This paper presents a novel turbo-equalizer based on Backward Information Forward Marginals (BIFM) Kalman smoothing. We extend this Kalman smoother with an Expectation Propagation (EP) based message passing strategy to derive an iterative time-domain turbo-equalizer, which exploits a sequential soft decision feedback to improve the detection performance. The application of this receiver to Faster-Than-Nyquist (FTN) signaling demonstrates gains of up to 13 dB compared to existing frequency-domain EP equalizers, at the cost of 12 times higher computational complexity, when operating at a high spectral efficiency of 5 bits/s/Hz.

Kalman smoothers have been successfully used in many digital communication receiver algorithms, including channel equalization. In this paper, we propose low complexity Kalman equalizers based on expectation propagation (EP), and we compare them with state-of-the-art EP-based Kalman equalizers to identify the structure with the most attractive complexity/performance trade-off. In addition, we provide a practical approach for further reducing the computational complexity of Kalman equalizers in time-varying channels. Our analysis show that the Kalman equalizer we derived based on the backward information forward marginals (BIFM) structure is the most efficient in terms of complexity/performance trade-off and numerical results attest its superiority in high-speed vehicle cellular communication scenarios above 50 km/h.

Underwater acoustic localization has a wide range of applications, such as tracking autonomous underwater vehicles, remotely operated vehicles, and surface vessel navigation. In this work, we focus on time difference of arrival (TDOA) based positioning to connect and track the fishing nets underwater. This application has environmental merit as it would contribute to long-term sustainability by preventing underwater waste. More precisely, we study the impact of underwater acoustic (UWA) channels on TDOA calculation. We introduce computation methods such as despreading and correlation and provide a performance comparison using the Watermark simulator. Finally, the result of a localization scenario in the ocean is presented as root mean square error (RMSE) using Bellhop for channel modeling.

The Cellular Internet of Things (CIoT) has seen significant growth in recent years. With the deployment of 5G, it has become essential to reduce the power consumption of these devices for long-term sustainability. The upcoming 6G cellular network introduces the concept of zero-energy CIoT devices, which do not require batteries or manual charging. This paper focuses on these devices, providing insight into their feasibility and practical implementation. The paper examines how CIoT devices use simultaneous wireless information and power transfer, beamforming, and backscatter communication techniques. It also analyzes the potential use of energy harvesting and power management in zero-energy CIoT devices. Furthermore, the paper explores how low-power transceivers can lower energy usage while maintaining dependable communication functions.

In digital wireless communications, the received signal can be strongly altered by the environment and may contain Inter-Symbol Interference (ISI). To remove or reduce the ISI i.e. equalize, the impulse response of the propagation channel can be estimated. The Kalman Filter (KF) is an inescapable estimation algorithm in linear systems because of its optimality in terms of Minimum Mean Square Error (MMSE) under certain assumptions. However, in real conditions, implementations of KF are often difficult because of the necessity of hand-tuning parameters. In this paper, we present the Smart Kalman Filter (SKF), a hybrid architecture that combines a KF and the power of neural networks to extract relevant features from data to benefit from an adaptive KF that is automatically well tuned over time. We demonstrate in this paper that the proposed SKF is up to 5dB better at low Signal-to-Noise Ratio (SNR) and 3dB better at high SNR than Least Square (LS) algorithm in a time-varying channel estimation context with abrupt Doppler frequency variations.

Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is an extension of the LoRa physical (PHY) layer that has been recently announced by Semtech and will be supported on their chips. This new PHY layer is designed to address extremely long-range and large-scale communication scenarios, such as satellite Internet of Things (IoT). LR-FHSS features high interference resistance that alleviates packet collisions by increasing the spectral efficiency with an uplink modulation based on the frequency hopping spread spectrum (FHSS). Subsequently, each data packet being sent from an LR-FHSS device is split into small pieces, which are randomly spread over a defined frequency bandwidth. In this paper, we propose several interference blanking techniques allowing increasing the number of simultaneously received LR-FHSS signals, that are accurately decoded, over the same hopping bandwidth. This processing would increase the capacity of LR-FHSS receivers and make this technology more suited to be deployed in satellite IoT applications, which entails the connectivity of a massive number of devices.

