Traditional centralized electromagnetic spectrum monitoring platforms collect energy detection data from time, frequency and space dimensions. This method has high data redundancy. Combining the propagation loss characteristics and the signal direction finding (DF) data of each detection node, we focus on the signal source compressed parameter estimation. We propose a minimum average distance (MAD) method to improve the accuracy of collaborative detection in Cognitive Radio Network (CRN). The collaborative estimated data is stored in the blockchain structure to establish the distributed electromagnetic spectrum database (BC-DSDB). Based on the consensus mechanism Proof of High Confidence (POHC), the detection nodes maintain BC-DSDB independently. To regulate the rational utilization of electromagnetic spectrum resources, we propose the Spectrum Resource Currency (SRC) to evaluate the priority of the secondary user (SU) for dynamic spectrum access. When a spectrum collision event occurs between SUs, the spectrum time slice resources can be allocated according to the SRC. The experimental results show that BC-DSDB accurately describes the distribution of electromagnetic spectrum resources based on the propagation loss characteristics. At the same time, the redundancy of spectral data storage is reduced. SUs can quickly formulate dynamic spectrum access policies based on BC-DSDB and SRC in distributed cognitive radio networks.
Francois Belletti, Evan Sparks, Michael J. Franklin, Alexandre M. Bayen · 5 authors
Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering. Much of the existing literature in linear causal analysis operates in the time domain. Unfortunately, the direct application of time domain linear causal analysis to many real-world time series presents three critical challenges: irregular temporal sampling, long range dependencies, and scale. Moreover, real-world data is often collected at irregular time intervals across vast arrays of decentralized sensors and with long range dependencies which make naive time domain correlation estimators spurious. In this paper we present a frequency domain based estimation framework which naturally handles irregularly sampled data and long range dependencies while enabled memory and communication efficient distributed processing of time series data. By operating in the frequency domain we eliminate the need to interpolate and help mitigate the effects of long range dependencies. We implement and evaluate our new work-flow in the distributed setting using Apache Spark and demonstrate on both Monte Carlo simulations and high-frequency financial trading that we can accurately recover causal structure at scale.
We study the compressed sensing reconstruction problem for a broad class of random, band-diagonal sensing matrices. This construction is inspired by the idea of spatial coupling in coding theory. As demonstrated heuristically and numerically by Krzakala et al. \cite{KrzakalaEtAl}, message passing algorithms can effectively solve the reconstruction problem for spatially coupled measurements with undersampling rates close to the fraction of non-zero coordinates. We use an approximate message passing (AMP) algorithm and analyze it through the state evolution method. We give a rigorous proof that this approach is successful as soon as the undersampling rate $δ$ exceeds the (upper) Rényi information dimension of the signal, $\uRenyi(p_X)$. More precisely, for a sequence of signals of diverging dimension $n$ whose empirical distribution converges to $p_X$, reconstruction is with high probability successful from $\uRenyi(p_X)\, n+o(n)$ measurements taken according to a band diagonal matrix. For sparse signals, i.e., sequences of dimension $n$ and $k(n)$ non-zero entries, this implies reconstruction from $k(n)+o(n)$ measurements. For `discrete' signals, i.e., signals whose coordinates take a fixed finite set of values, this implies reconstruction from $o(n)$ measurements. The result is robust with respect to noise, does not apply uniquely to random signals, but requires the knowledge of the empirical distribution of the signal $p_X$.
Open access
2 source records
Sparse and Compressive Sensing Techniques
Microwave Imaging and Scattering Analysis
Distributed Sensor Networks and Detection Algorithms