Performance Analysis for Multi-Antenna Small Cell Networks with Clustered Dynamic TDD

TL;DR

This paper develops an analytical framework to evaluate multi-antenna small cell networks with clustered dynamic TDD, demonstrating that clustering improves performance and identifying optimal cluster sizes for uplink and downlink.

Contribution

It introduces a stochastic geometry-based analytical model for clustered D-TDD in small cell networks, quantifies key system parameter effects, and compares clustered versus traditional D-TDD performance.

Findings

Clustered D-TDD outperforms traditional D-TDD.

Optimal cluster size exists for downlink performance.

Uplink performance benefits from larger clusters.

Abstract

Small cell networks with dynamic time-division duplex (D-TDD) have emerged as a potential solution to address the asymmetric traffic demands in 5G wireless networks. By allowing the dynamic adjustment of cell-specific UL/DL configuration, D-TDD flexibly allocates percentage of subframes to UL and DL transmissions to accommodate the traffic within each cell. However, the unaligned transmissions bring in extra interference which degrades the potential gain achieved by D-TDD. In this work, we propose an analytical framework to study the performance of multi-antenna small cell networks with clustered D-TDD, where cell clustering is employed to mitigate the interference from opposite transmission direction in neighboring cells. With tools from stochastic geometry, we derive explicit expressions and tractable tight upper bounds for success probability and network throughput. The proposed analytical framework allows to quantify the effect of key system parameters, such as UL/DL configuration, cluster size, antenna number, and SINR threshold. Our results show the superiority of the clustered D-TDD over the traditional D-TDD, and reveal the fact that there exists an optimal cluster size for DL performance, while UL performance always benefits from a larger cluster.