This paper proposes a new digitizer architecture, called MIXIC for MIXed signal Interference Cancellation, in order to reach Rx dynamic range performance beyond state-of-the art converters. The proposed solution combines a couple of analog-to-digital converters and a digital-to-analog converter in order to reduce the influence of thermal noise intrinsic to direct conversion architectures. The solution is detailed alongside its impact on the overall digitiser noise factor by comparing it with an architecture using classic Automatic Gain Control (AGC) techniques. Results are also presented by using digital communication signals to obtain both BER and spectral representation which highlight a 10 dB gain for the dynamic range.

In recent years, chirp spread spectrum (CSS) has been used in the Long Range (LoRa) technology, which has been widely deployed for terrestrial low power and long-range wireless Internet of Things (IoT) communications. In order to ensure the connection of any object whatever its position on earth, constellations of satellites in low Earth orbit (LEO) can be used. However, the sensitivity of the receivers to establish earth-space CSS communications requires modifications. In this work, we propose a modification of the LoRa physical layer that we refer to as differential chirp spread spectrum (DCSS) to cope with the requirements of LEO satellite communications. The latter modulation is naturally insensitive to any carrier frequency offset (CFO) induced by the Doppler effect and existing mismatches between local oscillators at transmission and reception. Hence we propose a time synchronization approach regardless of the CFO using non-chirp-based learning sequences. This processing makes our receiver have no limitation on the maximum CFO estimable as in the current LoRa receivers. In addition, in the presence of a Doppler shift variable in time, DCSS is much more efficient than CSS, making our proposed receiver a good candidate for the LEO satellite communications.

Pollination is essential in fruit cultivation. It has a strong impact on fruit production, both for the quantity of fruit and for their quality. Its efficiency is therefore essential to the economic profitability of farms. However, populations of pollinating insects have been decreasing in France due to the many threats they face. These include the destruction and degradation of their habitats and the use of agricultural inputs. The objective of this paper is to present the BEESY project which aims to develop a pollination monitoring system. The idea behind this project is to work on a low signal to noise ratio case where the spectrogram doesn't allow the algorithm to distinct a precise time-frequency pattern corresponding to the sound to detect. A different version of the spectrogram is therefore constructed based on the harmonic characteristic of the bee buzz, allowing a better classification.

This paper proposes unprecedented Expectation Propagation (EP)-based receivers for Circular Faster-Than-Nyquist (CFTN) signaling. This concept yields a Minimum Mean-Square-Error equalizer for InterSymbol Interference (ISI) processing combined with a block which we call Constellation Matcher in charge of the symbol estimates realignment with the constellation. From this framework, we explore different scheduling strategies leading to iterative EP-based receivers with our without decision feedback. Then, by extending the family of the EP process to a subset of non-circular Gaussian distributions results in a Widely Linear (WL) equalization. This new WL-EP receiver best fits the CFTN model, allowing enhanced performance at the cost of a slightly increased complexity. Also, we propose to restrict the Gaussian family to circular Gaussian distributions with identity covariance matrices up to a scaling factor. Combined with CFTN, this particular family allows a low-complexity Frequency-Domain processing of the equalization without requiring any cyclic prefix. The proposed EP-based receivers for CFTN are then evaluated for different spectral efficiencies and computational complexities. Our simulations show that they completely handle the ISI up to 5 bits/s/Hz and double the spectral efficiency compared to Nyquist signaling with almost no performance loss.

The Internet of Things (IoT) finds widespread applications spanning from consumer services such as smart home or domotic to strategic use cases, including infrastructure or energy management. Different technologies have been proposed to support IoT and this paper aims at considering the Long Range one (LoRa). The LoRa physical layer is based on the chirp spread spectrum (CSS) technique where the bit error rate can strongly degrade in the presence of synchronization errors. To alleviate this constraint, we propose a modification of the LoRa physical layer which we refer to as differential CSS (DCSS) associated to an original synchronization algorithm. The main contribution of this paper is to propose a receiver, based on this original synchronization algorithm associated to the DCSS technique, able to demodulate the received signals without performing a complete frequency synchronization and by tolerating some timing synchronization errors. In addition, our receiver has no limitation on the maximum carrier frequency offset as it is actually the case in the deployed LoRa receivers.

