Adaptive RSMA-OMA for Resilient MIMO Networks Under Imperfect CSI and SIC

This paper addresses the challenge of power control in Rate-Splitting Multiple Access (RSMA) systems for downlink Multi-Input Multi-Output (MIMO) networks under practical impairments such as spatial correlation, imperfect Channel State Information (CSI), and residual Successive Interference Cancellation (SIC) errors. We propose a novel degeneracyaware framework that adaptively adjusts the power allocation between the common and private streams, ensuring optimal performance despite CSI uncertainty and imperfect SIC. Our approach incorporates a dynamic switching mechanism between RSMA and Orthogonal Multiple Access (OMA) to maintain system feasibility and resilience in the face of these impairments. Extensive analytical and simulation results demonstrate that the proposed framework significantly enhances power efficiency, mitigates outage probability, and improves overall system robustness, making RSMA a viable and efficient solution for modern wireless networks with realistic CSI and SIC conditions.

Degeneracy-Aware Agent-Based Resource Allocation for Multi-User MIMO RSMA Network

This paper proposes a pilot-aware, degeneracy-driven Agent-Based Modelling (ABM) framework for distributed resource allocation in RSMA-enabled multi-user MIMO systems under imperfect Channel State Information (CSI) and residual Successive Interference Cancellation (SIC) error. The centralized RSMA power allocation problem is reformulated as a distributed multi-agent system, where users operate as autonomous agents that iteratively adapt transmit powers based on locally observed feasibility conditions. To capture the joint impact of interference coupling, CSI estimation errors, pilot overhead, and residual SIC error, a novel degeneracy index defined as the ratio of target to achieved signal-to-interference-plus-noise ratio (SINR) is introduced as a unified feasibility metric. This enables a scalable fixed-point power control mechanism that characterizes the feasible operating region without requiring global CSI. Analytical expressions for user-level and system-level outage probabilities are derived under spatially correlated fading, providing insights into reliability under practical impairments. The fundamental interplay between degeneracy, outage probability, and effective throughput is established, revealing that system performance is governed by the feasibility of the bottleneck user. To further enhance resilience, Degeneracy-Weighted Path Robustness (DWPR) and Functional Substitution Score (FSS) are incorporated to exploit path diversity and functional redundancy. Numerical results show that the proposed framework achieves near-centralized performance in sparse networks, while providing notable throughput gains and improved scalability in dense deployments, highlighting its effectiveness for robust and distributed resource management in next-generation wireless systems.

TSN-IoT: A Two-Stage NOMA-Enabled Framework for Prioritized Traffic Handling in Dense IoT Networks

With the growing applications of the Internet of Things (IoT), a major challenge is to ensure continuous connectivity while providing prioritized access. In dense IoT scenarios, synchronization may be disrupted either by the movement of nodes away from base stations or by the unavailability of reliable Global Navigation Satellite System (GNSS) signals, which can be affected by physical obstructions, multipath fading, or environmental interference, such as such as walls, buildings, moving objects, or electromagnetic noise from surrounding devices. In such contexts, distributed synchronization through Non-Orthogonal Multiple Access (NOMA) offers a promising solution, as it enables simultaneous transmission to multiple users with different power levels, supporting efficient synchronization while minimizing the signaling overhead. Moreover, NOMA also plays a vital role for dynamic priority management in dense and heterogeneous IoT environments. In this article, we proposed a Two-Stage NOMA-Enabled Framework "TSN-IoT" that integrates the mechanisms of conventional Precision Time Protocol (PTP) based synchronization, distributed synchronization and data transmission. The framework is designed as a four-tier architecture that facilitates prioritized data delivery from sensor nodes to the central base station. We demonstrated the performance of "TSN-IoT" through a healthcare use case, where intermittent connectivity and varying data priority levels present key challenges for reliable communication. Synchronization speed and end-to-end delay were evaluated through a series of simulations implemented in Python. Results show that, compared to priority-based Orthogonal Frequency Division Multiple Access (OFDMA), TSN-IoT achieves significantly better performance by offering improved synchronization opportunities and enabling parallel transmissions over the same sub-carrier.

Blockage-aware Hierarchical Codebook Design for RIS-Assisted Movable Antenna Systems

In this paper, we propose a novel blockage-aware hierarchical beamforming framework for movable antenna (MA) systems operating at millimeter-wave (mm-Wave) frequencies. While existing works on MA systems have demonstrated performance gains over conventional systems, they often neglect the design of specialized codebooks to leverage MA's unique capabilities and address the challenges of increased energy consumption and latency inherent to MA systems. To address these aspects, we first integrate blockage detection into the codebook design process based on the Gerchberg-Saxton (GS) algorithm, significantly reducing inefficiencies due to beam evaluations done in blocked directions. Then, we use a two-stage approach to reduce the complexity of the joint beamforming and Reconfigurable Intelligent Surfaces (RIS) optimization problem. The simulations demonstrate that the proposed adaptive codebook successfully improves the Energy Efficiency (EE) and reduces the beam training overhead, substantially boosting the practical deployment potential of RIS-assisted MA systems in future wireless networks.

Pattern and Polarization Diversity Multi-Sector Annular Antenna for IoT Applications

This work proposes a small pattern and polarization diversity multi-sector annular antenna with electrical size and profile of ${ka=1.2}$ and ${0.018\lambda}$, respectively. The antenna is planar and comprises annular sectors that are fed using different ports to enable digital beamforming techniques, with efficiency and gain of up to 78% and 4.62 dBi, respectively. The cavity mode analysis is used to describe the design concept and the antenna diversity. The proposed method can produce different polarization states (e.g. linearly and circularly polarized patterns), and pattern diversity characteristics covering the elevation plane. Owing to its small electrical size, low-profile and diversity properties, the solution shows good promise to enable advanced radio applications like wireless physical layer security in many emerging and size-constrained Internet of Things (IoT) devices.

