# Joint Constellation Shaping Using Gradient Descent Approach for MU-MIMO Broadcast Channel

arXiv: https://arxiv.org/abs/2407.07708 · HTML: https://mlsanity.com/paper/2407.07708/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Machine Learning for MU-MIMO Receive Processing in OFDM Systems](https://mlsanity.com/paper/2012.08177/) (2020)
- [Is NOMA Efficient in Multi-Antenna Networks? A Critical Look at Next Generation Multiple Access Techniques](https://mlsanity.com/paper/2101.04802/) (2021)
- [Multiple Antenna Assisted Non-Orthogonal Multiple Access](https://mlsanity.com/paper/1801.08170/) (2018)

## Abstract (arXiv)

We introduce a learning-based approach to optimize a joint constellation for a multi-user MIMO broadcast channel (T Tx antennas, K users, each with R Rx antennas), with perfect channel knowledge. The aim of the optimizer (MAX-MIN) is to maximize the minimum mutual information between the transmitter and each receiver, under a sum-power constraint. The proposed optimization method do neither impose the transmitter to use superposition coding (SC) or any other linear precoding, nor to use successive interference cancellation (SIC) at the receiver. Instead, the approach designs a joint constellation, optimized such that its projection into the subspace of each receiver k, maximizes the minimum mutual information I(Wk;Yk) between each transmitted binary input Wk and the output signal at the intended receiver Yk. The rates obtained by our method are compared to those achieved with linear precoders.