Orthogonal multiple access schemes based on assignment of communication resource blocks among multiple contenders, although widely available, still necessitate an upper limit on the number of concurrent users for minimization of multiple-user interference. The feature thwarts efforts to cater for pressing connectivity demands posed by modern-day cellular communication networks. Non-orthogonal multiple access, regarded as a key advancement towards realization of high-speed 5G wireless communication networks, enables multiple users to access the same set of resource blocks non-orthogonally in terms of power with controllable interference, thereby allowing for overall performance enhancement. Owing to the combinatorial nature of the underlying optimization problem involving user pairing/grouping scheme, power control and decoding order, the computational complexity in determining optimal and sub-optimal solutions remains considerably high. This work proposes three novel alternative approaches (Randomly, 2-Opt and Hybrid) for arriving at a near-optimal solution for the problem of user pairing/grouping. The algorithms not only offer reduced computational complexity but also outperform orthogonal multiple access and existing schemes reported in the literature for uplink non-orthogonal multiple access systems.

The non-orthogonal multiple access (NOMA) scheme has proven to be a potential candidate to enhance spectral potency and massive connectivity for 5G wireless networks. To achieve effective system performance, user grouping, power control, and decoding order are considered to be fundamental factors. In this regard, a joint combinatorial problem consisting of user grouping and power control is considered, to obtain high spectral-efficiency for NOMA uplink system with lower computational complexity. To solve the joint problem of power control and user grouping, for Uplink NOMA, we have used a newly developed meta-heuristicnature-inspired optimization algorithm i.e., whale optimization algorithm (WOA), for the first time. Furthermore, for comparison, a recently initiated grey wolf optimizer (GWO) and the well-known particle swarm optimization (PSO) algorithms were applied for the same joint issue. To attain optimal and sub-optimal solutions, a NOMA-based model was used to evaluate the potential of the proposed algorithm. Numerical results validate that proposed WOA outperforms GWO, PSO and existing literature reported for NOMA uplink systems in-terms of spectral performance. In addition, WOA attains improved results in terms of joint user grouping and power control with lower system-complexity when compared to GWO and PSO algorithms. The proposed work is a novel enhancement for 5G uplink applications of NOMA systems.

Fifth-generation wireless communications provide several benefits, including high throughput, lower latency, massive connectivity, considerable improvement in the number of users, higher base station capacity, and achieved quality of service. Non-orthogonal multiple access, an effective approach for sharing the same radio resources, has been highlighted as a viable technology in the fifth-generation wireless networks to achieve the demands of available bandwidth, user connectivity, and application latency. Non-orthogonal multiple access and heterogeneous networks have recently emerged as promising network infrastructures for enhancing the spectrum capacity and accommodating more users by sharing the same resources with high throughput. This potential capability has made the non-orthogonal multiple access–enabled heterogeneous networks a new research topic in the modern era. In this survey, the concept of non-orthogonal multiple access and its significance in different emerging technologies has been well explored. Furthermore, this survey covers a systematic overview of the state-of the-art techniques based on non-orthogonal multiple access–enabled heterogeneous networks and devising taxonomy for uplink non-orthogonal multiple access–enabled heterogeneous networks. In addition, this survey provides critical insights and identifies several open research challenges considering the uplink non-orthogonal multiple access–enabled heterogeneous networks.

Wireless communication is evolving rapidly to meet growing demands for higher data rates and seamless connectivity, especially with the rise of the Internet of Things (IoT). Among the latest advancements, 5G technology stands out by enabling ultra-fast data transmission, high capacity, and efficient spectrum utilization through millimeter-wave frequencies. This study presents a comparative analysis of six Microstrip Patch Antennas (MPAs) designed for 5G applications, addressing the challenge of limited spaceand increasing performance demands. The novelty of this work lies in the evaluation of how substrate materials and feeding techniques influence MPA performance, providing insights not thoroughly addressed in prior research. The antennas were designed using three substrates1:FR-4 (εr = 4.4), 2-Rogers RT5880 (εr = 2.2), and 3-Taconic RF-35TC (εr =3.5), and two feeding techniques: Microstrip line feed and coaxial probe feed. All antennas were tuned to resonate at 38 GHz, suitable for 5G millimeter-wave applications. Feeding technique also significantly affects impedance matching and Gain.It is found out that using Roggers RT Duroid 5880 substrate with Microstrip feedline technique provides the highest gain whereas the largest bandwidth is achieved using coaxial feed with FR4 substrate.A quarter-wave transformer was additionally implemented for optimal impedance matching between the source and antenna. The findings guide substrate and feed selection in compact 5G antenna designs.

This research presents the design and implementation of a non-invasive sugar level monitoring system with an early warning mechanism using embedded systems and Internet of Things (IoT) technologies. The system integrates an Arduino Uno microcontroller with sensors such as the MLX90614 infrared temperature sensor, MAX30102 blood oxygen and heart rate sensor to monitor vital health parameters. The system correlates temperature, blood oxygen levels, heart rate, and glucose levels to provide early warnings for high or low sugar conditions. Experimental results demonstrate the system's accuracy, reliability, and effectiveness in providing real-time health data. Also, this research highlights the potential of non-invasive health monitoring systems in diabetes management and paves the way for future advancements in IoT-based healthcare solutions.