This paper evaluates the downlink performance of cellular networks in terms of coverage and electromagnetic field exposure (EMFE), in the framework of stochastic geometry. The model is constructed based on datasets for sub-6 GHz macro cellular networks but it is general enough to be applicable to millimeter-wave networks as well. On the one hand, performance metrics are calculated for β-Ginibre point processes which are shown to faithfully model a large number of motion-invariant networks. On the other hand, performance metrics are derived for inhomogeneous Poisson point processes with a radial intensity measure, which are shown to be a good approximation for motion-variant networks. For both cases, joint and marginal distributions of the EMFE and the coverage, and the first moments of the EMFE are provided and validated by Monte Carlo simulations using realistic sets of parameters from two sub-6 GHz macro urban cellular networks, i.e., 5G NR 2100 (Paris, France) and LTE 1800 (Brussels, Belgium) datasets. In addition, this paper includes the analysis of the impact of the network parameters and discusses the achievable trade-off between coverage and EMFE.

A novel unified frequency diverse array (FDA) and spatial data focusing (SDF) approach is proposed to simultaneously overcome time-variance and precision constraints of conventional FDA in geocasting, i.e., spatially confined broadcasting, scenarios. This paper describes a free space FDA-based SDF (FDA-SDF) system model for 2-dimensional range-angle-based focusing, including a generalized multi-purpose baseband approach for time-invariant FDA, complemented by SDF processing for improved spatial focusing precision and reduced array size. Comprehensive analytical derivations – general for any frequency offset configuration – describe the geographical FDA-SDF properties and design rules, such as geocast delivery zone steering, location, uniqueness, and size. Simulations of the proposed scheme validate theoretical derivations and demonstrate FDA-SDF’s superior spatial precision and minimal design complexity. In particular, using novel alternating logarithmic frequency offsets, a 3-antenna FDA-SDF setup is shown to match the radial and azimuthal precision of its beamforming-based FDA counterpart using, respectively, 64 and 24 antennas.

This paper presents the first-ever experimental validation of both spatial data focusing (SDF) and time-invariant frequency diverse array (FDA) range-angle-based focusing, in both free space and multipath environments. In particular, a hybrid FDA-based SDF (FDA-SDF) approach is considered for spatially confined broadcasting, i.e. geocasting. First, the base FDA-SDF free space system model is reviewed. Next, a novel OFDM-based FDA-SDF system model is introduced to ensure robust operation in multipath channels. The schemes are validated, respectively, in an anechoic chamber and outdoor measurement setup using software defined radios. Results confirm FDA-SDF's anticipated supreme spatial precision: a 0.9 m × 4.0° range-azimuth geocast delivery zone is generated by a 4-antenna array in free space. Additionally, they illustrate that OFDM-based FDA-SDF provides all necessary performance improvements for practical operation in outdoor multipath environments. Most notably, FDA-SDF is shown to overcome the time-variance flaw of conventional FDA.

In this paper, we present the fusion of passive Time-Difference-of-Arrival (TDoA) and active monostatic radar measurements in the context of single object localization and tracking. We assume a target equipped with a wireless transmitter so that the passive-TDoA and active-radar sensors can simultaneously detect it. For the localization problem, we base our approach on the Constrained-Weighted-Least-Squares (CWLS) method, which has better accuracy than the Least-Squares (LS) and Weighted-LS approaches. Nevertheless, CWLS suffers from estimation bias when the measurement noise is large. We show that it is possible to reduce such a bias by using both TDoA and monostatic radar measurements. For the tracking problem, we base our approach on Bayesian filtering. Therefore, we detail the TDoA, radar and combined measurement models required for the filtering process regardless of the Bayesian Filter implementation. We show that the combined measurements result in enhanced tracking capabilities, e.g., improved reliability and larger tracking area compared to the radar-only case. In addition, we present other possible combinations of radar and TDoA measurements that one could encounter in practical scenarios. Finally, we present experimental results to validate and support our work.

