This paper presents a planar multi-circular loop antenna with a wide impedance bandwidth for next generation mm-wave systems. The proposed antenna comprises three circular rings with a partial ground plane with a square slot. The resonating structure is designed on a 0.254 mm thin RO5880 substrate with a relative permittivity of 2.3. The single element of the proposed design showed a resonance response from 26.5 to 41 GHz, with a peak gain of 4 dBi and radiation efficiency of 96%. The proposed multicircular ring antenna element is transformed into a four-element array system. The array size is kept at 18.25 × 12.5 × 0.254 mm³ with a peak gain of 11 dBi. The antenna array is fabricated and measured using the in-house facility. The simulated and measured results are well agreed upon and are found to be suitable for mm-wave communication systems.

In this article, we propose a light weight, low profile Multiple Input Multiple Output (MIMO) antenna system for compact 5th Generation (5G) mmwave devices. Using a RO5880 substrate that is incredibly thin, the suggested antenna is made up of circular rings stacked vertically and horizontally on top of one another. The single element antenna board has dimensions of 12 × 12 × 0.254 mm${}^3$ while the size of the radiating element is 6 × 2 × 0.254 mm${}^3$ (0.56$\lambda$${}_0$ × 0.19$\lambda$${}_0$ × 0.02$\lambda$${}_0$). The proposed antenna showed dual band characteristics. The first resonance showed a bandwidth of 10 GHz with a starting frequency of 23 GHz to an ending frequency point of 33 GHz followed by a second resonance bandwidth of 3.25 GHz ranging from 37.75 to 41 GHz, respectively. The proposed antenna is transformed into a four element Linear array system with size of 48 × 12 × 0.254 mm${}^3$ (4.48$\lambda$${}_0$ × 1.12$\lambda$${}_0$ × 0.02$\lambda$${}_0$). The isolation levels at both resonance bands were noted to be >20 dB which shows high levels of isolation among radiating elements. The MIMO parameters such as Envelope Correlation Co-efficient (ECC), Mean Effective Gain (MEG) and Diversity Gain (DG) were derived and were found to be in satisfactory limits. The proposed MIMO system model is fabricated and through validation and testing of the prototype, the results were found to be in good agreement with simulations.

Many underdeveloped countries are facing acute shortage of electric power and short term measures are important to consider to address the problems of power outage, power plant failures, and disaster areas. Distributed generation (DG) is a promising approach for such cases as it allows quick on-site installation and generation of electric power. Injection of DG can improve the system voltage profile and also reduce the system's total power losses. However, the placement and sizing of the DG unit is an optimization problem in the radial distribution system. As a test case, this study examines voltage profile improvement and system power losses for an 11 KV residential feeder at the Abdul Rehman Baba grid station in Pakistan, which is modelled using the Electrical Transient Analyzer Program (ETAP). For various scenarios, several tests are conducted to assess the effects of DG on the distribution system. The results show that proper design considerations of size and location of a DG, to be inserted in to the system, lead to significant reduction in power losses and improvement in voltage profile and thus improvement in the overall efficiency of the power system. The projections of this work can be used to optimize the expansion of a power system and tackling different issues related to voltage profile in distribution sector worldwide.

Enhanced bandwidth issues for 5G system are fruitfully resolved by organizing free space optics (FSO) communication frameworks. The high bandwidth, the maximum number of channel transmission requirements, and high data rate have been boosted during the last years because of the COVID-19 pandemic. The online services and digital applications have increased pressure on installed optical network models. In addition, the optical networks with high capacity transmission produce nonlinear distortions, which degrade system efficiency. This paper presents a mixed FSO and fiber network to tackle the factors of nonlinearities and enrich the system capacity and range. Furthermore, the issues related to radio frequency, FSO pointing, and co-channel interference are considered in this work. The theoretical and simulation structures are validated using advanced measuring parameters, such as bit error rate (BER), peak to average power ratio (PAPR), cumulative distribution function (CDF), and outage probability. The nonlinear factors are addressed successfully, and the capacity is developed from current models. Finally, the proposed model’s limitations and future direction are discussed in this paper.

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users’ diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users’ data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness ($\gamma$), and 6% performance gain in terms of backhaul saving vs. cache capacity.

One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.