A novel fast two stage method for wideband spectrum sensing

Molazadeh, Amirhossein; Ghasimi, Mohsen; Ardebilipour, Mehrdad

doi:10.1016/j.phycom.2019.01.001

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…In other words, a sparse vector

\mathbf{x}

can be recovered from

\mathbf{y}=\boldsymbol{\Phi} \mathbf{x}

where

\mathbf{y}

is the measurement vector and

\boldsymbol{\Phi}

is the sensing matrix. Compressed sensing is widely applicable to many applications 14 like wideband spectrum sensing 15 …”

Section: Introductionmentioning

confidence: 99%

“…Compressed sensing is widely applicable to many applications 14 like wideband spectrum sensing. 15 In the case of mmWave channel estimation, Alkhateeb et al 16 introduce an approach found on the fusion of beam training concept and compressed sensing. The algorithm divides the estimation process into several stages and use predesigned beam patterns.…”

Section: Introductionmentioning

confidence: 99%

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Sensing matrix design for wideband channel estimation in millimeter‐wave hybrid multiple‐input multiple‐output system

Molazadeh

Ardebilipour

Maroufi

2022

Trans Emerging Tel Tech

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Hybrid analog/digital multiple‐input multiple‐output (MIMO) system is proposed to support low complexity multi‐stream communication in millimeter‐wave (mmWave) frequencies. The main idea of channel estimation methods for mmWave hybrid MIMO systems is based on leveraging the sparsity nature of the channel in the angle and delay domain. Sparse signal recovery algorithms have been used to reconstruct the channel coefficients. Due to the lack of information about the angle of arrival and angle of departure before channel estimation, random values are common choices for precoders and combiners. Moreover, the training pilot symbols are considered to be random variables. In this article, we propose sequential time domain sensing matrix design and sequential frequency domain sensing matrix design algorithms to design these variables in order to enhance sparse signal recovery performance and consequently the channel estimation accuracy. Our methods are based on reducing the correlation between the sensing matrix columns, which is defined in the sparse formulation of the channel estimation problem. We design the training radio frequency precoders/combiners phase angle values and pilot symbol vectors with the aid of a sequential procedure that has a reasonable complexity. Simulation results show that utilizing the designed values instead of random variables increased the performance of the channel estimation, especially when we deal with a higher number of variables.

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“…In other words, a sparse vector