Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
June 18, 2020 Β· Declared Dead Β· π IEEE Journal on Selected Areas in Communications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Shuaifei Chen, Jiayi Zhang, Emil BjΓΆrnson, Jing Zhang, Bo Ai
arXiv ID
2006.10275
Category
eess.SP: Signal Processing
Cross-listed
cs.IT
Citations
236
Venue
IEEE Journal on Selected Areas in Communications
Last Checked
3 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Signal Processing
R.I.P.
π»
Ghosted
π
π
The Cartographer
1D Convolutional Neural Networks and Applications: A Survey
R.I.P.
π»
Ghosted
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
π
π
The Cartographer
Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond
R.I.P.
π»
Ghosted
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
R.I.P.
π»
Ghosted
A New Wireless Communication Paradigm through Software-controlled Metasurfaces
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted