This paper addresses the peak-to-average power ratio (PAPR) reduction of Orthogonal Frequency Division Multiplexing (OFDM) waveforms under unit modulus constraint for Integrated Sensing and Communication systems (ISAC). We conduct a comparative analysis of radar and communication performance for waveforms optimized using classical PAPR reduction techniques, primarily Tone Reservation (TR) and Selected Mapping (SLM), as well as their state-of-the-art sensing-aware variants, across radar- and communication-centric scenarios. Additionally, we investigate the pilot-aided case with pseudorandom pilot tones. The results reveal a trade-off between PAPR reduction, radar sensing capability, and computational cost, offering practical insights for efficient OFDM-based ISAC system design.

This work investigates Generalized Receiver Spatial Modulation (GRSM) in a massive MIMO downlink scenario over millimetre wave channels. While GRSM enhances spectral efficiency (SE) and reduces power consumption, indexing additional bits using the spatial dimension increases the vulnerability to detection errors. These errors primarily stem from thresholddependent spatial detection. We propose a novel predefined threshold computation method minimizing spatial detection errors, rigorously validated through the Maximum A Posteriori (MAP) criterion. Furthermore, we derive an analytical Average Bit Error Probability (ABEP) expression tailored for energy detection, exploiting inherent constellation energy distributions. The analytical derivation was validated via link-level simulations under two scenarios: (i) perfect spatial detection, and (ii) practical spatial detection. The results show spatial errors dominating overall performance, shifted from theoretical values by practical spatial detection using multiple thresholds for different energy levels in 16QAM. Crucially, an inverted error trend revealed between GRSM-4QAM and GRSM-16QAM, highlighting a tradeoff between error resilience, complexity, and energy efficiency.

Energy consumption remains a predominant constraint in numerous Internet of Things (IoT) applications, as microcontrollers typically exhibit excessively high power consumption. To address this issue, novel circuit designs have been introduced, and the utilization of spiking neurons and analog computing has emerged as a promising approach, enabling substantial reductions in power consumption. However, operating within the analog domain poses challenges in managing the sequential processing of incoming signals, which is essential for processing wireless communication signals. In this study, we leverage a bio-inspired phenomenon known as Saturating Synapses to create a temporal filter. We present a model of neurons whose synapses respond specifically within a defined range of delays between two incoming spikes, but remain unresponsive when the Inter-Spike Timing (IST) falls outside this range. Our investigation delves into the model’s parameters to better understand their selection criteria and adaptation for the IST. Subsequently, we show the system’s efficiency in recognizing specific sequences defined by these ISTs. This paper’s novelty lies in the proposal of a new analog processing approach for managing temporal sequences, which permits a much lower energy consumption compared to classical approaches.

In Spectrum cartography (SC), the generation of exposure maps for radio frequency electromagnetic fields (RFEMF) spans dimensions of frequency, space, and time, which relies on a sparse collection of sensor data, posing a challenging ill-posed inverse problem. Cartography methods based on models integrate designed priors, such as sparsity and low-rank structures, to refine the solution of this inverse problem. In our previous work, EMF exposure map reconstruction was achieved by Generative Adversarial Networks (GANs) where physical laws or structural constraints were employed as a prior, but they require a large amount of labeled data or simulated full maps for training to produce efficient results. In this paper, we present a method to reconstruct EMF exposure maps using only the generator network in GANs which does not require explicit training, thus overcoming the limitations of GANs, such as using reference full exposure maps. This approach uses a prior from sensor data as Local Image Prior (LIP) captured by deep convolutional generative networks independent of learning the network parameters from images in an urban environment. Experimental results show that, even when only sparse sensor data are available, our method can produce accurate estimates.