A unified frequency diverse array (FDA) and spatial data focusing (SDF) system, or FDA-SDF, is proposed as a novel approach to perform spatially confined broadcasting of information, i.e. geocasting. It is shown that SDF processing exploits FDA range-angle-dependency more effectively than conventional power focusing implementations, resulting in improved spatial focusing precision and reduced array size. Additionally, the time-variance flaw in conventional FDAs is addressed and mitigated by introducing a generalized baseband FDA model. This paper describes the FDA-SDF system model in free space, including dedicated SDF precoding, beamsteering, and equalization, exploiting FDA multi-frequency transmission for 2-dimensional range-angle-based time-invariant geocasting. Simulations of the FDA-SDF system illustrate compatibility with conventional FDA frequency offset schemes and highlight degrees of freedom for geocast delivery zone manipulation. Finally, FDA-SDF's superior spatial precision is demonstrated: a 3-antenna FDA-SDF setup matches the radial and angular precision of a conventional FDA using, respectively, 13 and 23 antennas.

OFDM-based spatial data focusing (OFDM-SDF) is proposed as a novel means of performing wireless physical layer geocasting, i.e. spatially confined broadcasting. It is shown that this approach overcomes beamforming and directional modulation (DM) limitations by exhibiting higher spatial precision with a reduced number of antennas and offering uncoupled range-angle-dependent focusing. This paper describes the OFDM-SDF system model for multipath channels, including multipath robust equalization, design rules for steering phases and sidelobe mitigation, analytical geocast delivery zone derivation, and optimized symbol mapping. Using density-based clustering of the spatial bit error rate distribution, a procedure for identifying a practical geocast delivery zone and evaluating its precision and connectivity is proposed. OFDM-SDF’s performance and multipath robustness are evaluated through Rice channel simulations as a function of the Rice factor. In particular, it is shown that a 2-antenna OFDM-SDF array matches the radial and angular precision of, respectively, a 6 and 12-antenna DM array in recent literature, while robustness is ensured for 5G small cell channels.

In this paper, we introduce a fully distributed localization algorithm based on self-synchronization mechanism. The proposed algorithm reaches consensus for the posterior distribution of the transmitter position at each base station. To reduce the communication overhead at each iteration, we propose to represent the state variable matrices of the self-synchronization mechanism with only four parameters (radial and angular means and variances). The performance of the algorithms is numerically assessed by the mean distance error and mean Kullback–Leibler divergence. Finally, we show through Monte-Carlo simulations that our approach gets very close to the direct-centralized-localization performance after a few iterations.

Iterative localization is currently seen as an attractive solution to localize a transmitter in a cellular network. It has been shown that, by iterating between a range estimation step and a multi-lateration step, it is possible to refine the estimation in the first step, where only local information is used at iteration one. The iterative approach gets close to the performance of direct localization; nevertheless, it does not seem to converge to the direct localization performance for medium and low signal-to-noise-ratio values, due to the fact that it still suffers from loss of information due to projections and data representation. In this work, we propose to approximate the range log-likelihood at the base station with a Dirichlet kernel and to perform all the processing in a common xy-domain so that projections are no longer needed. We numerically show that our approach brings significant performance gains as compared to the time-of-arrival based iterative position estimation algorithm, getting really close to the performance of direct localization.

Recently, event organizers and researchers have advocated the development of novel technologies supporting crowd control, notably for public events. This paper presents a crowd monitoring system based on probe requests (PRs), which are Wi-Fi packets smartphones send periodically. By estimating the global rate at which nearby smartphones send PRs, Wi-Fi sensors can estimate crowd counts. The core contribution of this paper is a computationally tractable method that forecasts crowd counts up to thirty minutes in the future, with forecasts becoming available as soon as two hours of data are available. The forecasting method relies on autoregressive integrated moving average (ARIMA) models. Contributions also include two methods that compute prediction intervals associated with the forecasts, one of which is based upon generalized autoregressive conditional heteroskedasticity (GARCH) models. Recent real-world data from Winter Wonders 2018/2019 (an event that took place in Brussels, Belgium) notably demonstrate that the proposed forecasting method outperforms its immediate variations as well as baseline models (i.e., random walk models).

