In order to meet the extra traffic demand of hot-spot users not expected in the original network planning, it is desirable to deploy new small cells on top of the existing heterogeneous network (HetNet) without replanning the overall network. In this paper, we propose to maximize the minimum user throughput in a HetNet with unexpected recurring hot-spots by jointly optimizing the number and locations of new small cells and user associations of all cells. A reduced-complexity iterative algorithm is devised to solve the joint optimization problem. The simulation results show that the proposed iterative algorithm significantly outperforms the random deployment of new small cells and achieves performance very close to numerically solving the joint optimization in terms of minimum user throughput and required number of new small cells, especially for a large number of unexpected hot-spot users.Introduction: The appropriate deployment of small cells is crucial to the success of heterogeneous networks (HetNets). Existing deployment strategies mainly focus on designing the whole HetNet to offer good service quality at low cost [1], [2]. After a HetNet has been established, it is desirable to meet the extra traffic demands from recurring hot spots (HSs) of user equipments (UEs), which were not expected in the original network planning, by deploying new small cells on top of the existing HetNet without replanning the whole network. Mobile small cells mounted on vehicles can be used in this situation, however, the operators need to know the optimal number and locations of new small cells to be deployed. In this paper, we propose to maximize the minimum UE throughput in a HetNet with unexpected recurring HSs by jointly optimizing the number and locations of new small cells and user associations of all cells. Due to the high computational complexity of the joint optimization problem, we propose a reduced-complexity iterative algorithm to solve this optimization. Performance of the iterative new small-cell deployment algorithm in terms of minimum UE throughput and required number of new small cells is evaluated through simulations, in comparison with numerically solving the joint optimization and the random deployment of new small cells.