IoT devices, constrained by limited resources, must balance energy consumption and performance, with the communication module often being the largest energy consumer. Among various communication technologies, Bluetooth Low Energy (BLE) is notable for its efficient design and performance. However, despite recent BLE optimizations, the Neighbor Discovery Process (NDP) remains energy-intensive. We propose a new approach that uses a modern microcontroller's multicore architecture to dynamically adjust the scanning duration based on the number of devices to be discovered. Our simulations show that avoiding a fixed scanning duration by concluding the NDP once the required devices are found can significantly reduce energy consumption. Validation through simulations and real-world experiments demonstrates up to 50% energy savings compared to a fixed 1-second scan using a single core during the NDP.

Energy consumption is a critical issue for the deployed nodes in the area of Internet of Things (IoT). This is the reason why many research projects focus on Wake-up Radio (WuR) receivers that permit the nodes to remain in sleep mode for as long as possible and to wake them up only if needed. However, current WuR use classic microcontrollers that are still too energy consuming. Meanwhile, Spiking Neural Networks (SNN) offer much lower power consumption. Thus, we propose to adapt those neural networks as a wake-up radio receiver in the IoT context. We aim at waking up the concerned node by recognising one or many activation sequences in a bit flow. We propose here a configuration for the neurons along with the design of appropriate sequences. We present the performances of our system and the impact of different parameters on the accuracy of the recognition system.

Impulsive noise, where large amplitudes arise with a relatively high probability, arises in many communication systems including interference in Low Power Wide Area Networks. A challenge in coping with impulsive noise, particularly alpha-stable models, is that tractable expressions for the log-likelihood ratio (LLR) are not available, which has a large impact on soft-input decoding schemes, e.g., low-density parity-check (LDPC) packets. On the other hand, constraints on packet length also mean that pilot signals are not available resulting in non-trivial approximation and parameter estimation problems for the LLR. In this paper, a new unsupervised parameter estimation algorithm is proposed for LLR approximation. In terms of the frame error rate (FER), this algorithm is shown to significantly outperform existing unsupervised estimation methods for short LDPC packets (on the order of 500 symbols), with nearly the same performance as when the parameters are perfectly known. The performance is also compared with an upper bound on the information-theoretic limit for the FER, which suggests that in impulsive noise further improvements require the use of an alternative code structure other than LDPC.

A key challenge for random access communications arising in the monitoring of physical phenomena is optimizing the access policy. This is particularly the case when the activity of each sensor is correlated, contrasting with the independence assumption underpinning standard slotted ALOHA schemes. In this paper, we propose a stochastic resource allocation algorithm to reduce outages via maximization of the expected number of sensors that are able to reliably communicate with an access point. Allowing for devices to transmit data over multiple consecutive frames, we show that the proposed algorithm converges with probability one to a locally optimal solution. Moreover, our algorithm significantly outperforms existing methods in terms of the average number of successful transmissions when utilizing successive interference cancellation.

The Internet of Things faces a significant scaling issue due to the rapid growth of the number of devices and asynchronous communications. Different technologies in the license-free industrial, scientific, and medical (ISM) band have been widely deployed to fill this gap. LoRa and Sigfox are the most common. Many devices can use the ISM band if they obey the regulations and cope with internal and external interference. However, when there is massive connectivity, the effect of the inter and intra-network interference between multiple networks is significant. This study uses a deep learning-based technique to decode signals and deal with the interference in the uplink of a LoRa network. Two classification-based symbol detection methods are proposed using a deep feedforward neural net-work (DFNN) and a convolutional neural network (CNN). The proposed receivers can decode the signals of a selected user when many LoRa users transmit simultaneously using the same spreading factor over the same frequency band (intra-spreading factor interference), and multiple Sigfox users interfere (inter-network interference). Simulation results show that both receivers outperform the conventional LoRa receiver in the presence of interference. For a target symbol error rate (SER) of 10−3, the proposed DFNN and CNN-based receivers attain around 2 dB and 3.5 dB gain, respectively.