An OFDM-based Spatial Data Focusing (OFDM-SDF) approach is proposed as an improvement over standard Time-based Spatial Data Focusing (T-SDF) as a means of wirelessly broadcasting information towards confined spatial locations, i.e. wireless geocasting. It is shown that this approach allows for 2-dimensional focusing, hence leading to far greater flexibility in terms of geocasting scenarios compared to T-SDF and beamforming, both limited to angular focusing only. This increased flexibility does not come at any trade-off costs in terms of spatial selectivity, and hence, just as T-SDF, OFDM-SDF is shown to establish a considerable increase in attainable geocasting accuracy when compared to traditional power focusing methods. This paper describes the free space OFDM-SDF system model for uniform linear antenna arrays, including beamsteering and sidelobe mitigation techniques. Based on simulations, the performance of OFDM-SDF is compared to both T-SDF, as well as classical beamforming.

Secret-key generation exploiting the channel reciprocity between two legitimate parties is an interesting alternative solution to cryptographic primitives for key distribution in wireless systems as it does not rely on an access infrastructure and provides information-theoretic security. Many works in the literature assume that the eavesdropper gets no side information about the key from her observations provided that: (i) it is spaced more than a wavelength away from a legitimate party and (ii) the channel is rich enough in scattering. In this paper, we show that this condition is not always verified under realistic propagation conditions and we study the resulting secret-key capacity.

In practice, the subspace-based algorithms such as Multiple Signal Classification (MUSIC) suffer from sensitivity to antenna-array response errors and therefore they require the assessment of the calibration gain and phase perturbations. This paper evaluates experimentally the accuracy of Angle-of-Arrival (AoA) estimation based on the MUSIC algorithm only coming from these perturbations in the context of Internet-of-Thing (IoT) applications. First of all, a new Over-the-Air (OTA) calibration method is proposed and gain and phase uncertainties are investigated. The impact of these uncertainties on the accuracy of AoA estimation is then studied and compared with the theoretical analysis. The experimental results show that the calibration errors coming from hardware imperfections can cause some degrees of uncertainty in AoA estimation.

A relay network can provide the extended link capacity and coverage thanks to its good spatial diversity. In order to analyze the relay network performance, most of the works in literature assume the independent relaying channels. In practice, the received signals experience the common scatterers, hence the correlated relaying channels should be considered. In this paper, we first derive a novel and accurate probability density function (PDF) of a modulus of the sum of products of two correlated zero-mean complex Gaussian random variables (RVs), which is useful for the statistical analysis of the cascaded relaying fading channels. Based on this result, we secondly investigate the received signal-to-noise ratio (SNR) outage probability of a relay network in the presence of correlated relaying channels. It is found that the correlation magnitude is more important than the correlation phase for the analysis of power-based signal-detection of relay networks. Numerical simulations have been carried out to confirm the correctness of the derivation.

Multiple-antenna technologies provide a good spatial diversity gain for emerging communication systems. In combination to the orthogonal frequency division multiplexing (OFDM) modulation, a precoding system can be easily implemented and efficient to combat the effect of fading channels. However, the precoding system requires a perfect knowledge of the channel, which is not usually hold in practical cases. In this paper, we investigate the impact of the imperfect channel state information (CSI) on the downlink performance of multiple-input single-output (MISO) OFDM systems using a matched-filter (MF) precoder. Particularly, the exact mean-square-error (MSE) expression of the equalized received signal of the MF precoding system is derived. Numerical simulations with Rayleigh fading channels are carried out to validate the analysis. The results show that the imperfect CSI-based precoder causes a MSE plateau compared to the ideal case of using the perfect CSI.

Angle-of-arrival (AoA) estimation-based localization systems are a promising technology in the context of Internet-of-Things. The system considered is a densely deployed set of anchors equipped with arrays of antennas able to measure the AoA of the signal emitted by the device to be located. Naturally, the AoA measurements are prone to uncertainties due to noise, channel conditions and uncertainties on the anchors placement and orientations. In this work, a method based on Polynomial Chaos Expansions is proposed to perform anchor selection in order to enhance the precision of the positioning. An experimental setup with six anchors is implemented to evaluate the performance of the proposed method. The experimental results demonstrate that by applying our anchor selection method, the performance of the localization system improves.

The goal of this work is to identify the possible degradations on a Wi-Fi based Passive Radar in the presence of an interferer. We assume that the signal-of-opportunity and the interference are 802.11ax compliant. The mathematical model derived for the interference shows that in a synchronized case, the interference may yield ghost targets. When a more occasional interference scenario is considered, the range/Doppler Map accuracy decreases significantly. Furthermore, numerical results are provided to quantify certain effects of OFDM radar interference on range/Doppler Maps.

