Abstract
With the rapid increase of energy consumption and environment problems, the need for green techniques and harvesting energy is emerging. Network coding can provide the solution by the reduction of the unnecessary data transmission and the estimated traffic patterns. It can also amplify the synergy in an energy harvesting capable cognitive radio (CR) network since the CR has the recognition and optimal decision functionalities. In this paper, we propose stochastic policy based wireless energy harvesting in green cognitive radio network. With the simulations, we show that the proposed scheme is better up to 20 % of the previous work.
Highlights
Researchers believe that one of the causes for the recent increase in carbon dioxide and serious environmental problems worldwide is the rise in the amount of data due to the advancements in information and communications technology
We suggest a technique of restoring a naturally integrated signal of primary user (PU) and secondary user (SU) with active noise control (ANC) technique, using the minimum amount of energy from the cognitive radio (CR) network, which functions network coding and energy harvesting
This is possible since this paper, similar to [4] which assumed that the packets involved in analog coding started with the known pilot sequence of 64 bits to distinguish the beginning of transmission, supposes that each of PU’s k linear network coding starts with the known sequence
Summary
Researchers believe that one of the causes for the recent increase in carbon dioxide and serious environmental problems worldwide is the rise in the amount of data due to the advancements in information and communications technology. The energy harvesting capable cognitive radio (CR) network, which senses its environment and surroundings, is effective in the development of eco-friendly, low-carbon protocols since its nodes can each recognize the energy consumptions and environmental changes [1]. Unlike existing studies on CR network, we propose a technique for eco-friendly, low-carbonate communication by which SU can predict and recognize the frequency, the energy consumption of PU and the gain of energy harvesting, thereby choosing the optimum in false negatives or false positives. This process is modeled by partially observable Markov decision process (POMDP).
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