The Internet of Everything is leading to an increasingly connected intelligent digital world. Envisaged sixth-generation wireless networks require new solutions and technologies due to stringent network requirements. The benefits of cell-free massive MIMO (CF-mMIMO) and non-orthogonal multiple access (NOMA) have brought substantial attention to these approaches as potential technologies for future networks. In CF-mMIMO, numerous distributed access points are linked to a central processing unit, which allocates the same time-frequency resources to a smaller group of users. On the other hand, NOMA can support more users than its orthogonal counterparts by utilizing non-orthogonal resource allocation. This paper provides a comprehensive review and survey of NOMA-aided CF-mMIMO (CF-mMIMO-NOMA). Specifically, we present a comprehensive review of massive MIMO, CF-mMIMO, and NOMA. We then present a state-of-the-art research review of CF-mMIMO-NOMA. Finally, we discuss the challenges and potential of combining CF-mMIMO-NOMA with other enabling technologies to enhance performance.

Users have increasingly been having more use cases for the network while expecting the best Quality of Service (QoS) and Quality of Experience (QoE). The Fifth Generation of mobile telecommunications technology (5G) network had promised to satisfy most of the expectations and network slicing had been introduced in 5G to be able to satisfy various use cases. However, creating slices in a real-life environment with just the resources required while having optimized QoS has been a challenge. This has necessitated more intelligence to be required in the network and machine learning (ML) has been used recently to add the intelligence and ensure zero-touch automation. This research addresses the open question of creating slices to satisfy various use cases based on their QoS requirements, managing, and orchestrating them optimally with minimal resources while allowing the isolation of services by introducing a Deep reinforcement Machine Learning (DRL) algorithm. This research first evaluates the previous work done in improving QoS in the 5G core. 5G architecture is simulated by following the ETSI NFV MANO (European Telecommunications Standards Institute for Network Function Virtualization Management and Orchestration) framework and uses Open5G in 5G core, UERANISM for RAN, Openstack for Virtual Infrastructure Manager (VIM), and Tacker for Virtual Network Function Management and orchestration (VNFMO). The research simulates network slicing at the User Plane Function (UPF) level and evaluates how it has improved QoS. The network slicing function is automated by following ETSI Closed Loop Architecture and using Deep Reinforcement Learning (DRL) by modeling the problem as a Markov Decision Problem (MDP). Throughput is the Reward for the actions of the DRL agent. Comparison is done on the impact of slicing on throughput and compares models that have not been sliced, the ones that have been sliced and combined to work together, and models with slices that have been assigned more bandwidth. Sliced networks have better throughput than the ones not sliced. If more slices are load-balanced the throughput is increased. Deep Reinforcement Learning has managed to achieve the dynamic assigning of slices to compensate for declining throughput.

Integrated sensing and communication (ISAC) is evolving from dual-functional waveform and transceiver design toward an end-to-end network capability in which sensing observations are acquired, coordinated, processed, protected, and exposed to applications. This paper presents an architecture-centric synthesis of that evolution, organized using six operational contracts: input/output, uncertainty, timescale, decision, evaluation, and admissibility–applied across seven functional layers. These contracts provide a consistent basis for examining whether information produced at one layer remains valid, timely, interpretable, and implementable at the next. The survey covers physical-layer signal models; waveform and beamforming design; sensing metrics and communication–sensing tradeoffs; cooperative and service-oriented ISAC; intelligence and edge computation; propagation and hardware constraints; security and trust; and standards, datasets, prototypes, and testbeds. We pay particular attention to metric compatibility, uncertainty propagation, information age, computation and deadline reliability, sensing-service exposure, and the distinction between achieved performance and feasible operating points. The strength of reported results depends on their supporting evidence, ranging from analytical formulations and simulations to hardware measurements, real-time prototypes, and networked testbeds. A representative literature audit and research-gap matrix identify recurring requirements for cross-layer operation, including the transfer of estimator uncertainty and calibration validity, synchronization, characterization of latency tails and deadline violations, and feasibility checks for learned actions. The resulting synthesis identifies end-to-end interface design, trustworthy decision making, and reproducible validation as central requirements for deployable ISAC systems.

Channel estimation is important for Orthogonal Frequency-Division Multiplexing (OFDM) in wireless channel communication and requires algorithms that offer the best accuracy while at the same time have very low computational and runtime complexities. Newtonised Orthogonal Matching Pursuit (NOMP) is a promising algorithm for channel estimation; however, it suffers from high computational complexity due to repeated refinement and least-squares updates. In this paper, we propose a low complexity NOMP variant that reduces the dominant computational cost through three modifications: (i) a residual energy-based stopping criterion for NOMP to avoid expensive CFAR evaluation, (ii) a partial cyclic refinement with frozen atoms, and (iii) approximate one-sweep per atom least-squares updates. Complexity analysis shows a reduction from O(K³) to O(KN) in the gain update and from O(K²N) to O(KN) in refinement. Simulation results show that the proposed method achieves ∼87% reduction in runtime, while the symbol error rate (SER) performance is comparable to classical NOMP and outperforms Oversampled OMP at high signal-to-noise ratio (SNR). These results show that NOMP can be computationally efficient for OFDM systems without sacrificing estimation accuracy.