Spatial Data Focusing (SDF) is introduced as a novel technique that allows wireless broadcasting of information towards specific spatial locations only. It is shown that this approach allows one to target geographic areas more accurately than traditional power focusing methods, using limited equipment at the transmitter. This paper describes the SDF system model for linear arrays, based on simple modulation techniques and transmitter architectures, both in pure line-of-sight and multipath environments. In particular, the robustness of the scheme is proven for over-the-ground propagation environments. Theoretic results are illustrated by simulations, confirming the increased spatial selectivity of SDF and showing the influence of various design parameters of the scheme on the resulting beam.

Internet of Things (IoT) integrates numerous technologies to obtain the location information of the sensors in various ways. The most common localization methods are based on measuring a location dependent parameter such as Received Signal Strength (RSS) or Angle of Arrival (AoA). Then the measured parameters are used to estimate the sensor location by using a multilateration algorithm. AoA estimation can provide good localization accuracy for narrowband array-based systems. The research has drawn considerable attention in the literature and numerous methods have been proposed to improve the localization accuracy. The two-step method suffers from information loss from the received signals when the estimation of the parameters is communicated to the multilateration step. In this paper, we propose a new localization algorithm which iterates between AoA estimation and multilateration steps to refine the position estimate. We develop a localization scenario for the assessment and validation of the algorithm in Bluetooth Low Energy (BLE) sensor networks. Simulation results show that the proposed solution achieves a better localization accuracy when compared to the common two-step and achieving this after only a few iterations. Additionally, our method comes very close to the performance of the position estimation relying directly on the received signal, known as Direct Position Estimation (DPE), while reducing its complexity.

Time reversal-based indoor positioning system (TRIPS) is a promising technology for the centimeter-accuracy indoor positioning, since it exploits the rich multipath propagation in indoor environments as a specific signature for each location. In TRIPS, a database is first constructed via channel probing. Well-calibrated devices are usually assumed in this process, i.e., no hardware impairments. However, a low cost terminal to be located, whose typical impairment is the I/Q imbalance (IQI) at the front-end transmitter, can significantly influence the TRIPS performance. More specifically, IQI creates an interference image of the signal that reduces the metric value used in TRIPS and hence decreases the localization accuracy. In this paper, we analytically investigate the impact of the IQI on the metric of TRIPS. A closed-form approximation of the localization metric inherent to the IQI is derived. In order to improve the TRIPS performance, an effective IQI mitigation method is proposed. Numerical simulations are carried out to validate the derived analytical expression under the IQI impact and the proposed compensation method.

Iterative localization is arising as a promising solution to determine the position of a mobile station in a cellular network. We recently showed that in a perfect line-of-sight environment, iterating between the conventional delay estimation and multi-lateration steps allows to approach the performance of the direct localization based on the observation of the received signals. In this paper we extend our iterative localization method to operate in rich multipath environments. Simulation results prove that given some prior knowledge on the power delay profile of the channel, the proposed iterative algorithm is robust to harsh propagation environments and performs very close to the direct localization approach.

The introduction of the Bluetooth Low Energy (BLE) standard significantly streamlines the development of the Internet of Things (IoT) applications. These applications often require the sensor positioning to link the measurements with its location. Therefore, it is essential to conduct studies on BLE transmitter positioning methods. Power fingerprinting transmitter positioning approaches are commonly used in BLE network. However, these methods have their own limitations in terms of practical use and ease of implementation. The purpose is to develop a simple BLE transmitter positioning of high accuracy. In this paper, a BLE transmitter positioning method is proposed based on Angle of Arrival (AOA) estimation. Multiple Signal Classification (MUSIC) algorithm is used for angle estimation due to the high angular resolution and sensitivity. Several experiments have been conducted in an indoor environment and the results compared with simulation results. Experimental results show that our proposed BLE transmitter positioning method achieves a promising positioning accuracy.

