Vol. 75, n° 11-12, November-December 2020
Content available on Springerlink
Patrick Abry, CNRS, ENS de Lyon, Lyon, France
Olivier Michel, Grenoble-Alpes, Grenoble-INP, Grenoble, France
Henri Maître, Telecom-Paris, Institut Polytechnique de Paris, Palaiseau, France
“GRETSI 2019” colloquium
Patrick Abry, Olivier Michel, Henri Maître
Energy optimization of quantized min-sum decoders
for protograph-based LDPC codes
Mohamed Yaoumi1, Elsa Dupraz1, François Leduc-Primeau2, Frederic Guilloud1
(1) IMT Atlantique, Lab-STICC, UBL, Brest, France
(2) Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC, Canada
Abstract This paper considers protograph-based LDPC codes, and proposes an optimization method to select protographs that minimize the energy consumption of quantized min-sum decoders. This method first estimates the average number of iterations required by the decoder, and includes this estimate into two high-level models that evaluate the decoder energy consumption. The optimization problem is then formulated as minimizing the energy consumption of the decoder while satisfying a performance criterion on the frame error rate. Finally, an optimization algorithm based on differential evolution is introduced. Protograph optimized for energy consumption shows a gain in energy of approximately 15% compared with a baseline protograph optimized for performance only.
Keywords LDPC codes · Protographs · Min-sum decoder · Energy
Excess rate for model selection in interactive compression using belief propagation decoding
Navid Mahmoudian Bidgoli1, Thomas Maugey1, Aline Roumy1
(1) INRIA, Univ. Rennes, CNRS, IRISA, Rennes, France
Abstract Interactive compression refers to the problem of compressing data while sending only the part requested by the user. In this context, the challenge is to perform the extraction in the compressed domain directly. Theoretical results exist, but they assume that the true distribution is known. In practical scenarios instead, the distribution must be estimated. In this paper, we first formulate the model selection problem for interactive compression and show that it requires to estimate the excess rate incurred by mismatched decoding. Then, we propose a new expression to evaluate the excess rate of mismatched decoding in a practical case of interest: when the decoder is the belief propagation algorithm. We also propose a novel experimental setup to validate this closed-form formula. We show a good match for practical interactive compression schemes based on fixed-length Low-Density Parity-Check (LDPC) codes. This new formula is of great importance to perform model and rate selection.
Keywords Source coding · Interaction · Model selection · Mismatched decoding
On the optimization of resources for short frame synchronization
Alex The Phuong Nguyen1, Frédéric Guilloud1, Raphaël Le Bidan1
(1) IMT Atlantique, Lab-STICC, 29238 Brest, France
Abstract We consider the transmissions of successive short packets. Each of them combines information to be transmitted (codeword) with information for synchronizing the frame (syncword). For short packets, the cost of including syncwords is no longer negligible and its design requires careful optimization. In particular, a trade-off arises depending on the way the total transmit power or the total frame length is split among the syncword and the codeword. Assuming optimal finite-length codes, we develop tight upper bounds on the probability of erroneous synchronization, for both frames with concatenated syncword and frames with superimposed syncword. We use these bounds to optimize this trade-off. Simulation results show that the proposed bounds and analysis have practical relevance for short packet communication system design.
Keywords Frame synchronization · Short packet transmission · Finite blocklength · MMTC · URLLC
Quantum signal processing for quantum phase estimation:
Fourier transform versusmaximum likelihood approaches
François Chapeau-Blondeau1, Etienne Belin1
(1) Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Angers, France
Abstract The phase in quantum states is an essential information carrier for quantum telecommunications, signal processing, and computation. Quantum phase estimation is therefore a fundamental operation to extract and control useful information at the quantum level. Here, we analyze various approaches to quantum phase estimation, when a phase parameter characterizing a quantum process gets imprinted in a relative phase attached to a quantum state serving as a probe signal. The estimation approaches are based on standard concepts of signal processing (Fourier transform, maximum likelihood), yet operated in the quantum realm. We also exploit the Fisher information, both in its classical and its quantum forms, in order to assess the performance of each approach to quantum phase estimation. We demonstrate a possibility of enhanced estimation performance, inaccessible classically, which is obtained via optimized quantum entanglement. Beyond their significance to quantum phase estimation, the results illustrate how standard concepts of signal processing can contribute to the ongoing developments in quantum information and quantum technologies.
