Backscatter-assisted data offloading in OFDMA-based wireless powered mobile edge computing for IoT networks

Mobile edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities. Nevertheless, the limited energy resources also seriously hinders IoT devices from offloading tasks that consume high power in active RF communications. Despite the development of energy harvesting (EH) techniques, the harvested energy from surrounding environments could be inadequate for power-hungry tasks. Fortunately, Backscatter communications (Backcom) is an intriguing technology to narrow the gap between the power needed for communication and harvested power. Motivated by these considerations, this paper investigates a backscatter-assisted data offloading in OFDMA-based wireless-powered (WP) MEC for IoT systems. Specifically, we aim at maximizing the sum computation rate by jointly optimizing the transmit power at the gateway (GW), backscatter coefficient, time-splitting (TS) ratio, and binary decision-making matrices. This problem is challenging to solve due to its non-convexity. To find solutions, we first simplify the problem by determining the optimal values of transmit power of the GW and backscatter coefficient. Then, the original problem is decomposed into two sub-problems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closedform expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two sub-problems, an efficient algorithm, termed the fast-efficient algorithm (FEA), is proposed by leveraging the block coordinate descent method. Then, it is compared with exhaustive search (ES), bisection-based algorithm (BA), edge computing (EC), and local computing (LC) used as reference methods. As a result, the FEA is the best solution which results in a near-globally-optimal solution at a much lower complexity as compared to benchmark schemes. For instance, the CPU execution time of FEA is about 0.029 second in a 50-user network, which is tailored for ultralow latency applications of IoT networks

Future Green Mobile Communication Technology Facing the “Double Carbon” Goal

The goal of “double carbon” (namely “peak carbon dioxide emissions” and “carbon neutrality”) proposed by China for the first time is an important layout in the Tenth Five-Year Plan, and it is also the key goal to realize the green and sustainable development of mobile communication networks in the future, and it is also the foundation for China’s international carbon asset pricing right and the world carbon trading platform. Among them, the difficulty in realizing green communication lies in maintaining the growth of business volume. Reduce network energy consumption and carbon emissions. This paper studies the green communication technology from the perspective of energy saving and emission reduction on the mobile communication network side and the perspective of the integrated architecture of communication network and multi-energy energy network. The research results show that the key to realize green communication technology lies in the mutual matching of network resources, energy resources and business distribution, while the existing technology can only achieve one-way matching of network resources and business distribution. Or the one-way matching of energy resources and service distribution. Based on this, this paper proposes a native green grid architecture with communication, perception and energy fusion, which has the ability of energy perception and service perception, supports the two-way matching method of network resources, energy resources and service distribution, and realizes the continuous growth of service while significantly reducing the energy consumption and carbon emissions on the mobile communication network side by eliminating the randomness and suddenness of service distribution and energy distribution