Efficient Resource Allocation and User Management in XL-RIS-assisted Vision-aided mmWave Networks
Year: 2025
Type: conference-paper
Abstract: The performance of millimeter-wave (mmWave) networks is heavily influenced by line-of-sight (LoS) blockages. Extremely large reconfigurable intelligent surfaces (XL-RIS) integrated with vision-aided wireless communications (VAWC) are envisioned as a key solution to overcome LoS beam blockages, ensuring uninterrupted communication services for users. However, as mmWave-enabled XL-RIS operates in the near-field, channel modeling and estimation become more complex. Additionally, the large number of RIS elements increases this complexity. This work addresses this challenge by determining the optimal configuration for subdividing the XL-RIS into smaller units, each with a minimal number of RIS elements. We also determine the optimal number of subcarriers allocated to each user, aiming to achieve two objectives: first, to enable the XL-RIS to efficiently support multiple users, and second, to minimize the channel estimation overhead, all while maintaining high link quality. To achieve this, we formulate an optimization problem to minimize the number of RIS elements within each sub-group unit and apply a particle swarm optimization (PSO) algorithm to find the optimal number of RIS elements. Our results show that allocating more subcarriers can compensate for the need to increase the gain achieved through larger sub-groups, leading to better data rates. The results further indicate that with 10,000 XL-RIS elements, the optimal combination of subcarriers and subgroup area allows seamless connectivity for 16 users, each with different desired achievable data rates and service requirements.
Authors: Alaa Adnan Bibi, Mohammad Al-Quraan, Omer Waqar, Ahmed Zoha, Muhammad Ali Imran, Lina Mohjazi
Institutions: University of Glasgow, University of the Fraser Valley
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414405282