LoRa is a chirp spread-spectrum modulation that operates in industrial, scientific, and medical (ISM) free bands. The modulation has been developed for low power and long-range wireless Internet of Things (IoT) communications. Operating in unlicensed bands requires connected objects to reduce their energy consumption. Thereby, it is very likely that their access to the radio channel is random, which leads to an increase in the probability of packet collisions. In this paper, we design a new receiver able to decode several IoT LoRa-Like signals simultaneously received on the same channel and with the same spreading factor which leads to destructive collisions. Based on the particular structure of the superposed received signals and the Successive Interference Cancellation (SIC) algorithm, we propose a novel approach to detect, synchronize and decode each of them iteratively.

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.

To cope with the exponential rise of emerging technology, there have been significant developments in intelligent communication systems aimed at low-power and long-range wireless Internet-of-Things (IoT) communication. Among the various number of leading low-power wide-area network (LPWAN) technologies, long range (LoRa) has emerged to be an attractive solution to connect devices in free bands. Operating in unlicensed bands requires connected objects to reduce their energy consumption. To this end, one of the adopted techniques is random access to the radio channel, which leads to an increase in the probability of packet collisions. In this article, we propose a new receiver capable to decode several IoT LoRa-like signals simultaneously received with the same spreading factor which leads to destructive collisions. Based on the particular structure of the received signals and the successive interference cancellation (SIC) algorithm, we propose a novel approach to decode superposed LoRa-like signals iteratively. Simulations and real LoRa deployments are presented to validate the efficiency of our receiver.

This paper proposes a new Widely Linear Minimum Mean Square Error with Decision Feedback Equalizer (WL-MMSE-DFE) based on the Expectation Propagation (EP) formalism for Faster-Than-Nyquist (FTN) communications. The system model is first derived using a matrix formalism avoiding any aliasing on the Nyquist filter. Then, after recalling the principles of EP, we consider a WL exponential family for estimating the transmitted symbols. This leads us to an iterative receiver composed of a new WL-MMSE-DFE equalizer dedicated to FTN which exchanges messages with a WL constellation matcher. Moreover, we develop a WL-EP-MMSE-DFE algorithm that outperforms existing solutions at the price of a minor complexity increase. Finally, the proposed receiver allows FTN to achieve its Matched Filter Bound while doubling the spectral efficiency of Nyquist signaling up to 3 bits/s/Hz.

Word population has almost doubled during the last century increasing dramatically the need for food to support a population over 7 billion persons. According to FAO, by 2050, the agricultural production will have to increase by 70%. Plants growth depends on several factors such as nutrients (NPK), soil characteristics, soil Ph, soil moisture, temperature, weather, and light. To manage all the required information and the complexity of plants growth a system, based on IoT technology, able to measure, analyze, and act is needed. IoT is a solution for precision agriculture. A system for precision agriculture, that will be distributed in the field, far from energy and communication sources needs to be low power and able to process the received information and just sending the most relevant information to the cloud for further statistical analysis. This system will be able to measure the most important parameters for plant growth through a set of sensors and act to fix some of those parameters through actuators when needed as well.

This paper discusses a new resolution requirement for Analog to Digital Converter in the case of the simultaneous reception of two signals with high power ratio. This issue was previously studied in the case of two single tone signals. This work is extended to the demodulation of digital communication signals and shows the dynamic range, and thus the required resolution, is enhanced by the presence of a strong signal carrying the weaker one and crossing the quantum level.

The Internet of Things (IoT) has many applications such as health monitoring or home automation. All of this leads to an increase of connected objects and thus interference between them. Various technologies are used to connect these devices. Among them, we find the Long Range (LoRa) technology. In this paper we propose a new receiver able to decode two IoT LoRa-Like signals simultaneously received with the same spreading factor. To our knowledge this problem has never been addressed before. Based on the particular structure of the received signal we show how to estimate the time shift between the two received signals in order to process the unsynchronized signal. Different algorithms are proposed to improve actual LoRa-Like receivers through simulations and real LoRa communications.

