# Online Distributed User Association for Heterogeneous Radio Access Network
## 2 Related Works  
Recent surveys [14; 21] show different schemes regarding user association for load-balancing in a HetNet environment. In Ref. [14], the authors surveyed different schemes to solve the load-balancing problem, such as centralized optimization, the game theory, Markov decision process, and biasing schemes, and also mentioned some of the open challenges. They also discussed a few myths in detail, such as signal quality (which is the main driver of user experience) and spectrum crunch. UE will not always get the best experience from the strongest BS in its range, and the current focus should be on the development of infrastructure rather than spectrum shortage. The authors in Ref. [22] surveyed various user-association schemes and summarized the challenges into four parts, being complexity, energy efficiency, spectrum efficiency, and interference management. They also stated that the large number of UEs, together with the increasing number of BSs requires simple user-association algorithms, with minimal signaling and processing overhead. Authors in Ref. [23] proposed a load-balancing scheme based on a machine learning technique that uses both unsupervised learning and supervised learning, as well as the Markov Decision Process. Authors in Ref. [21] discussed the control mechanisms for user association in a cellular network which can be divided into three categories, namely centralized control, distributed control, and hybrid control. In centralized control, a single processing unit takes all the decisions for user association. Some centralized controls were presented in Refs. [24; 25; 26; 27]. The authors in Ref. [24] formulated the resource allocation with a load-balancing problem as a mixed-integer nonlinear non-convex combinatorial optimization problem. Then, a nonlinear fractional programming scheme and dual decomposition scheme were applied to search the optimal solutions. A two-tierscenario with a centralized controller for load-balancing was considered in Ref. [25], where the load-balancing problem was formulated as a nonlinear integer problem, and a centralized entity was responsible for the load-balancing task. The authors in Ref. [26] proposed an ON-OFF transmission coordination model which can control the BS to either transmit at the maximum power or none. An optimization problem was formulated to maximize the throughput and balance the load in Ref. [27], where the user-association task was done centrally. Centralized controls have been proven to show good results in terms of optimal resource allocation, with a fast convergence. However, the required amount of signaling overhead grows exponentially, and is intractable with the increase in network size [17; 21]. Hence, a scheme having decentralized implementation with low-signaling overhead is desirable [28]. Distributed controls [17; 19; 20; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43] can be further divided into the static/offline and dynamic/online environment, and are guaranteed to be the simple and low-complexity alternative to centralized controls. In a distributed control, the UEs select the BSs based on the received signal strength and information received, such as load information, from BSs in range. BSs and UEs make autonomous user association decisions by themselves through mutual interaction. Both the BSs and the UEs execute the load-balancing algorithm on their respective ends. The BSs broadcast the load parameter periodically, and the concerned UEs take the decision of associating to a BS based on these load values and signal strengths. These schemes achieve higher performance in terms of network throughput and load balance.