Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks

Area of Science:

• Telecommunications Engineering
• Wireless Communication Systems
• Network Resource Management

Background:

• Internet of Things (IoT) networks require efficient radio resource planning to balance performance and cost.
• Uplink radio resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) networks presents challenges in supporting device traffic and minimizing access delay.
• Over-provisioning radio resources leads to increased operational costs and spectrum wastage.

Purpose of the Study:

• To statistically determine the optimal number of radio resources (RRs) for a typical cell in licensed OFDMA-based IoT networks.
• To develop statistical tools for evaluating and adapting allocated bandwidth based on network parameters and Quality of Service (QoS) requirements.
• To minimize spectrum wastage and operational costs for network operators.

Main Methods:

• Utilizing stochastic geometry to model the average network behavior, assuming random distribution of sensors and collectors (Poisson Point Process).
• Developing statistical tools to evaluate, adjust, and adapt allocated bandwidth based on QoS (rate, access delay), sensor density, collector intensity, antenna configurations, and transmission modes.
• Calculating the optimal number of RRs by considering constraints such as low access delay, limited power per RR, target data rate, and network outage probability.

Main Results:

• Statistical tools derived from stochastic geometry enable precise evaluation and adaptation of bandwidth allocation.
• The study provides a method to calculate the optimal total number of RRs for a typical cell.
• Performance analysis across different antenna configurations (SISO, SIMO, MU-MIMO) is facilitated.

Conclusions:

• Efficient radio resource planning is crucial for OFDMA-based IoT networks.
• Stochastic geometry offers a robust framework for optimizing resource allocation in complex wireless networks.
• The proposed methodology allows for dynamic adaptation of resources to meet evolving network demands and QoS requirements.