An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characteristics. Although most modern systems use orthogonal frequency-division multiplexing (OFDM) for its efficient equalization, this waveform suffers from multiple limitations such as a high adjacent channel leakage ratio (ACLR) and high peak-to-average power ratio (PAPR). In this paper, we propose a learning-based method to design OFDM-based waveforms that satisfy selected constraints while maximizing an achievable information rate. To that aim, we model the transmitter and the receiver as convolutional neural networks (CNNs) that respectively implement a high-dimensional modulation scheme and perform the detection of the transmitted bits. This leads to an optimization problem that is solved using the augmented Lagrangian method. Evaluation results show that the end-to-end system is able to satisfy target PAPR and ACLR constraints and allows significant throughput gains compared to a tone reservation (TR) baseline. An additional advantage is that no dedicated pilots are needed.

翻译：对未来通信系统来说,一个有吸引力的研究方向是设计新的波形,这种波形既能支持高吞吐量,又能显示有利的信号特性。虽然大多数现代系统都使用正方位频率分流多功能(OFDM)来有效均衡,但这种波形受到多种限制,如相邻通道渗漏率(ACLR)高和峰值对平均功率(PARP)高。在本文中,我们提出了一个基于学习的办法来设计基于DM的波形,既能满足选定的限制,又能最大限度地达到可实现的信息速率。为此,我们把发射机和接收机模拟成共振神经网络(CNNs),分别执行高维调制方案和检测传输的位元。这导致一个优化问题,通过增强的拉格朗加法解决。评价结果显示,端对端系统能够满足目标PPR和ACLR的制约,并允许与音质保留基线相比,显著的吞吐量收益。另一个好处是不需要专门的飞行员。