100% isolation between slices despite any inter-slice or ICI. Method C1 provides the best overall implementation for maintaining complete isolation without adding the complexity of compensation and the rollback can be applied independently of the resource allocation, SO it is not specific to the game-theory approach. However, this strict approach means that fewer users achieve the required QoS: in B2 for example, there is no rollback and SO there is a high QUR overall, but a small number of existing PrUs are dropped. It should also be noted that the compensation in B2 is included as part of the game, SO something equivalent would need to be implemented in any other optimization technique. 8.6 Conclusions This chapter has sought to highlight the advantages and the issues surrounding network slicing when it is implemented in a dynamic spectrum-sharing manner. These have been considered in a generic way, but the merits of the dynamic approach have been demon- strated using results from research simulations. Of course, the detail of the results would have changed with different implementations, but the principles remain the same. Clearly, the use of dynamic sharing of a whole block of spectrum leads to improvements in the number of users meeting their QoS requirements and allows flexible changes to the load between operators/slices. However, allowing all users access to all the spectrum does raise issues about ICI being not only between cells but also between different slices whereas fixed allocation limits interference to being within a slice. This means that an interference mitigation strategy needs to be implemented between cells SO that the SINR seen by users is sufficient to allow them to meet their QoS requirements. The approach used in the example results shown is to use game theory to perform the necessary multi-cell optimization of resources; while other optimization techniques could be used, non-cooperative game theory has the advantage of avoiding the need to have some form of messaging between cells. A po