1,720,999 research outputs found
Localization With Joint Diffusion-Based Molecular Communication and Sensing Systems: Fundamental Limits and Tradeoffs
Full text linkThis paper introduces and examines a novel joint communication and sensing system based on molecular diffusion. Using a configuration of at least four fully absorbing spherical receivers, the proposed system achieves precise 3D-localization of a pointwise transmitter by counting the same molecules emitted for communication purposes. We develop an analytical framework to explore the fundamental limits of communication and localization within this context. Exact closed-form expressions for the bit error probability and the Cramér-Rao bound on localization error are derived, considering both Poisson concentration and timing transmitter models, with and without accounting for molecule degradation. For the first time, theoretical trade-offs between communication and localization performance are established, taking inter-symbol interference and molecule degradation into account. In scenarios without molecule degradation, inter-symbol interference detrimentally affects communication but enhances localization. Conversely, the introduction of degradation improves communication performance but partially compromises localization effectiveness. These trade-offs are navigated by adjusting number of transmitted symbols or degradation rate, respectively. Furthermore, we compare communication and localization ranges, alongside the associated costs measured in terms of average emitted molecules required to meet performance requirements
Spatially Distributed Molecular Communications: An Asynchronous Stochastic Model
No full textThis letter studies large-scale molecular communication systems by using point processes theory. A swarm of point transmitters randomly placed in a bounded space are considered in conjunction with a single fully absorbing receiver. The transmitters' positions are modeled by a spatial point process, but the global clock assumption, adopted by prior works, is here removed. More precisely, the emission times for each point transmitter are considered as random and are modeled by a non-stationary time-domain point process. We show that, if the intensity function is the same for all time point processes (thus taking the meaning of a distributed input), the average number of received molecules per time unit (receiving rate) can be computed through a convolution: the collective response to a one-molecule emission can be properly interpreted as the impulse response. This models unifies all the widely known transmitter models (exact concentration, Poisson concentration, and timing transmitter), which result as special cases. Analytical expressions for the receiving rate are provided and validated by Monte-Carlo simulations
Inhomogeneous Poisson Sampling of Finite-Energy Signals with Uncertainties in Rd
Full text linkSpatiotemporal signal reconstruction from samples randomly gathered in a multidimensional space with uncertainty is a crucial problem for a variety of applications. Such a problem generalizes the reconstruction of a deterministic signal and that of a stationary random process in one dimension, which was first addressed by Whittaker, Kotelnikov, and Shannon. In this work we analyze multidimensional random sampling with uncertainties jointly accounting for signal properties (signal spectrum and spatial correlation) and for sampling properties (inhomogeneous sample spatial distribution, sample availability, and non-ideal knowledge of sample positions). The reconstructed signal spectrum and the signal reconstruction accuracy are derived as a function of signal and sampling properties. It is shown that some of these properties expand the signal spectrum while others modify the spectrum without expansion. The signal reconstruction accuracy is first determined in a general case and then specialized for cases of practical interests. The optimal interpolator function is derived and asymptotic results are obtained to show the impact of sampling non-idealities. The analysis is corroborated by verifying that previously known results can be obtained as special cases of the general one and by means of a case study accounting for various settings of signal and sample properties
Performance Limits in 3D Localization via Molecular Diffusion
No full textThis paper investigates the performance limit of position estimation in a three dimensional (3D) molecular diffusion environment. Specifically, we consider a realistic molecular transmission process based on chemical-physical laws and a Poisson distribution of the received molecules. We derive a closed form expression of the Cramér-Rao Bound as a function of the system parameters, such as the number of molecules, the estimation time, the distance of the target, the constant reaction rate and the reagents concentration showing how they impact the localization estimation
On random sampling with nodes attraction: The case of Gauss-Poisson process
No full textAbstract—The deployment of sensing nodes is crucial for appli- cations relying on the reconstruction of spatial fields. Theoretical analysis usually assumes that nodes are distributed according to a homogeneous Poisson point process (PPP), in which nodes positions are stochastically independent. However, realistic scenarios for crowd sourcing and Internet of Things call for clustered layouts of sensing nodes, for which homogeneous PPP is not appropriate. This paper analyzes sampling and reconstruction of finite-energy signals in Rd, with samples gathered in space according to a Gauss-Poisson point process (G-PPP), which has been recently proposed to model node spatial distribution with attraction (as in clustering). In particular, it is shown that attraction between nodes modeled by G-PPP reduces the reconstruction accuracy with respect the case of homogeneous PPP with the same intensity. This represents the opposite case of the repulsion effect, which was investigated in a previous work relying on Ginibre point process sampling.The deployment of sensing nodes is crucial for applications relying on the reconstruction of spatial fields. Theoretical analysis usually assumes that nodes are distributed according to a homogeneous Poisson point process (PPP), in which nodes positions are stochastically independent. However, realistic scenarios for crowd sourcing and Internet of Things call for clustered layouts of sensing nodes, for which homogeneous PPP is not appropriate. This paper analyzes sampling and reconstruction of finite-energy signals in Rd, with samples gathered in space according to a Gauss-Poisson point process (G-PPP), which has been recently proposed to model node spatial distribution with attraction (as in clustering). In particular, it is shown that attraction between nodes modeled by G-PPP reduces the reconstruction accuracy with respect the case of homogeneous PPP with the same intensity. This represents the opposite case of the repulsion effect, which was investigated in a previous work relying on Ginibre point process sampling
