Energy-Efficient Multi-Radio Microwave and IAB-Based Fixed Wireless Access for Rural Areas

Deploying fiber optics as a last-mile solution in rural areas is not economically viable due to low population density. Nevertheless, providing high-speed internet access in these regions is essential to promote digital inclusion. 5G Fixed Wireless Access (5G FWA) has emerged as a promising alternative; however, its one-hop topology limits coverage. To overcome this limitation, a multi-hop architecture is required. This work proposes a unified multi-hop framework that integrates long-haul microwave, Integrated Access and Backhaul (IAB), and FWA to provide wide coverage and high capacity in rural areas. As the number of hops increases, total energy consumption also rises, a challenge often overlooked in existing literature. To address this, we propose an energy-efficient multi-radio microwave and IAB-based FWA framework for rural area connectivity. When the network is underutilized, the proposed approach dynamically operates at reduced capacity to minimize energy consumption. We optimize the off, start-up, serving, deep sleep, and wake-up sates of microwave radios to balance energy use and satisfying data rate requirements. Additionally, we optimize resource block allocation for IAB-based FWA nodes connected to microwave backhaul. The formulated optimization problems aim to minimize the energy consumption of long-haul microwave and multi-hop IAB-based network while satisfying data rate constraints. These problems are solved using dual decomposition and multi-convex programming, supported by dynamic programming. Simulation results demonstrates

💡 Research Summary

The paper addresses the challenge of providing high‑speed broadband to sparsely populated rural areas where laying fiber is economically prohibitive. While 5G Fixed Wireless Access (FWA) offers a cost‑effective alternative, its conventional single‑hop architecture limits coverage, especially when using high‑frequency millimeter‑wave (mmWave) bands that suffer from high path loss. To overcome this limitation, the authors propose a unified multi‑hop framework that combines long‑haul microwave backhaul, Integrated Access and Backhaul (IAB), and FWA.

Key elements of the proposed system are:

1. Multi‑radio microwave nodes – each backhaul node can host several radios operating in different frequency bands (narrow, wide, or very wide).
2. Five power states per radio – beyond the usual OFF/ON, the authors introduce “startup”, “serving”, “deep‑sleep”, and “wake‑up”. Deep‑sleep retains a minimal power level to prevent moisture buildup, while “completely off” consumes zero power and allows the radio to be relocated.
3. Dynamic state transition – a controller monitors traffic demand and switches radios among the five states to match the required aggregate data rate. When demand is low, radios are placed in deep‑sleep or completely off; when demand spikes, radios are quickly woken up or started.
4. IAB‑based FWA nodes – each IAB donor or node carries both a mmWave interface and a mid‑band interface. The Distributed Unit (DU) at each node schedules resource blocks (RBs) separately for the two bands, exploiting 5G mixed numerologies and the maximum allowable transmission bandwidths.
5. Joint optimization problem – the authors formulate two coupled sub‑problems: (a) minimize the total energy consumption of the multi‑radio microwave backhaul while satisfying the downlink data‑rate requirements of all terminals, and (b) allocate RBs across mmWave and mid‑band links to minimize the energy of the multi‑hop IAB‑FWA segment. The combined problem is expressed as a constrained minimization with decision variables for radio state‑action pairs and RB allocations.

To solve the problem, the paper employs:

• Dual decomposition – separates the backhaul energy‑minimization from the IAB‑FWA RB‑allocation, linking them via Lagrange multipliers that enforce the overall data‑rate constraints.
• Disciplined multi‑convex programming (DMCP) – each sub‑problem is cast into a multi‑convex form, enabling efficient convex solvers to find locally optimal solutions.
• Dynamic programming – used to derive optimal state‑transition policies over time for each microwave radio, accounting for startup and wake‑up delays and the energy cost of each state.

Simulation methodology reflects realistic rural traffic patterns: a set of CPEs and IAB‑MTs with minimum data‑rate demands, variable user density, and time‑varying load. The baseline comparisons include (i) a single‑hop FWA with a single microwave radio, (ii) a multi‑radio backhaul that only switches between OFF and ON, and (iii) an IAB‑FWA system without power‑state optimization.

Results demonstrate that the proposed framework reduces total network energy consumption by 30 %–45 % compared with the baselines while meeting all per‑terminal data‑rate constraints. Energy savings are most pronounced during low‑utilization periods, where many radios reside in deep‑sleep or completely off. The system also reacts quickly to sudden traffic surges, waking up the necessary radios within the startup/wake‑up latency budget, thereby preserving Quality of Service.

The paper’s contributions can be summarized as follows:

1. Introduction of a five‑state power model for multi‑radio microwave backhaul, capturing practical considerations such as moisture protection in deep‑sleep.
2. Formulation of a joint energy‑minimization and resource‑allocation problem that simultaneously handles backhaul power management and IAB‑FWA RB scheduling.
3. Development of a solution methodology based on dual decomposition, DMCP, and dynamic programming that is computationally tractable for realistic network sizes.
4. Comprehensive performance evaluation under rural‑specific traffic scenarios, showing substantial energy savings without sacrificing throughput.

Overall, the work provides a concrete, analytically grounded blueprint for operators seeking to deploy cost‑effective, energy‑efficient broadband in rural regions, leveraging existing microwave and 5G IAB technologies while intelligently managing radio resources across multiple hops.