Enabling Ultra-Dense, Open-RAN, Vehicular Networks with Non-Linear MIMO Processing
George N. Katsaros, Konstantinos Nikitopoulos

TL;DR
This paper introduces a non-linear MIMO processing method for ultra-dense vehicular networks, significantly increasing device connectivity and reducing infrastructure costs in open-RAN deployments.
Contribution
It proposes a novel Massively Parallelizable Non-linear (MPNL) MIMO processing technique that outperforms linear methods in dense vehicular networks, enabling higher connectivity with fewer antennas.
Findings
Over 300% increase in concurrent single-antenna vehicle connections
Significant reduction in required antennas without throughput loss
Enabling simpler, more dense radio unit deployments
Abstract
Future autonomous transportation systems necessitate network infrastructure capable of accommodating massive vehicular connectivity, despite the scarce availability of frequency resources. Current approaches for achieving such required high spectral efficiency, rely on the utilization of Multiple-Input, Multiple-Output (MIMO) technology. However, conventional MIMO processing approaches, based on linear processing principles, leave much of the system's capacity heavily unexploited. They typically require a large number of power-consuming antennas and RF-chains to support a substantially smaller number of concurrently connected devices, even when the devices are transmitting at low rates. This translates to inflated operational costs that become substantial, particularly in ultra-dense, metropolitan-scale deployments. Therefore, the question is how to efficiently harness this unexploited…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWireless Body Area Networks · Molecular Communication and Nanonetworks · Energy Efficient Wireless Sensor Networks