Internet of things (IoT) is considered as the next technological revolution. Therefore, many solutions are developed either in free, i.e. ISM bands or in non free bands. However, if the IoT growth forecasts are confirmed by 2020, the collisions between packets will multiply and thus degrade the system spectral and energy efficiencies. The LoRa modulation is one of the most popular solutions used in free bands. Based on the LoRa PHY layer principle, we propose in this paper an architecture able to detect the spreading factor and we show how this can enhance the communication efficiency.

This paper focus on the problem of spectrum sensing based on multiple receive antennas. In this context, we propose a novel kurtosis based Combined Second Order Moments (CSOM) as a decision metric allowing to enhance the decision accuracy for both very low SNR environment and an uncertainty on the noise level estimation. Besides, a novel kurtosis based Square-Law Combining (SLC) test metric is proposed. Comparing the proposed approaches to the former sensing techniques shows its better performance in terms of Receiver Operating Characteristics (ROC) and robustness to the noise level estimation errors for the disadvantage of an acceptable complexity loss. Furthermore, the proposed approaches does not necessitate a priori knowledge about the primary signal.

Cross-correlation metrics introduce a challenging performance for multiple antennas spectrum sensing in cognitive radio networks, since it permits to alleviate the drawback of the energy based approaches under low SNR and noise level uncertainty, while maintaining the low complexity. In this paper, we propose a novel sensing approach based on a linear soft weighting of the Combined Second Order Moment (CSOM). Two different optimization criteria are developed, namely, the Normal Deflection Criteria (NDC) and the signal to noise power ratio maximization criteria. We demonstrate that both yield to a similar optimum results, given a certain samples number, thus, same philosophy in decision of the band occupancy. Simulation results show decision accuracy improvement compared to the existing approaches as the BCED, MME and SLC based energy detector. In fact, the proposed sensing strategy guarantees minimal interference to the primary user (high detection probability) while maintaining a good amount of spectral efficiency (low false alarm).

In the field of cognitive radio (CR), radio frequency (RF) transceivers must be efficient to save the terminal battery autonomy. Therefore, when designing the CR power amplifier (CR-PA), an obvious objective is to optimize efficiency over a large bandwidth. As a consequence, the CR-PA operates in its non-linear region and then frequency-dependent distortions are generated. This issue is all the more critical as one deals with high peak-to-average power ratio (PAPR) like those of OFDM signals. In this paper, we develop a digital post-distortion and detection technique in order to compensate the non-linearities generated by the CR-PA. It is based on a dynamic Volterra model to take into account the non-linear behavior of the CR-PA. The key feature of the proposed technique is the joint estimations of the model parameters and the CR-PA input samples. For this reason, an extended Kalman filter (EKF) is considered. However, as the model parameters can vary over time, several EKFs are combined by means of an interacting multiple model (IMM) algorithm. Simulation results confirm the relevance of the proposed postdistortion and detection technique.

This study deals with an interweave cognitive‐radio (CR) system, where an uplink orthogonal frequency‐division multiple access system is considered for the primary users (PUs). Our purpose is to estimate the PU carrier frequency offsets (PU‐CFOs) as well as the channels to estimate the transmitted symbols. However, in wideband wireless communications, the PU received‐signal spectrum usually exhibits a localised fading because of the multipath propagation channel. When the PU faded frequencies are used by the CR system, the PU system is contaminated by a CR narrowband interference (CR‐NBI). In that case, if a Kalman filter (KF)‐based approach is used for the estimations of the CFOs and the channels, the state‐space representation does not necessarily take into account the CR‐NBI; this hence has a negative impact on the algorithm performance. Therefore the authors propose to make this solution more robust to the CR‐NBI by detecting when it appears and disappears to avoid using the data disturbed by the CR‐NBI. The authors’ contribution is to assume that the CR‐NBI is because of the transmission of contiguous blocks and to propose various criteria based on the covariance matrix of the KF innovation.

