Mid Sweden University

Accurate traffic forecasting in high-speed railway (HSR) systems is hindered by abrupt signal fluctuations and varied mobility scenarios. Conventional approaches that rely on fixed weighted combinations of local and global features are unable to adjust rapidly to real-time changes, resulting in suboptimal performance. To address this limitation, we propose a novel gated guided serial CNN and Transformer network (GsCT) that employs a dynamic combination mechanism implemented via a multilayer perceptron (MLP). In GsCT, CNNs capture fine-grained local variations while Transformers model long-range dependencies, and the adaptive MLP-based gating module adjusts the contribution of each branch based on time-window statistics. This dynamic fusion improves prediction quality by 6.5% compared to conventional fixed weighting mechanisms. Evaluations on both public and real-world HSR datasets demonstrate that GsCT achieves a 2.4% reduction in RMSE relative to LSTM-based methods, and the learned gating coefficients offer transparent interpretability of the feature fusion process. Overall, GsCT provides an effective solution for real-time railway traffic forecasting, paving the way for next-generation HSR services.

This paper investigates adaptive user pairing (UP) under different non-orthogonal medium access choices in 5G-and-beyond cellular IoT networks to balance the uplink performance of mission-critical (MC) and enhanced mobile broadband (eMBB) services. Our objective is to enhance eMBB rates while ensuring quality of service (QoS) for MC users, assessed through average age of information (AoI) and peak AoI (PAoI) violation probabilities. By deriving a signal-to-noise ratio (SNR) gap threshold between a pair of eMBB and MC users, we identify optimal access scheme—puncturing, non-orthogonal mul tiple access (NOMA), or rate-splitting multiple access (RSMA)—with respect to activation probability (pm) and cellular network radius. By using this derived threshold, we design an adaptive pairing algorithm that achieves near-optimal QoS for MC users and maximizes eMBB data rates. To realize different spatial associations among users in the cell, the proposed pairing strategy for eMBB and MC services is evaluated for three user distributions around the base station: concave (eMBB users concentrated near the BS), uniform (evenly spread eMBB and MC users), and convex (MC users concentrated near the BS). The extensive numerical analysis of the proposed solution demonstrates significant performance gains over random and traditional NOMA-based pairings, especially under concave scenarios. In concave distributions, our strategy reduces MC users’ QoS outage by 85% at pm=0.1, achieving zero outage for pm≥0.3. Uniform and convex distributions confirm method robustness, maintaining low or zero outage probabilities across all pm values. We also analyzed the impact of network radius and MC user activation probabilities on access scheme selection. Results show that RSMA generally outperforms other multiple access schemes in terms of eMBB rate, but NOMA exhibits superior performance compared to RSMA and puncturing in larger networks.

Enhancing reliability and energy efficiency is a key objective of sixth-generation (6G) wireless communication systems. To support this goal, we propose a novel ambient backscatter-assisted NOMA (AmBC-NOMA) system that jointly supports device-to-device (D2D) and cellular uplink communications, aiming to improve both energy efficiency and spectral utilization for low-power IoT networks. The system introduces a passive backscatter device (BSD) that enhances data forwarding without requiring active RF transmission. We derive closed-form expressions for the outage probabilities and SINR of both D2D and cellular links, considering realistic channel impairments and interference. Numerical results reveal that our architecture achieves up to 40% improvement in D2D outage performance and 10% better cellular reliability compared to OMA counterparts under practical SINR thresholds. These findings demonstrate the feasibility of integrating AmBC with NOMA for future green 6G wireless systems.

