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We address the problem of opportunistic multiuser scheduling in downlinknetworks with Markov-modeled outage channels. We consider the scenario in whichthe scheduler does not have full knowledge of the channel state information,but instead estimates the channel state information by exploiting the memoryinherent in the Markov channels along with ARQ-styled feedback from thescheduled users. Opportunistic scheduling is optimized in two stages: (1)Channel estimation and rate adaptation to maximize the expected immediate rateof the scheduled user; (2) User scheduling, based on the optimized immediaterate, to maximize the overall long term sum-throughput of the downlink. Thescheduling problem is a partially observable Markov decision process with theclassic 'exploitation vs exploration' trade-off that is difficult to quantify.We therefore study the problem in the framework of Restless Multi-armed BanditProcesses (RMBP) and perform a Whittle's indexability analysis. Whittle'sindexability is traditionally known to be hard to establish and the indexpolicy derived based on Whittle's indexability is known to have optimalityproperties in various settings. We show that the problem of downlink schedulingunder imperfect channel state information is Whittle indexable and derive theWhittle's index policy in closed form. Via extensive numerical experiments, weshow that the index policy has near-optimal performance.  Our work reveals that, under incomplete channel state information, exploitingchannel memory for opportunistic scheduling can result in significantperformance gains and that almost all of these gains can be realized using aneasy-to-implement index policy.

Exploiting Channel Memory for Joint Estimation and Scheduling in Downlink Networks