We present new high-order modulation schemes for reliable and high capacity 6G communications, that outperform the state-of-the-art schemes adopted in the cellular and broadcasting communication standards. The performance of modulation schemes are mainly determined by the modulation symbol constellation and the mapping of the encoded bits to a modulation symbol. The square Quadrature Amplitude Modulation(QAM) schemes of 16/64/256/1024 modulation orders have been widely adopted in various communication standards, however they fundamentally exhibit a shaping loss of up to 1.53 dB loss with respect to the Shannon capacity bound, in terms of the required SNR for a target date rate. We develop machine learning (ML) based modulation optimization methodologies and present optimal modulation schemes (symbol constellation, bit-to-symbol mapping) for various modulation orders and SNRs. The neural network architecture and the training methods are designed taking into account the desired properties of well performing modulations. This significantly helps the training converge to a global optimal state and result in the symbol constellations and bit-to-symbol mappings that reduces the shaping loss to the Shannon capacity bound to a large extent. The new high order modulation schemes obtained through the ML methodologies outperform the state-of-the-art modulations adopted in the ATSC (Advanced Television Systems Committee) 3.0 standards, at least a few dB in case of 1024-ary modulations with LDPC coding, and will enable us to achieve higher reliability and capacity for 6G communications.

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10279091