Non-orthogonal multiple access (NOMA) is a promising technology for next-generation wireless networks with emerging demands on low latency, high throughput, and massive connectivity. Unlike orthogonal multiple access, NOMA allows multiple users to share the same radio resources, which significantly improves spectral efficiency (SE). To achieve green wireless communications for numerous networked devices, NOMA helps reduce energy consumption while satisfying rate fairness and quality-of-experience requirements. The goal of this paper is to introduce the innovative approaches for NOMA in terms of the SE and energy efficiency, and discuss emerging technologies involved with NOMA. Further, its challenges and future research directions are highlighted.

The substantial volume of data generated by Earth observation (EO) satellites poses a significant challenge to the limited-rate satellite-to-ground links. This paper addresses the downlink communication problem of change detection in multi-spectral satellite images for EO purposes. The proposed method is based on a cohesive strategy capable of eliminating clouds and performing semantic encoding during image processing. This approach is a manifestation of semantic communication, as it encodes vital information for the target application, in the form of changed multi-spectral pixels (MPs) to minimize energy consumption. The proposed method is based on a three-stage end-to-end scoring mechanism, which quantifies the significance of each MP before determining its transmission. Specifically, the sensing image is (1) normalized and passed through a high-performance cloud filtering via the Cloud-SLR model, (2) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them, and forward to the ground station, and (3) reconstructed at ground gateway based on the reference image and received data. The numerical results show the effectiveness of the proposed framework in achieving energy savings of up to 58% while upholding the transmission of high-quality data for satellite-based EO applications.

This paper presents an approach to schedule observations from different sensors in an environment to ensure their timely delivery and build a digital twin (DT) model of the system dynamics. At the cloud platform, DT models estimate and predict the system’s state, then compute the optimal scheduling policy and resource allocation strategy to be executed in the physical world. However, given limited network resources, partial state vector information, and measurement errors at the distributed sensing agents, the acquisition of data (i.e., observations) for efficient state estimation of system dynamics is a non-trivial problem. We propose a Value of Information (VoI)-based algorithm that provides a polynomial-time solution for selecting the most informative subset of sensing agents to improve confidence in the state estimation of DT models. Numerical results confirm that the proposed method outperforms other benchmarks, reducing the communication overhead by half while maintaining the required estimation accuracy.

This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer–Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency.

This letter considers a multi-pair decode-and-forward relay network where a power-splitting (PS) protocol is adopted at the energy-constrained relay to provide simultaneous wireless information and energy harvesting (EH). To achieve higher efficiency of EH, we propose a new PS-based EH architecture at the relay by incorporating an alternating current (AC) computing logic, which is employed to directly use the wirelessly harvested AC energy for computational blocks. Under a nonlinear EH circuit, our goal is to maximize the fairness of end-to-end rate among user pairs subject to power constraints, resulting in a non-convex problem. We propose an iterative algorithm to achieve a suboptimal and efficient solution to this challenging problem by leveraging the inner approximation framework. Numerical results demonstrate that the proposed algorithm outperforms the traditional direct current computing and other baseline schemes.

This paper investigates the latency minimization at the network edge in mobile edge computing (MEC)-enabled Cell-Free massive MIMO systems. We introduce a new edge computing model that integrates both task offloading and local execution. To minimize overall system latency while considering power allocation constraints, we formulate an optimization problem aimed at reducing maximum computing time. This mixed-integer non-convex problem is then reformulated into a more tractable form, which is solved using an iterative convex approximation method to achieve locally-optimal solutions. Additionally, we propose a convolutional neural network-based algorithm as an alternative solution to further improve system efficiency. Numerical results are provided to validate the theoretical framework and demonstrate the effectiveness of the proposed approaches in accelerating the data processing in MEC-enabled cell-free networks.