Load-aware dynamic RRH assignment in Cloud Radio Access Networks

Abstract

Due to spatio-temporal variation of mobile subscriber's data traffic requirements, traffic load experienced by base stations present at different cell sites exhibit highly dynamic behavior in traditional cellular systems. This non-uniform and dynamic traffic load leads to under utilization of the base station computing resources at cell sites. Cloud Radio Access Network (C-RAN) is an innovative architecture which addresses this issue and keeps the Total Cost of Ownership (TCO) under safe limit for cellular operators. In C-RAN, the baseband processing units (BBUs) are segregated from cell sites and are pooled in a central cloud data center thereby facilitating shared access for a set of Remote Radio Heads (RRHs) present at cell sites. In order to truly exploit the benefits of C-RAN, the BBU pool deployed in the cloud has to efficiently serve clusters of RRHs (i.e., manyto-one mapping between RRHs and BBUs in the BBU pool) and thereby minimizing the required number of active BBUs. In this work, potential benefits of C-RAN are studied by considering realistic traffic loads of base stations deployed in urban areas by using statistical models. We propose a lightweight and load-aware algorithm, Dynamic RRH Assignment (DRA), which achieves BBU pooling gain close to that of a well known First-Fit Decreasing (FFD) bin packing algorithm. Using extensive simulations, we show that DRA consumes only 25% of time on average compared to FFD for the case of urban cellular deployment of 1000 RRHs. DRA slightly overestimates the required number of active BBUs as compared to FFD by 1.7% and 1.4% for weekdays and weekends, respectively.

User-Centric Performance Optimization With Remote Radio Head Cooperation in C-RAN

“…Theorem 5 gives the solution method for solving (20) (and equivalently (13)). The optimality of the method is guaranteed by Theorem 6.…”

“…Theorem 5 and Theorem 6 guarantee that for an arbitrary set of UEs S in the network, one can iteratively compute the maximum demand scaling factor α * for S. As a special case, when π j (α) = 1 for all j ∈ J, solving the problem in (20) is to find the eigenvalue µ and the eigenvector 1/α of the equation system f(µ) = (1/α)µ such that H(µ) = 1.…”

Novel Cloud-RRH Architecture With Radio Resource Management and QoS Strategies for 5G HetNets

“…The overall end-user QoS is defined as a weighted sum of SIR (signal to interference ratio) and the average number of users assigned to one RRH. In another work [6], authors proposed a lightweight and load aware dynamic RRH assignment (DRA) algorithm. The proposed algorithm reduced the complexity of the clustering procedure and offered quite close BBU resource savings as compared to FFD (First Fit Decreasing).…”

Latency-Aware Power Management in Software-Defined Radios

“…Cellular networks exhibit spatial and temporal patterns in the load they experience [15,16]. Daytime load is higher than during the night, when many users are asleep.…”