In ultra-dense networks (UDNs), the dense deployment of base stations (BSs) is facing challenges due to the pronounced unbalanced traffic loads, severe inter-cell interference, and uncertain traffic demands. In this paper, we tame traffic uncertainty for the joint optimization of BS activation and user association in UDNs to mitigate interference and balance traffic loads among BSs. Specifically, we address the traffic uncertainty by using chance constraint programming with the known first- and second-order statistics of the uncertain traffic. We formulate the joint BS activation and user association problem as a mixed integer non-linear programming problem, which is then decomposed into a set of user association sub-problems by modeling the BS states (active or idle) as a Markov chain. We solve the user association sub-problem at each BS state by transforming it into a convex problem over the positive orthant. In particular, at each BS state, the candidate serving BSs that lead to the optimal load balancing performance are identified for each user and parts of the user's traffic are offloaded to the identified BSs. Based on the obtained solutions, we propose a distributed near-optimal BS activation and user association scheme. Numerical results demonstrate that our proposed scheme is more robust to traffic uncertainty and provides better load-balancing performance than the existing schemes.

中文翻译：

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流量不确定性下超密集网络中基站激活和用户关联联合优化


在超密集网络（UDN）中，由于流量负载明显不均衡、小区间干扰严重、流量需求不确定，基站的密集部署面临挑战。在本文中，我们通过联合优化 UDN 中的 BS 激活和用户关联来控制流量不确定性，以减轻干扰并平衡 BS 之间的流量负载。具体来说，我们通过使用机会约束规划以及已知的不确定流量的一阶和二阶统计来解决流量不确定性。我们将联合基站激活和用户关联问题表述为混合整数非线性规划问题，然后通过将基站状态（活动或空闲）建模为马尔可夫链，将其分解为一组用户关联子问题。我们通过将每个 BS 状态下的用户关联子问题转化为正向问题上的凸问题来解决它。具体地，在每个BS状态，为每个用户识别导致最佳负载平衡性能的候选服务BS，并且将用户的部分业务卸载到所识别的BS。基于所获得的解决方案，我们提出了一种分布式近乎最优的BS激活和用户关联方案。数值结果表明，我们提出的方案对流量不确定性更加鲁棒，并且比现有方案提供更好的负载平衡性能。