# Learning-Based Latency-Constrained Fronthaul Compression Optimization in C-RAN

arXiv: https://arxiv.org/abs/2311.03899 · HTML: https://mlsanity.com/paper/2311.03899/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Safe Policies for Reinforcement Learning via Primal-Dual Methods](https://mlsanity.com/paper/1911.09101/) (2019)
- [Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling](https://mlsanity.com/paper/1812.01608/) (2018)
- [Actor Prioritized Experience Replay](https://mlsanity.com/paper/2209.00532/) (2022)
- [6G White Paper on Machine Learning in Wireless Communication Networks](https://mlsanity.com/paper/2004.13875/) (2020)
- [Cloud-RAN functional split for an efficient fronthaul network](https://mlsanity.com/paper/2101.04216/) (2021)
- [Balancing detectability and performance of attacks on the control channel of Markov Decision Processes](https://mlsanity.com/paper/2109.07171/) (2021)
- [Quantisation-aware Precoding for MU-MIMO with Limited-capacity Fronthaul](https://mlsanity.com/paper/2202.08925/) (2022)
- [Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs](https://mlsanity.com/paper/2205.07518/) (2022)
- [Graph-based Framework for Flexible Baseband Function Splitting and Placement in C-RAN](https://mlsanity.com/paper/1501.04703/) (2015)
- [Modulation Compression in Next Generation RAN: Air Interface and Fronthaul trade-offs](https://mlsanity.com/paper/2011.03734/) (2020)
- [Fronthaul Compression Control for shared Fronthaul Access Networks](https://mlsanity.com/paper/2205.08329/) (2022)

## Built on by (most cited first, 1 of 1)

- [Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO](https://mlsanity.com/paper/2409.06715/) (2024)

## Abstract (arXiv)

The evolution of wireless mobile networks towards cloudification, where Radio Access Network (RAN) functions can be hosted at either a central or distributed locations, offers many benefits like low cost deployment, higher capacity, and improved hardware utilization. Nevertheless, the flexibility in the functional deployment comes at the cost of stringent fronthaul (FH) capacity and latency requirements. One possible approach to deal with these rigorous constraints is to use FH compression techniques. To ensure that FH capacity and latency requirements are met, more FH compression is applied during high load, while less compression is applied during medium and low load to improve FH utilization and air interface performance. In this paper, a model-free deep reinforcement learning (DRL) based FH compression (DRL-FC) framework is proposed that dynamically controls FH compression through various configuration parameters such as modulation order, precoder granularity, and precoder weight quantization that affect both FH load and air interface performance. Simulation results show that DRL-FC exhibits significantly higher FH utilization (68.7% on average) and air interface throughput than a reference scheme (i.e. with no applied compression) across different FH load levels. At the same time, the proposed DRL-FC framework is able to meet the predefined FH latency constraints (in our case set to 260 μs) under various FH loads.