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Extracting the parameters of the multipath with high accuracy can be achieved by using high-resolution algorithm for time-domain ultra wideband (UWB) channel modeling. The CLEAN algorithm has been used as such a high-resolution algorithm for UWB time-domain characterization. This paper presents a compressed sensing (CS) based high-resolution deconvolution algorithm for time-domain UWB channel modeling...
Deconvolution is a key operation for the post-processing of ultra-wideband (UWB) channel modeling. Due to the wideband of the radio channel sounding pulse, the nature of the UWB channel can be frequency selective. This paper presents a multi-template compressed sensing (CS) based high-resolution deconvolution algorithm for time-domain UWB channel modeling, considering the pulse distortion. UWB channels...
In this paper, we propose a channel allocation approach based on sparse channel model and channel state reconstructed by compressed sensing. The graphical model is proposed based on the sparse property of the primary users and resolved by the min-sum algorithm. Simulation results show that the proposed algorithm can convert channel allocation problem to physical distance and frequency-domain distance...
Noncoherent ultra-wideband (UWB) receivers are suboptimal but have advantage in low-complexity and low-power consumption. The theory of compressed sensing (CS) enables the reconstruction or approximation of sparse or compressible signals from a small set of incoherent projections. This paper presents a noncoherent detection approach based on CS for pulse ultra-wideband systems. The Matching Pursuit...
In this paper, we propose a channel estimation approach based on Bayesian compressive sensing that has the advantage of computation simplicity, noise robust to some extent and the sparse result. UWB channel estimation is absolutely vital to the design of the receiver and is predicament of the UWB system implement. We show that our proposed method relies on the time domain sparse of the impulse response...
Bayesian compressive sensing (BCS) utilizes the prior distribution of signal coefficients to reconstruct the original signal. The widely used prior is Laplace and Gaussian distributed. In this paper, we use the scene of L sets of signal sparse coefficients which are statistically related and take advantage of Laplace prior and statistically interrelationship among signals to propose the Laplace prior...
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