Abstract
A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today’s digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.
Highlights
A growing trend for information technology is to not just react to changes, but anticipate them as much as possible
The main body of the anticipatory networking literature can be split into four categories based on the context used to characterize the system state and to determine its evolution: geographic, such as human mobility patterns derived from location-based information; link, such as channel gain, noise and interference levels obtained from reference signal feedback; traffic, such as network load, throughput, and occupied physical resource blocks based on higher-layer performance
Simulations show that an energy saving up to 80% with respect to the baseline approach can be achieved and that the performance of the heuristic solution is quite close to the optimal MixedInteger Linear Programming (MILP) approach
Summary
Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. Can researchers tailor their solutions to specific places and users, and they can anticipate the sequence of locations a user is going to visit or to forecast whether connectivity might be worsening, and to exploit the forecast information to take action before the event happens This enables the possibility to take full advantage of good future conditions (such as getting closer to a base station or entering a less loaded cell) and to mitigate the impact of negative events (e.g., entering a tunnel).
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