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Evolution of cellular networks into dynamic, dense, and heterogeneous networks have introduced new challenges for cell resource optimization, especially in the imbalanced traffic load regions. Numerous load balancing schemes have been proposed to tackle this issue; however, they operate in a reactive manner that confines their ability to meet the top‐notch quality of experience demands. To address this challenge, we propose a novel proactive load balancing scheme. Our framework learns users' mobility and demands statistics jointly to proactively cache future contents during their stay at lightly loaded cells, which results in quality of experience maximization and load minimization. System level simulations are performed and compared with the state‐of‐the‐art reactive schemes.

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In this paper we study a multi-robot path planning problem for persistent monitoring of an environment. We represent the areas to be monitored as the vertices of a weighted graph. For each vertex, there is a constraint on the maximum time spent by the robots between visits to that vertex, called the latency, and the objective is to find the minimum number of robots that can satisfy these latency constraints. The decision version of this problem is known to be PSPACE-complete. We present a O(log ρ) approximation algorithm for the problem where ρ is the ratio of the maximum and the minimum latency constraints. We also present an orienteering based heuristic to solve the problem and show through simulations that in most of the cases the heuristic algorithm gives better solutions than the approximation algorithm. We evaluate our algorithms on large problem instances in a patrolling scenario and in a persistent scene reconstruction application. We also compare the algorithms with an existing solver on benchmark instances.

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