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In our paper, we have addressed the power control issue of the cognitive radio network maintaining insignificant interference level to the primary users (PUs) as the power control algorithms ensure less interference to PUs as well as maximum SINRs for all secondary users (SUs) minimizing the total transmitting power of the secondary network. Power control through weighted least square (WLS) approach provides protection to PUs from the harmful interference induced by SUs but fails to support all the SUs with SINRs above minimum required value. To overcome this drawback, we propose an iteratively reweighted least squares (IRLS) based power control algorithm that provides SINRs to all the SUs above a threshold value maintaining acceptable interferences to PUs caused by SUs in the cognitive radio network. Our simulation result compares the performance of IRLS solution with the WLS solution and shows the convergence behavior of our algorithm.Index Terms-Cognitive radio, power control, SINR, interference constraint, IRLS.

Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence, effective resource management is essential; nevertheless, state-ofthe-art research lacks efficient economic models to support the exponential growth of the MEC-enabled applications market. We focus on designing a MEC offloading service market based on a repeated auction model with multiple resource sellers (e.g., network operators and service providers) that compete to sell their computing resources to the offloading users. We design a computationally-efficient modified Generalized Second Price (GSP)-based algorithm that decides on pricing and resource allocation by considering the dynamic offloading requests arrival and the servers' computational workloads. Besides, we propose adaptive best-response bidding strategies for the resource sellers, satisfying the symmetric Nash equilibrium (SNE) and individual rationality properties. Finally, via intensive numerical results 1 , we show the effectiveness of our proposed resource allocation mechanism.

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