Mg 0.8 Zn 0.2 Er x Fe 2-x O 4 (x = 0.00, 0.005, 0.01, 0.015, 0.02, and 0.025) nanoparticles made using the citrate gel autocombustion process were examined for their structure, morphology, and behavior. The single-phase cubic spinel structure was formed according to the diffraction pattern. Observations revealed that as the Er focus went from to 0.0 to 0.025, the average crystallite size ( D ) increased from 12.4 to 18.6 nm. The findings of the SEM and EDAX analyses reveal that the particles are uniform, with just a little amount of agglomeration and no impurity pickup. Nanoparticles from transmission electron microscopes (TEM) were present, the range from 12 to 19 nm. In IR spectroscopy using the Fourier transform (FTIR), nearly all the spinel ferrites presented generate two absorption bands that have wavelengths of around 400 cm and 600 cm. The BET surface area of the Er 3+ ion doping Zn-Mg ferrites rises from 23.860 m 2 /gm to 29.845 m 2 /gm. The TG–DTA analysis of the prepared samples confirms the thermal stability of the samples; the temperature ranges from 100 to 750 °C.The transition temperature (Seebeck coefficient) of the samples was studied using thermoelectric power (TEP) measurement studies, and it was found that all the samples showed N-type semiconductor behavior. With an increase in erbium concentration, DC conductivity decreases. At room temperature, the magnetic characteristics of hysteresis loops, squareness ratio (SQR), anisotropy constant (K), magnetic moment (), coercivity (Hc), saturation magnetization (Ms), and retentivity (Mr) were examined. When erbium concentration rises, the magnetic moment (B) increases. The saturation magnetization values were 288.4615 and 244.5266 emu/g, and squareness ratio values from 0.01554 to 0.03303 were observed. These materials are converted from hard permanent magnet materials to soft magnet materials.

It's not a myth that transition in next generation technology brings with it a set of exciting applications as well as challenges to the telecom ecosystem and in-turn paves way for new revenue streams. 5G enables ultra-high data rates, exceptional low latencies which enables the telecom operator for the facilitation of interesting parallels like IoT and Next-Gen Industrial enhancements like autonomous vehicles, connected mines, connected agriculture and mission critical communications by enhancing infrastructure, software and hardware components of the 5G system. As imminent new features of 5G like Multiple Input Multiple Output (MIMO), network slices, virtual network functions, indoor localization, Machine to Machine (M2M) capabilities are highly appreciated, it also opens new set of challenges like real time dynamic configurations, low latency handovers. These challenges can be addressed with the application of AI technologies to components at the crux of 5G system. Here in this paper, we discuss some of the major challenges such as data burst, improving performance, fault tolerance and traffic management with new components appended to the 5G system, required upgrades to existing technology and how Machine Learning (ML), Artificial Intelligence (AI), becomes the self-evident answer to these stumbling blocks.