Cost-optimal caching for D2D networks with user mobility: Modeling, analysis, and computational approaches

TL;DR

This paper models and analyzes a cost-optimal caching problem in D2D networks considering user mobility, proposing approximation and heuristic algorithms with proven performance bounds and demonstrating their effectiveness through simulations.

Contribution

It introduces a novel COCP model accounting for mobility, cache size, and encoded segments, and develops approximation and heuristic algorithms with performance guarantees.

Findings

ACOCP achieves near-optimal solutions for small to medium systems.

The MAUU algorithm provides efficient caching placements in large scenarios.

Simulation results confirm the effectiveness of proposed methods.

Abstract

Caching popular files at user equipments (UEs) provides an effective way to alleviate the burden of the backhaul networks. Generally, popularity-based caching is not a system-wide optimal strategy, especially for user mobility scenarios. Motivated by this observation, we consider optimal caching with presence of mobility. A cost-optimal caching problem (COCP) for device-to-device (D2D) networks is modelled, in which the impact of user mobility, cache size, and total number of encoded segments are all accounted for. Compared with the related studies, our investigation guarantees that the collected segments are non-overlapping, takes into account the cost of downloading from the network, and provides a rigorous problem complexity analysis. The hardness of the problem is proved via a reduction from the satisfiability problem. Next, a lower-bounding function of the objective function is derived. By the function, an approximation of COCP (ACOCP) achieving linearization is obtained, which features two advantages. First, the ACOCP approach can use an off-the-shelf integer linear programming algorithm to obtain the global optimal solution, and it can effectively deliver solutions for small-scale and mediumscale system scenarios. Second, and more importantly, based on the ACOCP approach, one can derive the lower bound of global optimum of COCP, thus enabling performance benchmarking of any suboptimal algorithm. To tackle large scenarios with low complexity, we first prove that the optimal caching placement of one user, giving other users' caching placements, can be derived in polynomial time. Then, based on this proof, a mobility aware user-by-user (MAUU) algorithm is developed. Simulation results verify the effectivenesses of the two approaches by comparing them to the lower bound of global optimum and conventional caching algorithms.