# Sum-Rate Maximization of Multicell MISO Networks with Limited Information Exchange

arXiv: https://arxiv.org/abs/1904.05121 · HTML: https://mlsanity.com/paper/1904.05121/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Enhancing Sum-Rate Performance in Constrained Multicell Networks: A Low-Information Exchange Approach](https://mlsanity.com/paper/2404.02477/) (2024)
- [Selective Experience Sharing in Reinforcement Learning Enhances Interference Management](https://mlsanity.com/paper/2501.15735/) (2025)

## Abstract (arXiv)

Although there have been extensive studies on transmit beamforming in multi-input single-output (MISO) multicell networks, achieving optimal sum-rate with limited channel state information (CSI) is still a challenge even with a single user per cell. A novel cooperative downlink multicell MISO beamforming scheme is proposed with highly limited information exchange among the base stations (BSs) to maximize the sum-rate. In the proposed scheme, each BS can design its beamforming vector with only local CSI based on limited information exchange on CSI. Unlike previous studies, the proposed beamforming design is non-iterative and does not require any vector or matrix feedback but requires only quantized scalar information. The proposed scheme closely achieves the optimal sum-rate bound in almost all signal-to-noise ratio regime based on non-iterative optimization with lower amount of information exchange than existing schemes, which is justified by numerical simulations.