# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
###### Abstract
In [10] the authors investigate the issue of parallel transmissions over multiple RATs, focusing their attention on the Quality of Service (QoS) perceived by the final users. A simple but efficient LB algorithm is proposed and evaluated in WiFi-UMTS and mobile WiMAX systems scenarios. In [11] an improved mechanism for routing the Internet traffic over several communication paths in heterogeneous networks is proposed. The heterogeneous network is represented as a combination of multiple single technology networks available simultaneously, and each network is modeled as a single server queue. The number of jobs in each queue is minimized subject to constraints using the Lagrange multiplier. A soft LB mechanism that divides the traffic of the users into sub-flows and routes these sub-flows through different wireless access networks is presented in [12]. Soft LB involves the combined use of load distribution and vertical handover techniques. In [13] the authors adopt a fuzzy neural network approach to determine the optimal load sharing of the traffic among the heterogeneous networks and in [14] heterogeneous wireless networks are mapped to distributed grids. The authors of [14] present a hierarchical semi-centralized architecture for balancing the traffic among heterogeneous wireless networks and the proposed solution is based on the grid concept in data transmission networks. The LB mechanisms in networks involving the WiMAX and WLAN technologies are investigated in [15]. The authors consider the different service characteristics of these two networks and the QoS requirements of real-time and best effort applications and distribute all streaming flows to WiMAX networks, the remaining capacities of WiMAX and the entire capacity of WLAN being used for transmission of flows associated with non-real-time services. The advantages of LB over heterogeneous wireless links in what concerns the resource utilization, system capacity, and fulfillment of QoS requirements is proved in [16]. The authors propose a LB algorithm that is acting based on the scheduling mechanism used and the wireless link quality information. The proposed solution is more time effective and can operate in a distributed way. In [17] a new approach is proposed to compute a metric expressing the network load. The new metric computation hides the heterogeneity of network technologies from the LB module and can be applied to any packet injected into the system. In [18] a utility function-based approach, which can support multiple client classes is introduced. A bandwidth sharing policy in heterogeneous networks and a \"controlled unfairness\" scheme are achieved by using logarithmic utility functions that characterize the bandwidth allocated to different users. In [19] a Markov decision process is proposed for LB operations in heterogeneous cellular networks using multiple carriers, and the LB solution proposed is based on the usage of the Sarsa algorithm of online learning. The authors of [20] jointly address the network LB and the server LB and propose a two-phase algorithm that concurrently deals with the two LB problems in the context of new network architectures integrating virtualized network functions. In [21] the authors present a LB algorithm that selects a suitable network interface for each data flow in a dynamic way. The paper considers the growing importance and the heterogeneity of Local Area Networks (LAN) and the increasing requirements of the services in terms of latency and throughput. The interface selection is based on service requirements and the LB operations are modeled as a mixed integer linear program. In [22] the LB technique is proposed to be implemented among collocated BSs for better usage of the transmission resources and increased energy efficiency in the context of sustainable green wireless networks. In [23] the authors propose solutions to solve the problem of resource allocation optimization in heterogeneous 5G networks while satisfying the QoS requirements of the users.