joint caching and resource allocation. By jointly incorporating user associ- ation, spectrum allocation, and content caching, a resource allocation problem was formed and a distributed algorithm based on the Liquid State Machine (LSM) was proposed. The proposed LSM algorithm would enable the cloud to predict users' content request distri- bution with limited information about network and users. The proposed algorithm will also help UAVs choose the optimal resource allocation strategies depending on the network 10.2 ML-Based Methodologies for SS states autonomously. It was shown that the proposed approach yields up to 33.3% and 50.3% gains in terms of the number of users that have stable queues compared to Q-learning with cache and Q-learning without cache. It was also shown that LSM significantly improves the convergence time of up to 33.3% compared to Q-learning. 10.2.4 Ambient Backscatter Networks To increase SE, a cutting-edge technology named AmBC has received significant attention as a new SS framework [107]. In backscatter communication (e.g. RFID), a device commu- nicates by modulating its reflections of an incident RF signal without generating its own radio waves. Hence, it is in the orders of magnitude more energy-efficient than conventional radio communication. AmBC system enables two devices to communicate using ambient RF as the only source of power. It leverages existing TV and cellular transmissions to elim- inate the need for wires and batteries, thus enabling ubiquitous communication where devices can communicate among themselves at unprecedented scales and in locations that were previously inaccessible. In particular, in an AmBC system as illustrated in Figure 10.8, the backscatter transmitter can transmit data to the backscatter receiver by modulating and reflecting surrounding ambient signals. Hence, the communication in the AmBC system does not require dedi- cated frequency spectrum. Based on the received signals from the backscatter transmitter and the RF source or carrier emitter, the receiv