As communication systems are foreseen to enable new services such as joint communication and sensing and utilize parts of the sub-THz spectrum, the design of novel waveforms that can support these emerging applications becomes increasingly challenging. We present in this work an end-to-end learning approach to design waveforms through joint learning of pulse shaping and constellation geometry, together with a neural network (NN)-based receiver. Optimization is performed to maximize an achievable information rate, while satisfying constraints on out-of-band emission and power envelope. Our results show that the proposed approach enables up to orders of magnitude smaller adjacent channel leakage ratios (ACLRs) with peak-to-average power ratios (PAPRs) competitive with traditional filters, without significant loss of information rate on an additive white Gaussian noise (AWGN) channel, and no additional complexity at the transmitter.

翻译：由于预计通信系统能够提供联合通信和遥感等新服务,并利用次HHZ频谱的某些部分,因此设计能够支持这些新兴应用的新波形越来越具有挑战性,我们在工作中采用端至端学习方法,通过联合学习脉冲形状和星座几何以及神经网络接收器来设计波形。优化是为了最大限度地提高可实现的信息率,同时满足对带外排放和电力封套的限制。我们的结果显示,拟议办法使相邻通道渗漏率(ACLR)达到规模较小、与传统过滤器具有峰值和平均功率比率(PAPRs)具有竞争力的水平,同时不会在添加白高斯噪音(AWGN)频道上大量丢失信息率,而且发射机上也不会增加复杂性。