Keywords Quantum signal · Quantum phase · Quantum estimation · Quantum Fourier transform · Maximum likelihood · Fisher information
Parameter-free and fast nonlinear piecewise filtering: application to experimental physics
Barbara Pascal1, Nelly Pustelnik1, Patrice Abry1, Jean-Christophe Géminard1,
(1) Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS, Laboratoire de Physique, Lyon, France
Abstract Numerous fields of nonlinear physics, very different in nature, produce signals and images that share the common feature of being essentially constituted of piecewise homogeneous phases. Analyzing signals and images from corresponding experiments to construct relevant physical interpretations thus often requires detecting such phases and estimating accurately their characteristics (borders, feature differences, …). However, situations of physical relevance often comes with low to very low signal-to-noise ratio precluding the standard use of classical linear filtering for analysis and denoising and thus calling for the design of advanced nonlinear signal/image filtering techniques. Additionally, when dealing with experimental physics signals/images, a second limitation is the large amount of data that need to be analyzed to yield accurate and relevant conclusions requiring the design of fast algorithms. The present work proposes a unified signal/image nonlinear filtering procedure, with fast algorithms and a data-driven automated hyperparameter tuning, based on proximal algorithms and Stein unbiased estimator principles. The interest and potential of these tools are illustrated at work on low-confinement solid friction signals and porous media multiphase flows.
Keywords Nonlinear physics · Solid friction · Porous media multiphase flow · Nonlinear filtering · Proximal operators ·Fast algorithms · Automated hyperameter tuning · Large size dataset
A review on machine learning–based approaches for Internet traffic classification
Ola Salman1, Imad H. Elhajj1, Ayman Kayssi1, Ali Chehab1
(1) Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
Abstract Traffic classification acquired the interest of the Internet community early on. Different approaches have been proposed to classify Internet traffic to manage both security and Quality of Service (QoS). However, traditional classification approaches consisting of modifying the Transmission Control Protocol/Internet Protocol (TCP/IP) scheme have not been adopted due to their complex management. In addition, port-based methods and deep packet inspection have limitations in dealing with new traffic characteristics (e.g., dynamic port allocation, tunneling, encryption). Conversely, machine learning (ML) solutions effectively classify traffic down to the device type and specific user action. Another research direction aims to anonymize Internet traffic and thwart classification to maintain user privacy. Existing traffic surveys focus on classification and do not consider anonymization. Here, we review the Internet traffic classification and obfuscation techniques, largely considering the ML-based solutions. In addition, this paper presents a comprehensive review of various data representation methods, and the different objectives of Internet traffic classification. Finally, we present the key findings, limitations, and recommendations for future research.
Keywords Machine learning · Internet traffic · Classification · Obfuscation · Survey ·
Decentralized spectrum learning for radio collision mitigation in ultra-dense IoT networks: LoRaWAN case study and experiments
Christophe Moy1, Lilian Besson2, Guillaume Delbarre1, Laurent Toutain3
(1) Univ Rennes, CNRS, IETR – UMR 6164, Rennes, France
(2) CentraleSupélec, CNRS, IETR – UMR 6164, Cesson-Sévigné, France
(3) IMT Atlantique, IRISA, Rennes, France
Abstract This paper describes the theoretical principles and experimental results of reinforcement learning algorithms embedded into IoT devices (Internet of Things), in order to tackle the problem of radio collision mitigation in ISM unlicensed bands. Multi-armed bandit (MAB) learning algorithms are used here to improve both the IoT network capability to support the expected massive number of objects and the energetic autonomy of the IoT devices. We first illustrate the efficiency of the proposed approach in a proof-of-concept, based on USRP software radio platforms operating on real radio signals. It shows how collisions with other RF signals are diminished for IoT devices that use MAB learning. Then we describe the first implementation of such algorithms on LoRa devices operating in a real LoRaWAN network at 868 MHz. We named this solution IoTligent. IoTligent does not add neither processing overhead, so it can be run into the IoT devices, nor network overhead, so that it requires no change to LoRaWAN protocol. Real-life experiments done in a real LoRa network show that IoTligent devices’ battery life can be extended by a factor of 2, in the scenarios we faced during our experiment. Finally we submit IoTligent devices to very constrained conditions that are expected in the future with the growing number of IoT devices, by generating an artificial IoT massive radio traffic in anechoic chamber. We show that IoTligent devices can cope with spectrum scarcity that will occur at that time in unlicensed bands.
Keywords Internet of Things (IoT) . Machine learning . MAB . Bandit . UCB . Radio spectrum . Collision mitigation . Interference . LoRa . Artificial intelligence . LoRaWAN . Cognitive radio . Spectrumscarcity . ISMband
A blind separation algorithm for heterogeneous mixed signals
Tingting Huo1, Yong Gao1
(1) School of electronic information, Sichuan University, Chengdu, Sichuan, China
Abstract Aiming at the problem that the existing algorithms can only achieve single-channel blind separation (SCBS) for signals with co-frequency and co-modulated, the gravitational field resampling particle filter (GFR-PF) algorithm was proposed. The GFR-PF can realize SCBS of binary phase shift keying (BPSK) and binary frequency shift keying (2FSK) that overlap in both time and frequency domain and are mixed in a single-channel with environmental noise. The GFR-PF was used to jointly detect symbols and estimate unknown parameters of the BPSK and 2FSK signals. Besides, the blind separation of different frequencies and different modulations signals, which was called the mixed heterogeneous signal blind separation (HSBS). The proposed algorithm not only improved the global search performance and tracking accuracy of the blind separation of the same frequency and same modulation mixed signals but also realized the HSBS of BPSK and 2FSK. Thus, the blind separation of heterogeneous mixed signals containing BPSK and 2FSK with different amplitudes and bit rates were achieved by the proposed algorithm. Based on this heterogeneous mixed signal model, it was proposed by this paper that anti-interception capability was improved through mixed transmission of BPSK and 2FSK signals with different amplitudes.
