## 2 State-of-the-Art Overview and Problem Formulation
### State-of-the-Art in the Location-Aware Communications and Handover Schemes  
A location-aware adaptive communication system has been investigated in ([33] Ch. 9), where adaptive modulation and coding (AMC) together with location information was cooperatively combined in order to achieve macro diversity. It was suggested in [33] that wireless communications can benefit from a proactively updated location information of the UEs. This is because location-aware communication systems are capable of predicting more precisely the channel state information (CSI) at the transmitter for adaptive beamforming as compared to location-unaware communication systems. With a perfect knowledge of location and a fingerprint database, a location-aware system showed decent improvements in terms of the mean capacity over pure CSI-based systems especially for the applications with long feedback delays and large channel variations. However, the advantage of a location-aware system may not hold when positioning error increases above a certain threshold. In practical situations, it is likely that positioning errors may be excessive for the UE to benefit from a location-aware communication mechanism. As an indispensable component of the handover process, network selection algorithms have been studied with location awareness, i.e., LNS. In [6], a TCP/IP based LNS architecture was proposed on top of IEEE 802.21 MIH standard. Specifically, location awareness of mobile users as well as the available network information were monitored periodically by a location & network monitor (LNM). The former was then exploited to predict the distances to the currently available networks, user trajectory, as well as the mobility patterns, for designing the LNS algorithms. Despite better handover performance than without location awareness, the impact of location errors of the mobile users towards the LNS was not considered therein. Additionally, cloud-based network selection for vehicular networks was proposed in [7] for leveraging the rich computing power and data storage of a cloud computing server. The efficiency of the aforementioned network selection algorithms stemmed from offloading complex computations to a geographically distributed cloud server, which in turn provided better decision-making in the network selection, based on a broader network information. Moreover, a fast convergence algorithm for solving a coalition formation game was proposed in [7] to enable optimization over larger scale networks and practical implementation. Works on LHO under different scenarios can be found in [8; 9; 10] and the references therein. Taking into account the information of both user location and network load, the authors in [8] studied the LHO scheme in multi-cell networks. Two metrics have been utilized to optimize the handover process, namely, the angle of handover and the load-balancing index. The former suggests that the moving direction of the UEs can be computed based on the previous location estimates by the GNSS, which is then exploited to predict the most likely target cell that the UE will connect to. Further, the consideration of the load balancing index is to ensure that the QoS of the UEs does not drop if the target cell is already heavily loaded. The simulation results in [8] showed that the proposed LHO algorithm outperforms the conventional one, especially for UEs with high-but-constant velocity and UEs that make turns less frequently. Similarly, relying on the location estimation provided by GNSS, authors of [9] proposed an efficient network selection scheme based on not only the estimated location of the users (via GNSS technique) but also maps reflecting the channel quality information (i.e., the average of signal strength) as well as the traffic load. Due to the rapid changes of traffic load, frequent updates of such information are therefore necessary which poses a stringent requirement in terms of the latency and throughput of back-haul link of the HetNets.