Researchers have developed a new algorithm that identifies the right amount of power level, enhancing the energy efficiency of an RF-energy harvesting network.
A team of researchers from the Indian Institute of Technology (IIT) Bombay and Monash University, Australia, has developed a new algorithm that identifies the right amount of power level, enhancing the energy efficiency of a radio frequency (RF)-energy harvesting network.
The algorithm, in this study, uses a statistical tool called the multi-armed bandit method that does not depend on channel state information parameters, and the source identifies the optimal power output, IIT Bombay statement said. The performance results of the algorithm are presented in the journal IEEE Wireless Communications Letters.
RF signals are electromagnetic radiations used in wireless communication. They transmit information and carry an inherent small electrical energy component. Emerging technology harvests this electrical energy and powers many wireless devices (called nodes) over a wide area, such as medical implants or IoTs.
The project is funded by the Department of Science & Technology (DST) and Science and Engineering Research Board (SERB), Govt of India, through the Innovation in Science Pursuit for Inspired Research (INSPIRE) Faculty Fellowship and Early Career Research Award (ECRA); and the Australian Research Councils Discovery Early Career Researcher Award (DECRA) Scheme.
“An actual transmission system is a complex network with several receivers spread over a region receiving different amounts of energy for harvesting. Also, they will require different amounts of energy for successfully transmitting the information,” says Prof Manjesh Hanawal, lead author of the study. As the environment is uncertain, reinforcing the algorithms with sequential decision making can quickly ascertain the status of the harvested energy, thereby improving the system’s energy efficiency, he adds. However, traditional optimisation techniques drastically increase computation costs as they require information on the channel state parameters.
To overcome this hurdle, the team used a sequential optimization method in the algorithm called the Multi-Armed Bandit technique that relies only on detecting if a receiver’s feedback signal was successfully decoded or not (a yes-no status). This technique is akin to exploring multiple levers and playing the best lever of a slot machine (gambling devices) at a given time. First, the player risks a few losses by exploring the levers; then, sequentially pulling the levers, the player learns the lever that maximizes the overall reward after some trials.
The source selects a power level in the harvesting network to transmit energy at a given time slot. The nodes harvest this energy, and using this energy, they send back information to the 3 sources. If the nodes could harvest enough energy, they will be able to transfer the information higher than a certain rate; otherwise, no information transfer will occur.
“The rate at which nodes could transmit the information is treated as the reward and is directly coupled with the amount of energy harvested,” explains Debamita Ghosh, first author of the study.
In traditional optimization methods, as the source power increases, the rate of transmission also increases. However, receivers cannot harness the energy indefinitely due to physical limitations, and the rate of information saturates, leading to transmission losses, thereby compromising the network’s energy efficiency.
So, the team considered the rate of information per unit of power, i.e. bits/second/Joules, instead of the rate of information from the nodes. “Since there could be multiple nodes in the network, we consider the total rate of information of all the nodes per unit power as the performance metric,” says Ghosh. The source selects the transmit power in each time slot to transmit energy such that it receives the maximum possible bits at the source per unit of power spent, she adds. Also, the algorithm estimates an upper limit of the mean of the total rate of information per unit power for each power level and uses the power level with the highest estimated bound level.
Thus, though there are few initial losses for not playing the best power level, overall, the accumulated losses are minimized due to the sequential learning. In addition, the team performed simulations of algorithm outputs to establish that it helps the source optimize the power output, thereby improving the system’s energy efficiency compared to current computation methods.