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We study the fundamental network capacity of a multi-user wireless downlinkunder two assumptions: (1) Channels are not explicitly measured and thusinstantaneous states are unknown, (2) Channels are modeled as ON/OFF Markovchains. This is an important network model to explore because channel probingmay be costly or infeasible in some contexts. In this case, we can use channelmemory with ACK/NACK feedback from previous transmissions to improve networkthroughput. Computing in closed form the capacity region of this network isdifficult because it involves solving a high dimension partially observedMarkov decision problem. Instead, in this paper we construct an inner and outerbound on the capacity region, showing that the bound is tight when the numberof users is large and the traffic is symmetric. For the case of heterogeneoustraffic and any number of users, we propose a simple queue-dependent policythat can stabilize the network with any data rates strictly within the innercapacity bound. The stability analysis uses a novel frame-based Lyapunov driftargument. The outer-bound analysis uses stochastic coupling and stateaggregation to bound the performance of a restless bandit problem using arelated multi-armed bandit system. Our results are useful in cognitive radionetworks, opportunistic scheduling with delayed/uncertain channel stateinformation, and restless bandit problems.

Exploiting Channel Memory for Multi-User Wireless Scheduling without Channel Measurement: Capacity Regions and Algorithms