Over the last few years, interference has been a major hurdle for successfully implementing various end-user applications in the fifth-generation (5G) of wireless networks. During this era, several communication protocols and standards have been developed and used by the community. However, interference persists, keeping given quality of service (QoS) provision to end-users for different 5G applications. To mitigate the issues mentioned above, in this paper, we present an in-depth survey of state-of-the-art non-orthogonal multiple access (NOMA) variants having power and code domains as the backbone for interference mitigation, resource allocations, and QoS management in the 5G environment. These are future smart communication and supported by device-to-device (D2D), cooperative communication (CC), multiple-input and multiple-output (MIMO), and heterogeneous networks (HetNets). From the existing literature, it has been observed that NOMA can resolve most of the issues in the existing proposals to provide contention-based grant-free transmissions between different devices. The key differences between the orthogonal multiple access (OMA) and NOMA in 5G are also discussed in detail. Moreover, several open issues and research challenges of NOMA-based applications are analyzed. Finally, a comparative analysis of different existing proposals is also discussed to provide deep insights to the readers.

In the last few years, there has been an exponential increase in the deployment of 5G-based test beds across the globe with an aim to reduce the latency for accessing various applications. The integration of generic services such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), critical machine-type communication (cMTC), and ultra-reliable low-latency communications (URLLC) can improve the performance of 5G-based applications. This service heterogeneity can be achieved by network slicing for an optimized resource allocation and an emerging technology, Tactile Internet, to achieve low latency, high bandwidth, service availability, and end-to-end security. In this paper, we discuss the application-specific nonorthogonal multiple access (NOMA)-based communication architecture for Tactile Internet which allows nonorthogonal resource sharing from a pool of eMBB, mMTC, cMTC, and URLLC devices to a shared base station. We summarize various variants of NOMA and their suitability for future low latency Tactile-Internet-based applications.

Federated learning (FL) is proposed as a different approach for distributed learning on the edges while maintaining privacy. Existing FL methods, are mainly focused on learning deep classifying and clustering models, with little consideration offered to the federated reinforcement learning (FRL) task on the edge, a difficult task in which several trained agents track local state and take local actions to train a global learning model without disclosing their local dataset. Several neural network models based on FRL have been presented recently to determine the best method for computation offloading (CO) and resource allocation (RA), particularly in Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC). However, because of the complexity and variety of computational tasks involved in 6G and beyond networks, the FRL algorithms are challenging to apply directly to complicated UAV-assisted MEC scenarios. In this study, we present a generalized FRL approach based on a meta learning technique that incorporates RL models explained by numerous smart devices into a generic model. This research uses a normalized characteristic matrix to divide a complex network into small-scale units and provides a normalized network model for complex network situations based on the FRL meta critic method to determine the CO and RA strategy in a Digital Twin (DT)-enabled UAV-assisted MEC system. Numerical results show that the proposed scheme achieves higher and more reliable overall rewards. Proposed method achieves a 73.15% reduction in reward variance and a 14.23% increase in average rewards over 570 continuous operations.

In this paper, we propose a joint channel allocation and power control algorithm by using cognitive radio nonorthogonal multiple access (CR-NOMA) for femtocell users (FUs). The aim is to maximize the sum rate of the FUs for guaranteed Quality of Service (QoS). With an aim to have guaranteed QoS for FUs, we use CR-NOMA at the femto base station (FBS). Then, an algorithm for pairing among strong and weak users is proposed by using the channel gain difference. Using pairing, the NOMA interference between them reduces which results in better channel utilization. Moreover, we differentiate the even/odd number of FUs in a femtocell to provide the QoS for weak users also. For this purpose, OMA is used to get a predefined data rate using a greedy channel allocation algorithm. The power of each FBS is controlled by using the successive convex approximation for low complexity (SCALE) protocol with Karush-Kuhn-Tucker (KKT) conditions. Numerical results demonstrate that the proposed scheme enhances the sum rate and provides guaranteed QoS for CR-NOMA based femtocell users in comparison to the existing conventional OMA based-femtocell techniques.

In this current era, cyber-physical systems (CPSs) have gained concentrated consideration in various fields because of their emergent applications. Though the robust dependence on communication networks creates cyber-physical systems susceptible to deliberated cyber related attacks and detecting these cyber-attacks are the most challenging task. There is the interaction among the components of the cyber and physical worlds, so CPS security needs a distinct approach from past security concerns. Deep learning (DL) distributes better performance than machine learning (ML) due to its layered architecture and the efficient algorithm for extracting prominent information from training data. So, the deep learning models are taken into consideration quickly for detecting cyber-attacks in cyber physical systems. As numerous attack detection methods have been proposed by various authors for enforcing CPS security, this paper reviews and analyzes multiple ways of attack detection presented for CPS using deep learning. We will be putting the excellent potential for detecting cyber-attacks for CPS concerning deep learning modules. The admirable performance is attained partly as highly quality datasets are eagerly obtainable for the use of the public. Moreover, various challenges and research inclinations are also discussed in impending research.

An unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks fulfil the on-demand computation services for the mobile terminals (MTs) with its high mobility and easy deployment. Thus, reduction in the latency is observed but the energy efficiency is still a major issue in this network, as both the UAV and MTs have limited energy storage batteries. Also, to support the massive connectivity, and to handle the data traffic generated by the MTs in an uplink scenario, using nonorthogonal multiple access (NOMA) is accomodated. The NOMA-based MEC provides flexible computing services to MTs. In this article, our goal is to minimize the total energy consumption of the NOMA-based MEC networks underlaying UAV with time, computation capacity, and UAV trajectory. The formulated model is a nonlinear programming problem; for the solution, it is divided into two subproblems: joint time allocation and task computation capacity, and UAV trajectory optimization. To solve the joint time allocation and computation task capacity, we proposed an iterative algorithm with low complexity, where closed-form solutions are obtained in each step. Next, to optimize the trajectory of the UAV, we used the successive convex approximation technique. Numerical results demonstrated that the proposed scheme achieves better results as compared to the orthogonal multiple access and equal resource allocation schemes.