Access Policy Design for Cognitive Secondary Users under a Primary Type-I HARQ Process

TL;DR

This paper proposes optimal access policies for secondary users in a cognitive radio network with a primary user employing Type-I HARQ, utilizing retransmission redundancy and Markov decision processes to maximize secondary throughput.

Contribution

It introduces centralized and decentralized optimal access policies leveraging HARQ retransmission information and Markov decision processes for cognitive radio networks.

Findings

Decentralized policies outperform centralized ones at high PU throughput.

Centralized policies are more effective when PU tolerates low throughput.

Proposed policies significantly improve secondary user throughput.

Abstract

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.