The increasing number of devices together with uncoordinated transmissions result in a major challenge of scalability in the Internet of things. This paper deals with signal detection in the uplink of a LoRa network through a deep learning-based approach. Two strategies are proposed: regression for bit detection based on a deep feedforward neural network and classification for symbol detection based on a convolutional neural network. These receivers can decode a selected user’s signals when multiple users simultaneously transmit over the same frequency band with the same spreading factor. Simulation results show that both receivers outperform the classical LoRa one in the presence of interference. The results show that the introduced approach is relevant to deal with the scalability issue.

The European Train Control System (ETCS) relies today on the 2nd generation cellular system GSM-R. GSM-R obsolescence has triggered the evolution of the current European Railway Traffic Management System (ERTMS) to the Future Railway Mobile Communication System (FRMCS), under development. FRMCS considers the use of several radio access technologies in parallel such as Wi-Fi, LTE, Satellite and 5G. Testing new communication systems along railway tracks is time and money consuming. Consequently, it is important to develop a zero-on-site-testing approach thanks to the development of emulation platforms. These platforms, combining hardware and software, are able to reproduce in laboratory real railway infrastructure behavior and scenarios. This paper presents the EMULRADIO4RAIL platforms, developed in the framework of Shift2Rail program and gives examples of results obtained.

Recently, there has been a proliferation of wireless communication technologies in unlicensed bands for the Internet of Things. A key question is whether these networks can coexist given that they have different power levels, symbol periods, and access protocols. The main challenge is to characterize the impact of mutual interference arising from distinct uncoordinated networks. It is known that when interferers form a homogeneous Poisson point process and transmit only on a single subband, the interference is often well-modeled by the heavy-tailed α-stable distribution. In this paper, we focus on the scenario where interferers transmit on multiple subbands. Under a policy where each interferer independently accesses each band with probability p, we provide an exact characterization of the interference random vector. Exploiting this characterization, we derive optimal linear combining weights and an analytical approximation for the bit error rate (BER), accurate for large transmit power. A key observation is that the expression for the BER admits an interpretation in terms of an array gain and a fractional diversity gain.

In this paper, we present a new receiver design, which significantly improves performance in the Internet of Things networks such as LoRa, i.e., having a chirp spread spectrum modulation. The proposed receiver is able to demodulate multiple users simultaneously transmitted over the same frequency channel with the same spreading factor. From a non-orthogonal multiple access point of view, it is based on the power domain and uses serial interference cancellation. Simulation results show that the receiver allows a significant increase in the number of connected devices in the network.

As the Internet of Things (IoT) is largely supported by wireless communication networks in unlicensed bands, there has been a proliferation of technologies that use a large variety of protocols. An ongoing challenge is how these networks can coexist given that they have different power levels, symbol periods, and access protocols. In this paper, we study the statistics of interference due to IoT networks that transmit small amounts of data. A key observation is that sets of active devices change rapidly, which leads to impulsive noise channels. Moreover, these devices operate on multiple partially overlapping resource blocks. As such, we characterize the joint distribution and propose a tractable model based on copulas. Using our copula model, we derive closed-form achievable rates. This provides a basis for resource allocation and network design for coexisting IoT networks.

In impulsive noise, the inputs of the belief propagation decoder can be complex to compute or even impossible when the noise distribution is not known. We propose a simple approximation of the log-likelihood ratio that maps the channel output to the input of the error correcting decoder, for instance, LDPC decoders. This approximation is designed for additive impulsive noise channels, nevertheless, it is not computationally demanding and easy to be implemented. It requires the estimation of three parameters and we propose an efficient way to do it. Moreover, in terms of performance, our solution is barely discernible from the optimal receiver which is computationally prohibitive.

In a usual MIMO framework when noise is assumed Gaussian, the channel matrix can be estimated with a Least Square approach. In addition, the Euclidean distance is optimal to detect the transmitted symbol. However, in dynamic interference, the Gaussian assumption is no longer valid. In this case, the α-stable model is better suited and the receiving strategy fails. In this paper we present a MIMO communication system based on QPSK modulation, Alamouti coding, 2 transmitting antennas, M receiving antennas and supervised channel estimation. We propose to modify both the channel estimation method and the distance metric to improve the receiver strategies. The Least Absolute Deviation estimator is used to estimate the channel matrix, instead of Least Squares, and the p-norm replaces the Euclidean distance (for which p = 2), with an estimator for the value of p. Our approach outperforms the traditional method.

