Lucas Dias Hiera Sampaio

Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation

Abstract: This paper discusses the power allocation with fixed rate constraint problem in multi-carrier code division multiple access (MC-CDMA) networks, that has been solved through game theoretic perspective by the use of an iterative water-filling algorithm (IWFA). The problem is analyzed under various interference density configurations, and its reliability is studied in terms of solution existence and uniqueness. Moreover, numerical results reveal the approach shortcoming, thus a new method combining swarm intelligence and IWFA is proposed to make practicable the use of game theoretic approaches in realistic MC-CDMA systems scenarios. The contribution of this paper is twofold: (i) provide a complete analysis for the existence and uniqueness of the game solution, from simple to more realist and complex interference scenarios; (ii) propose a hybrid power allocation optimization method combining swarm intelligence, game theory and IWFA. To corroborate the effectiveness of the proposed method, an outage probability analysis in realistic interference scenarios, and a complexity comparison with the classical IWFA are presented.

Energy and spectral efficiencies trade-off with filter optimisation in multiple access interference-aware networks

In this contribution, the optimised deployment of both spectrum and energy resources scarcely available in the mobile multiple access systems has been analysed, with special attention to the impact of the filter design on the energy efficiency EE of code division multiple access networks. Putting into perspective two conflicting metrics, namely throughput maximisation and power consumption minimisation, the distributed EE utility function is formulated. We also show that the best EE versus spectral efficiency trade-off is achievable when each node allocates exactly the power necessary to attain the maximum EE. In order to demonstrate the validity of our analysis, a low-complexity energy-spectral-efficient algorithm based on distributed instantaneous signal-to-interference-plus-noise ratio level is developed, and the impact of single and multi-user detection filters on the EE-spectral efficiency trade-off is extensively analysed by numerical simulation. Copyright

Energy Efficient OFDMA Networks Maintaining Statistical QoS Guarantees for Delay-Sensitive Traffic

An energy-efficient design is proposed under specific statistical quality-of-service (QoS) guarantees for delay-sensitive traffic in the downlink orthogonal frequency-division multiple-access networks. This design is based on Wus effective capacity (EC) concept, which characterizes the maximum throughput of a system subject to statistical delay-QoS requirements at the data-link layer. In the particular context considered, our main contributions consist of quantifying the effective energy-efficiency (EEE)-versus-EC tradeoff and characterizing the delay-sensitive traffic as a function of the QoS-exponent θ, which expresses the exponential decay rate of the delay-QoS violation probabilities. Upon exploiting the properties of fractional programming, the originally quasi-concave EEE optimization problem having a fractional form is transformed into a subtractive optimization problem by applying Dinkelbachs method. As a result, an iterative inner-outer loop-based resource allocation algorithm is conceived for efficiently solving the transformed EEE optimization problem. Our simulation results demonstrate that the proposed scheme converges within a few Dinkelbach algorithms iterations to the desired solution accuracy. Furthermore, the impact of the circuitry power, the QoS-exponent, and the power amplifier inefficiency is characterized numerically. These results reveal that the optimally allocated power maximizing the EEE decays exponentially with respect to both the circuitry power and the QoS-exponent, while decaying linearly with respect to the power amplifier inefficiency.

Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic

Multiple attributes from IP flows are combined to detect anomalous events.GA metaheuristic used for Digital Signature of Network Segment using Flow Analysis.Unsupervised training technique applied efficiently for network traffic profiling.Fuzzy Logic improved accuracy and false positives compared to state of art. Due to the sheer number of applications that uses computer networks, in which some are crucial to users and enterprises, network management is essential. Therefore, integrity and availability of computer networks become priorities, making it a fundamental resource to be managed. In this work, a scheme combining Genetic Algorithm and a Fuzzy Logic for network anomaly detection is discussed. The Genetic Algorithm is used to generate a Digital Signature of Network Segment using Flow Analysis, where information extracted from network flows data is used to predict the networks traffic behavior for a given time interval. Furthermore, a Fuzzy Logic scheme is applied to decide whether an instance represents an anomaly or not, differing from some approaches present in the literature. Indeed, it is proposed an expert system with the capability to monitor the networks traffic with IP flows while expected behaviors are generated in a regular time interval basis, issuing alarms when a possible problem is present. The proposed anomaly detection system exposes network problems autonomously. The results acquired from applying the proposed approach in a real network traffic flows achieve an accuracy of 96.53% and false positive rate of 0.56%. Moreover, our method succeeds in achieving higher performance compared to several other approaches.

