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IEEE Signal Processing Magazine

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ILN - Foundation Models & Human Centric AI

In Celebration: Professor Simon Haykin [In Memoriam]

Tim DavidsonJim Reilly

Keywords:ObituariesHaykin, SimonCommunication Research

Power-Efficient Sampling: Towards low-power analog-to-digital converters

Satish MulletiTimur ZirtilogluArman TanRabia Tugce YazicigilYonina C. Eldar

Keywords:Power demandQuantization (signal)Digital signal processorsPrototypesDynamic rangeHardwareBatteriesAnalog-digital conversionSignal resolutionEnergy efficiencyAnalog-to-digital ConverterSampling RatePower ConsumptionBitrateAnalog SignalSignal ConversionQuantization BitsQuantumEnergy ConservationInput SignalSignal ModelMultiple-input Multiple-outputReconstruction AlgorithmPower EfficiencyQuantization ErrorDigital FilterSignal BandwidthSignal RecoverySignal ReconstructionMultiple-input Multiple-output SystemsNyquist RateSampling FrameworkVector ModulationLevel CrossingsHardware PrototypeFeedback CircuitSwitching RateSpeech DetectionPower RequirementsSignal Classification

Abstracts:Analog-to-digital converters (ADCs) facilitate the conversion of analog signals into a digital format. While the specific designs and settings of ADCs can vary depending on the application, it is crucial in many modern applications to minimize the devices’ power consumption. The significance of low-power ADCs is particularly evident in fields like mobile and handheld devices reliant on battery operation. Key parameters that dictate ADCs’ power are the sampling rate, dynamic range (DR), and number of quantization bits. Typically, these parameters are required to be higher than a threshold value but can be reduced by using the structure of the signal and by leveraging preprocessing and the system application needs. In this article, we discuss four approaches relevant to a variety of applications.

ILN - Transformer Architecturess for Mutimodal

Lagrangian Grid-Based Filters With Application to Terrain-Aided Navigation

Jakub MatoušekJindřích DuníkOndřej Straka

Keywords:Terrain-aided NavigationComputational ComplexityOptimal ControlGlobal Navigation Satellite SystemNoisy MeasurementsConsiderable Research InterestSystem State EstimationMonte Carlo IntegrationSpoofing AttacksMeasurement OutliersProbability Density FunctionGrid PointsKalman FilterAdvectionHorizontal PositionInertial Measurement UnitState-space ModelNavigation SystemMean Sea LevelSimilar ComplexityConditional Probability Density FunctionUnscented Kalman FilterMeasurement UpdateUpdate TimeTerrain MapInertial NavigationStatistical NoiseOdometerDeterministic RulesDiffusion Part

Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

A Fast Multiplierless Discrete Fourier Transform Approximation by Sparse Factorization Optimization [Tips & Tricks]

Danyan LinGang WangK. C. HoDr. Xin Liao

Keywords:Discrete Fourier TransformFast EstimationSparse FactorizationMatrix ProductionSparse MatrixDiscrete Fourier Transform MatrixEstimation ErrorScaling FactorIdentity MatrixLeast Squares RegressionFast Fourier TransformScale ParameterBlock DiagonalFeasible SetBottom Of PageFrobenius NormMatrix EstimationNonzero ElementsAdditional NumberPermutation MatrixCalibration MatrixLinear Least Squares ProblemInverse Discrete Fourier Transform

Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Fast Computation of the Discrete Fourier Transform Square Index Coefficients

Saulo QueirozJoão P. VilelaEdmundo Monteiro

Keywords:Calculation Of CoefficientDiscrete Fourier TransformIndex CoefficientFast Fourier TransformTime ComplexityComputational TheoryRegularization AlgorithmComplex MultiplicationQuestions Of TheoryInput SignalFundamental FrequencyEven NumberSignal LengthFrequency Of InterestInput LengthHarmonic SignalSignal CompressionInverse Discrete Fourier TransformComplex ExponentialAsymptotic ComplexityPerfect Square

Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Incorporating Cross-Ambiguity Function-Based Radar in Wireless Orthogonal Frequency-Division Multiplexing Communication Receivers [Tips & Tricks]

Tomasz P. ZielinskiKrzysztof Duda

Keywords:WirelessOrthogonal Frequency Division MultiplexingChannel EstimationSignal ProcessingCommunication SystemsColumn VectorFast Fourier TransformCross-correlationSpectral MethodDiscrete Fourier TransformRadar SystemDoppler ShiftCommunication ServicesRadar SignalDoppler FrequencyBlock SamplesColumn ElementPeak CoordinatesChannel Impulse ResponseInverse Discrete Fourier TransformChannel Frequency ResponseOrthogonal Frequency Division Multiplexing SymbolDigital RadioTransmission FrameFrequency ShiftDecimal NumberObservation TimeHighest FrequencyFourier CoefficientsPseudo-random Sequence

Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Waveforms for Computing Over the Air: A groundbreaking approach that redefines data aggregation

Ana Pérez-NeiraMarc Martinez-GostAlphan ŞahinSaeed RazavikiaCarlo FischioneKaibin Huang

Keywords:Performance evaluationWireless sensor networksTransmittersWireless networksReceiversSignal processingPhysical layerInterference channelsInternet of ThingsReliabilitySignal ProcessingInternet Of ThingsWireless SystemsSensor NetworksPhysical LayerWireless Sensor NetworksInterference ChannelSuperposition Of SignalsMultiple-access ChannelArithmetic MeanWireless NetworksAmplitude ModulationMultiple-input Multiple-outputBeamformingBit Error RateNomogramMinimum Mean Square ErrorOrthogonal Frequency Division MultiplexingSignal Processing TechniquesNon-orthogonal Multiple AccessDigital ModulationRadio ResourcePulse Amplitude ModulationFusion CenterMultiple-input Multiple-output ChannelComputing NodesConstellation PointsQuadrature Amplitude ModulationCode Division Multiple Access

Abstracts:Over-the-air computation (AirComp) leverages the signal superposition characteristic of wireless multiple-access channels (MACs) to perform mathematical computations. Initially introduced to enhance communication reliability in interference channels and wireless sensor networks (WSNs), AirComp has more recently found applications in task-oriented communications like wireless distributed learning and in wireless control systems. Its adoption aims to address latency challenges arising from an increased number of edge devices or Internet of Things (IoT) devices accessing the constrained wireless spectrum. This article is the first one to focus on the physical layer (PHY) of these systems. We present a unified framework, specifically on the waveform and the signal processing aspects at the transmitter and receiver, to meet the challenges that AirComp presents within the different contexts and use cases.

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