The number of connected devices is growing and in the near future it is expected to become extremely large in cities. As a consequence, using simulators to study and prepare a project of installing new networks before their real deployment is of great importance. They can help to predict some important information like signal overload or the feasibility of the deployment in terms of location, interferences, communication and cost. In this paper we present a new architecture for the platform CupCarbon, developed within the research project PERSEPTEUR. The main objective of this platform is to design and simulate Wireless Sensor Networks dedicated to Smart-city and IoT applications. It allows to validate distributed algorithms in a 2D/3D environment, taking account of the city buildings in which to deploy the network, the mobiles, and using accurate models of radio propagation and interferences in that environment.

In the new generation of networks, not only computers are connected but also objects like cars, streets, buildings, or just, everything. In general, these things communicate by means of internet using gateways. Simulating these systems can help to validate specific algorithms and concepts. However, it cannot give any accurate information about reality when some problems arise such as delays, disconbectivity, attacks, insecurity, and especially in the case of large networks. Also, the real implementation of such networks is very complex, time consuming and can be impossible when the nodes are situated in different cities or countries. In this demo we propose a new platform called CupCarbon-Lab based on the existing simulator CupCarbon, where the codes used in simulation can be directly injected in real connected embedded cards like Raspberry Pi cards. This platform can automatically generate from the software a real IoT network even it is already deployed, which can be reconfigured without the need to go through each node. It also helps to test the feasibility and the scalability of an algorithm in real conditions.

Robust communication is necessary for many wireless applications. Making a decision at the receiver requires an evaluation of the likelihood. However, in impulsive noise, the traditional Gaussian-based receiver exhibits a very significant performance loss. This paper proposes to approximate the likelihood ratio in a binary transmission with a function adapted to impulsive noise conditions but also efficient when noise is purely Gaussian. We introduce a blind estimation of the two parameters defining the approximation and evaluate its performance when used as the inputs of the belief propagation decoder. Our proposal allows us not only to achieve performance close to the optimal decoding but also to have a simple implementation and to adapt to different environment, impulsive or not, independently of the underlying statistical noise model, without the need of a training sequence.

This paper focuses on the full-duplex Gaussian two-way diamond relay channel (TWDRC), where two users wish to exchange their messages with the help of two relays, when one relay performs a lattice-based Compress-and-Forward (CF) scheme. The other relay performs either Decode-and-Forward (DF), Amplify-and-Forward (AF) or a lattice-based CF scheme. We start by proposing a novel lattice-based CF/CF scheme, where both relays perform CF. As opposed to the single relay case, two main challenges arise: the quantization rate region is constrained by two Multiple Access Channel (MAC) rate regions, and each user has to combine two noisy observations to decode the sent message. These challenges are tackled by exploiting MAC techniques and Maximum Ratio Combining methods, which are used to combine the two noisy observations. We then characterize a general achievable rate region when one relay sends a version of both users' messages (eventually noisy), which encompasses both AF and DF, and the other relay performs CF and derive the corresponding achievable rate regions. Finally, the three proposed relaying schemes are compared via numerical evaluations. Clearly, the position of both relays has an impact on the best relaying strategy.

In cellular networks, the outage probability is the probability that the signal-to-interference-plus-noise-ratio (SINR) is less than a given threshold. In this article, firstly, we develop an explicit formula for outage probability for a wireless sensor network taking account of the problem parameters - i.e. the spatial density of nodes in the network, the noise power, the transmission power, the distance between the considered node and the base station, and finally the attenuation coefficient -. Secondly, we model a given node by a Markov chain to study the performance of the MAC layer in the case of a Slotted-ALOHA protocol and Slotted-ALOHA with preliminary channel reservation. Especially in this study, we theoretically estimate the transmission success likelihood after a certain number of attempts that we consider as a metric to evaluate the performance of the considered protocol. Finally we compare the theoretical results with experimental ones achieved on the large scale platform FIT IoT-Lab of Lille. To sum up, this paper responds to the following question: How many channels do we need to a achieve high performance distributed wireless sensor network? The answer is given by the necessary number of channels which maximizes the transmission success likelihood after a certain number of trials.

