Data-Locality-Aware User Grouping in Cloud Radio Access Networks

Cellular base band units of the future are expected to reside in a cloud data center which provides computation resources, content storage and caching, and a natural place to perform multi-user precoding, thus addressing both cost and performance concerns of cellular systems. Multi-user precoding relies on efficient user grouping schemes to maximize multiplexing gains. However, traditional user grouping schemes are unaware of data center constraints, and may induce a large number of data transfers across racks when fetching requested data to a certain rack for preceding. When congestion occurs in the data center network, the delay of data transfers across racks may exceed the channel coherence time. This would kill multi-user MIMO transmissions as channel state information becomes outdated. In this paper, we design a novel data-locality-aware user grouping schemes which preferentially group users whose requested data are located under the same rack. We also design user grouping algorithms which adapt to the congestion level in the cloud data center. Specifically, a regularized spectral efficiency maximization problem is proposed where the number of data transfers across racks is introduced as a regularization term. By adjusting the weight of the regularization term according to the congestion level, we gradually suppress data transfers across racks in forming user groups when congestion occurs. We reduce the above problem to a soft-capacitated facility location problem, and we devise a 2-approximation user grouping algorithm. At last, we conduct simulations which show that our proposed algorithm performs close to the optimal in practical scenarios, and study the tradeoff between higher spectral efficiency and lower data transfer cost.