This paper investigates a co-design framework for wireless networked control systems (WNCSs) that integrates multi-hop IEEE 802.15.4-based links under Wi-Fi interference, addressing the challenges of signal-to-interference-plus-noise ratio (SINR) degradation in adverse industrial environments. Multihop configurations are essential for extending the operational range and improving SINR in harsh propagation conditions, but they introduce trade-offs in control stability, latency, and computational complexity. We investigate the impact of multi-hop communication on system performance, comparing Bernoulli and Markovian control strategies. Our results demonstrate that multihop links effectively extend the operational range and mitigate SINR degradation, but at the cost of increased latency and computational cost. We analyze the spectral radius of the system stability verification matrix and control costs for Bernoulli and Markovian control strategies, illustrating that network latency and hop counts can be balanced while maintaining the stability of the multi-hop WNCS. Markovian strategy, although more computationally intensive, outperforms Bernoulli strategy under high interference, offering a robust solution for industrial WNCSs. The proposed framework provides a practical approach for deploying reliable WNCSs in interference-prone environments. 

Vehicular Ad Hoc Networks (VANETs) are the backbone of intelligent transport and enhanced passenger safety, but they face significant challenges related to authentication, security, and privacy. Existing distributed VANET authentication protocols struggle with issues like privacy preservation during vehicle handovers and inefficiency when handling large volumes of verifications. This paper proposes a novel authentication framework designed to address these limitations. First, we introduce zero-knowledge guarantees for Vehicle-to-Infrastructure (V2I) authentication and improve anonymity and unlinkability in authentication by eliminating explicit vehicle handover, thus enhancing privacy. Second, we propose a batch-verifiable Vehicle-to-Vehicle (V2V) message-sharing method utilizing an elliptic curve digital signatures scheme (ECDSA*). Unlike others, we provide a complete computational and efficiency analysis of batch verification in the presence of faulty signatures. A formal security analysis and proven security in the Scyther security verification tool provide the security guarantees of our proposed scheme. A thorough efficiency analysis shows that our scheme can perform at least 5-times more V2I authentication and can batch verify at least 2-times more V2V messages than other related schemes within a time threshold of 300ms.

The 6G massive Internet of Things envisions trillions of interconnected devices, set to revolutionize society by creating a more efficient and convenient ecosystem. However, this rapid expansion presents significant sustainability challenges, particularly in achieving alignment with the United Nations Sustainable Development Goals. A pressing concern is the "trillion-battery problem," where the need for frequent battery replacements poses environmental risks and disrupts data continuity. Zero-energy device technology is seen as a potential solution, yet it remains in its early stages, facing obstacles such as complex wireless power transfer behaviors, efficient charge scheduling, and vulnerability to malicious energy attacks. This article reviews the state-of-the-art in these areas, identifying existing knowledge gaps. A bibliometric analysis is conducted to reveal key research trends and developments. Building on these insights, we propose a versatile paradigm for autonomous networked wireless power transfer (AutoNetWPT), outlining research opportunities to address the challenges identified. This work is critical in shaping a sustainable, secure, and interconnected future for 6G zero-energy massive Internet of Batteryless Things.

Through the lens of average and peak age-of-information (AoI), this paper takes a fresh look into the uplink medium access solutions for mission-critical (MC) communication coexisting with enhanced mobile broadband (eMBB) service. Considering the stochastic packet arrivals from an MC user, we study three access schemes: orthogonal multiple access (OMA) with eMBB preemption (puncturing), non-orthogonal multiple access (NOMA), and rate-splitting multiple access (RSMA), the latter two both with concurrent eMBB transmissions. Puncturing is found to reduce both average AoI and peak AoI (PAoI) violation probability but at the expense of decreased eMBB user rates and increased signaling complexity. Conversely, NOMA and RSMA offer higher eMBB rates but may lead to MC packet loss and AoI degradation. The paper systematically investigates the conditions under which NOMA or RSMA can closely match the average AoI and PAoI violation performance of puncturing while maintaining data rate gains. Closed-form expressions for average AoI and PAoI violation probability are derived, and conditions on the eMBB and MC channel gain difference with respect to the base station are analyzed. Additionally, optimal power and rate splitting factors in RSMA are determined through an exhaustive search to minimize MC outage probability. Notably, our results indicate that with a small loss in the average AoI and PAoI violation probability the eMBB rate in NOMA and RSMA can be approximately five times higher than that achieved through puncturing. 