Several wild animal species are endangered by poaching. As a solution, deploying wireless sensors on animals able to send regular messages and also alert messages has been envisaged recently by several authorities and foundations. In that context, this paper proposes WildMAC, a multichannel, multihop wireless communication protocol for these specific wireless sensor networks that have to collect data from unknown large areas with different QoS requirements. WildMAC is a TDMA based MAC protocol that leverages long range communication properties to propose an efficient data collection mean. Its performance evaluation shows it meets QoS requirements.

Fast varying active transmitter sets are a key feature of wireless communication networks with very short transmissions arising in machine-to-machine communications. A consequence is that the interference is dynamic, leading to non-Gaussian statistics. In this paper, we study the behavior of large scale communication networks in the presence of isotropic α-stable interference, which forms a model for dynamic interference. We first characterize the achievable rate of each link by considering a non-Gaussian input distribution, which is shown to outperform a Gaussian input. Moreover, we analyze the area spectral efficiency, which is the total rate per square meter. Our analysis suggests that analogously to the common model of slowly varying active transmitter sets, dense networks maximize the area spectral efficiency.

Impulsive noise features in many modern communication systems-ranging from wireless to molecular-and is often modeled by the α-stable distribution. At present, the capacity of α-stable noise channels is not well understood, with the exception of Cauchy noise (α = 1) with a logarithmic constraint and Gaussian noise (α = 2) with a power constraint. In this paper, we consider additive symmetric α-stable noise channels with α ∈ (1, 2]. We derive bounds for the capacity with an absolute moment constraint. We then compare our bounds with a numerical approximation via the Blahut-Arimoto algorithm, which provides insight into the effect of noise parameters on the bounds. In particular, we find that our lower bound is in good agreement with the numerical approximation for α near 2.

Impulsive noise arises in many communication systems - ranging from wireless to molecular - and is often modeled via the α-stable distribution. In this paper, we investigate properties of the capacity of complex isotropic α-stable noise channels, which can arise in the context of wireless cellular communications and are not well understood at present. In particular, we derive a tractable lower bound, as well as prove existence and uniqueness of the optimal input distribution. We then apply our lower bound to study the case of parallel α-stable noise channels and derive a bound that provides insight into the effect of the tail index α on the achievable rate.

IEEE 802.15.4 protocol is very much associated with ZigBee protocol and targets low data rate, low power consumption and low cost wireless networking that fits the requirements of wireless sensor networks (WSNs). Due to increase in radio frequency (RF) based communication devices and spectrum sharing between these devices makes it impossible to neglect the affect of interference on wireless communications. This paper provides brief description about technical features of IEEE standard 802.15.4 and ZigBee based physical layer structure and wireless channel impulsive interference modeling for wireless sensor networks in smart cities applications. For more accurate estimation of wireless channel characteristics, we have modeled it using alpha-stable distribution function. We have then estimated two out of four parameters of alpha-stable distribution depending on spatial density of wireless devices in specified area around the wireless node in a network. This allows better modeling and estimation of wireless channel conditions. We have analyzed IEEE standard 802.15.4 based communication system performance in terms of the bit error rate (BER). We have varied the total number of active nodes in specified area around sensor node and evaluates its affect on overall BER performance. We have also integrated this PHY layer based on IEEE standard 802.15.4 in the CupCarbon simulator with the consideration of impulsive network interference and its modeling using alpha-stable distribution.

