# Structured Massive Access for Scalable Cell-Free Massive MIMO Systems

arXiv: https://arxiv.org/abs/2006.10275 · HTML: https://mlsanity.com/paper/2006.10275/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

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

- [Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2110.09001/) (2021)
- [Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2208.12453/) (2022)

## Abstract (arXiv)

How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE.