Track: Research Track |
| Maximizing Stable Throughput in Age of Information-Based Cognitive Radio |
| Cognitive radio can be considered as a viable frequency access framework that overcomes the disadvantages of the licensed-based transmission procedure by allowing the secondary users to access the spectrum to transmit their data. However, due to the evolution of latency-sensitive real-time communication applications such as gaming and extended reality, it becomes more vital that cognitive radio networks should be studied with latency requirements on the data transmission. Recently, Age of Information has introduced itself as a important metric for evaluating the freshness of the transmitted data. In this paper, we investigate the latency and stability analysis of a two-user cognitive radio network that consists of one primary user, one secondary user and their destinations. The latency requirements of the transmitted data packets are taken into consideration by imposing Age of Information constraints on the data transmission of the users. We present two optimization problems, in the first problem, the secondary user stable throughput is maximized under an Age of Information constraint imposed on the data transmission of the secondary user. While, in the second problem, we maximize the stable throughput of the secondary user with respect to Age of Information constraints set on the data transmission of both the primary and secondary users. The resultant problems are found to be non-linear programming optimization problems. An appropriate algorithm is used to solve the problems and provide the numerical solutions. Our results characterize the impact of setting Age of Information constraints on the stability region of the network; we demonstrate that the stability region, in certain cases, is reduced by only 11% with strict Age of Information restrictions if compared to the scenario where no latency requirements is considered. Our results also show the potential accuracy of the algorithm adopted in this paper to solve the formulated optimization problems. |
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