Cognitive radio technology is a promising solution to the exponential growth in bandwidth demand sustained by increasing number of ubiquitous connected devices. The allocated spectrum is opened to the secondary users conditioned on limited interference on the primary owner of the band. A major bottleneck in cognitive radio systems is to find the best available channel quickly from a large accessible set of channels. This work formulates the channel exploration-exploitation dilemma as a multi-arm bandit problem. Existing theoretical solutions to a multi-arm bandit are adapted for cognitive radio and evaluated in an experimental test-bed. It is shown that a Thompson sampling based algorithm efficiently converges to the best channel faster than the existing algorithms and achieves higher asymptotic average throughput. We then propose a multihop extension together with an experimental proof of concept.

This paper deals with the robustness of soft iterative decoders in impulsive interference modeled by Middleton class A distribution, epsilon-contaminated Gaussian noise or a sum of a Gaussian thermal noise and an impulsive alpha-stable noise. The inputs of belief propagation decoder should be the log-likelihood ratios of the received symbols. But in case of impulsive interference, their computation are highly non-linear and relies on the knowledge of the noise probability distribution. Besides, two of the most often used models - Middleton and alpha-stable noises - give complex analytical expression, for instance through infinite series. In a previous work, we proposed an easily computable approximation of the log likelihood ratio in an impulsive environment. Even with this simplification, performance stays close to the one obtained using the true probability density function. In this paper, we investigate the robustness of our approximation against different interference models and its parameters' estimation method.

In the era of Internet of Things (IoT), the development of Wireless Sensor Networks (WSN) arises different challenges. Two of the main issues are electromagnetic interference and the lifetime of WSN nodes. In this paper, we show and evaluate experimentally the relation between interference and energy consumption, which impacts the network lifetime. We present a platform based on commercially available low-cost hardware in order to evaluate the impact of electromagnetic interference in 2.4 GHz ISM band on energy consumption of WSN. The energy measurements are obtained separately from each electronic component in the node. Interference and energy measurements are conducted in an anechoic chamber and in an office-type lab environment. X-MAC protocol is chosen to manage the Radio Duty Cycle of the nodes and its energy performance is evaluated. The energy consumption transmitter nodes is analyzed particularly in this work. Moreover, this energy consumption has been quantified and differentiated according to the number of (re-)transmissions carried out by the transmitter as well as the number of ACK packets sent by the receiver for a single packet. Finally, we use a model of real battery to calculate the lifetime of the node for operation within different interference level zones. This study lays the basis for further design rules of communication protocols and development of WSNs.

Impulsive interference is a strong limitation in ultra wide band systems or ad hoc networks. However, many work rely on the assumption of independent interference samples which is in many situations an unrealistic assumption. We propose to model the dependence structure using natural extensions to existing interference models based on parameter copula models. We focus on a particular flexible class of models based on the skewed-t copula family. They allow one to capture interesting dependence features based on extremal concordance such as multivariate generalizations of joint extreme correlation known as tail dependence. In the skew-t copula family this can arise in both homogeneous and heterogeneous forms in the extreme quadrants of the multivariate distribution. Importantly, by considering the skew-t copula it is also amenable to efficient scalability to high dimensions. In a second step, we study the impact of these dependence in the receivers' performance when they are designed assuming i.i.d. signals.

We develop a novel distributional upper bound on the interference created in an ultra-wideband wireless communication systems under two general assumptions: the first is that there is an unknown number of interferers who are distributed according to a homogeneous Poisson point process randomly in space; and the second is that the frequency bands occupied by the unknown number of interferers is also a random variable in an ultra-wideband setting. Then given these two general assumptions, we derive a distributional upper bound representation of the total interference.

The design of a baseband decision feedback equalizer, featuring a continuous-time digital delay line, is discussed within the context of a wireless, Line-Of-Sight, communication scenario over the 60-GHz band. BER simulations advocate for a critical-tap cancellation scheme, which leads to the realization of an equalizer featuring a small number of taps. A two-step configurable architecture is suggested as the equalizer's feedback path, in order to mitigate the loss of robustness that is caused by the absence of the clock. Simulations reveal the system's channel-dependent power consumption character and a delay line power consumption reduction of 3 to 4 times, when compared with its clocked counterpart.

