  In this paper, an underlay cognitive radio network that consists of an
arbitrary number of secondary users (SU) is considered, in which the primary
user (PU) employs Type-I Hybrid Automatic Repeat Request (HARQ). Exploiting the
redundancy in PU retransmissions, each SU receiver applies forward interference
cancelation to remove a successfully decoded PU message in the subsequent PU
retransmissions. The knowledge of the PU message state at the SU receivers and
the ACK/NACK message from the PU receiver are sent back to the transmitters.
With this approach and using a Constrained Markov Decision Process (CMDP) model
and Constrained Multi-agent MDP (CMMDP), centralized and decentralized optimum
access policies for SUs are proposed to maximize their average sum throughput
under a PU throughput constraint. In the decentralized case, the channel access
decision of each SU is unknown to the other SU. Numerical results demonstrate
the benefits of the proposed policies in terms of sum throughput of SUs. The
results also reveal that the centralized access policy design outperforms the
decentralized design especially when the PU can tolerate a low average long
term throughput. Finally, the difficulties in decentralized access policy
design with partial state information are discussed.
