Delay Optimal Joint Scheduling-and-Power-Control for Cognitive Radio Uplinks

TL;DR

This paper proposes a delay-optimal joint scheduling and power control policy for uplink cognitive radio systems with multiple secondary users, ensuring delay guarantees and interference protection for the primary user, and demonstrates near-optimal performance through simulations.

Contribution

It introduces a dynamic programming-based policy for joint scheduling and power control that guarantees delay constraints and interference limits, and develops a low-complexity suboptimal policy with near-optimal performance.

Findings

The proposed policy is asymptotically delay optimal in light traffic.

The suboptimal policy achieves within 0.3% of the optimal in both traffic regimes.

Both policies outperform existing methods in delay performance.

Abstract

An uplink cognitive radio system with a single primary user (PU) and multiple secondary users (SUs) is considered. The SUs have an individual average delay constraint and an aggregate average interference constraint to the PU. If the interference channels between the SUs and the PU are statistically different due to the different physical locations of the SUs, the SUs will experience different delay performances. This is because SUs located closer to the PU transmit with lower power levels. A dynamic scheduling-and-power-allocation policy that uses the dynamic programming, is proposed. The proposed policy can provide the required average delay guarantees to all SUs irrespective of their location as well as protect the PU from harmful interference. The policy is shown to be asymptotically delay optimal in the light traffic regime. Motivated, by the high complexity of the dynamic programming algorithm in the optimal policy we exploit the structure of the problem's solution to present an alternative suboptimal policy. Through simulations we show that in both light and heavy traffic regimes, the delay performance of the suboptimal policy is within $0.3\%$ from the optimal policy and both outperforming existing methods.