| Summary: | 碩士 === 國立交通大學 === 電子工程學系 電子研究所 === 104 === In this work, we study the problem of maximizing the sum rate of the network, with quality-of-service (QoS) constraints, by properly allocating resource blocks to the users in small cell networks and in a sense to restrain the energy consumption by using as few resource blocks as possible in that situation. Centralized resource allocation algorithm (CA) is proposed where the centralized coordinator such as Data Center (DC) knows the information channels between all the small cell base stations (SBSs) and user equipments (UEs). To make the problem tractable, we relax the integer constraint of association indicator to any real value between 0 and 1. We first fix the number of used resources as a constant in each iteration and then employ bisection method to obtain the optimal solutions. Simulation results show that the optimum solutions of the relaxed problem are close to the boundary, i.e., either the result is close to 0 or 1, due to the fact that the objective function is linear and monotonic.
On the other hand, in order to further reduce the overhead of channel state information (CSI) feedback to the DC, we propose another hierarchical algorithm (HA) that decomposes the centralized algorithm into three phases. First, the load of each SBS is estimated. Second, based on each SBS’s load demand, the channels are allocated to SBSs by the technique of graph coloring. Finally, the resource allocation is performed at each SBS with QoS constraints and limited power budget.
We compare the proposed centralized algorithm (CA) and hierarchical algorithm (HA) with the algorithms proposed in [6] and [3]. Simulations show that the CA consumes less energy than the HA, while HA requires less overhead than CA for CSI feedback in the network. We also compare the proposed CA and HA with the heuristic algorithm studied in [3] in which RBs are allocated to users sequentially until QoS requirement is satisfied. Simulation results demonstrate that both the proposed CA and HA schemes requires less energy than the heuristic scheme in [3]. We also take rate-per-energy as a benchmark. The proposed CA is the best energy-efficient allocation among other algorithms in lower QoS requirements.
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