Time reversal has been shown as a promising technology for the indoor positioning. Instead of mitigating the multipath channel, the time reversal based indoor positioning system (TRIPS) exploits the rich multipath propagation in indoor environments as a specific signature for each location. In order to do this, a database should be first constructed by channel probing. The devices used in this process are assumed to have no hardware impairments or be well calibrated. However, the low cost terminal to be located can exhibit impairments, such as I/Q imbalance at the front end transmitter known to create an interference image of the signal that can particularly impact the performance of time reversal. In this paper, we investigate the impact of the I/Q imbalance on the TRIPS. We analytically show that the I/Q imbalance modifies the metric used for the localization and hence reduces the spatial focusing gain of TRIPS. Numerical simulations are carried out to evaluate this observation. The results further show that the I/Q imbalance creates errors in the positioning estimation. More specifically, at the gain and phase imbalances of respectively 1.1 and 10°, the focusing gain reduction is about 0.5 dB while the estimation error rate is increased by about 5% in the specified scenario.

Emerging cellular networks integrate the user terminal geo-localization function besides the communication function. The conventional positioning approach is to estimate the terminal location in two-steps: first the distance to all connected base stations is assessed based on signal time-of-flight measurements, then the location is deduced from the distances by multi-lateration. The two-step approach incurs a performance degradation because information is lost from the received signal when the multi-lateration is performed. In this paper, we propose to iterate between the two conventional steps to progressively refine the distance estimates based on the knowledge of the position estimate obtained from the previous iterations. The information exchanged between the two-steps not only consists in the mean of the estimates (distance or position) but also of their variance that convey information about the reliability of the estimates. Simulation results show that the achievable performance after a few iterations is close to the performance of the optimal approach that directly estimates the position based on the observation of the received signal.

In the field of 60 GHz high data rate wireless and Body Area Networks communications, fast computation and accurate analytic models are required to predict the budget link. In this paper, an indoor off-body channel model restricted to TE polarization transmitters and receivers is studied numerically. Some channel properties are extracted and compared to the indoor channel to study the impact of the human body on the 60 GHz propagation.

On-body channel dynamics are primarily affected by body motions, which not only cause dynamic scattering conditions but also modify the geometry of links over time. We experimentally investigate the Doppler spectra of narrowband on-body channels at 2.45 GHz in both anechoic and indoor environments. The body dynamics resulting from the motion are shown to directly affect the Doppler spectrum. An empirical model is proposed to describe the global and/or local Doppler spreads. The fit of a 3-cylinder-based physical model accounting for the arm swinging is also evaluated.

Cognitive radios impose challenges on the design of efficient signal detectors, including wide bandwidth sensing and large dynamic range support. The recently considered compressed sensing theory helps in relaxing the constraints on the design of the analog front-end. The maximum likelihood method introduced here is computationally simple since it does not require a signal reconstruction, unlike most methods introduced in the current literature. Moreover, the metric is optimum, works for any modulation scheme and is independent of the emitted signal knowledge and the number of occupied bands. The results are supported with Matlab simulations, a statistical study is performed and the probabilities of misdetection and false alarm are plotted for different scenarios, proving the efficiency of the estimator in a range of plausible SNRs and subsampling factors.

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. In this paper, a decentralized Q-learning power allocation algorithm is proposed to maximize the Signal on Interference-plus-Noise Ratio (SINR) at the secondary receivers while meeting the primary protection constraint. Numerical simulations allow to compare the performance of the proposed algorithm with the performance of the optimal centralized power allocation scheme in the scenario of multiple independent secondary cells located on the border of a primary network protection contour. The impact of the definition of the learning algorithm cost function on the secondary user fairness and the system performance is discussed. The optimal execution frequency of the algorithm is obtained and different strategies for the exploration of new solutions are compared.

We consider the sensing of the frequency spectrum for cognitive radios, based on energy detection. It has been shown that the sensing reliability can be improved by using several cooperating cognitive radios that exchange their individual sensing information to a coordinator node through specific control channels. The coordinator node then combines the received information in order to make a decision about the primary network presence. In this paper, we compute analytically the probability density functions of the noise coming from the two-bits non-uniform quantization of the energy measure at each node and from the non-uniform bit flipping on the control channel. The quantization step and the bit flipping probabilities are selected to reduce the impact of the error. A new optimal fusion rule is proposed that takes into account the control channel noise distribution. Numerical simulations show that this new scheme outperforms the Maximum Ratio Combining scheme when different false alarm probabilities are used by the nodes.

