Enrique Stevens-Navarro

Performance comparison between MADM algorithms for vertical handoff in 4G networks

In a fourth generation (4G) wireless environment, the need for an user to be always best connected (ABC) anywhere at anytime leads to execute a vertical handoff decision for guaranteeing service continuity and quality of service (QoS). Several strategies have been proposed in the literature for addressing this problem, being multiple attribute decision making (MADM) one of the most promising methods. A comparative analysis of these methods including SAW, MEW, TOPSIS, ELECTRE, VIKOR, GRA, and WMC is illustrated with a numerical simulation, showing their performance for different applications such as: voice and data connections, in a 4G wireless system.

VIKOR method for vertical handoff decision in beyond 3G wireless networks

The next generation of wireless networks also known as beyond 3G (B3G) networks is a mixture of heterogeneous wireless access technologies. One of the most important technical challenges in B3G wireless networks is the support of vertical handoff. In this paper, we propose the use of Multiple Attribute Decision Making (MADM) method: VIKOR for vertical handoff decision. The idea of VIKOR for decision making is based on an aggregating function representing closeness to the ideal solution. We execute simulation experiments to compare the performance of VIKOR for vertical handoff decision in B3G networks with other MADM schemes such as SAW and TOPSIS.

Spectrum Occupancy Measurements below 1 GHz in the City of San Luis Potosi, Mexico

The cognitive radio technology is a promising solution to solve the scarcity problem of electromagnetic spectrum caused by the increase in the demand of wireless communications. Throughout the world, several measurement campaigns have been conducted to determine the real occupancy of the spectrum. In several countries, including Mexico, these measurements have not been realized yet. In this paper, we present results of the spectral power and duty cycle of the first measurement campaign conducted in the city of San Luis Potosi and in Mexico. The frequencies of interest considered in this study lies within the range of 30 MHz to 910 MHz. In order to improve the system sensitivity, the measurements were made with a directional antenna considering the four cardinal points. The study measures the power spectrum for 7.5 hours on a weekday. The results clearly show the critical underutilization of the spectrum in our country since it show an average duty cycle of 12.5% in the above mentioned frequency range.

On spectrum occupancy measurements at 2.4 GHz ISM band for cognitive radio applications

The 2.4 GHz industrial, scientific and medical (ISM) band is a shared electromagnetic resource where many wireless communications technologies such as wireless local area networks, Bluetooth, ZigBee, cordless phones, amongst others, coexist. Due to the large amount of users of different technologies that might utilize the band at the same time and geographical region, it is reasonable to assume that the band is very busy all the time. In this paper, however, a spectrum measurement campaign is carried out to determine the actual spectrum occupancy of the ISM band in an environment with high density of users. Although many wireless technologies share this frequency band, results show its low spectrum utilization. Indeed, it is shown that the spectral occupation of the band ranges between 7% and 34% of a normal working day, and it is moderate even during busy hours. Therefore, the use of a more sophisticated approach is essential to exploit the unlicensed access to this band, for which cognitive radio is well suited. This is valid despite the fact that, formally speaking, there are no licensed (or primary) users in the ISM band. Nevertheless, ISM users could be treated as pseudo-primary. Thus, cognitive radio techniques can be implemented to improve the spectrum utilization.

A growth model for directed complex networks with power-law shape in the out-degree distribution

Many growth models have been published to model the behavior of real complex networks. These models are able to reproduce several of the topological properties of such networks. However, in most of these growth models, the number of outgoing links (i.e., out-degree) of nodes added to the network is constant, that is all nodes in the network are born with the same number of outgoing links. In other models, the resultant out-degree distribution decays as a poisson or an exponential distribution. However, it has been found that in real complex networks, the out-degree distribution decays as a power-law. In order to obtain out-degree distribution with power-law behavior some models have been proposed. This work introduces a new model that allows to obtain out-degree distributions that decay as a power-law with an exponent in the range from 0 to 1.

