For digital inclusion, high-capacity Internet access should be provided to rural areas to support a range of services and applications. Due to the high operating costs of fiber-optic deployment, Fixed Wireless Access (FWA) is becoming a more attractive internet solution for rural areas. However, 5G FWA is a one-hop solution with limited coverage. A multi-hop solution is needed for wider rural coverage. This work considers a unified solution combining long-haul microwave, 5G Integrated Access and Backhaul (IAB), and FWA to provide a multi-hop network for extended coverage and high network capacity in rural areas. A key challenge for such a network is that energy consumption increases with the number of hops, a problem that has been overlooked in the existing literature. To address this, we propose energy-efficiency microwave backhaul for IAB-based FWA as the Physical Twin (PT). We develop an energy-efficient strategy to optimize radio start-up, serving, sleeping, and wake-up states for microwave backhaul connecting 5G IAB-based FWA serving rural areas. By operating the network at reduced capacity during low utilization, we aim to minimize energy consumption. Then, we present a Digital Twin (DT) of PT to improve its performance. We solve the formulated optimization problem using deep Q-learning in DT and the optimization solver in PT. The simulation results show that our approach satisfies the data rate requirements while reducing energy consumption.
Anselme Ndikumana, Kim-Khoa Nguyen, Adel Larabi et al.· 0 citations
Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.
Anselme Ndikumana, K. Nguyen, Oscar Delgado et al.· IEEE Transactions on Network...· 0 citations