Networking Anomaly Detection Using DSNs and Particle Swarm Optimization with Re-Clustering

This paper presents an anomaly detection method using Digital Signature of Network Segment (DSNS) and Par- ticle Swarm Optimization-based clustering (PSO-Cls). The PSO algorithm is an evolutionary computation technique whose main characteristics include low computational complexity, ability to escape from local optima, and small number of input parameters dependence, when compared to other evolutionary algorithms, e.g. genetic algorithms (GA). In the PSO-Cls algorithm, swarm intelligence is combined with K-means clustering, in order to achieve high convergence rates. On the other hand, DSNS consists of normal network traffic behavior profiles, generated by the application of Baseline for Automatic Backbone Management (BLGBA) model in SNMP historical network data set. The proposed approach identifies and classifies data clusters from DSNS and real traffic, using swarm intelligence. Anomalous behaviors can be easily identified by comparing real traffic and cluster centroids. Tests were performed in the network of State University of Londrina and the obtained detection and false alarm rates are promising.

Game Theoretic Energy Efficiency Design in MC-CDMA Cooperative Networks

The energy efficiency (EE) maximization on the uplink of multicarrier code division multiple access (MC-CDMA) cooperative wireless networks is a NP-hard optimization problem of great interest for future networks systems. This paper presents two different noncoalitional game theoretic approaches to solve in a distributed fashion the EE maximization problem that arises in MC-CDMA wireless cooperative networks considering receiver multiuser filter design. A study over the quasi-concavity of the utility function is presented while numerical results and a complexity analysis are offered to corroborate the mathematical model and observe the tradeoff between EE, spectral efficiency, and average transmission power.

Achieving Maximum Effective Capacity in OFDMA Networks Operating Under Statistical Delay Guarantee

We propose a spectrally efficient design that guarantees the statistical delay quality-of-service (QoS) for delay-sensitive traffic in the downlink of orthogonal frequency-division multiple-access (OFDMA) networks. This design is based on the so-called effective capacity (EC) concept, which describes the maximum throughput, a system can achieve under a specific statistical delay-QoS violation probability constraint. We investigate the EC maximization problem, in which, the statistical delay profile of the traffic is characterized by the QoS-exponent

Firefly Algorithm in Telecommunications

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Energy efficiency design in MC-CDMA cooperative networks

determining the exponential decay rate of the delay-QoS violation probability. By exploiting the properties of concave programming and Slater’s condition, the Lagrangian dual decomposition method is applied and an iterative algorithm that does not depend on the instantaneous channel state information (CSI) is proposed for solving the concave problem formulated. Extensive simulations demonstrate the efficacy and robustness of the proposed iterative algorithm. Furthermore, we show that the system’s achievable EC does not depend on the specific choice of the subcarrier allocation, but rather on the number of subcarriers allocated to each user. This is, because, the EC is calculated using the channel’s statistics, instead of the instantaneous CSI, implying that the EC is more of a long term channel capacity metric.

Inteligência Swarm e Equilíbrio de Verhulst Aplicados à Alocação de Potência em Redes Ópticas CDMA Particionadas

Abstract This chapter discusses the nature-inspired metaheuristic firefly algorithm (FA) applied in telecommunications. FA has been developed based on the behavior of the fireflies and the light emitted, where the brightest firefly attracts the others in his direction. Besides combining stochastic behavior and a population-based multimodal characteristic, the FA approach is able to solve optimization problems in different areas of knowledge such as engineering, robotics, combinatorial optimization, and so on. This chapter aims to show the FA performance in two distinct network optimization problems: traffic characterization and energy-efficient cooperative networks. In the first optimization problem, FA is applied as a clustering algorithm to create a network traffic pattern called Digital Signature of Network Segment using Flow analysis (DSNSF); in the second, FA has been applied to the energy-efficiency maximization problem in multicarrier direct sequence code division multiple access (MC-DS/CDMA) cooperative networks.