Frame synchronization for M-ary modulation with phase offsets

Elzanaty, Ahmed; Koroleva, Ksenia; Gritsutenko, Stanislav; Chiani, Marco

doi:10.1109/icuwb.2017.8250962

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…Finding the packet arrival time at the received signal is called frame synchronization. Frame synchronization for burst communication is a well-established research field [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The receiver finds the packet arrival time by detecting the received preamble. Many frame synchronization techniques by using preamble have been suggested for binary phase-shift keying (BPSK) systems [4,5], M-ary PSK systems [6], continuous phase modulation (CPM) systems [7,8], and orthogonal frequency division multiple access (OFDM) systems [9,10]. Optimum frame synchronization is known as the maximum likelihood (ML) technique [4,8].…”

Section: Introductionmentioning

confidence: 99%

“…The huge computation is difficult to handle. Therefore, practical frame synchronization techniques have been researched so far [4][5][6][7][8][9][10]. Those practical frame synchronization techniques, regardless of modulation schemes, are based on the correlation peak search.…”

Section: Introductionmentioning

confidence: 99%

“…As the frame synchronization performance is highly sensitive to the SNR estimation accuracy [11], as one of the most intuitive methods, the peak detection of the correlator output within the time window is widely used [4]. Frame synchronization based on peak search at correlator output has a long history, but it is still the most widely used technique in recent communication systems [6,8,10].…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network (CNN)-Based Frame Synchronization Method

et al. 2020

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A new frame synchronization technique based on convolutional neural network (CNN) is proposed for synchronized networks. To estimate the exact packet arrival time, the receiver typically uses the correlator between the received signal and the preamble or pilot in front of the transmitted packet. The conventional frame synchronization technique searches the correlation peak within the time window. In contrast, the proposed method utilizes a CNN to find the packet arrival time. Specifically, in the proposed method, the 1D correlator output is converted into a 2D matrix by reshaping, and the resulting signal is inputted to the proposed 4-layer CNN classifier. Then, the CNN predicts the packet arrival time. To verify the frame synchronization performance, computer simulation is performed for two channel models: additive white Gaussian noise and fading channels. Simulation results show that the proposed CNN-based synchronization method outperforms the conventional correlation-based technique by 2dB.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network (CNN)-Based Frame Synchronization Method

et al. 2020

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“…The Gaussian codebook for the data is usually adopted as an approximation for higher order modulation[29],[30],[35]-[38].4 VOLUME 4, 2016 This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication.…”

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confidence: 99%

Semi-Blind Channel Estimation for Intelligent Reflecting Surfaces in Massive MIMO Systems

Alwakeel

Elzanaty

2022

IEEE Access

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Intelligent reflecting surface (IRS) is considered as a promising technology for enhancing the transmission rate in cellular networks. Such improvement is attributed to considering a large IRS with high number of passive reflecting elements, optimized to properly focus the incident beams towards the receiver. However, to achieve this beamforming gain, the channel state information (CSI) should be be efficiently acquired at the base station (BS). Unfortunately, the traditional pilot estimation method is challenging, because the passive IRS does not have radio frequency (RF) chains and the number of channel coefficients is proportional to the number of IRS elements. In this paper, we propose a novel semi-blind channel estimation method where the reflected channels are estimated using not only pilot but also data symbols, reducing the channel estimation overhead. The performance of the system is analytically investigated in terms of the uplink achievable sum-rate. The proposed scheme achieves higher energy and spectrum efficiency, while being robust to channel estimation errors. For instance, the proposed scheme achieves a 80% increase in spectrum efficiency compared to pilot-only based schemes, for IRSs with N = 32 elements.INDEX TERMS Intelligent reflecting surfaces; channel estimation; massive MIMO.

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Trade-Off Between Frame Synchronization and Channel Decoding for Short Packets

Bidan

Guilloud

2019

IEEE Commun. Lett.

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Frame synchronization for M-ary modulation with phase offsets

Cited by 5 publications

References 18 publications

Convolutional Neural Network (CNN)-Based Frame Synchronization Method

Convolutional Neural Network (CNN)-Based Frame Synchronization Method

Semi-Blind Channel Estimation for Intelligent Reflecting Surfaces in Massive MIMO Systems

Trade-Off Between Frame Synchronization and Channel Decoding for Short Packets

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