An Iterative Semi-Blind Channel Estimation Scheme and Uplink Spectral Efficiency of Pilot Contaminated One-Bit Massive MIMO Systems

Srinivas, Boddupelly; Mawatwal, Khushboo; Chakrabarti, Saswat

doi:10.1109/tvt.2019.2926037

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…According to the characteristics of the channel, this structure first accurately matches the channel characteristics through lowdimensional digital precoding, so as to improve system performance and support multistream transmission. Then, in the high-dimensional analog precoding, the analog phase shifter is used to match the spatial characteristics of the signal in the analog domain to obtain the array gain, which effectively compensates for the high path loss of the millimeter wave [19].…”

Section: Hybrid Precodingmentioning

confidence: 99%

Transceiver Decoupling of Multivariate Symmetric Hybrid Precoding Based on 5G

Zhang

Wang

et al. 2022

Wireless Communications and Mobile Computing

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Mobile Internet will promote the continuous change of human interaction, leading to an increase in mobile traffic, so the demand for network bandwidth and data volume is rising rapidly, which is also one of the problems that 5G needs to solve. The mobile communication network of the railway system has the characteristics of high-speed user mobility, large-scale group mobility of users, high certainty of user mobile lines, and high QoS requirements for dispatching information. In order to improve the transmission reliability requirements of the railway system for wireless communication, a quick search method algorithm based on GMCS model to encode the number of each subinterval is proposed. Hybrid precoding is designed according to multivariate symmetry rules. The target beam is designed according to the GMCS model, and the hierarchical training beam is designed to minimize the mean square error between the training beam and the target beam as the objective function. Then, the fast search model based on beam overlap is extended to NLoS to solve the problem of misjudgment caused by multipath. In the simulation experiment, it proves that the search success rate of the research in this paper is 10% higher than that of the traditional algorithm. It improves the search speed and has obvious advantages in complexity. It can provide a dynamic reliable conversion mechanism for the railway communication environment, reduce the transmission power of the base station, and optimize the actual effect of uplink and downlink service requirements.

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Section: Hybrid Precodingmentioning

confidence: 99%

Transceiver Decoupling of Multivariate Symmetric Hybrid Precoding Based on 5G

Zhang

Wang

et al. 2022

Wireless Communications and Mobile Computing

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“…The work in [14] proposed a variational Bayesiansparse Bayesian learning-based CE algorithm for the multiuser massive MIMO system where hybrid analog-digital processing and low-resolution ADCs are utilized at the BS. In [11], researchers studied multi-cell analysis, considering pilot interference and spatially/temporally associated channels. The CE using maximum a posteriori and maximum likelihood was explored in [12] and [13] respectively, focusing on sparse mmWave MIMO communication systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based One Bit-ADCs Efficient Channel Estimation Using Fewer Pilots Overhead for Massive MIMO System

Rahman,

Sejan,

Aziz

et al. 2024

IEEE Access

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The massive MIMO approach presents an exciting prospect for the upcoming generation of wireless transmission systems. However, the adoption of actual massive MIMO scenarios is hindered by high hardware expenses and increased energy usage, particularly as the quantity of RF modules expands. To address this issue and make massive MIMO more commercially viable, the design of 1-bit analog-to-digital converters (ADCs) has been considered as a solution. Various deep learning (DL) techniques for channel estimation (CE) with 1-bit ADCs have been developed in the literature. Nonetheless, most of these methods demonstrate limited performance in CE regarding pilot lengths and noise levels. In this paper, an efficient DL model known as bi-directional long short-term memory (BiLSTM) is proposed. This model enhances CE performance with limited pilot signals by training on long input sequence data within a bi-directional framework. The bi-directional (forward and backward) tasks in the hidden layers of BiLSTM contribute to its enhanced training ability, thereby enriching the CE of the proposed system. Moreover, in this paradigm, BiLSTM is utilized in conjunction with previous channel estimation data to learn the complex mapping from quantized incoming evaluations to channels. Consequently, the proposed model demonstrates superior CE efficiency for the same size of pilot sequencing as it deduces the necessary length and configuration of the pilot sequencing to ensure the existence of this mapping function. Therefore, lower pilot signals are needed with additional antennas for identical CE capability. Simulation outcomes verify that the proposed model exhibits satisfactory CE accuracy. It is confirmed that the increase of the number of antennas improves CE concerning the acquired signal-to-noise ratio per antenna and the normalized mean squared error.INDEX TERMS Bi-directional deep learning model, massive MIMO system, low-resolution ADC, channel estimation.

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“…Angular-domain channel estimation for one-bit massive MIMO systems was studied in [7]- [9]. Spatially/temporally correlated channels and multi-cell processing with pilot contamination were investigated in [10] and [11], respectively. For sparse millimeter-wave MIMO channels, ML and maximum a posteriori (MAP) channel estimations were examined in [12] and [13], respectively.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems

Nguyen¹,

Nguyen²,

Swindlehurst³

2021

Preprint

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Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the lowresolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of the domain knowledge in the few-bit quantization process. While the first data detection network, namely B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, namely FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the vanishing gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into its weight matrices of the channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gain in estimation accuracy by the proposed deep learning framework.

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An Iterative Semi-Blind Channel Estimation Scheme and Uplink Spectral Efficiency of Pilot Contaminated One-Bit Massive MIMO Systems

Cited by 9 publications

References 28 publications

Transceiver Decoupling of Multivariate Symmetric Hybrid Precoding Based on 5G

Transceiver Decoupling of Multivariate Symmetric Hybrid Precoding Based on 5G

Deep Learning Based One Bit-ADCs Efficient Channel Estimation Using Fewer Pilots Overhead for Massive MIMO System

Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems

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