This paper considers opportunistic scheduler (OS) design using statistical channel state information~(CSI). We apply max-weight schedulers (MWSs) to maximize a utility function of users' average data rates. MWSs schedule the user with the highest weighted instantaneous data rate every time slot. Existing methods require hundreds of time slots to adjust the MWS's weights according to the instantaneous CSI before finding the optimal weights that maximize the utility function. In contrast, our MWS design requires few slots for estimating the statistical CSI. Specifically, we formulate a weight optimization problem using the mean and variance of users' signal-to-noise ratios (SNRs) to construct constraints bounding users' feasible average rates. Here, the utility function is the formulated objective, and the MWS's weights are optimization variables. We develop an iterative solver for the problem and prove that it finds the optimal weights. We also design an online architecture where the solver adaptively generates optimal weights for networks with varying mean and variance of the SNRs. Simulations show that our methods effectively require $4\sim10$ times fewer slots to find the optimal weights and achieve $5\sim15\%$ better average rates than the existing methods.

翻译：本文研究了利用统计信道状态信息（CSI）的 opportunistic 调度器（OS）设计。我们采用最大权重调度器（MWS）来最大化用户平均数据速率的效用函数。MWS 在每个时隙调度具有最高加权瞬时数据速率的用户。现有方法需要数百个时隙根据瞬时 CSI 调整 MWS 的权重，才能找到最大化效用函数的最优权重。相比之下，我们的 MWS 设计仅需少量时隙即可估计统计 CSI。具体而言，我们利用用户信噪比（SNR）的均值和方差构建约束条件，界定用户的可行平均速率，从而提出权重优化问题。其中，效用函数为优化目标，MWS 的权重为优化变量。我们为该问题开发了迭代求解器，并证明其能求得最优权重。我们还设计了一种在线架构，使求解器能够针对 SNR 均值和方差变化的网络自适应生成最优权重。仿真表明，我们的方法所需时隙数比现有方法少 4∼10 倍，且平均速率提高 5∼15%。