# Joint Pilot and Payload Power Allocation for Massive-MIMO-enabled URLLC IIoT Networks

arXiv: https://arxiv.org/abs/1912.12438 · HTML: https://mlsanity.com/paper/1912.12438/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2311.16895/) (2023)
- [Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management](https://mlsanity.com/paper/2412.16167/) (2024)
- [Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2307.13415/) (2023)
- [Digital Twinning of a Pressurized Water Reactor Startup Operation and Partial Computational Offloading in In-network Computing-Assisted Multiaccess Edge Computing](https://mlsanity.com/paper/2407.12011/) (2024)

## Abstract (arXiv)

The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way the factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, and suffers from transmission rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low-complexity algorithms to solve the achievable data rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.