Joint uplink and downlink cell selection in cognitive small cell heterogeneous networks

A. Mesodiakaki, F. Adelantado, L. Alonso, C. Verikoukis
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引用次数: 12

Abstract

In the next few years, small cells (SCs) are expected to be densely deployed to achieve sustainable capacity enhancement. Due to the high SC density, some SCs will not have a direct connection to the core network, and thus will forward their traffic to their neighboring SCs through a multi-hop backhaul (BH). In such multi-hop architectures, the user association problem becomes challenging with BH energy consumption playing a key role. In parallel, the ever-increasing need to minimize the user equipment (UE) transmission power along with the uplink (UL) and downlink (DL) traffic asymmetry, predicate the joint study of UL and DL. Thus, in this paper, we study the joint UL and DL cell selection problem aiming at maximizing the total network energy efficiency, without compromising the UE quality of service. The problem is formulated as an optimization problem, which is NP-hard. Therefore, we propose a heuristic context-aware algorithm that associates the UEs in an energy-efficient way, while considering both access and BH energy consumption in UL and DL. We evaluate the proposed algorithm performance and we show that it can achieve significantly higher energy efficiency than the reference approaches, while maintaining high spectral efficiency and low UE power consumption.
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认知小蜂窝异构网络中的联合上行和下行小区选择
在未来几年,小型电池预计将密集部署,以实现可持续的容量提升。由于SC密度高,一些SC不会直接连接到核心网,因此会通过多跳回程(multi-hop backhaul, BH)将其流量转发给邻近的SC。在这种多跳架构中,BH能量消耗起着关键作用,用户关联问题变得很有挑战性。同时,用户设备(UE)传输功率最小化的需求日益增加,上行链路(UL)和下行链路(DL)的流量不对称也促使了UL和DL的联合研究。因此,在本文中,我们研究了以最大化网络总能量效率为目标的联合UL和DL小区选择问题,同时不影响UE服务质量。该问题被表述为一个np困难的优化问题。因此,我们提出了一种启发式上下文感知算法,该算法以节能的方式关联ue,同时考虑UL和DL中的访问和BH能耗。我们对该算法的性能进行了评估,结果表明该算法可以实现比参考方法更高的能量效率,同时保持高频谱效率和低UE功耗。
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