# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 5 Pricing: Creating Incentive for Caching
### Pricing Caching Service Using a Hierarchical Architecture  
We conceive a hierarchical architecture with virtual network operators (VNOs) [37], as shown in Figure 4. A RAN sells its bandwidth to VNOs, which buy bandwidth to serve their associated users, either by on-demand transmission or caching. If a user cannot find the requested file from the local memory, her or his VNO has to buy bandwidth to serve it. A VNO charges its user for the data volume that the user requests, no matter how a requested file is served.  
Figure 3: A Cross-Layer Perspective on Memory Scheduling: (Upper Sub-figure) Memory Reuse in the Time Domain; (Lower Sub-figure) Memory Cost vs. Hit Ratio [21; 22].  
A simple scenario in which VNOs schedule bandwidth only is discussed first. The RAN operator charges VNOs a higher bandwidth fee during peak times, because the price is determined by the demand-supply relationship from an economics perspective. If a user's requested file can be found in her or his local cache, the service cost is low. Otherwise, on-demand transmission has to be launched, even if the instantaneous cost is high. On the other hand, caching undesired content wastes the pushing cost. Consequently, VNOs have a strong incentive to maximize the cache hit ratio through accurate request prediction and careful scheduling. This incentive helps to better match the bandwidth demand and supply in the time domain [38]. An alternative way to advocate caching is nonlinear pricing in which the cost per unit spectrum increases with the total amount of spectrum acquired by a user.  
Caching should reduce a user's cost for telecommunication services, while increasing the income of spectrum owners and/or RAN operators. The two goals seem to be contradictory, but can be achieved simultaneously due to caching gains. More specifically, the overall service costs are reduced due to the EE and SE gains of caching. Pushing popular items in off-peak time helps to reduce the bandwidth demand during peak times. As such, proactive caching better matches the bandwidth demand and supply in the time domain, which also broadens the cross-disciplinary research of economics and wireless networks.  
A RAN may adopt an auction that allows VNOs to bid for bandwidth. In this case, the gap between bandwidth prices in peak and off-peak times can become even larger and hence caching saves more cost. Further, if a VNO fails in bidding bandwidth to serve its users, it fails in assuring the QoS, thereby losing users. Therefore, a bandwidth auction may not only increase the income of a RAN, but also eliminate VNOs with weak caching algorithms. To enhance caching policies, VNOs may adopt meta learning or imitation learning. Furthermore, cross-layer pricing mechanisms can be exploited to allow VNOs to control the physical layer directly. Radio resources are then priced dynamically. For  
Figure 4: A hierarchical pricing infrastructure for cache-empowered RANs, in which various bandwidth or radio resource pricing mechanisms can be adopted.  
instance, idle spectrum and renewable energy are usually cheap or even free. Physical-layer multicasting serves multiple users without additional bandwidth cost. As a result, a VNO can increase its own income by efficiently using radio resources in the physical layer.