For small cell technology to significantly increase the capacity of
tower-based cellular networks, mobile users will need to be actively pushed
onto the more lightly loaded tiers (corresponding to, e.g., pico and
femtocells), even if they offer a lower instantaneous SINR than the macrocell
base station (BS). Optimizing a function of the long-term rates for each user
requires (in general) a massive utility maximization problem over all the SINRs
and BS loads. On the other hand, an actual implementation will likely resort to
a simple biasing approach where a BS in tier j is treated as having its SINR
multiplied by a factor A_j>=1, which makes it appear more attractive than the
heavily-loaded macrocell. This paper bridges the gap between these approaches
through several physical relaxations of the network-wide optimal association
problem, whose solution is NP hard. We provide a low-complexity distributed
algorithm that converges to a near-optimal solution with a theoretical
performance guarantee, and we observe that simple per-tier biasing loses
surprisingly little, if the bias values A_j are chosen carefully. Numerical
results show a large (3.5x) throughput gain for cell-edge users and a 2x rate
gain for median users relative to a max received power association.

使用低复杂度的分布式算法通过对网络宽带优化来尝试解决小型细胞技术的优化问题，以及通过对偏置值的谨慎选择获得了大量吞吐量收益和速率增益。