In cognitive radio systems, the sparse assigned frequency bands are opened to secondary users. We consider the sensing of the frequency spectrum for cognitive radios, based on energy detection. It has been shown that the sensing reliability can be improved in the presence of fading by using several cooperating cognitive radios that exchange their individual sensing information to a coordinator node. The coordinator node combines the received information in order to make a decision about the primary network presence. The primary network sensing times used by the cognitive nodes should be chosen high enough to ensure the correct detection of the primary emitter but low enough so that the nodes still have enough time to communicate. In this paper, we propose a decentralized Q-Learning algorithm to allocate the sensing times of the cognitive radios in a way that maximizes the throughputs of the radios. A rigorous proof of the convergence of the proposed algorithm is provided. Numerical results show that it converges faster to the same throughputs as those obtained with a reference algorithm.

Compared to classical spatial MIMO wireless systems, cross-polarized MIMO systems are an interesting way to reduce equipment size while still maintaining low inter-antenna correlation. In this paper, the time-variation of the polarization of the received waves is investigated. In this scenario, a theoretical formulation is proposed in order to obtain the parameters of the elliptical polarization, based on the signals received on three perpendicularly polarized antennas. A measurement campaign has been performed in an indoor-to-indoor scenario and at a frequency of 3.6 GHz. Different measurement positions are considered in a Line-Of-Sight (LOS) and a Non-Line-of-Sight (NLOS) scenario. Based on these measurements and the proposed theoretical formulation, the time-variation of the parameters describing the polarization ellipse is analyzed and a time-variant statistical model is proposed.

In previous work, it has been observed that the specular and the diffuse component are linked in the angular domain. The idea of adding a diffuse component to each specular cluster has been proposed to model the specular-diffuse channel. In this paper, an approach is proposed to treat the specular and the diffuse measurement data simultaneously, with a clustering algorithm that is applied jointly on the specular and the diffuse component. The output of the clustering algorithm gives clusters that are characterized by their specular and their diffuse component. Measurement results are presented based on double-directional measurements, and parameter values are extracted using the proposed methodology.

In cognitive radios systems, the sparse assigned frequency bands are opened to secondary users. We consider the sensing of the frequency spectrum for cognitive radios, based on energy detection. It has been shown that the sensing reliability can be improved by using several cooperating cognitive radios that exchange their individual sensing information to a coordinator node. The coordinator node combines the received information in order to make a decision about the primary network presence. In this paper, we propose a decentralized Q-learning algorithm to share the sensing time among the cognitive radios in a way that maximize the throughputs of the radios. Numerical results show the convergence of the proposed algorithm and allow to discuss the exploration strategy, the choice of the cost function and the frequency of execution of the algorithm.

To improve the performance of a DVB-S2 communication channel, it is necessary to remove the non-linear interferences induced by the power amplifier. Due to the presence of filters in the channel, this non-linear interference is also interference with memory. One of the most used techniques to cancel out the interferences in this case is the order p compensation. The order p compensation can be mathematically described as a recursive algorithm. The non- linear interference is supposed to be smaller after each step of the algorithm. However this is not always the case and it is necessary to define upper bounds on the system to ensure convergence of the algorithm. Upper bounds were already defined for general nonlinear systems, but are however very inaccurate for the case of a DVB-S2 communication channel. In this paper, we will present two new methods to refine these upper bounds.

Cognitive radios have been introduced as a dynamic way to share the frequency spectrum within wireless networks. After sensing its environment to discover the available frequency resources, a cognitive network allocates the locally available carriers among its nodes, under the constraint of negligible interference on licensed operators. The allocation, based on partial knowledge of the network parameters, must be optimized in order to provide maximum rates in the cognitive network. In addition, having a stable allocation reduces the control traffic inherent in dynamic networks. This paper presents a new and fast iterative way to allocate the available carriers, that reduces the control traffic and provides fairness at the same time. Using the properties of a proportionally fair objective function, we fasten the optimum search in the space of possible allocations, and reach that optimum with no approximation. Numerical results show that the proposed iterative algorithm is less complex than an analytical solution in usual dynamic carrier allocation scenarios.

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.

