局部最优,是指对于一个问题的解在一定范围或区域内最优,或者说解决问题或达成目标的手段在一定范围或限制内最优。在应用数学和计算机科学中,优化问题的局部最优是在候选解决方案的相邻集合内最优(最大或最小)的解决方案。 这与全局最优相反,后者是所有可能的解决方案中的最优解决方案,而不仅仅是在特定值附近的最优解决方案。

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This paper investigates rate-splitting (RS) precoding for non-orthogonal unicast and multicast (NOUM) transmissions using fully-digital and hybrid precoders. We study the nonconvex weighted sum-rate (WSR) maximization problem subject to a multicast requirement. We propose FALCON, an approach based on sequential parametric optimization, to solve the aforementioned problem. We show that FALCON converges to a local optimum without requiring judicious selection of an initial feasible point. Besides, we show through simulations that by leveraging RS, hybrid precoders can attain nearly the same performance as their fully-digital counterparts under certain specific settings.

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This paper investigates rate-splitting (RS) precoding for non-orthogonal unicast and multicast (NOUM) transmissions using fully-digital and hybrid precoders. We study the nonconvex weighted sum-rate (WSR) maximization problem subject to a multicast requirement. We propose FALCON, an approach based on sequential parametric optimization, to solve the aforementioned problem. We show that FALCON converges to a local optimum without requiring judicious selection of an initial feasible point. Besides, we show through simulations that by leveraging RS, hybrid precoders can attain nearly the same performance as their fully-digital counterparts under certain specific settings.

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