Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
By Chongwen Huang, George C. Alexandropoulos, Alessio Zappone, Chau Yuen, Merouane Debbah. 2019
71 citations recorded by the source corpus.
One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transceivers, there exist inevitably nonlinear hardware components, like signal amplifiers and various analog filters, which complicates the calibration task. To deal with this challenge, we design a deep neural network for channel calibration between the UL and DownLink (DL) directions. During the initial training phase, the deep neural network is trained from both UL and DL channel measurements. We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlinear relationships between the UL and DL channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited.
Subfields: Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Chau Yuen
- Chongwen Huang
- Billy Pik Lik Lau
- U-Xuan Tan
- Yuren Zhou
- Meng Zhang
- Naveed Ul L. Hassan
- Marakkalage Sumudu Hasala
Citations in the corpus
- A survey of data fusion in smart city applications (2019, background): “Examples of O1 can be found in [27], [42]–[44].”
- Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, unknown): “enabled wireless network has appeared in [79].”
- Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning (2020, background): “Recently, the model-free artificial intelligence (AI) has emerged as an extraordinarily remarkable technology to address explosive mass data, mathematically intractable non-linear non-convex problems and high-computation issues [27]–[30].”