 since none of them can unilaterally increase their utility function given that the other players stick to their current strategies. In the example here, the game has converged to reach the equilibrium. A static scenario is considered: multiple users request bandwidth resource from RAEs in a multi-cell OFDMA network with wrap-around [45]. The players are the N RAEs and the action space is the combination of all possible resource allocation schemes of all players. To implement the game for the situation being considered, the utility function needs to be mapped to that situation. In general, the utility function is as follows: Maximize the number of users who achieve a capacity C = B X sSINR/3.01 C min where Cmin is the minimum capacity needed for that user to meet its QoS requirements. Subject to (i) the total transmission power of a single RB not exceeding Pmax, max' (ii) in any cell a particular RB can only be allocated to one user, and (iii) the maximum number of RBs allocated to a particular slice may be limited by a factor such as a predefined sharing ratio. This is the general approach, but the application to the static and fully dynamic network slicing types is different: Fixed slices: A portion of RBs is reserved for each slice according to some predetermined ratio. Each slice is only allowed to use its reserved RBs. In this case, it is straightfor- ward to apply the game theory as each slice is an RAE and independent games can be implemented, one for each slice. The players are the instances in each cell of a particular slice, SO in the multi-cell environment the players compete to maximize the number of qualified users. Fully dynamic slicing: In this case the RAE is a cell. All the RBs are available to all the users and the allocation is done in such a way that the overall sharing between slices is as specified in the overall design. Sharing is not orthogonal since each slice may have 8 Network Slicing with Spectrum Sharing users in different parts of the spectrum in neighboring cells and this leads to i