The quantum leap in mobile data traffic and high density of wireless electronic devices, coupled with the advancements in industrial radio monitoring and autonomous systems, have created great challenges for smooth wireless network operations. The fifth-generation and beyond (B5G) (also being referred to as sixth-generation (6G)) wireless communication technologies, due to their compatibility with the previous generations, are expected to overcome these unparalleled challenges. Accompanied by traditional and new techniques, the massive multiple input multiple output (mMIMO) approach is one of the evolving technologies for B5G/6G systems used to control the ever-increasing user stipulations and the emergence of new cases efficiently. However, the major challenges in deploying mMIMO systems are their high computational intricacy and high computing time latencies, as well as difficulties in fully exploiting the multi-antenna multi-frequency channels. Therefore, to optimize the current and B5G/6G wireless network elements proficiently, the use of the mMIMO approach in a HetNet structure with artificial intelligence (AI) techniques, e.g., machine learning (ML), distributed learning, federated learning, deep learning, and neural networks, has been considered as the prospective efficient solution. This work analyzes the observed problems and their AI/ML-enabled mitigation techniques in different mMIMO deployment scenarios for 5G/B5G networks. To provide a complete insight into the mMIMO systems with emerging antenna and propagation precoding techniques, we address and identify various relevant topics in each section that may help to make the future wireless systems robust. Overall, this work is designed to guide all B5G/6G stakeholders, including researchers and operators, aiming to understand the functional behavior and associated techniques to make such systems more agile for future communication purposes.

Forecasting the microeconomics of electricity will turn into a challenging process when electricity is produced through renewable energy technologies (RET). These technologies are mainly sunlight-based photovoltaic (PV), wind power, and tidal resources, which vigorously rely upon ecological conditions. For a reliable and livable energy supply to the electricity grid from renewable means, electrical energy storage technologies can play an important role while considering the weather effects in order to provide immaculate, safe, and continuous energy throughout the generation period. Energy storage technologies (ESTs) charge themselves during the low power demand period and discharge when the demand of electricity increases in such a way that they act as a catalyst to provide energy boost to the power grid. In this paper, we presented and discussed the renewable ESTs for each type with respect to their operational mechanism. In this regard, the renewable energy scenarios of Pakistan and Turkey are first discussed in detail by analyzing the actual potential of each renewable energy resource in both the countries. Then, policy for the EST utilization for both the countries is recommended in order to secure sustainable and reliable energy provision.

Internet traffic at various tiers of service providers is essentially a superposition or active mixture of traffic from various sources. Statistical properties of this superposition and a resulting phenomenon of scaling are important for network performance (queuing), traffic engineering (routing) and network dimensioning (bandwidth provisioning). In this article, the authors study the process of superposition and scaling jointly in a non‐asymptotic framework so as to better understand the point process nature of cumulative input traffic process arriving at telecommunication devices (e.g., switches, routers). The authors further assess the scaling dynamics of the structural components (packets, flows and sessions) of the cumulative input process and their relation with superposition of point processes. Classical and new results are discussed with their applicability in access and core networks. The authors propose that renewal theory‐based approximate point process models, that is, Pareto renewal process superposition and Weibull renewal process superposition can model the similar second‐order scaling, as observed in traffic data of access and backbone core networks, respectively.