TY - JOUR
T1 - A Novel Average Autoencoder-based Amplify-and-Forward Relay Networks with Hardware Impairments
AU - Gupta, Ankit
AU - Sellathurai, Mathini
N1 - Funding Information:
This work is supported in part by the U.K. Engineering and Physical Sciences Research Council under Grant EP/P009670/1, the COG-MHEAR: Towards cognitively-inspired 5G-IoT enabled, multi-modal Hearing Aids under Grant EP/T021063/1, and the U.K. Defence Science and Technology Laboratory: Signal Processing in the Information Age under Grant EP/S000631/1.
Publisher Copyright:
© 2015 IEEE.
PY - 2022/6
Y1 - 2022/6
N2 - In this paper, we propose a novel Average autoencoder (AE)-based amplify-and-forward (AF) relay networks impacted by the I/Q imbalance (IQI) and additional hardware impairments (AHI), where the source and destination nodes are equipped with neural network (NN)-based encoder and decoder, while a conventional AF relay node assists the transmission. The average AE employs multiple small NN-based decoders at the destination node, each decoding a soft probabilistic output that is averaged to obtain the final soft probabilistic output at the destination node. By considering multiple small NN decoders, we reduce the implementation complexity significantly while improving the performance compared to the AE with a single large but NN-based decoder. Within this Average AE framework, we propose a coded modulation design (CMD) with zero-forcing-based IQI compensation that considers the availability of the channel state information (CSI) and IQI knowledge. However, the IQI and CSI need to be estimated separately. Thus, we also propose a CMD with no IQI compensation that requires only the CSI knowledge. Finally, we propose a differential CMD that removes the necessity of both the CSI and IQI knowledge. Under low signal-to-interference-and-noise-ratio regimes, we show that the proposed Average AE outperforms the optimal maximum likelihood detector by considerable margin.
AB - In this paper, we propose a novel Average autoencoder (AE)-based amplify-and-forward (AF) relay networks impacted by the I/Q imbalance (IQI) and additional hardware impairments (AHI), where the source and destination nodes are equipped with neural network (NN)-based encoder and decoder, while a conventional AF relay node assists the transmission. The average AE employs multiple small NN-based decoders at the destination node, each decoding a soft probabilistic output that is averaged to obtain the final soft probabilistic output at the destination node. By considering multiple small NN decoders, we reduce the implementation complexity significantly while improving the performance compared to the AE with a single large but NN-based decoder. Within this Average AE framework, we propose a coded modulation design (CMD) with zero-forcing-based IQI compensation that considers the availability of the channel state information (CSI) and IQI knowledge. However, the IQI and CSI need to be estimated separately. Thus, we also propose a CMD with no IQI compensation that requires only the CSI knowledge. Finally, we propose a differential CMD that removes the necessity of both the CSI and IQI knowledge. Under low signal-to-interference-and-noise-ratio regimes, we show that the proposed Average AE outperforms the optimal maximum likelihood detector by considerable margin.
KW - AF relay networks
KW - I/Q imbalance
KW - additional hardware impairments
KW - and small neural networks
KW - average autoencoder
KW - block coding
KW - coded modulation design
KW - differential coded modulation design
UR - http://www.scopus.com/inward/record.url?scp=85127783106&partnerID=8YFLogxK
U2 - 10.1109/TCCN.2022.3164901
DO - 10.1109/TCCN.2022.3164901
M3 - Article
AN - SCOPUS:85127783106
SN - 2332-7731
VL - 8
SP - 615
EP - 630
JO - IEEE Transactions on Cognitive Communications and Networking
JF - IEEE Transactions on Cognitive Communications and Networking
IS - 2
ER -