Chun Hu

Zhen Gao, Minghui Wu, Chun Hu et al.

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.

Renjie Gu, Chaoyue Niu, Fan Wu et al.

In recent years, mobile devices have gained increasing development with stronger computation capability and larger storage space. Some of the computation-intensive machine learning tasks can now be run on mobile devices. To exploit the resources available on mobile devices and preserve personal privacy, the concept of client-based machine learning has been proposed. It leverages the users' local hardware and local data to solve machine learning sub-problems on mobile devices and only uploads computation results rather than the original data for the optimization of the global model. Such an architecture can not only relieve computation and storage burdens on servers but also protect the users' sensitive information. Another benefit is the bandwidth reduction because various kinds of local data can be involved in the training process without being uploaded. In this article, we provide a literature review on the progressive development of machine learning from server based to client based. We revisit a number of widely used server-based and client-based machine learning methods and applications. We also extensively discuss the challenges and future directions in this area. We believe that this survey will give a clear overview of client-based machine learning and provide guidelines on applying client-based machine learning to practice.