Volker Pauli | Searcharxiv

arXiv2025

Realistic UE Antennas for 6G in the 3GPP Channel Model

The transition to 6G has driven significant updates to the 3GPP channel model, particularly in modeling UE antennas and user-induced blockage for handheld devices. The 3GPP Rel.19 revision of TR 38.901 introduces a more realistic framework that captures directive antenna patterns, practical antenna placements, polarization effects, and element-specific blockage. These updates are based on high-fidelity simulations and measurements of a reference smartphone across multiple frequency ranges. By aligning link- and system-level simulations with real-world device behavior, the new model enables more accurate evaluation of 6G technologies and supports consistent performance assessment across industry and research.

eess.SP↗

arXiv2020

Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V Communication

We consider the distributed resource selection problem in Vehicle-to-vehicle (V2V) communication in the absence of a base station. Each vehicle autonomously selects transmission resources from a pool of shared resources to disseminate Cooperative Awareness Messages (CAMs). This is a consensus problem where each vehicle has to select a unique resource. The problem becomes more challenging when---due to mobility---the number of vehicles in vicinity of each other is changing dynamically. In a congested scenario, allocation of unique resources for each vehicle becomes infeasible and a congested resource allocation strategy has to be developed. The standardized approach in 5G, namely semi-persistent scheduling (SPS) suffers from effects caused by spatial distribution of the vehicles. In our approach, we turn this into an advantage. We propose a novel DIstributed Resource Allocation mechanism using multi-agent reinforcement Learning (DIRAL) which builds on a unique state representation. One challenging issue is to cope with the non-stationarity introduced by concurrently learning agents which causes convergence problems in multi-agent learning systems. We aimed to tackle non-stationarity with unique state representation. Specifically, we deploy view-based positional distribution as a state representation to tackle non-stationarity and perform complex joint behavior in a distributed fashion. Our results showed that DIRAL improves PRR by 20% compared to SPS in challenging congested scenarios.

cs.NI↗