To enhance the performance of massive multi-input multi-output (MIMO) detection using deep learning, prior research primarily adopts a model-driven methodology, integrating deep neural networks (DNNs) with traditional iterative detectors. Despite these efforts, achieving a purely data-driven detector has remained elusive, primarily due to the inherent complexities arising from the problem's high dimensionality. This paper introduces ChannelNet, a simple yet effective purely data-driven massive MIMO detector. ChannelNet embeds the channel matrix into the network as linear layers rather than viewing it as input, enabling scalability to massive MIMO scenarios. ChannelNet is computationally efficient and has a computational complexity of $\mathcal{O}(N_t N_r)$, where $N_t$ and $N_r$ represent the numbers of transmit and receive antennas, respectively. Despite the low computation complexity, ChannelNet demonstrates robust empirical performance, matching or surpassing state-of-the-art detectors in various scenarios. In addition, theoretical insights establish ChannelNet as a universal approximator in probability for any continuous permutation-equivariant functions. ChannelNet demonstrates that designing deep learning based massive MIMO detectors can be purely data-driven and free from the constraints posed by the conventional iterative frameworks as well as the channel and noise distribution models.

翻译：为利用深度学习提升大规模多输入多输出（Massive MIMO）检测的性能，既有研究主要采用模型驱动方法，将深度神经网络（DNN）与传统迭代检测器相结合。然而，由于问题的高维特性带来的固有复杂性，实现纯数据驱动的检测器仍难以达成。本文提出 ChannelNet，一种简洁高效的纯数据驱动 Massive MIMO 检测器。ChannelNet 将信道矩阵嵌入网络作为线性层而非将其视为输入，从而可扩展至 Massive MIMO 场景。ChannelNet 计算高效，其计算复杂度为 $\mathcal{O}(N_t N_r)$，其中 $N_t$ 和 $N_r$ 分别表示发射天线数和接收天线数。尽管计算复杂度低，ChannelNet 仍展现出稳健的实证性能，在各种场景下可匹配或超越现有最优检测器。此外，理论分析表明，ChannelNet 在概率意义上能通用逼近任意连续置换等变函数。ChannelNet 证实，基于深度学习的 Massive MIMO 检测器设计可完全采用纯数据驱动方式，不受传统迭代框架以及信道与噪声分布模型的约束。