A　critical　issue　in　many　wireless　networks　is　how　to　establish　the　best　possible　quality　connections　between　users　to　base　stations.　This　is　in　particular　challenging　when　users　are　randomly　located　over　a　geographical　region,　and　each　covered　by　a　number　of　heterogeneous　base　stations.　To　this　end,　we　design　a　smart　and　efficient　cell　selection　mechanism　to　improve　the　user-base　station　connection　in　heterogeneous　wireless　networks.　We　formulate　the　cell　selection　problem　as　an　asymmetric　congestion　game　with　consideration　of　users’　heterogeneity　in　their　locations　and　their　data　rates　to　various　cells.　We　show　the　existence　of　pure　Nash　equilibria　(PNE)　and　propose　a　concurrent　distributed　learning　algorithm　to　converge　to　them.　In　the　algorithm,　we　allow　users　to　perform　random　error-tolerant　updates　synchronously,　and　guarantee　them　to　reach　one　or　multiple　PNE　with　the　largest　utilities.　In　addition,　we　do　a　systematical　investigation　on　the　implementation　of　the　algorithm　in　practical　networks.　Simulation　results　show　that　the　algorithm　can　achieve　satisfactory　performance　with　acceptable　convergence　rate.　©　2017　Elsevier　B.V.