Prefetching is a technique for lowering the access delay by making data available in anticipation of future requests. Correspondingly, power control has been proposed in wireless networks for the efficient use of the wireless resources and low energy consumption at the transmitters. This work investigates the two techniques jointly (1) for communications over a fluctuating wireless channel whose dynamics and statistics are unknown, and (2) explore approximating schemes for exercising deep prefetching.

In short, a user uses a wireless terminal to access various data items residing at a server over a wireless network. Every requested item not found in the cache of the terminal incurs to the system (1) an access delay cost, and (2) an energy/network cost to download it over the wireless link. To minimize the total cost, the system may either (i) postpone the transmissions when the link quality is sensed to be low, or reversely, (ii) proactively prefetch data items during link quality ?highs?, in anticipation to future user requests. The decision therefore involves choosing when and what to (pre)fetch, and at what power level.

To quantify on the above, we formulate the problem in the context of controlled Markov chains using the technique of Dynamic Programming. After analyzing the structure of the problem, we construct a set of policies based on justified heuristics for taking near-to-optimal decisions. Simulation is then used to quantify on the performance gains over standard schemes.