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Spectral compressive sensing toolbox
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Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins; when this is not the case, CS recovery performance degrades signi cantly. In this paper, we introduce the spectral CS (SCS) recovery framework for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter esti
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new cooperative
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basic cooperative scheme for power control in cognitive radio, to estimate channel SNR,calculate signal parameter. Cooperative spectrum sensing is considered best sensing.so, i tried to learn about this by searching in google
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Adaptive Resource Allocation in Multiuser OFDM Sys
4.0
Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless LAN systems. The sum capacity of MU-OFDM is maximized when each subchannel is assigned to the user with the best channel-tonoise ratio for that subchannel, with power subsequently distributed by water-filling. However, fairness among the users cannot generally be achieved with such a scheme. In this paper, we impose a set of proportional fairness constraints to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, we propose a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation. In the proposed algorithm, subchannel allocation is first performed by assum
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communications
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mainly undergraduate course students can appreciate and easily understand the MATLAB/simulink which i am uploading
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