Deep Learning Based Pilot Assisted Channel Estimation for Rician Fading Massive MIMO Uplink Communication System

Rahman, Md. Habibur; Shahjalal, Md.; Ali, Md. Osman; Yoon, Suk-Jin; Jang, Yeong Min

doi:10.1109/icufn49451.2021.9528814

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Generally, regardless of the several developments that have been made in PACE and better channel estimation quality due to the large number of pilots/training sequences, the major drawback that is associated with PACE techniques is the wastage of the bandwidth. Another setback of PACE is that channel estimates are only dependent upon pilot tones, therefore interpolation technique is used to estimate data tones which could not be a perfect estimator all the time [28]- [32].…”

Section: ) Pilot Assisted Channel Estimation (Pace)mentioning

confidence: 99%

Data Aided Channel Estimation for MIMO-OFDM Wireless Systems Using Reliable Carriers

2023

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Wireless channel estimation is one of the challenging problems in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless systems. The MIMO-OFDM exploits the spatial resources and increases the reliability and capacity of wireless systems. However, the performance of these systems depends on accurate channel estimation since the receivers require perfect Channel State Information (CSI) for coherent signal detection. Thus, wireless channel estimation is a necessary component of OFDM systems. In this article, we have proposed an algorithm for MIMO-OFDM systems that combines pilot symbols with reliable data symbols for channel estimation. The reliable data symbols serve as virtual pilots and enhance the spectral efficiency. The proposed Data Aided Channel Estimation (DACE) algorithm eliminates the requirement of any additional resources such as excessive number of training symbols to attain the desired performance. Also, it outperforms the traditional Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) methods for channel estimation in terms of Mean Square Error (MSE) and Bit Error Rate (BER) performance of the system.

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Section: ) Pilot Assisted Channel Estimation (Pace)mentioning

confidence: 99%

Data Aided Channel Estimation for MIMO-OFDM Wireless Systems Using Reliable Carriers

2023

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“…During the training process, the neural network based on DL calculates the loss gradient of a single training sample, updates the model parameters along the gradient direction once, and repeats this step multiple times to traverse all training samples, thereby gradually approaching the optimal parameters [25]. The difference is that the MAML method performs the final update by calculating the gradient of the loss function obtained by the training samples of different tasks, so as to find a suitable initial parameter, which has the best generalization for different subtasks in the task set.…”

Section: Maml Networkmentioning

confidence: 99%

Meta-Learning based Time-varying Channel Estimation with ICI Cancellation in High-speed Mobile OFDM Systems

Yang

Ren

Shao

et al. 2023

Preprint

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A meta-learning (ML) based time-varying channel estimation method with inter-carrier interference (ICI) cancellation is proposed for the high-speed mobile OFDM systems. To reduce the effect of the ICI caused by the Doppler shift on the accuracy of channel estimation, a improved transformation matrix is given to transform the transmitted signal with the comb-type pilot in the frequency domain into the one with the block-type pilot in the time domain, which can reduce the interference of the neighboring data symbols to the pilots. Since the ML network has ability to quickly adapt to the new channel scenario, it is employed to estimate the time-varying channel only by the received ICI-free pilots. To improve the practicability of the estimation model, the training target of the network is set as the channel estimation with high accuracy rather than the ideal channel state information. Simulation results show that the proposed method has high estimation accuracy and low computational complexity, and it is robust to the fast time-varying channel in the high-speed mobile scenarios.

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“…As new technologies such as 5G and 6G emerge, DL will play a crucial role in all layers of the communication stack. Previous research has shown impressive results in various DL applications for wireless communications such as channel estimation [1]- [3], signal identification [4], decoding [5], and synchronisation [6].…”

Section: Introductionmentioning

confidence: 99%

Streaming Convolutional Neural Network FPGA Architecture for RFSoC Data Converters

Maclellan,

Crockett,

Stewart

2023

2023 21st IEEE Interregional NEWCAS Conference (NEWCAS)

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This paper presents a novel Convolutional Neural Network (CNN) FPGA architecture designed to perform processing of radio data in a streaming manner without interruption. The proposed architecture is evaluated for radio modulation classification tasks implemented on an AMD RFSoC 2x2 development board and operating in real-time. The proposed architecture leverages optimisation such as the General Matrix-to-Matrix (GEMM) transform, on-chip weights, fixed-point arithmetic, and efficient utilisation of FPGA resources to achieve constant processing of a stream of samples. The performance of the proposed architecture is demonstrated through accuracy results obtained during live modulation classification, while operating at a sampling frequency of 128 MHz before decimation. The proposed architecture demonstrates promising results for realtime, time-critical CNN applications.

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Deep Learning Based Pilot Assisted Channel Estimation for Rician Fading Massive MIMO Uplink Communication System

Cited by 9 publications

References 16 publications

Data Aided Channel Estimation for MIMO-OFDM Wireless Systems Using Reliable Carriers

Data Aided Channel Estimation for MIMO-OFDM Wireless Systems Using Reliable Carriers

Meta-Learning based Time-varying Channel Estimation with ICI Cancellation in High-speed Mobile OFDM Systems

Streaming Convolutional Neural Network FPGA Architecture for RFSoC Data Converters

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