Time-Dependent Energy and Resource Management in Mobility-Aware D2D-Empowered 5G Systems

The 5G mobile networks are expected to offer higher degrees of radio access heterogeneity across a wide range of technologies (LTE, WiFi, mmWave, etc.), which operate in both licensed and unlicensed spectrum providing dissimilar data rates and coverage. In light of the stringent requirements in terms of resource and energy efficiency, the performance optimization of such heterogeneous 5G systems becomes an involved task that demands abundant information on the states of users to solve a complex, large-scale optimization problem. However, the unpredictable mobility of communicating entities, including dual mobility of D2D partners, may lead to a rapid deviation from the initial, optimized system state and thus requires frequent re-optimizations of the entire network. In this article, we aim to deliver a comprehensive tutorial on the implications of system-wide energy and resource management with the emphasis on its time-dependent behavior. Proposing a novel network-centric 5G optimization framework, we employ insights from the random walk and Markov chain theories, and also confirm our findings with an extensive system-level simulation campaign. These results reveal an exponential performance degradation rate that primarily depends on the average user speeds. Together with reporting the divergence exponents for different 5G system configurations, we also provide practical insights into how to exploit them in order to non-incrementally improve the throughput and energy performance of the network.