Joint Scheduling and Resource Allocation in OFDMA Downlink Systems via ACK/NAK Feedback

In this paper, we consider the problem of joint scheduling and resource allocation in the OFDMA downlink, with the goal of maximizing an expected long-term goodput-based utility subject to an instantaneous sum-power constraint, and where the feedback to the base station consists only of ACK/NAKs from recently scheduled users. We first establish that the optimal solution is a partially observable Markov decision process (POMDP), which is impractical to implement. In response, we propose a greedy approach to joint scheduling and resource allocation that maintains a posterior channel distribution for every user, and has only polynomial complexity. For frequency-selective channels with Markov time-variation, we then outline a recursive method to update the channel posteriors, based on the ACK/NAK feedback, that is made computationally efficient through the use of particle filtering. To gauge the performance of our greedy approach relative to that of the optimal POMDP, we derive a POMDP performance upper-bound. Numerical experiments show that, for slowly fading channels, the performance of our greedy scheme is relatively close to the upper bound, and much better than fixed-power random user scheduling (FP-RUS), despite its relatively low complexity

Rate adaptation via link-layer feedback for goodput maximization over a time-varying channel

The variable nature of the wireless channel may cause the quality of service to be intolerable for certain applications. To combat channel variability, we consider rate adaptation at the physical layer. We build an adaptive communication system based on uncoded QAM in which the available information on the channel state is obtained using the mere packet-level ACK/NACK sequence. Our system chooses the constellation size that maximizes the expected packet level goodput for every single packet. Our simulations show that our system achieves a goodput, reasonably close to the highest possible goodput achievable with full-feedback on Rayleigh-fading Markov channels