For a battery driven terminal, the power amplifier (PA) efficiency must be optimized. Consequently, non-linearities may appear at the PA output in the transmission chain. To compensate these distortions, one solution consists of using a digital detector based on a Volterra model of both the PA and the channel and a Kalman filter (KF) based algorithm to jointly estimate the Volterra kernels and the transmitted symbols. Here, we suggest addressing this issue when dealing with cognitive radio (CR). In this case, additional constraints must be taken into account. Since the CR terminal may switch from one sub-band to another, the PA non-linearities may vary over time. Therefore, we propose to design a digital detector based on an interacting multiple model combining various KF based estimators using different model parameter dynamics. This makes it possible to track the time variations of the Volterra kernels while keeping accurate estimates when those parameters are static. Furthermore, the single and multicarrier cases are addressed and validated by simulation results. Our solution corresponds to a compromise between computational cost and bit-error-rate performance.

This paper deals with a system combining orthogonal frequency division multiplexing (OFDM) and interleave-division multiple access (IDMA). OFDM makes the system robust against intersymbol interference, whereas IDMA combats the multiple access interference. Nevertheless, two problems have to be solved: 1/ carrier frequency offset (CFO) must be estimated/corrected to guarantee the orthogonality between subcarriers. 2/ the conventional IDMA receiver requires a priori knowledge of the channel. Therefore, the CFO and the channel must be estimated. Our contribution is twofold. Firstly, we suggest a sigma point Kalman filter to solve the estimation issue. In addition, we propose a new scheme where a CFO correction is no longer necessary, unlike common OFDM approaches. We show that without a CFO correction, the transmitted bits can be recovered by modifying the conventional IDMA receiver. When considering an OFDM-IDMA network over Rayleigh fading channels, simulation results show the efficiency of the proposed algorithm.

In this paper, we consider the transmission coordination among multiple cell sites, namely the Coordinated MultiPoint (CoMP) transmission scheme, where several Base Stations (BSs) cooperate to serve a set of Mobile Stations (MSs) and both BSs and MSs are equipped with multiple antennas. We model this multicell cooperative system under the presence of channel estimation errors that can significantly influence the obtained results. We derive the exact sum rate expression of the multicell cooperative system in the presence of channel estimation errors and we evaluate the performance of the Block-Diagonalization (BD) and Zero-forcing (ZF) beamforming approaches. Furthermore, we investigate a power allocation policy that maximizes the system sum rate in the presence of channel estimation errors. We show that in the multicell cooperative system, the performance of the BD or ZF beamforming approaches in terms of the maximization of the system sum rate, depends essentially on the system configuration parameters (i.e., number of antennas per BS and per MS). Simulation results are given to support our claims.

In Cognitive Radio (CR) systems, spectrum sensing plays a key role to determine the free frequency bands. However, when the primary-user (PU) signal spectrum exhibits localized fading, PU detection cannot be guaranteed. In addition, as the CR may use the PU faded frequencies, the PU spectrum can be disturbed by a narrow-band interference (NBI) and synchronization algorithms used for the PU carrier frequency offset (CFO) estimation suffer degradations. In this paper, we propose a new scheme that jointly allows the CR-NBI to be detected and the PU-CFOs and the channels to be estimated in an orthogonal frequency division multiple access (OFDMA) system. It combines a sigma point Kalman filter and a test aiming at detecting a variation of the measurement-noise covariance matrix. Simulation results confirm that the proposed algorithm can accurately detect the CR-NBI and estimate the PU-CFOs.

Whereas lecturing is the most widely used mode of instruction, we have explored small-group learning projects to make signal processing more appealing at the University and in Engineering schools in Bordeaux (France). The projects cover a wide range of applications, from audio processing to mobile communication system analysis and can be based on problems suggested by industrial partners. After a state of the art in the area, the students develop signal processing algorithms and usually deliver new softwares. At the end of the semester, they provide a final 8-page report and present their work during a one-day workshop. The projects enable the students to experience cooperative works, which is mostly done in industry. In addition, they help the students to see more easily the links between the courses they follow, including project managing. The students are hence involved in dialog inside their own group, in writing English reports as well as in problem solving, analysis and synthesis. This paper presents four examples dealing with speech processing, spectral analysis, Universal Mobile Telecommunications System (UMTS) mobile positioning and orthogonal frequency division multiple access (OFDMA) synchronization.

