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Robust power allocation in cognitive radio networks with uncertain knowledge of interference

机译:认知无线电网络中的强大功率分配,具有不确定的干扰知识

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We study the secondary user power allocation in OFDM-based cognitive radio networks with underlay mode where secondary user is allowed to share the spectrum licensed to primary user, provided that limited interference is generated to primary user. While this problem has been studied extensively in the literature, most of the previous works in this context assume that secondary user has the exact information of how much interference it generates to primary user, which may not be the case in practice. We assume that the interference channel gains are known to secondary user with uncertainty, and develop a power allocation algorithm that can keep interference under a desired level. In particular, we formulate the power allocation problem as a chance-constrained program that finds a power allocation that guarantees limited interference with high probability. The formulation is non-convex in general, and we apply techniques from robust optimization theory to convexify the formulation. We show through simulations that our approach finds a power allocation that remains feasible under time-varying channel conditions.
机译:我们研究了基于OFDM的认知无线电网络中的辅助用户电力分配,其中允许辅助用户共享给主用户的频谱,只要为主用户生成有限的干扰。虽然在文献中已经广泛研究了这个问题,但在此上下文中的大多数作品都假定辅助用户具有将其生成到主用户的多少干扰的确切信息,这可能在实践中可能不是这种情况。我们假设有次级用户的干扰频道增益具有不确定性,并且开发了可以在期望水平下保持干扰的功率分配算法。特别是,我们将电力分配问题作为一个机会约束程序,找到功率分配,以保证对高概率的有限干扰。制剂一般是非凸的,并且我们应用于鲁棒优化理论的技术,以吞吐制剂。我们通过模拟显示我们的方法发现功率分配,该功率分配在时变频道条件下仍然可行。

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