Abstract
Ultra-Dense Network (UDN) is one of the key techniques for the next generation of mobile network due to providing high system throughput. However, severe interference often occurs in UDN, which greatly impact the data rates of cell-edge users. User-centric wireless access virtualization has been widely adopted in UDN to mitigate the interference of cell-edge users by sharing resources and eliminating cell boundary. However, it's only effective for moderate scale networks. Moreover, the efficiency needs further improvement. In this paper, we study effective cooperative clustering method for large scale UDN with less computations in order to improve the throughput of cell-edge users. We formulate a convex optimization problem in which the objective is to maximize the system throughput with overlapping virtual cells. We propose a clustering method to solve this optimization problem. We design a fast-convergent iterative algorithm called K-Nearest Neighbor (KNN) algorithm to perform users clustering. Simulation results show that our proposed algorithm has better throughput performance for both average and cell-edge users. Especially, the per-carrier throughput is improved, which leads to more serviceable users with limited resources.
Highlights
With the rapid development of wireless communications, the mobile traffic volume is exponentially increasing
Because the interference intra virtual cell has been eliminated by zero-forcing precoding, the user set in the gth cluster is represented as Vg, and V denotes the set of user clusters
In this paper, we have investigated user clustering for downlink cooperative receiving in virtual-cell wireless networks
Summary
With the rapid development of wireless communications, the mobile traffic volume is exponentially increasing. From the perspective of network, different users in the same cell would choose overlapping clusters due to the freedom of users selecting cooperative clusters, which will cause conflicts in resource allocation and greatly increase the complexity of system scheduling. Compared with traditional static non-overlapping virtual cell clustering, this scheme improved the throughput gains of average and cell-edge users, but it was limited by the number of antennas of transmitters and receivers. K-means algorithm has been widely used in clustering users or BSs. In [5], the authors proposed a clustering-based radio resource management scheme. [21] proposed a clustering-based resource allocation scheme with QoS guarantee In this scheme, k-means algorithm was used to cluster the nano-cellular according to the distribution density, which can bring about the dynamic clustering under different dynamic topologies. Extensive simulation results show that our KNN algorithm has low running time
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