Improved semi-blind spectrum sensing for cognitive radio with locally optimum detection

In cognitive radio, there might be some information about primary users’ signals available at secondary users’ receivers since communications systems usually employ training signals for channel estimation and synchronization purposes. This training information can be exploited along with data symbols to perform semi-blind detection of primary users’ signals. In the literature, it is considered that the locally optimal semi-blind detection metric is the linear combination of the energy detector (ED) and the matched filter, i.e. the hybrid detector. Locally optimum detection (LOD), known to be optimum in the low signal-to-noise ratio, is proposed here in the design of a weighted semi-blind locally optimum detector (WSBLOD) by focusing on linear modulation in presence of an unknown phase shift and additive white Gaussian noise. By using LOD, it is shown that for binary phase shift keying-modulated signals, the semi-blind detector test statistic consists not only in combining linearly the matched filter and the ED but also the pseudo-energy of the received signal. Then, the designed semi-blind detector is improved by optimising the weights of the matched filter, energy and pseudo-energy in the test statistic, which maximises the probability of detection. Simulation results show that the proposed WSBLOD outperforms the hybrid detector.

Reducing Spectrum Handoffs and Energy Switching Consumption of MADM-Based Decisions in Cognitive Radio Networks

In a cognitive radio network (CRN), the number of spectrum handoffs increases energy consumption of cognitive (or secondary) users due to the channel switching process. This might limit the operation of the CRN, especially in scenarios where secondary users terminals are battery-powered. Thus, reducing the number of times a cognitive user involved in a transmission switch to different spectrum holes is required to increase battery life-time. In this regard, available spectrum holes possess different attributes (e.g., bandwidth) that can be exploited to satisfy specific secondary users requirements (i.e., connection profile) for data transmission while saving energy. Here, three multiple attribute decision-making (MADM) algorithms for the spectrum decision functionality are evaluated using real spectrum measurements of TV bands. This is performed by proposing six decision parameters, which are extracted from the spectrum data to characterize its suitability. Then, these are used as inputs of the MADM algorithms to select the most suitable spectrum hole for a cognitive user. Thus, an enhanced MADM-based decision process is proposed to reduce the number of handoffs considering energy consumption due to channel switching (ECCS). Results quantify savings from 30% to 90% in ECCS and spectrum handoffs reductions from 47% to 90%.

Performance of MADM algorithms with real spectrum measurements for spectrum decision in cognitive radio networks

Spectrum decision is an important functionality of a cognitive radio terminal, which allows the selection of the appropriate frequency band from the available underutilized spectrum. Spectrum decision conducts itself in accordance to the communication requirements of the secondary (or cognitive) users in the forthcoming Cognitive Radio Networks (CRNs). Selecting the best spectrum for a given transmission involves making preference decisions over the set of available alternatives of frequency bands, which are indeed characterized by different attributes. Therefore, spectrum decision can be modeled as a multiple attribute decision making (MADM) problem. In this paper, we evaluate the performance of MADM decision algorithms such as Simple Additive Weighting (SAW), Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) and the Compromise Ranking Method VIKOR for spectrum decision. The study, however, is conducted using real spectrum occupancy measurements to evaluate the performance of the aforementioned algorithms in a practical scenario. Some important attributes of underutilized spectrum are proposed for consideration in the decisions. Results show that SAW algorithm performs well for the preferred spectrum attributes in the selected scenarios, while offering a good performance also in other parameters.

The Effects of Network Topology on Epidemic Algorithms

Epidemic algorithms can propagate information in a large scale network, that changes arbitrarily, in a self-organizing way. This type of spreading process allows rapid dissemination of information to all network nodes. However, the dynamics of epidemic algorithms can be strongly influenced by the network topology. In this paper, numerical simulations are used to illustrate such influences. We address networks with simple topologies for simplicity and in order to isolate other effects that occur in more complex networks.

On a growth model for complex networks capable of producing power-law out-degree distributions with wide range exponents

The out-degree distribution is one of the most reported topological properties to characterize real complex networks. This property describes the probability that a node in the network has a particular number of outgoing links. It has been found that in many real complex networks the out-degree has a behavior similar to a power-law distribution, therefore some network growth models have been proposed to approximate this behavior. This paper introduces a new growth model that allows to produce out-degree distributions that decay as a power-law with an exponent in the range from 1 to ∞.