Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of the distributed recovery of jointly sparse signals has attracted much attention recently. Let us assume that the nodes of a network observe different sparse signals with common support; starting from linear, compressed measurements, and exploiting network communication, each node aims at reconstructing the support and the non-zero values of its observed signal. In the literature, distributed greedy algorithms have been proposed to tackle this problem, among which the most reliable ones require a large amount of transmitted data, which barely adapts to realistic network communication constraints. ,  address the problem through a reighted   ${\ell }_{1}$   soft thresholding technique, in which the threshold is iteratively tuned based on the current estimate of the support. The proposed method adapts to constrained networks, as it requires only local communication among neighbors, and the transmitted messages are indices from a finite set.  analytically prove the convergence of the proposed algorithm and  show that it outperforms the state-of-the-art greedy methods in terms of balance beten recovery accuracy and communication load.
['Sophie M. Fosson', 'Javier Matamoros', 'Carles Antoan-Haro', 'Enrico Magli']
I am writing a research paper on a idea Distributed Recovery of Jointly Sparse Signals Under Communication Constraints
[{"from": "human", "value": "I am writing a research paper on a idea Distributed Recovery of Jointly Sparse Signals Under Communication Constraints"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of the distributed recovery of jointly sparse signals has attracted much attention recently. Let us assume that the nodes of a network observe different sparse signals with common support; starting from linear, compressed measurements, and exploiting network communication, each node aims at reconstructing the support and the non-zero values of its observed signal. In the literature, distributed greedy algorithms have been proposed to tackle this problem, among which the most reliable ones require a large amount of transmitted data, which barely adapts to realistic network communication constraints. ,  address the problem through a reighted   ${\\ell }_{1}$   soft thresholding technique, in which the threshold is iteratively tuned based on the current estimate of the support. The proposed method adapts to constrained networks, as it requires only local communication among neighbors, and the transmitted messages are indices from a finite set.  analytically prove the convergence of the proposed algorithm and  show that it outperforms the state-of-the-art greedy methods in terms of balance beten recovery accuracy and communication load."}]