We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector. The method maximizes an achievable information rate, while simultaneously satisfying constraints on the adjacent channel leakage ratio (ACLR) and peak-to-average power ratio (PAPR). This allows control of the tradeoff between spectral containment, peak power, and communication rate. Evaluation on an additive white Gaussian noise (AWGN) channel shows significant reduction of ACLR and PAPR compared to a conventional baseline relying on quadrature amplitude modulation (QAM) and root-raised-cosine (RRC), without significant loss of information rate. When considering a 3rd Generation Partnership Project (3GPP) multipath channel, the learned waveform and neural receiver enable competitive or higher rates than an orthogonal frequency division multiplexing (OFDM) baseline, while reducing the ACLR by 10 dB and the PAPR by 2 dB. The proposed method incurs no additional complexity on the transmitter side and might be an attractive tool for waveform design of beyond-5G systems.

翻译：我们建议采用基于学习的方法,共同设计传输和接收过滤器、星座几何和相关的位标以及神经网络探测器。该方法最大限度地提高可实现的信息率,同时满足邻近通道渗漏率(ACLR)和峰值对平均功率率(PARP)的限制,从而控制光谱封隔、峰值功率和通信率之间的权衡。对添加式白高子噪音(AWGN)频道的评价显示,与依赖二次振动调节和根升-cosine(RRC)的常规基线相比,ACCR和PPR显著减少,而没有重大信息率损失。在考虑第三代伙伴关系项目(3GPP)多路频道时,所学的波形和神经接收器能够使电速率超过或更高于一个或分频率多氧化值(OFDDM)基线,同时将ACLR和PPR减少10 dB和2 dB。拟议的方法在发射机侧没有产生额外的复杂性,并且可能是一种具有吸引力的波形设计系统以外的工具。