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Deep Learning Constellation Design for the AWGN Channel with Additive Radar Interference

Abstract : Radar and wireless communication coexistence is considered in this paper as a possible solution to face the exploding demand and rising congestion in wireless networks. The transmission medium is modeled as an AWGN channel with additive radar interference. Standard constellations are not optimal in this context and an auto-encoder (AE) is used to design proper constellations and corresponding receiver devices. AE is a powerful tool in neural networks that shares strong similarities with communication systems. This technique is particularly relevant in the lack of an analytical expression of the loss function. In the asymptotic region (high interference regime), the optimal constellation shape is known and the AE always converges towards this optimal solution. In the other regions, the AE is able to yield solutions that outperform the standard configurations. Several demapping alternatives are also considered leading to the conclusion that it is possible to maintain the communication link in the presence of radar interference independently of the interference power. This is a step further compared to previous works in which solutions were limited to low or high interference regimes.
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Contributor : Florence Alberge <>
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Florence Alberge. Deep Learning Constellation Design for the AWGN Channel with Additive Radar Interference. IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2019, 67 (2), pp.1413-1423. ⟨10.1109/tcomm.2018.2875721⟩. ⟨hal-01894498⟩

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