We consider the sensing of the frequency spectrum based on energy detection for cognitive radios. It has been shown that the reliability of the sensing can be improved by using several cooperating cognitive radios. In such a network, the cognitive nodes exchange their individual sensing information to a coordinator node through specific control channels. The coordinator node then combines the received sensing informations in order to make a decision about the primary network presence. In this paper, we assess the impact of the noise coming from the quantization of the energy measure at each node and from the imperfect communication on the control channels. We also propose two complementary approaches to compensate for the noise: the system designer can firstly reduce the noise impact by using an energy coding technique that smartly shares out the bit error probabilities and secondly compensate for the remaining noise by designing a new soft metric fusion rule at the coordinator node. Numerical simulations show that this new scheme significantly improves the performance compared to the MRC scheme in the presence of control transmission noise.

Compared to classical spatial MIMO wireless systems, cross-polarized MIMO systems are an interesting way to reduce equipment size while reducing the inter-antenna correlation. Cross-Polar Discrimination (XPD) and Co-Polar Ratio (CPR) are two important parameters describing multi-polarized channels. In this paper, the behavior of these parameters is investigated for different observation scales. A measurement campaign has been performed in both Outdoor-to-Indoor and Indoor-to-Indoor scenarios, at a frequency of 3.5GHz. Small-scale variations of XPD and CPR are analyzed in different spatial positions. The distance-related and large-scale variations of XPD and CPR are also investigated and a model is deduced.

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.

Cross-polarized antenna systems are an attractive way to reduce equipment size while maintaining low interantenna correlation. In this paper, the polarization behaviour of indoor channels is investigated. A measurement campaign has been conducted at 3.6 GHz for a dual-polarized transmitter and a tri-polarized receiver in a non-Line-of-Sight (NLOS) scenario. The spatial and delay characteristics are extracted using a pertap beamforming algorithm. Distinct paths are isolated and the polarization of each wave is expressed in terms of its spherical components. The cross-polarization discrimination (XPD) of the wave is investigated as a function of its physical propagation parameters. The XPD of the wave is shown to be sensitive to spatial characteristics, while being insensitive to delay.

The latest developments and experimentation in wireless ad-hoc networks show that their use might provide a reliable coverage extension to cellular networks. The integration between 3rd Generation/4th Generation (3G/4G) and ad-hoc networks may offer a great number of benefits in term of deployment cost, accessibility, peer-to-peer (P2P) traffic redirection, etc. While the Open Systems Interconnection (OSI) reference model advocates for a clear separation of routing, transmission and physical layers, in this paper we show that an effective routing algorithm requires knowledge and constructive interaction between these layers. More precisely, we propose the use of a new routing algorithm, derived from the Ad-Hoc On-Demand Distance Vector (AODV) algorithm, based on the physical aspects of the wireless medium and optimized for indoor transmissions. Our results emphasize the possibility of a reliable cellular coverage extension in indoor environments, on the basis of a beneficial cross-layer interaction between routing, power control, transmission, and propagation.

info:eu-repo/semantics/published

A cluster-based channel model is presented that includes polarization characteristics. Measurements have been carried out in an indoor environment at 3.6 GHz using a dual-polarized transmitter and a tri-polarized receiver. Individual propagation paths are extracted using the SAGE algorithm, and a cross-polar discrimination (XPD) per ray is defined. Clusters are identified in the co-elevation-azimuth-delay domain, with an automatic clustering algorithm. The cluster properties are investigated and polarization characteristics are identified on a per-cluster basis. Finally, the obtained model is simulated and extraction-independent parameters are compared with experimental parameters for validation.

This paper investigates the polarization behaviour of indoor multi-polarized MIMO channels at 3.6 GHz. Measurements have been carried out in non-line-of-sight (NLOS) and line-of-sight (LOS) using a dual-polarized transmitter and a tri-polarized receiver. The spatial and delay characteristics are extracted using a per-tap beamforming algorithm. Distinct waves are isolated and the polarization of each wave is expressed in terms of its spherical components. The evolution of the waves polarization is investigated as function of its physical propagation parameters. The polarization of the waves is shown to be sensitive to spatial characteristics, while being insensitive to delay. Direct line-of-sight rays show to have higher XPD than in the NLOS case, while the reflected rays have a similar XPD than in a NLOS case.

