Today we design wireless networks using mathematical models that govern communication in different propagation environments. We rely on measurement campaigns to deliver parametrized propagation models, and on the 3GPP standards process to optimize model-based performance, but as wireless networks become more complex this model-based approach is losing ground. Mobile Network Operators (MNOs) are counting on Artificial Intelligence (AI) to transform wireless by increasing spectral efficiency, reducing signaling overhead, and enabling continuous network innovation through software upgrades. They may also be interested in new use cases like integrated sensing and communications (ISAC). All we need is an AI-native physical layer, so why not simply tailor the offline AI algorithms that have revolutionized image and natural language processing to the wireless domain? We argue that these algorithms rely on off-line training that is precluded by the sub-millisecond speeds at which the wireless interference environment changes. We present an alternative architecture, a universal neural receiver based on convolution, which governs transmit and receive signal processing of any signal in any part of the wireless spectrum. Our neural receiver is designed to invert convolution, and we separate the question of which convolution to invert from the actual deconvolution. The neural network that performs deconvolution is very simple, and we configure this network by setting weights based on domain knowledge. By telling our neural network what we know, we avoid extensive offline training. By developing a universal receiver, we hope to simplify discussions about the proper choice of waveform for different use cases in the international standards. Since the receiver architecture is largely independent of technologies introduced at the base station, we hope to increase the rate of innovation in wireless.

翻译：当前，我们使用数学模型来设计无线网络，这些模型控制着不同传播环境中的通信。我们依赖测量活动来提供参数化的传播模型，并依赖3GPP标准化流程来优化基于模型的性能。但随着无线网络日益复杂，这种基于模型的方法正逐渐失去优势。移动网络运营商（MNOs）正寄希望于人工智能（AI）来变革无线通信，通过提升频谱效率、减少信令开销，以及通过软件升级实现持续的网络创新。他们可能也对集成感知与通信（ISAC）等新用例感兴趣。我们所需要的只是一个AI原生的物理层，那么为何不直接将那些已在图像和自然语言处理领域引发革命的离线AI算法适配到无线领域呢？我们认为，这些算法依赖于离线训练，而无线干扰环境以亚毫秒级速度变化，使得这种训练无法实现。我们提出了一种替代架构——一种基于卷积的通用神经接收器，它能够处理无线频谱中任意信号的发射和接收信号处理。我们的神经接收器旨在实现卷积的逆运算，并将“反卷积何种卷积”的问题与实际的反卷积操作分离开来。执行反卷积的神经网络结构非常简单，我们通过基于领域知识设置权重来配置该网络。通过告知神经网络我们所掌握的知识，我们避免了大量的离线训练。通过开发通用接收器，我们希望简化国际标准中关于不同用例波形选择合适性的讨论。由于该接收器架构在很大程度上独立于基站引入的技术，我们期望能提升无线领域的创新速率。