D-FROST: Distributed Frequency Reuse-Based Opportunistic Spectrum Trading via Matching With Evolving Preferences - 2018


Spectrum trading creates more accessing opportunities for secondary users (SUs), and economically edges the first users (PUs). Compared with centralized spectrum trading designs, e.g., spectrum auction, distributed spectrum trading captures instantaneous spectrum trading opportunities better over massive nations while not incurring additional infrastructure deployment and has no network scalability issues. But, the prevailing distributed spectrum trading styles have restricted concern relating to spectrum reuse. Considering spatial reuse, during this Project, we tend to propose a completely unique distributed frequency reuse-based opportunistic spectrum trading (D-FROST) theme, which will more improve spectrum utilization, provide a lot of accessing opportunities for SUs, and increase the revenues of PUs. During this Project, we employ conflict graph to characterize the SUs' co-channel and radio interferences, and mathematically formulate a centralized PUs' revenue maximization drawback under multiple wireless transmission constraints. Because of the NP-hardness to solve the matter and also the non-existence of centralized trading entity, we tend to develop the D-FROST algorithms based on matching with evolving preferences, and prove its stability. Through in depth simulations, we have a tendency to show that the proposed D-FROST algorithm is superior to other distributed spectrum trading algorithms without considering spectrum reuse, yields results close to the centralized optimal one, and is effective in increasing PUs' revenue and improving spectrum utilization.

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