# Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI arXiv: https://arxiv.org/abs/2201.06778 ยท HTML: https://mlsanity.com/paper/2201.06778/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 59 references that are in the map. - First posted on arXiv 18 Jan 2022. ## Builds on (most cited first, 18 of 18) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019) - [Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://mlsanity.com/paper/2006.08099/) (2020) - [Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO](https://mlsanity.com/paper/2007.06512/) (2020) - [Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems](https://mlsanity.com/paper/2004.07576/) (2020) - [Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO](https://mlsanity.com/paper/2003.05875/) (2020) - [Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems](https://mlsanity.com/paper/2104.11052/) (2021) - [Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks](https://mlsanity.com/paper/1810.07181/) (2018) - [Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming](https://mlsanity.com/paper/2007.00038/) (2020) - [Machine Learning for MU-MIMO Receive Processing in OFDM Systems](https://mlsanity.com/paper/2012.08177/) (2020) - [Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach](https://mlsanity.com/paper/1911.04239/) (2019) - [Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems](https://mlsanity.com/paper/2011.10709/) (2020) - [A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking](https://mlsanity.com/paper/2104.09204/) (2021) - [Principal Component Analysis Based Broadband Hybrid Precoding for Millimeter-Wave Massive MIMO Systems](https://mlsanity.com/paper/1903.09074/) (2019) - [Millimeter-Wave NOMA with User Grouping, Power Allocation and Hybrid Beamforming](https://mlsanity.com/paper/1907.12708/) (2019) - [High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO System](https://mlsanity.com/paper/2002.06377/) (2020) - [Compressive Sensing Based Channel Estimation for Millimeter-Wave Full-Dimensional MIMO with Lens-Array](https://mlsanity.com/paper/1912.10668/) (2019) - [Weighted Sum Rate Maximization of the mmWave Cell-Free MIMO Downlink Relying on Hybrid Precoding](https://mlsanity.com/paper/2201.04332/) (2022) ## Built on by (most cited first, 7 of 7) - [Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication](https://mlsanity.com/paper/2205.03770/) (2022) - [Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network](https://mlsanity.com/paper/2405.19889/) (2024) - [Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems](https://mlsanity.com/paper/2302.02401/) (2023) - [Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems](https://mlsanity.com/paper/2302.07601/) (2023) - [E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion](https://mlsanity.com/paper/2509.19312/) (2025) - [Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method](https://mlsanity.com/paper/2603.02745/) (2026) - [Deep Learning-Based Tri-Hybrid Multi-User MIMO Precoding: The Blessing of EM-Reconfigurable Antennas](https://mlsanity.com/paper/2609.39167/) (2026) ## Abstract (arXiv) In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI). For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network. Different from conventional approaches separately processing different modules, the proposed solution simultaneously optimizes all modules with the sum rate as the optimization object. Therefore, by perceiving the inherent property of air-to-ground massive MIMO-OFDM channel samples, the DL-based E2E neural network can establish the mapping function from the channel to the beamformer, so that the explicit channel reconstruction can be avoided with reduced pilot and feedback overhead. Besides, practical low-resolution phase shifters (PSs) introduce the quantization constraint, leading to the intractable gradient backpropagation when training the neural network. To mitigate the performance loss caused by the phase quantization error, we adopt the transfer learning strategy to further fine-tune the E2E neural network based on a pre-trained network that assumes the ideal infinite-resolution PSs. Numerical results show that our DL-based schemes have considerable advantages over state-of-the-art schemes.