Keywords SCBS . Gravitational field algorithm . Particle filtering . Heterogeneousmixed signals . HSBS
Dynamic-TDD interference tractability approaches
and performance analysis in macro-cell and small-cell deployments
Jalal Rachad1,2, Ridha Nasri1, Laurent Decreusefond2
(1) Orange Labs, Chatillon, France
(2) LTCI, Telecom Paris, Institut Polytechnique de Paris, Palaiseau, France
Abstract Meeting the continued growth in data traffic volume, Dynamic Time Division Duplex (D-TDD) has been introduced as a solution to deal with the uplink (UL) and downlink (DL) traffic asymmetry, mainly observed for dense heterogeneous network deployments, since it is based on instantaneous traffic estimation and provide more flexibility in resource assignment. However, the use of this feature requires new interference mitigation schemes capable of handling two additional types of interference between cells in opposite transmission direction: DL to UL and UL to DL interference. The aim of this work is to provide a complete analytical approach to model inter-cell interference in macro-cell and dense small-cell networks. We derive the explicit expressions of Interference to Signal Ratio (ISR) at each position of the network, in both DL and UL, to quantify the impact of each type of interference on the system performance. Also, we provide the explicit expressions of the coverage probability as functions of different system parameters by covering different scenarios. Finally, through system-level simulations, we analyze the feasibility of D-TDD implementation in both deployments and we compare its performance to the static-TDD (S-TDD) configuration.
Keywords Dynamic TDD · Interference · Macro-cells · Small-cells · Coverage probability · SINR · ISR · ASE · FeICIC
Comparison between LoRa and NB-IoT coverage in urban and rural Southern Brazil regions
Lucas Eduardo Ribeiro1, Davi Wei Tokikawa1, João Luiz Rebelatto1, Glauber Brante1
(1) CPGEI, Universidade Tecnológica Federal do Paraná, Curitiba, PR, Brazil
Abstract In this work, we resort to computer simulations to compare the coverage of long range (LoRa) and narrowband (NB)-IoT in two different realistic scenarios of southern Brazil, encompassing an overall area of 8182.6 km2. The first scenario is predominantly rural with a few base stations (BSs) while the other scenario corresponds to a mostly urban area with high density of BSs. Our analysis, which adopts the actual position and parameters of the BSs of a given operator, also takes into account the digital elevation model (DEM) of the environments in order to calculate the path loss, following a realistic propagation model from 3GPP. Our results indicate that for a mainly rural environment, when operating at a similar sub-GHz frequency band, NB-IoT outperforms LoRa due to the directivity associate with directional antennas which provide a better coverage for devices which are far from BS but near the main beam. However, LoRa presents a better coverage, regardless of the site deployment, when the NB-IoT is considered to operate in the 1900-MHz band.
Keywords Coverage · LoRa · NB-IoT · IoT
A social-aware content delivery scheme based on D2D
communications underlying cellular networks: a Stackelberg game approach
Junyue Qu1, Dianxu Zhang1, DanWu2, Yanshan Long3, Wendong Yang2, Lianxin Yang2, Lan Yang4, Yueming Cai2
(1) No.2 Jianquan, Jianquan Street, Tianshan District,Wulumuqi City, Xinjiang Province, China
(2) No.2 Biaoying, Yudao Street, Baixia District, Nanjing City, Jiangsu Province, China
(3) No.27 Wanshou Road, Haidian District, Beijing, China
(4) Beijing, China
Abstract Content delivery based on device-to-device (D2D) communications has been widely considered an effective response to the prevalence of content sharing and local services. In order to ensure its advantages, content requesters (CRs) should decide from which content providers (CPs) they obtain the desired content. Moreover, no CP will provide contents for free; thus, an incentive should be given to motivate the CPs. In this work, we solve the content delivery utilizing a monetary incentive. In particular, each CP just transmits a part of the content to CR to reduce the energy consumption. Simultaneously, we introduce the social tie to improve the incentive efficiency, with the popularity of mobile social networks. Specifically, considering the two-layer architecture consisting of the CR and the CPs, a Stackelberg game model is introduced to model the content delivery. The CR works as the leader, and decides the monetary incentive. The CPs work as the followers, and decide the proportion of the provided content. Then, the expression of the Stackelberg equilibrium is given, and a content delivery and pricing algorithm based on the Stackelberg game is designed to converge to the Stackelberg equilibrium through finite-time iterations. Simulation results provide evidences for its efficiency.
Keywords Device-to-device communication · Content delivery · Stackelberg game · Social-aware