l point of view, allocating 8 Network Slicing with Spectrum Sharing bandwidth in terms of the number of RBs fits the sharing model well. In Szigeti et al. [34] there is a detailed assessment of QoS requirements for different types of service and these can be mapped onto capacity (in the Shannon sense) as explained at the start of Section 8.4. Differentiating services by bitrate is quite common (e.g. [35]) and hence a com- mon approach is sharing through considering the frequency domain. This section is concerned with illustrating the effects that would be expected with dynamic network slicing, optimizing the frequency allocation to maximize the number of users meeting their QoS requirements. Any form of optimization that takes into account the ICI could be used, but the specific results are using the game-theoretic approach in [39] and extended in [18] to apply to network slicing. A different optimization, but still using game theory, is found in [40]. Other authors use different methods, such as auctions [41]. Each run of the game takes place until equilibrium is reached, as explained later, and while this does not guarantee that a global optimization has been reached, the approach stands up well when compared with using a Genetic Algorithm for the optimization [42]. In Liu et al. [39], OFDMA was used with each RB having a bandwidth B depending on the number of subcarriers per RB and bandwidth of each subcarrier. From the well-known Shannon theorem, the capacity C achieved by a user is B ,log (1+S/N) where S is the signal power and N the total noise power including interference. For SINR of more than a few dB this can be approximated by the well-known C = B SINR/3.01. Hence, the total capacity of a user with L RBs is and SINR can be denoted as the "sum of signal-to-noise-plus-interference ratios" (sSINR). This approximation to Shannon's theorem is reasonably valid for an SINR of >5 dB (Figure 8.9) but it is a pessimistic approximation as it gives a lower capacity than the actual value; allocating resource based 