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arXiv 0710.1190cs.NIcs.MA
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多用户无线信道中时延约束下的功率有效调度

Power Efficient Scheduling under Delay Constraints over Multi-user Wireless Channels

Nitin Salodkar, Abhay Karandikar, Vivek S. Borkar

摘要:

在本文中,我们考虑的问题,功率有效的上行链路调度在时分多址(TDMA)系统在衰落的无线信道。目标是在满足单个用户时延的前提下,最小化每个用户的功耗。我们实际上假设系统统计是未知的,即,用户到达和信道状态的概率分布是未知的。该问题具有约束马尔可夫决策问题(CMDP)的结构。确定CMDP下的最优策略面临着状态空间爆炸和未知系统统计的问题。为了解决状态空间爆炸的问题,我们建议确定一个特定的用户在每个时隙的传输速率的基础上,其信道条件和缓冲区占用。用于特定用户的速率分配算法是一种学习算法,其在系统执行期间学习该用户的缓冲器占用和信道状态,从而解决未知系统统计的问题。一旦每个用户的速率被确定,所提出的算法调度具有最佳速率的用户。我们在IEEE 802.16系统内的仿真表明,该算法确实能够满足用户指定的延迟约束。我们比较我们的算法与著名的M-LWDF算法的性能。此外,我们证明了我们的算法下的用户所消耗的功率是相当低的。

英文摘要:

In this paper, we consider the problem of power efficient uplink scheduling in a Time Division Multiple Access (TDMA) system over a fading wireless channel. The objective is to minimize the power expenditure of each user subject to satisfying individual user delay. We make the practical assumption that the system statistics are unknown, i.e., the probability distributions of the user arrivals and channel states are unknown. The problem has the structure of a Constrained Markov Decision Problem (CMDP). Determining an optimal policy under for the CMDP faces the problems of state space explosion and unknown system statistics. To tackle the problem of state space explosion, we suggest determining the transmission rate of a particular user in each slot based on its channel condition and buffer occupancy only. The rate allocation algorithm for a particular user is a learning algorithm that learns about the buffer occupancy and channel states of that user during system execution and thus addresses the issue of unknown system statistics. Once the rate of each user is determined, the proposed algorithm schedules the user with the best rate. Our simulations within an IEEE 802.16 system demonstrate that the algorithm is indeed able to satisfy the user specified delay constraints. We compare the performance of our algorithm with the well known M-LWDF algorithm. Moreover, we demonstrate that the power expended by the users under our algorithm is quite low.

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。

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