Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity

By Juan Andrés Bazerque, Georgios B. Giannakis. IEEE Transactions on Signal Processing, 2010

516 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/13c8c4a291a1ad752ef7544e0c5994ce77a20900

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Citations in the corpus

On the Linear Convergence of the ADMM in Decentralized Consensus Optimization (2013, background): “The values of x̃ can stand for average temperature of a room [5], [6], frequen cy-domain occupancy of spectra [11], [12], states of a smart grid syste m [13], [14], and so on.”
Fast Distributed Gradient Methods (2011, methodology): “d methods, the recent reference [24] shows that the alternating direction method of multipliers (ADMoM) converges at rate O(1=k), which may be possible to extend to distributed variants of ADMoM [4], [3], [22]. Hence, dual Lagrangian methods may have a smaller overall communication cost than gradien”
A geometrically convergent method for distributed optimization over time-varying graphs (2016, background): “Examples and works include distributed averaging [1], [2], distributed spectrum sensing [3], formation control [4], [5], power system control [6], [7], statistical inference and learning [8]–[10], and so on.”
Linear Convergence Rate of a Class of Distributed Augmented Lagrangian Algorithms (2013, methodology): “Many known methods to solve (1) fall into the framework of (2)–(3); see, e.g., [9], [4], [5], [6], [7], [8].”
Distributed Sparse Linear Regression (2010, methodology): “A cooperative approach to the sensing task of CR networks can be devised, utilizing a basis expansion model for the spectrum [3] (3) where the ﬁrst equality models the power spectrum density (PSD) at frequency , and at the location of the CR, as the superposition of the PSDs generated by sources…”
Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs (2016, background): “Some examples include distributed averaging [7,50,72], distributed spectrum sensing [1], formation control [49, 58], power system control [21, 55], statistical inference and learning [20, 45, 53].”
Structured Compressed Sensing: From Theory to Applications (2011, background): “Similar ideas were also developed in the context of array processing [21], [103], [104], equalization of sparse communication channels [105], [106], and more recently cognitive radio and multiband communications [9], [86], [107]–[109].”
Cooperative spectrum sensing in cognitive radio networks: A survey (2011, methodology): “Similar to [71], cooperative sensing is formulated as a sparse regression problem with timeweighted non-negative Lasso to exploit the sparsity of PU locations.”
A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates (2017, background): “Some examples include distributed average consensus [2]–[4], distributed spectrum sensing [5], information control [6], [7], power systems control [8], [9], statistical inference and learning [10]–[12].”
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization (2014, background): “Examples and works include decentralized averaging [7,15,34], learning [9,22,26], estimation [1,2,16,18, 29], sparse optimization [19, 35], and low-rank matrix completion [20] problems.”
A Method to Improve Consensus Averaging using Quantized ADMM (2019, background): “These problems arise in multi-agent cooperative control [1], [2], spectrum sensing in cognitive radio networks [3], distributed load balancing [4] and distributed learning [5], [6].”
Quantized consensus ADMM for multi-agent distributed optimization (2015, methodology): “Examples of such models include least squares [8], [9] and re gularized least squares [10]–[12].”
D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization (2012, background): “These reformulations, except the one for LA SSO, are not new: see [13], [14], [16], [15].”
Expander graph and communication-efficient decentralized optimization (2016, background): “Some important works include decentralized averaging [1], [2], [3], learning [4], [5], [6], estimation [7], [8], [9], [10], sparse optimization [11], [12], and low-rank matrix completion [13] problems.”
DEXTRA: A Fast Algorithm for Optimization Over Directed Graphs (2017, background): “D ISTRIBUTED computation and optimization have gained great interests due to their widespread applications in, e.g., large-scale machine learning [1], [2], model predictive con-trol [3], cognitive networks [4], [5], source localization [6], [7], resource scheduling [8], and message routing [9].”
Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks (2011, methodology): “This framework is adopted in [13] to develop cooperative spectrum sensing solutions for CR networks which are sparse in both the power spectral density (PSD) and the spatial locations of the primary transmitters.”
NEXT: In-Network Nonconvex Optimization (2016, methodology): “, non-negativity an d upper bound limits;φi = {φik} Nf k=1 collects noisy samples of the received power over Nf frequencies by sensor i; Bi ∈ Rfbs is the matrix of regressors, whose specific expression is not relevant in our discussion, with Ns andNb being the number of sources and the number of bas”
On the Convergence of Decentralized Gradient Descent (2013, background): “These properties are important for applications where data are collected by distributed agents, communication to a fusion center is expensive or impossible, and/or agents tend to keep their raw data private; such applications arise in wireless sensor networks [16, 24, 27, 38], multivehicle and multi”