Cognitive radio inspired RSMA-MEC under hardware impairments: Performance analysis and optimization

Abstract

In this paper, we investigate a cognitive radio (CR) inspired rate splitting multiple access (RSMA) aided mobile edge computing (MEC) network, where hardware impairments (HIs) in all transceivers are considered. Given that allowing the secondary user (SU) to share the resource with the primary user (PU) for data offloading does not degrade the PU’s offloading performance, we derive closed-form expressions for the rate-splitting parameter and power allocation coefficient at the SU to maximize its offloading rate. Using the derived parameters, we then derive the successful computation probability (SCP), which is defined as the probability that both the PU and SU can successfully compute their task bits within a given latency budget, into the closed form. To further enhance the performance of the CR inspired RSMA-MEC network with HIs, a SCP maximization-based problem is formulated to jointly optimize the task offloading ratio and task offloading time of both the SU and PU. Leveraging the convex theory, we obtain the optimal solutions in the closed form. Simulation results confirm the following two insights. First, the presence of HIs leads to a decreasing on the SCP and the SCP approaches to a constant which is less than 1 with the increase of the transmit power. Second, with the optimal task offloading ratio and time, the CR inspired RSMA-MEC achieves the highest SCP compared to the existing schemes.