Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Farzan Haddadi"'
Publikováno v:
IEEE Access, Vol 12, Pp 111565-111578 (2024)
This article addresses the problem of channel estimation and pilot allocation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) networks for sparse doubly selective (DS) channels. In DS channels, due to
Externí odkaz:
https://doaj.org/article/41ff93809c464756860e7abc53af5a71
Publikováno v:
IEEE Access, Vol 11, Pp 21921-21933 (2023)
This paper develops an efficient channel estimation algorithm based on second-order statistics of time division duplex (TDD) multiuser massive multiple-input multiple-output (MIMO) systems. The algorithm uses the received signal correlation to determ
Externí odkaz:
https://doaj.org/article/9284b8bc03374291a568c4f0cef2e454
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2022, Iss 1, Pp 1-13 (2022)
Abstract One-bit compressive sensing (CS) is an advanced version of sparse recovery in which the sparse signal of interest can be recovered from extremely quantized measurements. Namely, only the sign of each measure is available to us. The ground-tr
Externí odkaz:
https://doaj.org/article/71fe4d12816540338767eba5118b816f
Autor:
Shayan Shojaei, Farzan Haddadi
Publikováno v:
Multidimensional Systems and Signal Processing. 31:1029-1049
In this paper we discuss recovering two signals from their convolution in 3 dimensions. One of the signals is assumed to lie in a known subspace and the other one is assumed to be sparse. Various applications such as super resolution, radar imaging,
Autor:
Abbas Omidi, Amirhossein Heydarian, Farzan Haddadi, Aida Mohammadshahi, Behnam Asghari Beirami
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
Autor:
Pourya Behmandpoor, Farzan Haddadi
Publikováno v:
Multidimensional Systems and Signal Processing. 31:711-723
This paper is concerned with source localization when path loss is taken into account. We modify multiple signal classification method to localize near-field sources whose received power is different in the sensors of the array due to path loss. Trad
Publikováno v:
IEEE Transactions on Signal Processing. 67:5093-5102
We study the problem of recovering a block-sparse signal from under-sampled observations. The non-zero values of such signals appear in few blocks, and their recovery is often accomplished using a $\ell_{1,2}$ optimization problem. In applications su
Publikováno v:
Circuits, Systems, and Signal Processing. 39:2730-2743
This paper proposes a spectrum sensing algorithm from one-bit measurements in a cognitive radio sensor network. A likelihood ratio test (LRT) for the one-bit spectrum sensing problem is derived. Different from the one-bit spectrum sensing research wo
Autor:
Pourya Behmandpoor, Farzan Haddadi
Publikováno v:
Multidimensional Systems and Signal Processing. 31:317-328
In this paper, we consider circular array design in the presence of a far-field or a near-field signal source. The location of the source is introduced to our optimization problem by its probability density function (PDF) as a priori information. We
Publikováno v:
IEEE Signal Processing Letters. 26:528-532
We study the problem of reconstructing a block-sparse signal from compressively sampled measurements. In certain applications, in addition to the inherent block-sparse structure of the signal, some prior information about the block support, i.e., blo