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Reliability-Aware Batch-Based Power-Efficient Spectrum Assignment in CR-MIMO UAV Networks
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DOI：10.1109/OJCOMS.2026.3654538.png)
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Abstract
En 中文
The increasing demands for high spectral efficiency, low energy consumption, flexible deployment, and stringent reliability in beyond 5G/6G systems motivate integrating unmanned aerial vehicles (UAVs) with cognitive radio (CR) and multi-antenna (MIMO) technologies. In CR–MIMO UAV networks, CR improves spectrum efficiency by allowing secondary UAVs to opportunistically exploit underutilized licensed spectrum while protecting primary users (PUs). Furthermore, MIMO technology increases spectral and energy efficiency by using spatial multiplexing, diversity, and array/beamforming gains. Due to the UAVs’ limited battery capacity, a key challenge in enabling efficient CR MIMO UAV networking is to maximize the number of served UAVs while minimizing the required transmit power under a set of quality-of-service, power, and spectrum access constraints. To address this, we propose a reliability-aware, batch-based framework for power allocation and channel assignment in CR–MIMO UAV networks. Unlike traditional sequential methods, this batching paradigm assigns power/channels to multiple UAVs simultaneously, resulting in more power-efficient, concurrent UAV transmissions. Specifically, the joint power allocation and channel assignment problem for multiple contending UAVs is formulated as a mixed-integer nonlinear program, which is known to be NP-hard. For a scalable solution, we introduce a two-stage, polynomial-time, batch-based framework that decouples power allocation from channel assignment. First, the framework formulates and solves a convex per-antenna power minimization problem for each UAV–channel pair, enforcing rate, reliability, and power budget constraints, which leads to a closed-form per-antenna power solution. Based on the computed powers, the second stage performs batch-based channel assignment to minimize required transmit power under exclusive-assignment and maximum-matching constraints. This is achieved by formulating and solving a totally unimodular binary linear program that corresponds to a minimum-weight maximum matching problem, which can be solved optimally using the Hopcroft–Karp algorithm. The polynomial-time complexity of the proposed algorithm is established through analytical computational analysis. Simulations in realistic indoor scenarios demonstrate that the proposed approach consistently satisfies the imposed constraints under varying PU traffic, serves more UAVs with higher success probability, and reduces the total transmit power compared to baseline methods with comparable computational complexity for practical network conditions.
Keywords:
Cognitive radio (CR)
multiple-input
multiple-output (MIMO)
unmanned aerial vehicles (UAVs)
binary linear programming (BLP)
channel assignment batching
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