The paper presents the principles, techniques and tools for the efficient modeling and simulation, at the component level, of an heterogeneous system composed of Wireless Sensor Network nodes that exhibits complex multi-discipline feedback loops that are likely to be found in many state-of-the-art applications such as cyber-physical systems. A Precollision Mitigation Braking System (PMBS) is used as a pragmatic case study to validate the whole approach. The component models presented (60 GHz communication channel, QPSK RF transceiver, CMOS video sensor, digital microcontroller, simplified car kinetic engine) are written in SystemC and its analog Mixed-Signal extensions, SystemC-AMS, and belong to five distinct yet highly interwoven disciplines: newtonian mechanics, op to-electronics, analog RF, digital and embedded software. The paper clearly exhibits the complex multi-discipline feed- back loop of this automotive application and the related model composability issues. Using the opto-electrical stimulus and the received RF inter-vehicle data, a car is able to exploit its environmental data to autonomously adjust its own velocity. This adjustment impacts the physical environment that in turns modifies the RF communication conditions. Results show that this holistic first-order virtual prototype can be advantageously used to jointly develop the final embedded software and to refine any of its hardware component part.

In this paper, we tackle the problem of interference mitigation in ad hoc networks. In such context, the multiple access interference (MAI) is known to be of an impulsive nature. Therefore, the conventional Gaussian assumption can not be considered to model this type of interference. Contrariwise, it can be accurately modeled by stable distributions. Here, this issue is addressed within an Orthogonal Frequency Division Multiplexing (OFDM) transmission link assuming a symmetric α-stable model for the signal distortion due to MAI. For this purpose, we propose a method for the joint estimation of the transmitted multicarrier signal and the noise parameters. Based on sequential Monte Carlo (SMC) methods, the proposed scheme allows the online estimation using a Rao-blackwellized particle filter.

In this paper, we introduce a statistical model of the ultra wide band channel impulse response at 60 GHz. The novelty of our model is to consider the transfer function as an α-stable random process. It leads to a characterization of the channel by a deterministic measure that can be estimated from observed transfer functions. We present the theoretical framework of our proposed solution and the necessary tools to estimate the characteristic measure and generate the impulse responses. Simulations based on 2 GHz wide channel measurements in the 60 GHz band show the validity of our model.

In this paper we investigate ad hoc networks based on impulse radio ultra wideband. Due to multiple access, the interference distribution is not Gaussian. One important reason for errors is the presence of close interferers generating pulse collision. However such events are rare and we propose an α-stable model compatible with this fact due to its heavy tailed distribution. We derive the analytical expression of the two significant parameters. They depend on the attenuation coefficient, the users' density, the pulse collision probability and the pulse shape. We finally propose receiver strategies (Cauchy receiver and p-norm) that outperforms the classical Gaussian receiver.

In order to make realistic simulations of the radio propagation mechanism in ultra-wide band channels, an appropriate model is needed. In this paper we propose a new technique to model the impulse response of the 60 Ghz channel. This new approach is based on the spectral analysis of alpha-stable processes. Our new model present many advantages: firstly, the channel is characterized only by a one deterministic function(spectral density) in the place of four parameters. Secondly, the estimations procedure deals directly with the measured transfer functions which avoids loosing information in data pretreatment. Finally, an estimation of the spectral measure permits to directly generate the impulse response.

This paper presents the performance of impulse radio ultra wide band up converted to the 60 GHz band for high rate ad hoc networks. The performance of a multiuser transmission system based on time hopping code division multiple access is evaluated. The bit and packet error rates depending on the number of users in the room are presented. It takes into account the network configuration, two types of antennas (omnidirectional and directional), the channel modeling and the received useful and interference power distributions. Finally the feasibility of the proposed system is shown

This paper presents the theoretical performance of an asynchronous wideband DS-CDMA system with as application the future 60 GHz indoor networks. We make an analytical study, separating the different noise sources of the communication link. We derive the error probability using the density probability function of the channel parameters in the case of an adapted filter and of a rake receiver. To validate the analytical study, we simulate the complete communication link using Monte Carlo simulation which takes into account the asynchronous character of the transmission. The results show a good agreement between the theoretical and simulation approaches and the feasibility of DS-CDMA for a 60 GHz high rate link.

