Multi-connectivity ( $MC$ ) is proposed in Fifth-Generation mobile communications systems ( $5G$ ) to mitigate the deterioration of Quality-of-Service owing to line-of-sight blockage and lack of communication resources. The main idea of MC is to associate single user equipment with multiple network layers and multiple radio access technologies, simultaneously. In previous studies, MC is demonstrated to increase capacity, provide high reliability and decrease outage probability. However, for the first time in literature, in this letter, we incorporate MC into the optimization of the total power consumption in 5G Heterogeneous Cloud Radio Access Networks ( $HCRAN$ )s. Upon formulation of the problem as a binary integer linear programming problem, proving its NP-hardness, a heuristic algorithm is proposed consisting of linear programming relaxation and rounding and generalized assignment problem heuristic, which takes the outputs of the linear programming relaxation and rounding as inputs and checks if it further minimizes the power consumption. We analyze power consumption, time complexity and achievable rate of the proposed algorithm and verify its superiority over existing methods by simulations.

中文翻译：

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5G 异构 CRAN 中基于多连接的上行/下行解耦节能用户关联

多连接（ $MC$ ) 是在第五代移动通信系统 ( $5G$ ) 以减轻由于视线阻塞和缺乏通信资源而导致的服务质量恶化。MC 的主要思想是将单用户设备与多个网络层和多种无线接入技术同时关联。在之前的研究中，MC 被证明可以增加容量、提供高可靠性并降低中断概率。然而，在文献中第一次，在这封信中，我们将 MC 纳入了 5G 异构云无线接入网络总功耗的优化中（ $HCRAN$ )s。将该问题表述为二元整数线性规划问题，证明其NP难度，提出了一种启发式算法，包括线性规划松弛和舍入和广义分配问题启发式，以线性规划松弛和舍入的输出为输入并检查它是否进一步降低了功耗。我们分析了所提出算法的功耗、时间复杂度和可实现率，并通过仿真验证了其优于现有方法的优势。