Optimal Spectrum Partitioning and Licensing in Tiered Access Under Stochastic Market Models
Optimal Spectrum Partitioning and Licensing in Tiered Access Under Stochastic Market Models
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DOI：
10.1109/tnet.2021.3077643
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发表时间：
2021-10-01
影响因子：
3.7
通讯作者：
Abouzeid, Alhussein A.
中科院分区：
文献类型：
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作者：
Saha, Gourav;Abouzeid, Alhussein A.
我们考虑将一个频带划分为M个带宽相等的信道，然后将这M个信道进一步分配为P个许可信道和M-P个非许可信道的问题。许可信道可以被访问以用于许可使用和机会使用，其遵循对于许可使用具有更高优先级的分层结构。未经许可的通道只能用于机会性使用。我们在本文中解决以下问题。给定一个市场设置，M和P的值是多少使频谱带的净频谱利用率最大化？虽然这个问题是根本性的，但它在实践中是高度相关的，例如，在划分最近提出的公民宽带无线电服务频带的上下文中。如果M太高或太低，则可能分别由于有限的信道容量或由于信道容量的浪费而降低频谱利用率。如果P太高（低），则它不会激励主要对未授权信道（授权信道）感兴趣的无线运营商加入市场。这些权衡在我们的优化问题中得到了体现，该问题表现为两阶段Stackelberg博弈。我们设计了一个算法来解决Stackelberg游戏，从而找到最佳的M和P。该算法的设计还涉及到一个有效的蒙特卡罗积分器来评估所涉及的随机变量，如频谱利用率和运营商的收入的期望值。我们还基准我们的算法使用数值模拟。
We consider the problem of partitioning a spectrum band into M channels of equal bandwidth, and then further assigning theseM channels into P licensed channels andM- P unlicensed channels. Licensed channels can be accessed both for licensed and opportunistic use following a tiered structure that has a higher priority for licensed use. Unlicensed channels can be accessed only for opportunistic use. We address the following question in this paper. Given a market setup, what values of M and P maximize the net spectrum utilization of the spectrum band? While this problem is fundamental, it is highly relevant practically, e.g., in the context of partitioning the recently proposed Citizens Broadband Radio Service band. IfM is too high or too low, it may decrease spectrum utilization due to limited channel capacity or due to wastage of channel capacity, respectively. If P is too high (low), it will not incentivize the wireless operators who are primarily interested in unlicensed channels (licensed channels) to join the market. These tradeoffs are captured in our optimization problem which manifests itself as a two-stage Stackelberg game. We design an algorithm to solve the Stackelberg game and hence find the optimal M and P. The algorithm design also involves an efficient Monte Carlo integrator to evaluate the expected value of the involved random variables like spectrum utilization and operators' revenue. We also benchmark our algorithms using numerical simulations.