In this work we give a statistical model of the paths' arrival time in the 60 GHz wide band channel. This model is based on the class of stable distributions, we show its accurateness on modeling real data. We also give a theoretical justification of its compatibility.

In this paper we investigate observed data of 60 Ghz channel impulse responses collected in IEMN. We first investigate the wide sense stationary and uncorrelated scatterers properties of the channel. We then develop a tapped delay line model and MC simulate a DS-CDMA transmission with a RAKE receiver. Results show the good performances of such a scheme and make DS-CDMA a good solution for high rate multi-media transmission at 60 GHz. Besides, the simulation rapidity allows to use this model for channel coding simulation.

The goal of the paper is to show that a Gaussian approximation can be used to model the global noise in a DS-CDMA system on a 60 GHz frequency band. We show that this approximation can also include the noise due to multipath and to imperfections in power control. The model can then be used as a first step and a reference when designing a communication architecture adapted to this frequency band for mobile indoor cellular systems.

This paper analyzes the suitability of various multiple access schemes for a 60 GHz mobile ad hoc network. The investigated techniques include multi-carrier modulation (OFDM-TDMA), the spectrum spreading method (DS-CDMA) and the combined schemes (MC-CDMA, MC-DS-CDMA) benefiting from the advantages of both techniques. The comparison between these different multiple access schemes is focussed on two aspects: (i) sensitivity to phase noise; the large available bandwidth around 60 GHz is very suitable for transmission of high data rate in indoor environments, but one issue in the 60 GHz band is the design of oscillators with moderate phase noise; (ii) robustness to imperfect power control; ad hoc networks consist of a set of mobile terminals communicating among themselves without any central controller; radio resource management has to be conducted in a distributed way and the power control is inherently imperfect.

Transmission of high rate data in an indoor environment is an important issue for personal or industrial applications. Millimetric waves are one possibility for such transmissions. Because they are strongly attenuated they are very interesting to simplify the frequency planning and to reduce the electro-magnetic pollution. They also offer a large available bandwidth to allow high rate transmissions. However, the first encountered problem to build systems at this frequency is the absence of accurate data about the channel and the consequences of fading or interference between symbols for high data rate links (155 Mbits/s). We propose a contribution to the indoor 60 GHz channel characterization. We also try to define a stationary model in order to use it in simulations. The model is based on a statistical study of the measured data.

We present a multipath channel impulse response model for 60 GHz indoor wireless systems. During the propagation, the electromagnetic wave is reflected on various objects present in the environment. On each object, the impacts of the reflections contributing to the received signal form clusters whose size depends on the nature of the object and on its orientation. By considering the physical mechanism responsible for indoor propagation and also the methodology of measurement, we show that our statistical channel impulse response model fits our measurements well, that the Rayleigh model is a special case, and that our model can be extended to any kind of room.

Significant investigation has been conducted towards combining OFDM which mitigates intersymbol interference and SDMA which improves the bandwidth efficiency. OFDM-SDMA systems need accurate channel estimation, because it is from this information that the separation of co-channel users is performed. Usually, the channel is assumed stationary because the frame duration is chosen smaller than the coherence time. However this choice limits the system efficiency. This paper discusses the performance of an OFDM-SDMA based system under a time-varying multi-path channel. The system under study uses the basic combining algorithm based on the linear minimum mean square error (MMSE) criterion for user separation and a pilot based scheme independent from channel statistics for channel estimation. We investigate in particular the effect of the pilot subcarrier arrangement on the BER performance and deduce an optimal configuration in a channel with both delay and Doppler spread.