Resource Optimization in Multi-Hop IAB Networks: Balancing Data Freshness and Spectral Efficiency

Abstract

This work proposes a multi-objective resource optimization framework for integrated access and backhaul (IAB) networks, tackling the dual challenges of timely data updates and spectral efficiency under dynamic wireless conditions. Conventional single-objective optimization is often impractical for IAB networks, where objective preferences are unknown or difficult to predefine. Therefore, we formulate a multi-objective problem that minimizes the age of information (AoI) and maximizes spectral efficiency, subject to a risk-aware AoI constraint, access-backhaul throughput fairness, and other contextual requirements. A lightweight proportional fair (PF) scheduling algorithm first handles user association and access resource allocation. Subsequently, a Pareto Q-learning-based reinforcement learning (RL) scheme allocates backhaul resources, with the PF scheduler’s outcomes integrated into the state and constrained action spaces of a Markov decision process (MDP). The reward function balances AoI and spectral efficiency objectives while explicitly capturing fairness, thereby resulting in robust long-term performance without imposing fixed weights. Furthermore, an adaptive value-difference-based exploration technique adjusts exploration rates based on Q-value estimate variances, promoting strategic exploration for optimal trade-offs. Simulations show that the proposed method outperforms baselines, reducing the convexity gap between approximated and optimal Pareto fronts by 68.6% and improving fairness by 16.9%.