Computer Networks and Communications

A Deep-SIC Channel Estimator Scheme in NOMA Network

Sumita Majhi — 2026-08-24

In 5G and next-generation mobile networks, reliable handover is a key requirement that ensures connection continuity, especially for mobile users and in high-density scenarios. However, conventional handover triggers based on instantaneous channel measurements are prone to failures and the ping-pong effect due to outdated or inaccurate channel state information. To address this, we propose Deep Successive Interference Cancellation (Deep-SIC), a knowledge-based channel prediction model that employs a Transformer-based approach to estimate channel quality and optimise handover decisions. Deep-SIC utilises Partially Decoded Data (PDD), a byproduct of SIC in Non-Orthogonal Multiple Access (NOMA), as a feedback signal to continually improve its predictions. This mechanism enables the model to learn quickly and stabilise their learning. Our model learns faster than existing state-of-the-art algorithms under the evaluated conditions, such as Graph-NOMA, while providing verifiable stability and resilience to user mobility (Theorem 2). The system-level simulation can substantially enhance network performance by mitigating the ping-pong effect and reducing the handover failure (HOF) rate, especially at vehicular speeds (e.g., 60 km/h). Moreover, Deep-SIC has a smaller normalised root mean square error (NRMSE) in low-SNR situations than state-of-the-art algorithms with linear computational complexity, O(K).

Energy Consumption Model for Full-Duplex LoRa Backscatter-Based Sensor Node

Siaka Konaté — 2026-08-17

The integration of Wireless Sensor Networks (WSNs) into Internet of Things (IoT) brings new challenges in terms of range and energy consumption. Specifically, energy consumption is of great importance for future IoT sensors. Therefore, a thorough study must be performed to understand and evaluate the impact of different parameters that affect sensor node energy consumption. However, LoRa backscatter has been seen as a promising technology for long-range low-power IoT applications. Hence, this paper addresses the modeling of the energy consumption of a LoRa backscatter-based sensor node. We first derive the energy model for single-node communication and extend it to multiple-sensor-node communication where several sensors concurrently transmit their information. Furthermore, we derive an energy model for a battery-free sensor node that relies on the energy harvested from ambient sources to drive its operation. The simulation results show that both the LoRa parameters (such as payload size), the number of nodes in the network, and the number of retransmissions can impact the total energy consumption of the sensor node. Additionally, a single node powered by a battery with a capacity of 225 mAh and a transmission of 20 bytes every 5 minutes has a lifetime of about 14 years.

Energy-Efficient MIMO Diversity: Operating Regimes, Hardware Constraints and Geometry-Aware Propagation Effects

Nikolaos Mouziouras — 2026-09-18

Multiple-Input Multiple-Output (MIMO) diversity techniques are widely employed to improve communication reliability in wireless systems; however, their energy-efficiency benefits under realistic propagation and hardware constraints remain insufficiently understood. This paper investigates the energy–reliability trade-off in MIMO diversity systems by jointly considering distance-dependent path loss, spatial correlation, retransmission effects and implementation-related energy costs. Representative diversity configurations, including Single-Input Single-Output (SISO) (1 × 1), Single-Input Multiple-Output (SIMO) (1 × 2) and Alamouti-based schemes (2 × 1 and 2 × 2), are evaluated across varying transmission distances and operating Signal-to-Noise Ratio (SNR) conditions. The results reveal a strongly operating-regime-dependent and non-monotonic relationship between diversity order and energy efficiency. Higher-order diversity is beneficial mainly in intermediate regimes, where reducing retransmissions can outweigh the additional implementation energy cost. Under favorable propagation conditions, however, implementation overhead dominates and lower-complexity configurations are more energy efficient. Conversely, under severely degraded conditions, all schemes enter a reliability-limited regime in which additional diversity provides diminishing energy returns. Furthermore, comparison with conventional Independent and Identically Distributed (i.i.d.) Rayleigh fading shows that simplified channel models systematically overestimate the achievable energy benefits of diversity techniques. The analysis identifies distinct operating regimes governing diversity-related energy efficiency and highlights the combined influence of propagation characteristics, reliability requirements, and hardware constraints. Overall, the results demonstrate that diversity is advantageous only under specific operating conditions, emphasizing the importance of operating-point-aware antenna configuration in practical wireless systems.

Simple Compressive Data Gathering Model for Wireless Sensor Network- Based Medical Internet of Things Applications

Mohammad Reza Ghaderi — 2026-09-18

Wireless Sensor Networks (WSNs) are a fundamental enabling technology for the Medical Internet of Things (MIoT), yet their deployment is severely constrained by the limited energy capacity of battery-powered sensor nodes. Compressive Data Gathering (CDG) has emerged as a promising technique to reduce communication overhead by exploiting the sparsity of sensed signals. However, existing CDG-based clustering models often rely on complex adaptive mechanisms that are computationally expensive for resource-constrained MIoT devices. This paper presents a simple, practical CDG-based clustered routing model specifically designed for WSN-based MIoT applications. The model employs fixed virtual circular clusters with random Cluster Head (CH) selection and a two-candidate next-hop routing strategy, significantly reducing computational overhead. We derive a closed-form energy consumption model that separately quantifies intra-cluster and inter-cluster energy expenditures. Simulation results demonstrate that the proposed model extends network lifetime by up to 38% compared to the state-of-the-art adaptive clustering approach, with a packet delivery ratio exceeding 97% and reconstruction error below 0.025. The model’s simplicity and energy efficiency make it well-suited for real-world MIoT deployments.