Classical Capacity of Arbitrarily Distributed Noisy Quantum Channels

With the rapid deployment of quantum computers and quantum satellites, there is a pressing need to design and deploy quantum and hybrid classical-quantum networks capable of exchanging classical information. In this context, we conduct the foundational study on the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information. The rationale behind considering such mixed noise is that quantum noise can arise from different entanglement and discord in quantum transmission scenarios, like different memories and repeater technologies, while classical noise can arise from the coexistence with the classical signal. Towards this end, we derive the distribution of the mixed noise from a classical system's perspective, and formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise. Numerical results demonstrate that capacity increases with the increase in the number of photons per usage.

Low-Complexity Dynamic Directional Modulation: Vulnerability and Information Leakage

In this paper, the privacy of wireless transmissions is improved through the use of an efficient technique termed dynamic directional modulation (DDM), and is subsequently assessed in terms of the measure of information leakage. Recently, a variation of DDM termed low-power dynamic directional modulation (LPDDM) has attracted significant attention as a prominent secure transmission method due to its ability to further improve the privacy of wireless communications. Roughly speaking, this modulation operates by randomly selecting the transmitting antenna from an antenna array whose radiation pattern is well known. Thereafter, the modulator adjusts the constellation phase so as to ensure that only the legitimate receiver recovers the information. To begin with, we highlight some privacy boundaries inherent to the underlying system. In addition, we propose features that the antenna array must meet in order to increase the privacy of a wireless communication system. Last, we adopt a uniform circular monopole antenna array with equiprobable transmitting antennas in order to assess the impact of DDM on the information leakage. It is shown that the bit error rate, while being a useful metric in the evaluation of wireless communication systems, does not provide the full information about the vulnerability of the underlying system.

Semantic-Functional Communications in Cyber-Physical Systems

This paper explores the use of semantic knowledge inherent in the cyber-physical system (CPS) under study in order to minimize the use of explicit communication, which refers to the use of physical radio resources to transmit potentially informative data. It is assumed that the acquired data have a function in the system, usually related to its state estimation, which may trigger control actions. We propose that a semantic-functional approach can leverage the semantic-enabled implicit communication while guaranteeing that the system maintains functionality under the required performance. We illustrate the potential of this proposal through simulations of a swarm of drones jointly performing remote sensing in a given area. Our numerical results demonstrate that the proposed method offers the best design option regarding the ability to accomplish a previously established task -- remote sensing in the addressed case -- while minimising the use of radio resources by controlling the trade-offs that jointly determine the CPS performance and its effectiveness in the use of resources. In this sense, we establish a fundamental relationship between energy, communication, and functionality considering a given end application.

Wavelet Packet Division Multiplexing (WPDM)-Aided Industrial WSNs

Industrial Internet-of-Things (IIoT) involve multiple groups of sensors, each group sending its observations on a particular phenomenon to a central computing platform over a multiple access channel (MAC). The central platform incorporates a decision fusion center (DFC) that arrives at global decisions regarding each set of phenomena by combining the received local sensor decisions. Owing to the diverse nature of the sensors and heterogeneous nature of the information they report, it becomes extremely challenging for the DFC to denoise the signals and arrive at multiple reliable global decisions regarding multiple phenomena. The industrial environment represents a specific indoor scenario devoid of windows and filled with different noisy electrical and measuring units. In that case, the MAC is modelled as a large-scale shadowed and slowly-faded channel corrupted with a combination of Gaussian and impulsive noise. The primary contribution of this paper is to propose a flexible, robust and highly noise-resilient multi-signal transmission framework based on Wavelet packet division multiplexing (WPDM). The local sensor observations from each group of sensors are waveform coded onto wavelet packet basis functions before reporting them over the MAC. We assume a multi-antenna DFC where the waveform-coded sensor observations can be separated by a bank of linear filters or a correlator receiver, owing to the orthogonality of the received waveforms. At the DFC we formulate and compare fusion rules for fusing received multiple sensor decisions, to arrive at reliable conclusions regarding multiple phenomena. Simulation results show that WPDM-aided wireless sensor network (WSN) for IIoT environments offer higher immunity to noise by more than 10 times over performance without WPDM in terms of probability of false detection.

IoT Localization and Optimized Topology Extraction Using Eigenvector Synchronization

Internet-of-Things (IoT) devices are low size, weight and power (SWaP), low complexity and include sensors, meters, wearables and trackers. Transmitting information with high signal power is exacting on device battery life, therefore an efficient link and network configuration is absolutely crucial to avoid signal power enhancement in interference-rich environment and resorting to battery-life extending strategies. Efficient network configuration can also ensure fulfilment of network performance metrics like throughput, coding rate and spectral efficiency. We formulate a novel approach of first localizing the IoT nodes and then extracting the network topology for information exchange between the nodes (devices, gateway and sinks), such that overall network throughput is maximized. The nodes are localized using noisy measurements of a subset of Euclidean distances between two nodes. Realizable subsets of neighboring devices agree with their own position within the entire network graph through eigenvector synchronization. Using communication global graph-model-based technique, network topology is constructed in terms of transmit power allocation with the aim of maximizing spatial usage and overall network throughput. This topology extraction problem is solved using the concept of linear programming.