# ISDNN: A Deep Neural Network for Channel Estimation in Massive MIMO systems

arXiv: https://arxiv.org/abs/2410.20110 · HTML: https://mlsanity.com/paper/2410.20110/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures](https://mlsanity.com/paper/1409.2574/) (2014)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [A Model-Driven Deep Learning Method for Massive MIMO Detection](https://mlsanity.com/paper/2004.06949/) (2020)
- [Deep Learning based Channel Estimation for Massive MIMO with Hybrid Transceivers](https://mlsanity.com/paper/2202.03220/) (2022)
- [Channel Hardening in Massive MIMO: Model Parameters and Experimental Assessment](https://mlsanity.com/paper/2004.06772/) (2020)

## Abstract (arXiv)

Massive Multiple-Input Multiple-Output (massive MIMO) technology stands as a cornerstone in 5G and beyonds. Despite the remarkable advancements offered by massive MIMO technology, the extreme number of antennas introduces challenges during the channel estimation (CE) phase. In this paper, we propose a single-step Deep Neural Network (DNN) for CE, termed Iterative Sequential DNN (ISDNN), inspired by recent developments in data detection algorithms. ISDNN is a DNN based on the projected gradient descent algorithm for CE problems, with the iterative iterations transforming into a DNN using the deep unfolding method. Furthermore, we introduce the structured channel ISDNN (S-ISDNN), extending ISDNN to incorporate side information such as directions of signals and antenna array configurations for enhanced CE. Simulation results highlight that ISDNN significantly outperforms another DNN-based CE (DetNet), in terms of training time (13%), running time (4.6%), and accuracy (0.43 dB). Furthermore, the S-ISDNN demonstrates even faster than ISDNN in terms of training time, though its overall performance still requires further improvement.