Fifth-generation (5G) cellular networks are being developed to meet the ever-growing data traffic across mobile devices and their applications. The core of 5G cellular networks is leveraging wider and higher frequencies available at millimeter wave frequency (mmWave) bands, thus providing very high data rates for mobile devices. Multi-input multi-output (MIMO) is an essential technology for overcoming the high propagation loss at mmWave communications. In non-orthogonal multiple access (NOMA), multiple cellular user equipments (CUEs) communicate over the same time-frequency resources using a multiplexed power domain. In device-to-device (D2D) communications, two D2D user equipments (DUEs) communicate without passing through the base station. In the underlaying scenario, DUEs reuse the frequency resources allocated to CUEs for spectrum utilization but DUEs cause interferences for cellular and D2D communications. Integrating D2D communications with other 5G technologies has great potential for spectral efficiency improvement. Unfortunately, interference management and resource allocation are becoming increasingly challenging due to aggressive frequency reuse. In this paper, D2D communications at mmWave underlaying MIMO-NOMA cellular network system model is developed. Consequently, a novel resource allocation for D2D communications underlaying MIMO-NOMA cellular network is proposed. A resource allocation optimization problem is formulated for spectral efficiency maximization. To solve this NP-hard problem, the problem is decomposed into three subproblems: interference-aware graph-based user clustering, MIMO-NOMA beamforming design, and optimized power allocation based on particle swarm optimization. Simulation results demonstrate that the proposed algorithm for D2D communications at mmWave underlaying MIMO-NOMA cellular network delivers a greater spectral efficiency compared to the conventional D2D communications that operate underlay MIMO-orthogonal multiple access cellular networks.

Fifth-generation of wireless cellular systems has the potential to increase capacity, spectral efficiency, and fairness among users. The Non-Orthogonal Multiple Access based wireless networks (NOMA) is the next generation multiplexing technique. NOMA breaks the orthogonality of traditional multiple access to allow multiple users to share the same radio resource simultaneously. The main challenge in designing NOMA is the selection of the resource allocation algorithms since user pairing and power allocation are coupled. This paper compares the performance of three power allocation schemes: fixed power allocation, fractional transmit power allocation and full search power allocation. The algorithms are analyzed in different simulation scenarios using three performance metrics of the spectrum efficiency and energy efficiency and sum rate. Additionally, the impact of user pairing algorithms studied through two user pairing schemes: random user pairing and channel state sorting based user pairing. Results indicate the superiority of NOMA to increase the capacity compared to traditional orthogonal multiple access. On the other hand, full search power allocation is the best performance compared to the other power allocation schemes though it is highly complex compared to fractional transmit power that gives a suboptimal performance.

Advanced metering infrastructures (AMIs) aim to enhance the efficiency, reliability, and stability of electrical systems while offering advanced functionality. However, an AMI collects copious volumes of data and information, making the entire system sensitive and vulnerable to malicious attacks that may cause substantial damage, such as a deficit in national security, a disturbance of public order, or significant economic harm. As a result, it is critical to guarantee a steady and dependable supply of information and electricity. Furthermore, storing massive quantities of data in one central entity leads to compromised data privacy. As such, it is imperative to engineer decentralized, federated learning (FL) solutions. In this context, the performance of participating clients has a significant impact on global performance. Moreover, FL models have the potential for a Single Point of Failure (SPoF). These limitations contribute to system failure and performance degradation. This work aims to develop a performance-based hierarchical federated learning (HFL) anomaly detection system for an AMI through (1) developing a deep learning model that detects attacks against this critical infrastructure; (2) developing a novel aggregation strategy, FedAvg-P, to enhance global performance; and (3) proposing a peer-to-peer architecture guarding against a SPoF. The proposed system was employed in experiments on the CIC-IDS2017 dataset. The experimental results demonstrate that the proposed system can be used to develop a reliable anomaly detection system for AMI networks.