Researchers at the Indian Institute of Technology-Bombay (IIT-Bombay) and Monash University in Australia have developed a new AI-powered algorithm that identifies the right amount of power level required to enhance the energy efficiency of wireless communication systems.
A statement from IIT-Bombay explained that radio frequency (RF) signals are electromagnetic radiations used in wireless communication that transmit information and carry an inherent small electrical energy component. Emerging technology harvests RF energy to power wireless devices in various sectors such as medical implants or the Internet of things.
RF energy is harvested either by scavenging the ambient energy or by having a dedicated energy source. Such a system facilitates the continuous charging of the nodes’ batteries, enhances their life, and overcomes the energy limitations of conventional battery-powered wireless devices. It also reduces the need for frequent battery replacement. RF energy harvesting networks consume high energy for both energy and information transmission. Therefore, optimising energy losses is crucial. The new algorithm uses a statistical tool that aids the source in identifying the optimal power output without having to depend on other parameters.
Wireless nodes detect, monitor, and report the energy harvesting status to the energy source. Based on the feedback, the source regulates the right power level to meet the demand and avoid power wastage. Researchers use statistics-based algorithms driven by AI to automate the power-optimising process. Current algorithms are designed on a metric called channel state information — the feedback from receivers, such as how good the link is or how much of the received energy they could use. In such smart systems, power sources act as both transmitters and receivers of information. The power source has to assess how much energy has to be transmitted so that all the nodes get enough energy to transmit the information. However, more energy to the nodes does not translate to more information transmission. Hence, it is important to evaluate the overall energy efficiency of the RF energy harvesting network.
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 to successfully transmit the information. As the environment is uncertain, reinforcing algorithms with sequential decision-making can quickly ascertain the status of the harvested energy, improving energy efficiency. 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 optimisation 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). 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 source. 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.
In traditional optimisation 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, which compromises the network’s energy efficiency.
The team considered the rate of information per unit of power, instead of the rate of information from the nodes. Since there could be multiple nodes in the network, the team considered the total rate of information of all the nodes per unit power as the performance metric. 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. 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 minimised due to the sequential learning. In addition, the team performed simulations of algorithm outputs to establish that it helps the source optimise the power output, improving the system’s energy efficiency compared to current computation methods.
