# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 1 Introduction  
Recently, the exponential growth of mobile data traffic has culminated in the development of fifth-generation (5G) wireless communication systems, with the ultimate goals of gigabit-level data rate, millisecond latency, billions of connected devices, and so on. Confronting these demands, academia and industry have developed several technologies, such as massive multiple-input multiple-output (MIMO), full-duplex (FD) communication, satellite communication [1, 2], and network densification. However, these cutting-edge techniques still cannot meet the ever-increasing demands for wireless traffic, which is expected to increase by about 1000 fold by the year 2020. Meanwhile, the current available wireless spectrum is almost saturated even if cognitive radio technology is applied, leaving limited room to exploit. Hence, it is imperative to provide a paradigm shift from the wireless spectrum to higher frequencies. With this regard, the millimeter wave (mmWave) band with abundant unused or lightly used bandwidths becomes a viable way to satisfy 5G communication demands [3]. The availability of rich spectrum resources makes mmWave communications a predominant technique to acquire gigabit-level data rates, millisecond low latency, and billions of connected devices in the 5G era. Moreover, owing to the short wavelength, mmWave communications can realize directional pencil-beam transmission with limited interference [4, 5]. However, the practical implementation of mmWave communications still faces various challenges, such as high propagation loss, susceptibility to blockage in daily environments (such as buildings, walls, humans, foliage, and even rain drops), and high hardware complexity [6, 7]. These challenges historically make utilizing mmWave communications infeasible for mobile communications due to insufficient coverage and a lack ofsupport for mobility, especially in non-line-of-sight (NLOS) environments [8]. However, mmWave communications are much more applicable for the 5G heterogeneous network (HetNet) with ultra-dense small cell deployment [9; 10]. On the other hand, with the ultra-dense deployment of mmWave access points (APs), self-optimizing network management mechanisms are essential to improve system performance while simultaneously reducing both operational expenditure and capital expenditure [11; 12]. Therefore, in this article, we specifically investigate self-optimizing traffic steering in an mmWave HetNet. The goal of traffic steering is to optimally and intelligently allocate traffic to different networks and different spectra to achieve diverse optimization objectives [13]. There have been several existing works studying the application of traffic steering in different scenarios, such as LTE in unlicensed networks (LTE-U) [14] and LTE HetNets [15]. However, to the best of our knowledge, there still has been no work that investigates traffic steering in the mmWave-based 5G HetNet. Different from the traditional dual-band network architecture, which only considers licensed and mmWave bands [16], we first introduce a novel triple-band network structure by integrating the sub-6 GHz unlicensed band. Compared to the traditional dual-band structure with only the mmWave band and licensed band, we can steer the traffic of mmWave and licensed bands to the additional sub-6 GHz unlicensed band. In this way, we can relieve traffic congestion in the licensed band and compensate for the blockage sensitivity of mmWave communications, thus better ensuring quality-of-experience (QoE) for users. Based on the proposed architecture, centralized and distributed cooperative mechanisms are further developed to reduce the computational complexity of network operation. The main contributions of this paper can be summarized as follows. * We propose a novel triple-band network structure which utilizes the licensed bands, sub-6 GHz unlicensed bands, and mmWave bands to support various user types.