654 research outputs found
Missing Spectrum-Data Recovery in Cognitive Radio Networks Using Piecewise Constant Nonnegative Matrix Factorization
In this paper, we propose a missing spectrum data recovery technique for
cognitive radio (CR) networks using Nonnegative Matrix Factorization (NMF). It
is shown that the spectrum measurements collected from secondary users (SUs)
can be factorized as product of a channel gain matrix times an activation
matrix. Then, an NMF method with piecewise constant activation coefficients is
introduced to analyze the measurements and estimate the missing spectrum data.
The proposed optimization problem is solved by a Majorization-Minimization
technique. The numerical simulation verifies that the proposed technique is
able to accurately estimate the missing spectrum data in the presence of noise
and fading.Comment: 6 pages, 6 figures, Accepted for presentation in MILCOM'15 Conferenc
-Box Optimization for Green Cloud-RAN via Network Adaptation
In this paper, we propose a reformulation for the Mixed Integer Programming
(MIP) problem into an exact and continuous model through using the -box
technique to recast the binary constraints into a box with an sphere
constraint. The reformulated problem can be tackled by a dual ascent algorithm
combined with a Majorization-Minimization (MM) method for the subproblems to
solve the network power consumption problem of the Cloud Radio Access Network
(Cloud-RAN), and which leads to solving a sequence of Difference of Convex (DC)
subproblems handled by an inexact MM algorithm. After obtaining the final
solution, we use it as the initial result of the bi-section Group Sparse
Beamforming (GSBF) algorithm to promote the group-sparsity of beamformers,
rather than using the weighted -norm. Simulation results
indicate that the new method outperforms the bi-section GSBF algorithm by
achieving smaller network power consumption, especially in sparser cases, i.e.,
Cloud-RANs with a lot of Remote Radio Heads (RRHs) but fewer users.Comment: 4 pages, 4 figure
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