This paper studies the scheduling problem in a co-located wireless network under both the deadline and power constraints. We consider a frame-based time-slotted system. The channel condition of a link remains constant within each frame but varies from frame to frame. Packets with hard deadlines arrive at the transmitters at the beginning of each frame, and will be discarded if missing their deadlines, which are in the same frame. Each of the links is associated with a quality of service (QoS) constraint and an average transmit power constraint. A MaxWeight-type problem is formulated for achieving throughput optimality. The computational complexity of solving the MaxWeight-type problem using the exhaustive search is exponential even for a single-link system. To overcome this difficulty, we propose a greedy algorithm, named PDMax (Power and Deadline constrained MaxWeight), with complexity $O(nlog\left(n\right))$. PDMax schedules packets according to their deadlines and incremental weight gains to the objective of the MaxWeight-type problem. We prove that PDMax is throughput optimal. Our simulations further show that PDMax outperforms both the Largest-Debt-First and the greedy-MaxWeight algorithms in terms of average packet delivery ratio and average transmit power.

本文研究了在最后期限和功率约束下位于同一地点的无线网络中的调度问题。我们考虑基于帧的时隙系统。链路的信道条件在每个帧内保持恒定，但在帧与帧之间变化。具有严格截止日期的数据包在每个帧的开始时到达发送器，如果丢失了它们在同一帧中的截止时间，则将其丢弃。每个链路与服务质量（QoS）约束和平均发射功率约束相关联。提出了MaxWeight类型的问题，以实现吞吐量的优化。即使对于单链接系统，使用穷举搜索来解决MaxWeight类型问题的计算复杂度也是指数级的。为了克服这个困难，我们提出了一个贪婪算法，$Ø（ñ日志（ñ））$。PDMax根据其截止日期和增加的重量增加来调度数据包，以达到MaxWeight类型问题的目的。我们证明PDMax是最佳吞吐量。我们的仿真进一步表明，PDMax在平均数据包传输率和平均发射功率方面均优于最大债务优先算法和贪婪最大权重算法。