Downlink channel estimation of FDD based massive MIMO using spatial partial-common sparsity modeling

Shalavi, N.; Atashbar, Mahmoud; Feghhi, Mahmood Mohassel

doi:10.1016/j.phycom.2020.101138

Cited by 8 publications

(6 citation statements)

References 46 publications

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“…𝐸{‖𝒉‖ 2 } 𝐿 𝑙=1 (26) in which 𝒉 𝑙 and 𝒉 ̂𝑙 are the actual and estimated channel vector of l-th user. In the first simulation, we select L=2, Ks = 8, and Nφ = 8, and compare the NMSE versus SNR for the proposed and the classical algorithms.…”

Section: Comparing the Methods Via Nmsementioning

confidence: 99%

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Channel Estimation For The Multiuser Multipanel Massive Multiple Input Multiple Output System

Ghadiri

Atashbar

Feghhi

2022

Preprint

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By increasing the number of antennas, the size and weight of massive Multiple Input Multiple Output (MIMO) become much larger and heavier, respectively. To deal with these problems, the 3GPP standardization group captured the multipanel in New Radio communications. Channel estimation is needed for accurate uplink signal recovery and downlink precoding of massive MIMO. Due to the use of hybrid structure and the nonuniform distribution of the antenna arrays, the conventional channel estimation methods cannot be used in multipanel massive MIMO. In this paper, we propose the channel estimation for the millimeter-wave multipanel MIMO systems in multiuser environments, in which the channel estimation problem is transformed to an angular domain block sparse signal recovery problem. In the following, we solve the obtained sparse model using the proposed generalized block orthogonal matching pursuit method. Also, the proper pilot sequence is designed in the proposed method. Finally, we check the accuracy of the proposed estimation algorithm, which simulation results show the superior performance of the proposed method compared to that of the classic method.

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Section: Comparing the Methods Via Nmsementioning

confidence: 99%

“…For solving a sparse problem using compressive sensing theory, mutual coherence has an important role in the accuracy of estimation. This parameter defines as follow [26]…”

Section: | Pilot Sequence Designmentioning

confidence: 99%

Channel Estimation For The Multiuser Multipanel Massive Multiple Input Multiple Output System

Ghadiri

Atashbar

Feghhi

2022

Preprint

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“…In 2020, N. Shalavi et al [2], modeled the FDD massive MIMO downlink frequency selective channel estimation. An appropriate pilot series was modeled for the developed technique; also an estimation model was presented related to this technique to resolve the issue.…”

Section: Literature Surveymentioning

confidence: 99%

“…Conversely, for precise detection, the CSI is needed in the receiver of both Mobile MS and BS. Hence, for the estimation of CSI with maximum precision, it is very important [2]. Moreover, the estimation of the uplink CSI information is simpler than the downlink channel.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid PSO-GSA Algorithm for Channel Estimation in Massive MIMO System

Kallur¹

2021

JNACS

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For advanced communication MILLIMETER wave (mmWave) is represented as a buoyant technology against wireless networks because of its prosperous frequency spectral resources in Multiple Input Multiple Output (MIMO). Nevertheless, the mmWave is diagnosed in MIMO remnants as a complex task that appears as the problems such as maximized propagation loss. Hence, this work introduces a novel optimization helped-out estimation technique to calculate the mm-wave channel parameters. On mm-wave massive MIMO system the performance of hybrid precoding, as well as channel estimation, is developed through using optimization procedure in codebook model principles. In reality, existing models are carried out the uniform distribution of azimuth angles in the codebook model, the wherein developed method estimates it as a single objective optimization issue without contravening characteristics of angle. To resolve the aforesaid optimization issue, the Hybrid Particle Swarm Optimization (PSO)-Gravitational Search Algorithm (GSA) technique is developed which hybridizes the idea of PSO and GSA method correspondingly. At last, the developed model performance is evaluated and examined with the conventional methods regarding the Channel State Information (CSI) and error metrics.

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Optimized deep learning based hypernet convolution neural network and long short term memory for joint pilot design and channel estimation in MIMO‐OFDM model

Silpa,

Vani,

Naidu

2023

Trans Emerging Tel Tech

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In multiple input multiple output‐orthogonal frequency division multiplexing (MIMO‐OFDM) systems, efficient pilot design (PD) and channel estimation (CE) greatly influences the reliability and robustness of pilot‐based CE methods. But, accurate estimationof the channel remains a challenge in a high‐mobility environment with non‐linear channel characteristics. Many techniques have been introduced to overcome the pilot contamination and CE problems in the MIMO‐OFDM system to overcome this issue. However, these techniques take multiple paths at the receiver, resulting in delay spread and interference in the communication. Hence, this study presents a novel deep learning (DL) based technique for channel estimation based on channel state information (CSI). A DL technique based on a hyper convolutional neural network (Hyper‐CNN) is introduced for the optimal pilot design. The selection of the pilot position can be made using the tunicate swarm optimization (TSO) approach. Finally, a DL‐based long short‐term memory (LSTM) model is proposed for the CE in the MIMO‐OFDM system. The proposed method is implemented in the MATLAB platform, and the outcome is compared under different metrics like mean square error (MSE) and bit error rate (BER). In the experimental scenario, the proposed method attains the BER of 0.20 and 0.03 for CP (cyclic prefix) and without CP, respectively. In addition, the proposed method attains the MSE of 1.14, 0.99, 1.08 and 0.97 for 8, 16, 48 and 64 pilots, respectively. The performance of a proposed method is compared with the existing method and proves the efficacy of the proposed method.

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Downlink channel estimation of FDD based massive MIMO using spatial partial-common sparsity modeling

Cited by 8 publications

References 46 publications

Channel Estimation For The Multiuser Multipanel Massive Multiple Input Multiple Output System

Channel Estimation For The Multiuser Multipanel Massive Multiple Input Multiple Output System

Hybrid PSO-GSA Algorithm for Channel Estimation in Massive MIMO System

Optimized deep learning based hypernet convolution neural network and long short term memory for joint pilot design and channel estimation in MIMO‐OFDM model

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