Scheduling with Rate Adaptation under Incomplete Knowledge of Channel/Estimator Statistics

In time-varying wireless networks, the states of the communication channels are subject to random variations, and hence need to be estimated for efficient rate adaptation and scheduling. The estimation mechanism possesses inaccuracies that need to be tackled in a probabilistic framework. In this work, we study scheduling with rate adaptation in single-hop queueing networks under two levels of channel uncertainty: when the channel estimates are inaccurate but complete knowledge of the channel/estimator joint statistics is available at the scheduler; and when the knowledge of the joint statistics is incomplete. In the former case, we characterize the network stability region and show that a maximum-weight type scheduling policy is throughput-optimal. In the latter case, we propose a joint channel statistics learning - scheduling policy. With an associated trade-off in average packet delay and convergence time, the proposed policy has a stability region arbitrarily close to the stability region of the network under full knowledge of channel/estimator joint statistics.

💡 Research Summary

The paper tackles the problem of joint rate adaptation and scheduling in a single‑hop wireless network where the channel state varies randomly over time and must be estimated before transmission. Two distinct uncertainty regimes are considered. In the first regime the scheduler possesses full knowledge of the joint probability distribution of the true channel state and its estimate; in the second regime this joint distribution is unknown and must be learned online.

Full‑knowledge regime.
The authors model the system as a set of N queues sharing a time‑slotted wireless medium. At each slot a random channel state (C_i(t)) is realized for each user i, and an estimator produces a possibly erroneous observation (\hat C_i(t)). The joint statistics (P_{C,\hat C}(c,\hat c)) are assumed known. Using these statistics the expected service rate for a chosen transmission rate r given the estimate (\hat c) is computed as
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