具有严格下溢约束的节能输电调度
Energy-Efficient Transmission Scheduling with Strict Underflow Constraints
摘要：
我们考虑单个源通过共享无线信道向一个或多个接收器/用户发送数据。由于随机衰落，无线信道条件随时间和用户而变化。每个用户都有一个缓冲区来存储接收到的数据包，然后再将其清空。在每个时间步长，源确定使用多少功率来传输到每个用户。该源的目标是以最小化预期成本度量的方式分配功率，同时满足严格的缓冲器下溢约束和每个时隙中的总功率约束。预期成本测量由与来自传输的功率消耗和分组保持成本相关联的成本组成。引起这个问题的主要应用是无线媒体流。对于该应用，缓冲器下溢约束防止用户缓冲器清空，以便维持播出质量。在单个用户的情况下，线性的功率率曲线，我们表明，修改后的基本库存政策是最优的有限的地平线，无限的地平线折扣，和无限的地平线平均期望成本标准。对于单个用户的分段线性凸功率率曲线，我们证明了有限的广义基本库存政策是最优的所有三个期望成本标准。我们还提出了序列的临界数，完成表征的最优控制法律在这些情况下，当一些额外的技术条件得到满足。然后，我们分析了两个用户的情况下的最优策略的结构。最后，我们讨论的方法，以确定可实施的接近最优的政策，最一般的情况下，M用户。
英文摘要：
We consider a single source transmitting data to one or more receivers/users over a shared wireless channel. Due to random fading, the wireless channel conditions vary with time and from user to user. Each user has a buffer to store received packets before they are drained. At each time step, the source determines how much power to use for transmission to each user. The source's objective is to allocate power in a manner that minimizes an expected cost measure, while satisfying strict buffer underflow constraints and a total power constraint in each slot. The expected cost measure is composed of costs associated with power consumption from transmission and packet holding costs. The primary application motivating this problem is wireless media streaming. For this application, the buffer underflow constraints prevent the user buffers from emptying, so as to maintain playout quality. In the case of a single user with linear power-rate curves, we show that a modified base-stock policy is optimal under the finite horizon, infinite horizon discounted, and infinite horizon average expected cost criteria. For a single user with piecewise-linear convex power-rate curves, we show that a finite generalized base-stock policy is optimal under all three expected cost criteria. We also present the sequences of critical numbers that complete the characterization of the optimal control laws in each of these cases when some additional technical conditions are satisfied. We then analyze the structure of the optimal policy for the case of two users. We conclude with a discussion of methods to identify implementable near-optimal policies for the most general case of M users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
