Jean-Michel Dricot

High-accuracy physical layer model for wireless network simulations in NS-2

While there exist many papers that compare the performances of different routing protocols for wireless ad hoc networks, these simulations are often using the same simplistic assumptions for the physical layer modeling. In this paper, we propose to combine techniques like ray tracing, Markov chains and experimental data in order to design an high-accuracy physical layer that can be implemented in the NS-2.

Dynamic Channel Modeling for Multi-Sensor Body Area Networks

A channel model for time-variant multi-link wireless body area networks (WBANs) is proposed in this paper, based on an extensive measurement campaign using a multi-port channel sounder. A total of 12 nodes were placed on the body to measure the multi-link channel within the created WBAN. The resulting empirical model takes into account the received power, the link fading statistics, and the link auto- and cross-correlations. The distance dependence of the received power is investigated, and the link fading is modeled by a log-normal distribution. The link autocorrelation function is divided into a decaying component and a sinusoidal component to account for the periodical movement of the limbs caused by walking. The cross-correlation between different links is also shown to be high for a number of specific on-body links. Finally, the model is validated by considering several extraction-independent validation metrics: multi-hop link capacity, level crossing rate (LCR) and average fade duration (AFD). The capacity aims at validating the path-loss and fading model, while the LCR and AFD aim at validating the temporal behavior. For all validation metrics, the model is shown to satisfactorily reproduce the measurements, whereas its limits are pointed out.

Sensitivity of Spectrum Sensing Techniques to RF Impairments

Cognitive radios are devices capable of sensing a large range of frequencies in order to detect the presence of primary networks and reuse their bands when they are not occupied. Due to the large spectrum to be sensed and the high power signal dynamics, low-cost implementations of the analog front-ends leads to imperfections. Two of them are studied in this paper: IQ imbalance and sampling clock offset (SCO). Based on a mathematical system model, we study analytically the impact of the two imperfections on the sensing performance of the energy detector and of the cyclostationarity detector. We show that the IQ imbalance does not impact the performance of the two detectors, and that the SCO only impacts significantly the performance of the cyclostationarity detector.

Electromagnetic fields estimation using spatial statistics

The spatial statistics formalism is applied to electromagnetic fields analysis. Fields are considered as realizations of a random function. Their spatial structure is studied by a method known as variographic analysis. To infer unknown values of the fields, an interpolation method called kriging is then used. It is shown how kriging can be performed on experimental or numerical data to speed up the fields estimation process.

A time-variant statistical channel model for tri-polarized antenna systems

Polarized MIMO systems are an efficient solution for reducing inter-antenna correlation while maintaining compact terminal size. In this paper, a time-variant statistical channel model is proposed for tri-polarized antenna systems. The model is based on a coherent and a scattered component, where each component includes inter-channel correlation and cross-polar discriminations. The temporal variations of the channel are separated in slow and fast channel variations. A measurement campaign has been performed at 3.6 GHz to parameterize the model, in both static and mobile cases. A variant of the variogram technique has been adopted for extracting the slow-varying channel parameters. Experimental results are investigated and presented. The Doppler spectrum of the fast channel variations show fundamental differences between the static case and the mobile case. Finally, it is explained how the proposed model can be generated.

Higher-Order cyclostationarity detection for spectrum sensing

Recent years have shown a growing interest in the concept of Cognitive Radios (CRs), able to access portions of the electromagnetic spectrum in an opportunistic operating way. Such systems require efficient detectors able to work in low Signal-to-Noise Ratio (SNR) environments, with little or no information about the signals they are trying to detect. Energy detectors are widely used to perform such blind detection tasks, but quickly reach the so-called SNR wall below which detection becomes impossible Tandra (2005). Cyclostationarity detectors are an interesting alternative to energy detectors, as they exploit hidden periodicities present in man-made signals, but absent in noise. Such detectors use quadratic transformations of the signals to extract the hidden sine-waves. While most of the literature focuses on the second-order transformations of the signals, we investigate the potential of higher-order transformations of the signals. Using the theory of Higher-Order Cyclostationarity (HOCS), we derive a fourth-order detector that performs similarly to the second-order ones to detect linearly modulated signals, at SNR around 0 dB, which may be used if the signals of interest do not exhibit second-order cyclostationarity. More generally this paper reviews the relevant aspects of the cyclostationary and HOCS theory, and shows their potential for spectrum sensing.

Impact of the environment and the topology on the performance of hierarchical body area networks

Personal area networks and, more specifically, body area networks (BANs) are key building blocks of future generation networks and of the Internet of Things as well. In this article, we present a novel analytical framework for network performance analysis of body sensor networks with hierarchical (tree) topologies. This framework takes into account the specificities of the on-body channel modeling and the impact of the surrounding environment. The obtained results clearly highlight the differences between indoor and outdoor scenarios, and provide several insights on BAN design and analysis. In particular, it will be shown that the BAN topology should be selected according to the foreseen medical application and the deployment environment.

Development of distributed self-adaptive instrumentation networks using Jini technology

This paper describes a client-server architecture for the creation of dynamic and self-adaptive instrumentation over the network, either local or the Internet. The proposed solution allows multiuser, multi-instrument sessions by the means of a new cooperative concept known as Jini. Client applications take advantage of this system-independent technology by using the Java programming language.

Primary Exclusive Region and Throughput of Cognitive Dual-Polarized Networks

Diversity techniques are of importance in the context of cognitive radio networks since they enable the primary and secondary terminals to simultaneously and efficiently share the spectral resources in the same location. In this paper, we investigate a simple, yet powerful, diversity scheme based on the exploitation of the polarimetric dimension. More precisely, we consider a scenario where the cognitive terminals use cross- polarized communications with respect to the primary users. Our approach is network-centric, i.e., the performance of the proposed dual- polarized system is investigated in terms of link throughput in the primary and the secondary networks. Our results suggest that the polarimetric dimension represents a remarkable (and simple to implement) opportunity in the context of cognitive radio networks.

Sensing time and power allocation for cognitive radios using distributed Q-learning

In cognitive radios systems, the sparse assigned frequency bands are opened to secondary users, provided that the aggregated interferences induced by the secondary transmitters on the primary receivers are negligible. Cognitive radios are established in two steps: the radios firstly sense the available frequency bands and secondly communicate using these bands. In this article, we propose two decentralized resource allocation Q-learning algorithms: the first one is used to share the sensing time among the cognitive radios in a way that maximize the throughputs of the radios. The second one is used to allocate the cognitive radio powers in a way that maximizes the signal on interference-plus-noise ratio (SINR) at the secondary receivers while meeting the primary protection constraint. Numerical results show the convergence of the proposed algorithms and allow the discussion of the exploration strategy, the choice of the cost function and the frequency of execution of each algorithm.