Robust Digital Signal Recovery for LEO Satellite Communications Subject to High SNR Variation and Transmitter Memory Effects

Chen, Qingyue; Zhang, Yufeng; Jalili, Feridoon; Wang, Zhugang; Huang, Yonghui; Wang, Yübo; Liu, Ying; Pedersen, Gert Frølund; Shen, Ming

doi:10.1109/access.2021.3117517

Cited by 10 publications

(9 citation statements)

References 28 publications

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“…The satellite communication system starts when the satellite establishes a communication link with the ground station [2], [21], [22], [25]- [28]. The window for an LEO satellite starts when the elevation angle ϵ is greater than 5 • .…”

Section: Communication System and Problem Formulationmentioning

confidence: 99%

“…Based on the equation the variation for a satellite in LEO at an altitude of 800 km is approximately 12 dB in the link budget between 5 • and 90 • [22]. To have the possibility of an efficient as possible satellite downlink, the transmitter should be able to vary transmission power such that the power received is constant.…”

Section: Communication System and Problem Formulationmentioning

confidence: 99%

“…The idea was initially proposed in [21]. It was shown possible to recover the received signal and compensate for the non-linear distortion before the actual receiver and BER performance was later shown in [22]. However, it lacked more realistic channels and results for how it compensates for different amplifiers, steering angles, and power levels without retraining the model.…”

Section: Introductionmentioning

confidence: 99%

“…Complex valued networks are similar to deep neural networks but they can process and understand complex numbers without preprocessing. This paper proposes to combine the use of a complex-valued neural network [23] with the idea of recovering signals [21], [22] to provide a new receiver structure that can both decode and compensate for the non-linearity in a single complexvalued neural network for an OFDM system. This paper proposes a two-stage Deep Complex Convolution Neural Network (DCCN) which is trained in two steps to achieve the wanted performance.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Stage Deep Learning Receiver for High Throughput Power Efficient LEO Satellite System With Varied Operation Status

2022

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Low Earth orbit satellites are expected to be one of the biggest suppliers of wireless communication within the coming years. For this to happen 5G and 6G networks, are crucial to be implemented in satellite communication. This comes with the problem of power-efficient transmissions. This paper exploits recent advances in complex-valued deep learning to cope with this challenge. The proposed approach is based on the autoencoder structure, where a legacy orthogonal frequency division modulation (OFDM) transmitter is used as an encoder and a deep complex convoluted network (DCCN) is used as decoder/receiver. Different from other state-of-the-art receiver architectures based on one-stage trained neural networks, our proposed DCCN adopts a two-stage training scheme, where the first stage is trained using AWGN channel and a fixed non-linear front end. The second stage uses a transfer learning to adapt to the flat fading channels and the front end model can be changed to compensate for different front ends, significantly reducing training time.This allows for power-efficient transmission at different operation statuses (e.g. radiated power levels and steering angles) without compromising the bit error rate in both average white Gaussian noise (AWGN) and flat fading channels. A K-band (28 GHz) active phased array in package (AiP) transmitting a 5G NR OFDM signal with a bandwidth of 100 MHz was used as the main front end test vehicle for validating the proposed DCCN. Satisfactory bit error rates were achieved while the AiP was driven into saturation with high power efficiency at different power levels and steering angles. This work demonstrates, for the first time, the promising capability of deep neural networks in processing varied operation staged non-linear OFDM waveforms in the form of an auto-decoder receiver.

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Section: Communication System and Problem Formulationmentioning

confidence: 99%

Section: Communication System and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Two-Stage Deep Learning Receiver for High Throughput Power Efficient LEO Satellite System With Varied Operation Status

2022

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“…For wideband signals, in particular, the memory effects have a significant impact [19,20]. To take care of memory effects, two dynamic neural structures have been proposed in the NN literature [21].…”

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confidence: 99%

Highly non‐linear and wide‐band mmWave active array OTA linearisation using neural network

Jalili

Zhang

Hintsala³

et al. 2021

IET Microwaves Antenna & Prop

Self Cite

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This paper proposes a neural network (NN)-based over-the-air (OTA) linearisation technique for a highly non-linear and wide-band mmWave active phased array (APA) transmitter and compares it with the conventional memory polynomial model (MPM)based technique. The proposed NN effectively learns the distinctive non-linear distortions, which may not easily fit to existing MPM solutions, and can, therefore, successfully cope with the challenges introduced by the high non-linearity and wide bandwidth. The proposed technique has been evaluated using a state-of-the-art 4 � 4 APA operating in highly non-linear regions at 28 GHz with a 100-MHz-wide 3GPP base-station signal as input. Experimental results show the pre-distortion signal generated by the NN exhibits the peak-to-average power ratio (PAPR) much lower than the one generated by MPM and consequently superior linearisation performance in terms of adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) for high non-linearity cases. Using the proposed NN-based linearisation technique, an improvement of 5-dB ACLR and 7% points in EVM are achieved, which demonstrates the promising potential of this technique for emerging broadband communication systems such as 5G/6G and low Earth orbit (LEO) satellite networks. K E Y W O R D S 5G mobile communication, active antenna arrays, learning (artificial intelligence), linearisation techniques, millimetre wave amplifiers, millimetre wave antenna arrays, satellite communication 1 | INTRODUCTION Recent wireless communication systems operating at mmWave are using active phased array (APA) transmitters together with multi-input multi-output (MIMO) systems to improve the system capacity and data rates of the wireless networks. The traditional linearisation techniques like digital pre-distortion (DPD) that are mainly based on the memory polynomial model (MPM) are facing new challenges [1] such as the following.� Highly integrated front-ends and a large number of PA's do not allow placement of feedback circuits for each branch so a single-input single-output (SISO) over-the-air (OTA) model is needed.� mmWave frequencies and wide-band linearisation must be handled. � The increasing desire for high power efficiency requires linearisation of power-efficient but highly non-linear APAs.Several solutions using a SISO model and modified DPD algorithms for linearisation have been proposed to combat the above challenges [2][3][4][5][6]. A SISO modelling where the entire transmitter is considered as a two-port system has been presented in [7-10] using an observation receiver in far-field together with using an MPM-based DPD technique for linearisation of the antenna array in the presence of crosstalk. In [11], the potential mismatches between PAs have been This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Hybrid VLC Vehicle to Vehicle and LEO Satellite Communication System for Highway Road Coverage

Okasha,

Zekry,

Newagy

2022

Lecture Notes in Electrical Engineering

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Robust Digital Signal Recovery for LEO Satellite Communications Subject to High SNR Variation and Transmitter Memory Effects

Cited by 10 publications

References 28 publications

A Two-Stage Deep Learning Receiver for High Throughput Power Efficient LEO Satellite System With Varied Operation Status

A Two-Stage Deep Learning Receiver for High Throughput Power Efficient LEO Satellite System With Varied Operation Status

Highly non‐linear and wide‐band mmWave active array OTA linearisation using neural network

Hybrid VLC Vehicle to Vehicle and LEO Satellite Communication System for Highway Road Coverage

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