Future sixth-generation (6G) networks require efficient resource management to support a variety of services. This paper addresses the issue of maximizing user rates in a beyond directional reconfigurable intelligent surface (BD-RIS)-assisted network with non-orthogonal multiple access (NOMA) and integrated sensing and communication (ISAC) users. However, exploiting the gains offered by these frameworks necessitates joint tuning of BD-RIS phases and NOMA power, which is an inherently non-convex problem. We model this coupling as a continuous-action Markov decision process and solve it using twin-delayed deep deterministic policy gradient (TD3) reinforcement learning. The learned policy adaptively selects power-allocation factors and BD-RIS phase shifts, thereby boosting both communication and sensing rates under quality-of-service constraints. Simulation results confirm that the proposed deep reinforcement learning (DRL) scheme significantly outperforms conventional heuristics, demonstrating its potential for real-time resource optimization in 6G networks. 

The advancement of energy harvesting technologies, coupled with the adoption of ultra-low-power IoT devices, have enabled the emergence of the Internet of Batteryless Things (IoBT). This innovative paradigm envisions a sustainable future by employing radiative wireless power transfer (RWPT) to support battery-free IoT device operation. Despite significant progress in optimizing one-to-one RWPT, a systematic understanding of the dynamics of concurrent RWPT is still lacking. This includes addressing complex situations such as one-to-many, many-to-one, and many-to-many concurrent power transfer. These gaps hinder the scalability and practical implementation of distributed RWPT networks in real-world IoBT environments. This study addresses these challenges by conducting comprehensive experimental evaluations and developing detailed theoretical models for concurrent RWPT across all four fundamental scenarios. By leveraging state-of-the-art wireless power development kits, the novel experimental observations provide key insights into spatial power distribution. For instance, the movement of a neighboring IoT node can either double a battery-free IoT node's received power or reduce it to nearly zero. Furthermore, analytical models grounded in electromagnetic field theory, circuit principles, and wave propagation dynamics are proposed to predict and optimize RWPT performance across diverse scenarios, and are validated by MATLAB simulation. This work represents the first unified framework for concurrent RWPT, offering valuable contributions to the design of scalable, energy-efficient IoBT systems. 

The rapid growth in mobile communication technologies has turned mobile edge computing (MEC) into a paradigm-shifting technology that extends cloud-like capabilities and storage resources to the edge of the network. This allows computation-intensive and latency-sensitive applications to be performed at close proximity to the end-users, thereby overcoming the bottleneck issues of resource-constrained devices. However, ensuring efficient operations in MEC-empowered systems requires intelligent task execution and resource allocation across MEC servers. To this end, MEC-empowered non-terrestrial wireless networks (MeNT-WiN) systems are one of the applications in which deep reinforcement learning (DRL) is seen as a powerful method to enhance the MEC abilities in edge servers and network entities. This paper presents a thorough overview of the applications of DRL in MeNT-WiNs. In particular, it underlines the main contribution of DRL in enhancing the performance of MeNT-WiNs, including unmanned aerial vehicles (UAV) and satellite communications networks. This paper investigates how DRL can meet the unique requirements of MeNT-WiNs by enhancing system efficiency, scalability, and decision-making processes across MEC architectures. First, the article reviews the fundamentals of DRL, it later goes on to discuss its integration with MeNT-WiNs and demonstrates its relevance for the optimization of satellite communications and management of UAV swarms, as well as enhancing connectivity in remote areas. The survey also identifies key challenges for DRL-driven MeNT-WiN systems, such as computational complexity and real-time adaptability, while being scalable. Finally, it discusses future research possibilities, emphasizing the importance of new solutions that integrate DRL with MEC in order to fully exploit the potential of MeNT-WiNs.