1 research outputs found
Distributed Caching for Data Dissemination in the Downlink of Heterogeneous Networks
Heterogeneous cellular networks (HCN) with embedded small cells are
considered, where multiple mobile users wish to download network content of
different popularity. By caching data into the small-cell base stations (SBS),
we will design distributed caching optimization algorithms via belief
propagation (BP) for minimizing the downloading latency. First, we derive the
delay-minimization objective function (OF) and formulate an optimization
problem. Then we develop a framework for modeling the underlying HCN topology
with the aid of a factor graph. Furthermore, distributed BP algorithm is
proposed based on the network's factor graph. Next, we prove that a fixed point
of convergence exists for our distributed BP algorithm. In order to reduce the
complexity of the BP, we propose a heuristic BP algorithm. Furthermore, we
evaluate the average downloading performance of our HCN for different numbers
and locations of the base stations (BS) and mobile users (MU), with the aid of
stochastic geometry theory. By modeling the nodes distributions using a Poisson
point process, we develop the expressions of the average factor graph degree
distribution, as well as an upper bound of the outage probability for random
caching schemes. We also improve the performance of random caching. Our
simulations show that (1) the proposed distributed BP algorithm has a
near-optimal delay performance, approaching that of the high-complexity
exhaustive search method, (2) the modified BP offers a good delay performance
at a low communication complexity, (3) both the average degree distribution and
the outage upper bound analysis relying on stochastic geometry match well with
our Monte-Carlo simulations, and (4) the optimization based on the upper bound
provides both a better outage and a better delay performance than the
benchmarks.Comment: tco