Optimizing Caching Policy and Bandwidth Allocation Towards User Fairness

User fairness is an important metric for cellular systems. It has been widely considered for wireless transmission when optimizing radio resource allocation but rarely considered for femto-caching. In this paper, we optimize caching and bandwidth allocation policies to improve long-term user fairness during content placement and content delivery by harnessing heterogeneous user preference. To this end, we maximize the minimal average data rate, where the average is taken over large-and small-scale channel gains as well as individual user requests. This gives rise to a complicated two-timescale optimization problem involving functional optimization. The objective function of the problem does not have closed-form expression due to unknown user preference and channel distributions, and the 'variables' to be optimized include a function. To solve such a challenging problem, we first optimize bandwidth allocation policy given arbitrary caching policy, user locations and user requests, whose structure can be found. We next optimize the caching policy given the optimized bandwidth allocation policy. To handle the difficulty of unknown distributions, we resort to stochastic optimization. Simulation results show that optimizing caching policy exploiting user preference can support much higher minimal average rate than optimizing caching policy based on content popularity when user preferences are less similar. Besides, better user fairness can be achieved by optimizing caching policy than by optimizing bandwidth allocation.