Cross-polarized antenna systems are used to reduce equipment size in MIMO systems while still achieving low inter-antenna correlation. One fundamental parameter of cross-polarized MIMO systems is the cross-polar discrimination (XPD). This paper proposes a model to determine the XPD as a function of channel condition and under different antenna configurations. The environment is supposed to have a truncated Laplacian Power Angular Spectrum (PAS) that is widely used in standardization bodies. Antenna XPD is shown to be sensitive to different channel conditions, as well as to different receiver orientations. Measurements were conducted at 3.5 GHz to validate the theoretical model. Good agreement is obtained between theoretical XPD and experimental results.

The impulse response in a reverberation chamber is investigated from an experimental and theoretical point of view. A full description is thus proposed based on measurements and confirmed by means of theoretical insights. In particular, the K factor of each tap is emphasized. BER simulations for OFDM and single carrier frequency domain equalization (SC-FDE) systems are applied on this channel model and compared to the 802.11n channel model showing some differences mainly for the OFDM transmission.

This paper proposes a closed-form expression for the correlation coefficient and the cross-polarization discrimination (XPD) of a randomly oriented dual- or tri-polarized MIMO system. The environment is supposed to have a truncated Laplacian power azimuth spectrum that is widely used in standardization bodies. correlation and XPD are shown to be highly sensitive to receiver orientation, azimuth spread and environment depolarization behavior. Measurements were conducted at 3.5 GHz to validate the theoretical model. Good agreement is obtained between theoretical curves and experimental results for correlation and XPD when comparing at different receiver orientations.

Using wireless sensors worn on the body for pervasive monitoring is expected to become more and more common in the next years. The achievement of this application requires solving some challenges, especially in the power saving domain. In order to develop efficient power radio communications, an interesting investigation field is the multi-sensor MIMO (MS-MIMO) ultra-wideband (UWB) network. It is realistic to assume that each sensor carries one antenna, while the central device uses an antenna array. In this paper, a new analytical channel model for the diffracted waves mechanism is proposed. It is derived from the existing IEEE 802.15.4a standard channel model and innovates with space-time considerations.

In this paper, the wideband channel is studied inside a vehicle. Propagation inside a recent car (model bought in 2006) is investigated and the power delay profile is measured for three different scenarios. Two kinds of transmission bandwidths are studied, 200 MHz around 5 Ghz and an ultra-wideband bandwidth of 3 Ghz around 4.5 GHz. The ultra-wideband measurements allow to clearly identify the propagation mechanism in this kind of environment. For each scenario, an exponential model for the power delay profile is shown to be in good agreement. A theoretical model based on reverberation chamber theory is proposed. It allows to predict the rms delay spread inside the car. A comparison with measurements inside a reverberation chamber is presented.

In this paper, a wideband characterization of coupled reverberation chambers is presented. This testbed allows to model dense multipath environments and to control the channel by means of a waveguide connecting the two chambers. A wideband model is first proposed. This model emphasizes the presence of different independent paths between the emitter and the receiver. Some measurements of impulse response are then presented. Very high values of rms delay spread are obtained between the chambers leading to inadequacy with indoor environments for example. A modification of the testbed by means of absorbing materials is proposed. Measurements results are presented showing that the best position for placing absorbing materials is in the transmitter chamber. The values of rms delays spread obtained pave the way to a new testbed for MIMO systems

SCOPUS: cp.p

Using ultra-wideband (UWB) wireless sensors placed on a person to continuously monitor health information is a promising new application. However, there are currently no detailed models describing the UWB radio channel around the human body making it difficult to design a suitable communication system. To address this problem, we have measured radio propagation around the body in a typical indoor environment and incorporated these results into a simple model. We then implemented this model on a computer and compared experimental data with the simulation results. This paper proposes a simple statistical channel model and a practical implementation useful for evaluating UWB body area communication systems.

Using wireless sensors placed on a person to continuously monitor health information is a promising new application. However, there are currently no models describing the radio channel around the human body making it difficult to design a suitable communication system. To address this problem, we have simulated electromagnetic wave propagation around the body and incorporated these results into a simple model. We then compared this model with measurements taken around the human torso and with previous studies in the literature. This paper proposes a simple statistical channel model useful for evaluating both UWB and (after resampling) narrow-band body area communication systems.