Modem Optimization of High-Mobility Scenarios: A Deep-Learning-Inspired Approach
arxiv(2024)
摘要
The next generation wireless communication networks are required to support
high-mobility scenarios, such as reliable data transmission for high-speed
railways. Nevertheless, widely utilized multi-carrier modulation, the
orthogonal frequency division multiplex (OFDM), cannot deal with the severe
Doppler spread brought by high mobility. To address this problem, some new
modulation schemes, e.g. orthogonal time frequency space and affine frequency
division multiplexing, have been proposed with different design criteria from
OFDM, which promote reliability with the cost of extremely high implementation
complexity. On the other hand, end-to-end systems achieve excellent gains by
exploiting neural networks to replace traditional transmitters and receivers,
but have to retrain and update continually with channel varying. In this paper,
we propose the Modem Network (ModNet) to design a novel modem scheme. Compared
with end-to-end systems, channels are directly fed into the network and we can
directly get a modem scheme through ModNet. Then, the Tri-Phase training
strategy is proposed, which mainly utilizes the siamese structure to unify the
learned modem scheme without retraining frequently faced up with time-varying
channels. Simulation results show the proposed modem scheme outperforms OFDM
systems under different highmobility channel statistics.
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