Modern communication technologies need faster analog-to-digital converters (ADC). To significantly increase the sampling rate of an ADC, time-interleaved ADC (TIADC) is an efficient solution. A M-channels TIADC is composed of M ADCs which operate at interleaved sampling times. Due to the manufacturing process, the main drawback of a TIADC system is that the M ADCs are not exactly the same. This means that offset, gain and time mismatch errors are introduced. As a result, these errors cause distortions in the output sampled signal and introduce unwanted tones and noise, and hence, reduce the spurious free dynamic range (SFDR) as well as the signal to noise ratio (SNR). In this paper, we propose a new orthogonal online digital calibration, for timing skew, offset and gain mismatches, based on Code Division Multiple Access (CDMA) technique already used in communications. Our calibration is online, this means that errors can be estimated while the ADC is running. Since most of the calibration processes are carried out on the digital outputs, very little change is needed on the analog part of the ADC. Simulations results showed the efficiency of our proposed calibration architecture.

We propose in this paper a maximum likelihood (ML) estimator to jointly obtain time and frequency synchronization together with channel estimation in a MIMO OFDMA system. The proposed algorithm is planned to be implemented in a base station (BS) environment and so it is mainly devoted to uplink asynchronous transmission schemes. We propose a new EM based procedure which enables to convert a multidimensional maximization problem into a number of substantially smaller separate maximization problems. Simulation results are presented which clearly show the accuracy of our estimator and its ability to combat the near-far effect.

Channel parameter estimation is an important task for coherent detection and constitutes a key challenge in MIMO systems. Turbo-Bell Labs Laboratory Architecture Systems (T-BLAST) with orthogonal frequency division multiplexing (OFDM) can be used to improve communication quality and capacity. This paper proposed a modified expectation maximization (EM) algorithm for T-BLAST systems in frequency selective channels. It is based on a combination of data and pilot based channel estimate with approximated coefficients. These coefficients are obtained by resolving an optimization problem using the Lagrangian multipliers. The algorithm we present takes advantage of the exchange message between the two soft input soft output (SISO) modules of the turbo receiver to attain the best estimation performance. The effectiveness of this technique is demonstrated through the simulation of an OFDM T-BLAST system with two-transmit and three-receive antennas.

This paper introduces new studies on the codes named Super Orthogonal Space Time Trellis Codes (SOSTTC). Using set partitioning rules combined with a set of orthogonal designs, SOSTTC's are able to combine the coding gain of STTC's together with diversity advantage of orthogonal STBC's. We propose new application fields of these codes in the particular context of three transmit antenna systems. We introduce new STTC's built on STBC designs that outperform some STTC's schemes based on the determinant and rank criteria. Further- more, we demonstrate by simulation that the use of these codes in a turbo parallel concatenated scheme permits working very close to the channel outage probability.

We present a new architecture of layered space-time codes as a combination of BLAST architecture and of special STTC codes which are variations about the codes first introduced by Jafarkhani et al. and named super orthogonal space-time trellis codes (SOSTTC). Using powerful set partitioning, these codes are able to combine the coding advantage of STTC's together with the advantage diversity of orthogonal space-time block codes (STBC). The system we propose is named super quasi orthogonal horizontal layered space time trellis code (SQOHLSTTC). It consists in a powerful STBC based STTC originally derived for a three transmit antenna system, together with an original block based decoding algorithm which enables to use a higher number of transmit antennas. The decoding of SQOHLSTTC combines group interference suppression and group interference cancellation techniques. To implement the system, we propose a low complexity hard decision iterative decoding method. System performances are illustrated and confirm the great interest of the proposed transmission scheme

In this paper, we propose a multi-user detector for space-time uplink transmission in multi-carrier CDMA systems using a new combination of STBC, OFDM and CDMA spreading. To achieve high rate, CDMA spreading codes are shared between a few users and we use linear STBC decoding to further separate users. The OFDM codewords are built from chips issued from different symbols. The STBC encoder and decoder are placed at the last stage of our multi-user transmitter and receiver respectively. We show that the system is particularly easy to implement when exploiting the space diversity after CDMA detection. Besides, the performance is comparable to the case of a single user STBC system. The proposed system allows for a very high rate multi-user system compared to the conventional CDMA system or combined array-processing technique previously proposed.