## 2 Related Works
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **Reference** & **Features** \\ \hline \multirow{6}{*}{Range} & [27] & SINR \\ \cline{2-3}  & [28] & Almost blank subframe (ABS), expected user data rate \\ \cline{2-3}  & [29] & Proportional fairness \\ \cline{2-3}  & [30] & Bias design \\ \cline{2-3}  & [31] & Asymmetric downlink/uplink ICI coordination \\ \cline{2-3}  & [32] & ABS, user association probability, throughput \\ \hline \multirow{6}{*}{Association} & [33] & Downlink max-min sum rate, convex \\ \cline{2-3}  & [34] & Total capacity and coverage gains across network \\ \cline{2-3}  & [35] & Outage probability, average ergodic rate, minimum user throughput \\ \cline{2-3}  & [36] & Load balancing, gradient descent method, online algorithm \\ \cline{2-3}  & [37] & Load-aware, best biasing, convex \\ \cline{2-3}  & [38] & Network capacity, load balancing, pricing based \\ \cline{2-3}  & [39] & Generalized algebraic framework, load balancing, greedy \\ \cline{2-3}  & [40] & Energy efficiency, spectral efficiency, cognitive radio \\ \cline{2-3}  & [41] & Load balancing, distributed near-optimal solution \\ \cline{2-3}  & [42] & Network utility maximization, proportional fairness, pricing based \\ \hline \multirow{6}{*}{Joint CS-RA} & [43] & Water filling, bisection search, sum-power and sum-rate constraint \\ \cline{2-3}  & [44] & Benders’ decomposition, non-convex BS association, power control \\ \cline{2-3}  & [45] & Joint user association, channel assignment, beamforming, power control \\ \cline{2-3}  & [46] & Orthogonal, co-channel, partially shared, non-convex integer program \\ \cline{2-3}  & [47] & Maximum capacity, max-min fairness \\ \hline \multirow{2}{*}{Fast CS} & [48] & Gain in throughput \\ \cline{2-3}  & [49] & SINR \\ \hline \multirow{6}{*}{Performance} & [50] & SINR, capacity \\ \cline{2-3}  & [51] & SINR, RSRP, RSRQ \\ \cline{2-3}  & [52] & RSRP, range expansion with static/adaptive offset or with ABS \\ \cline{2-3}  & [53] & Path loss, SINR, capacity \\ \hline \multirow{6}{*}{Others} & [54] & Knapsack, assignment problem \\ \cline{2-3}  & [55] & Maximum expected bitrate \\ \cline{1-1} \cline{2-3}  & [56] & Maximum ergodic capacity \\ \cline{1-1} \cline{2-3}  & [57] & Percentage of the total earned profit \\ \cline{1-1} \cline{2-3}  & [58] & Aggregate energy consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of CS optimization of the entire system. users to the femtocell. A Stackelberg game was considered between the femtocell BS as the leader and the macrocell user as the follower to improve their performance by establishing links between them. [PERSON] and [PERSON] [61] proposed a CS algorithm based on competition among a group of users, each of which decided the probability of choosing a microcell as its serving cell. [PERSON] _et al._[62] considered a Bayesian game and showed by means of a Stackelberg formulation that the operator could optimize its global utility while the end-users maximized their individual utilities. [PERSON] _et al._[63] proposed a Nash bargaining solution, which formulated the user association optimization as a Nash bargaining problem, so as to maximize the sum data rate related utility of all users in the overall system while guaranteeing the user's minimal data rate and considering user fairness. [PERSON] _et al._[64] modeled the game on two levels, _i.e._, inter-cell CS game and intra-cell RA game, proved that the CS game had mixed strategy Nash equilibrium (MSNE) and proposed a distributed algorithm that converged to MSNE. Some studies considered intelligent learning algorithms to search for the PSNE. [PERSON] _et al._[65] proposed an evolutionary game model for CS in a two-tier femto/macro scenario, and the reinforcement learning algorithm was used to search for the evolutionary equilibrium. [PERSON] _et al._[68] studied the CS problem in a two-tier femtocell network. To achieve PSNE without a centralized controller, the Q-learning algorithm was used to help distributed individual users adapt and make decisions independently.