Design Criteria for FIR-Based Echo Cancellers
No full textThe most relevant impairment experienced by on-channel repeaters (OCRs) in single frequency networks scenarios is the presence of a coupling-channel between the transmitting and receiving antennas, that generates unwanted echoes. This phenomenon causes a degradation of the repeated signal and, above all, could lead to the instability of OCRs, owing to the positive feedback that could result. For this reason, OCRs are usually equipped with echo canceller units, aimed at removing the coupling contributions. In this paper, we consider a realistic OCR setup and we analytically derive proper design criteria for echo-cancellers, showing the role of system parameters and implementation aspects on their performance. Here, in particular, we investigate the joint effect of the estimation noise and the finite precision arithmetic of digital systems, the system sensitivity to different design parameters and the relation between the echo-cancelling performance and the probability of instability
Ginibre sampling and signal reconstruction
Full text linkThe spatial distribution of sensing nodes plays a crucial role in signal sampling and reconstruction via wireless sensor networks. Although homogeneous Poisson point process (PPP) model is widely adopted for its analytical tractability, it cannot be considered a proper model for all experiencing nodes. The Ginibre point process (GPP) is a class of determinantal point processes that has been recently proposed for wireless networks with repulsiveness between nodes. A modified GPP can be considered an intermediate class between the PPP (fully random) and the GPP (relatively regular) that can be derived as limiting cases. In this paper we analyze sampling and reconstruction of finite-energy signals in Rd when samples are gathered in space according to a determinantal point process whose second order product density function generalizes to Rd that of a modified GPP in R2. We derive closed form expressions for sampled signal energy spectral density (ESD) and for signal reconstruction mean square error (MSE). Results known in the literature are shown to be sub-cases of the proposed framework. The proposed analysis is also able to answer to the fundamental question: does the higher regularity of GPP also imply an higher signal reconstruction accuracy, according to the intuition? Theoretical results are illustrated through a simple case study
On Molecular Communications via Diffusion with Multiple Transmitters and Multiple Receivers
No full textThis paper analyzes distributed molecular communications with multiple transmitters and multiple receivers randomly placed in a 3D space. Receivers, whose positions are modeled by a homogeneous Poisson point process, are assumed to be passive and spherical. Since they are not dimensionless, their presence affects the statistics of transmitter points, resulting in a Poisson hole process, instead of the Poisson process usually adopted in previous works on multiple transmitters. Moreover, molecules emissions from different transmitters are considered asynchronous, according to a recently proposed stochastic model. Here, the intensity function of the time point processes modeling molecules emissions is given a further meaning; in particular, it is considered as the propensity function of a chemical reaction. The average amount of received molecules per time unit (receiving rate) is evaluated accordingly for each receiver. When the swarm of receivers is considered as an equivalent, single, spatially distributed receiver, the average amount of received molecules per time and volume units (receiving rate density) is introduced and analytically evaluated
Joint Sensing and Communication with Multiple Antennas and Bistatic Configuration
No full textThe optimal trade-off between sensing and commu-
nication in perceptive networks is gaining importance. However,
many studies consider the mono-static radar configuration and
evaluate the sensing performance in terms of detection prob-
ability and/or angle of arrival (AoA) estimation accuracy of
the target. In this work, we investigate a joint sensing and
communication (JSC) system with a bistatic sensing configuration
where two different base stations (BSs) jointly perform the
sensing task while ensuring communication with their user
equipments (UEs) exploiting multiple antennas at both ends.
In particular, we consider a scenario where the transmitter
uses beamforming to accommodate communication and sensing
functionalities. In this setting, we derive a closed-form expression
for the Cram ́er-Rao Bound (CRB) of target position estimation,
which reveals the relationship with system parameters, scenario
geometry, and the beamforming vector. The CRB is then used
to analyze the JSC trade-off and to design the corresponding
beamforming at the transmitter. Finally, numerical results show
the effectiveness of the proposed solution
A Raspberry Pi-Based Platform for Signal Processing Education [SP Education]
No full textOne of the most important application areas of signal processing (SP) is, without a doubt, the software-defined radio (SDR) field [1]-[3]. Although their introduction dates back to the 1980s, SDRs are now becoming the dominant technology in radio communications, thanks to the dramatic development of SP-optimized programmable hardware, such as field-programmable gate arrays (FPGAs) and digital signal processors (DSPs). Today, the computational throughput of these devices is such that sophisticated SP tasks can be efficiently handled, so that both the baseband and intermediate frequency (IF) sections of current communication systems are usually implemented, according to the SDR paradigm, by the FPGA's reconfigurable circuitry (e.g., [4]-[6]), or by the software running on DSPs
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