This paper introduces a deterministic optimization framework for spectrum resource allocation, primarily focusing on the Citizens Broadband Radio Service (CBRS). The proposed framework aims to maximize spectrum utilization with minimum possible transmit power while ensuring that operational constraints are met for diverse users. We present mixed-integer linear programming (MILP) formulations for different spectrum allocation configurations, including contiguous and non-contiguous channels and uniform and non-uniform power allocation. To overcome the computational challenges encountered in solving MILPs for large-scale networks, we propose a computationally efficient heuristic method called Sequential Resource Allocation for Warm Start (STRAWS) that addresses resource allocation on one channel at a time. Using extensive computational experiments, we establish that, compared to the conventional contiguous model, the non-contiguous, non-uniform configuration demonstrates an average 15% improvement in low-demand and 75% in high-demand scenarios across all desired Signal-to-Noise ratios. The experiments also reveal that leveraging the STRAWS solution to warm start the optimization process enhances the overall solution quality within tight computational time limits. We also quantify the superior performance of STRAWS in several situations involving trade-offs between the number of users and the total network power. Despite a CBRS focus, our approach readily extends to cognitive radio, IoT, and vehicular networks.

中文翻译：

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公民宽带无线电服务的最优频谱分配


本文介绍了频谱资源分配的确定性优化框架，主要关注公民宽带无线电服务（CBRS）。所提出的框架旨在以尽可能小的发射功率最大化频谱利用率，同时确保满足不同用户的操作限制。我们提出了针对不同频谱分配配置的混合整数线性规划（MILP）公式，包括连续和非连续信道以及均匀和非均匀功率分配。为了克服在解决大规模网络的 MILP 时遇到的计算挑战，我们提出了一种计算高效的启发式方法，称为热启动顺序资源分配 (STRAWS)，该方法一次解决一个通道上的资源分配问题。通过大量的计算实验，我们发现，与传统的连续模型相比，非连续、非均匀配置在所有所需的信号到信号的低需求场景中平均提高了 15%，在高需求场景中平均提高了 75%。噪声比。实验还表明，利用 STRAWS 解决方案来热启动优化过程可以在严格的计算时间限制内提高整体解决方案质量。我们还量化了 STRAWS 在涉及用户数量和总网络功率之间权衡的几种情况下的卓越性能。尽管重点关注 CBRS，但我们的方法很容易扩展到认知无线电、物联网和车辆网络。