policies designed to operate under heterogeneous traffic environments. In particular, this paper proposes the application of bankruptcy policies to guarantee user fairness, and compares their performance to other schemes. The proposed policies can also be extended to manage radio resources in heterogeneous wireless systems.

MAC Layer Performance of Different Channel Estimation Techniques in UTRAN LTE Downlink

Long Term Evolution (LTE) is the new standard proposed by the 3GPP to evolve towards 4G. Evolved UTRAN (E-UTRAN) specifications are currently completed and research groups are studying the performance of the last Release 8. Nevertheless, these studies lack a full modeling of the MAC layer because they either leave out retransmissions and turbo coding or assume ideal channel estimation. This paper uses an accurate LTE MAC layer simulator to perform a complete downlink LTE performance study. Results compare different channel estimation techniques showing significant difference among them, most of all regarding the robustness of the estimator against errors. Finally, LTE system performance assessment is presented employing a realistic channel estimator.

User bandwidth usage-driven HNN neuron excitation method for maximum resource utilization within packet-switched communication networks

Mobile and wireless systems beyond 3G are being designed under the user-centric paradigm. Dynamic resource allocation (DRA) is a topic on intensive research to address efficiently such paradigm. Hopfield neural networks (HNN) have proved useful in the past to solve this kind of complex optimization problems. Recently, various approaches have been proposed to realize HNN-based user-centric DRA. However, the initial algorithms suffer from severe instability problems impacting the overall performance. This letter analyses the source of the existing limitations and proposes an enhanced formulation, ensuring maximum resource utilization while optimizing the convergence of the neural network. The letter highlights the improved performance in terms of optimum convergence and bandwidth utilization.