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Due to the drastic increase of mobile traffic, wireless caching is proposedto serve repeated requests for content download. To determine the cachingscheme for decentralized caching networks, the content preference learningproblem based on mobility prediction is studied. We first formulate preferenceprediction as a decentralized regularized multi-task learning (DRMTL) problemwithout considering the mobility of mobile terminals (MTs). The problem issolved by a hybrid Jacobian and Gauss-Seidel proximal multi-block alternatingdirection method (ADMM) based algorithm, which is proven to conditionallyconverge to the optimal solution with a rate $O(1/k)$. Then we use the tool of\textit{Markov renewal process} to predict the moving path and sojourn time forMTs, and integrate the mobility pattern with the DRMTL model by reweighting thetraining samples and introducing a transfer penalty in the objective. We solvethe problem and prove that the developed algorithm has the same convergenceproperty but with different conditions. Through simulation we show theconvergence analysis on proposed algorithms. Our real trace driven experimentsillustrate that the mobility-aware DRMTL model can provide a more accurateprediction on geography preference than DRMTL model. Besides, the hit ratioachieved by most popular proactive caching (MPC) policy with preferencepredicted by mobility-aware DRMTL outperforms the MPC with preference fromDRMTL and random caching (RC) schemes.

Mobility-aware Content Preference Learning in Decentralized Caching Networks