International Journal of Autonomous and Adaptive Communications Systems (8 papers in press)

Regular Issues

• A QR Code Secret Sharing Scheme Based on Extended Hamming Code  
  by Xuehua Cao, Xinwei Zhong, Genlin Ji 
  Abstract: Secret sharing, as a crucial method for privacy protection, has seen rapid development in fields such as cloud computing and the Internet of Things in recent years. As a very popular information cover at present, QR code is used far more frequently than traditional images, so the application of secret sharing in QR code has highly promising. However, because the QR code is often presented in paper form, it is easy to be stained or damaged; At the same time, stego-QR codes may be tampered with by attackers during transmission. These issues will inevitably lead to bit errors in stego-QR code, and the existing research schemes are unable to recover the secret losslessly. To address this, this paper proposes a QR code secret sharing scheme based on Extended Hamming Code. This scheme not only ensures high embedding capacity but also implements error correc tion capability of stego-QR codes, which can accurately detect and correct bit errors in the stego-QR code, and then recover the secret. Both theoretical analysis and experimental results demonstrate the effectiveness and practicality of this scheme.
  Keywords: secret sharing; QR code; bit errors; Extended Hamming Code; error correction.
  DOI: 10.1504/IJAACS.2026.10075089
   
  
• Overview of Power Line Communication: Innovations and the Integration of Artificial Intelligence  
  by Samir Laksir 
  Abstract: This paper provides a comprehensive review of the evolution, standardisation, applications, and advancements of power line communication (PLC) technologies. It presents a historical overview of key standards, from EN 50065 to the latest IEEE 1901c amendment, alongside an examination of significant patents and licensing efforts. The analysis highlights the diverse roles of PLC in smart grids, industrial automation, electric vehicle infrastructure, and broadband access. A central contribution of this work is its focus on the emerging integration of Artificial Intelligence (AI) into PLC systems, a topic not yet extensively synthesised in the literature. We show how AI augments PLC by enabling adaptive fault detection, intelligent security, noise mitigation, and performance optimisation under variable conditions. By consolidating global research and practical implementations, this review identifies a unique gap at the intersection of PLC and AI, offering insights into how their convergence can enhance versatility, resilience, and scalability. Finally, the study outlines future research avenues, including hybrid communication architectures, sustainable designs, and AI-driven frameworks for secure and scalable PLC innovations.
  Keywords: Power Line Communication; PLC Standards; Artificial Intelligence; Smart Grids; Noise Mitigation; Hybrid Communication; IoT Integration; Sustainable Networks; PLC Security.
  DOI: 10.1504/IJAACS.2026.10075666
   
  
• Spectrum Allocation in 6G Communication Networks Using Enhanced Dual Deep Q-Network  
  by Monika Gupta, Ishani Mishra, Sonika Katta, Pavithra G, Swapnil S. Ninawe, Preeti Khanwalkar 
  Abstract: In 6G communication networks, spectrum allocation process is crucial for effectively managing and utilising the limited spectrum resources available. However, traditional spectrum allocation approaches encounter significant challenges, particularly in dynamic environments; often result in suboptimal spectrum utilisation and delays in the allocation process. To overcome this issue, we propose an Enhanced Dual Deep Q-Net (EDDQN) scheme for spectrum allocation. EDDQN is an advanced version of DQN that makes use of two neural networks: an online network for action selection and a target network for Q-value estimation. By using two separate neural networks, EDDQN overcomes overestimation bias in Q-value estimation, a significant drawback of the original DQN. The zebra optimisation algorithm is used to optimise the hyper-parameters of EDDQN in order to improve its performance. Through extensive simulations, we prove the superiority of proposed spectrum allocation compared with traditional spectrum allocation approaches.
  Keywords: 6G communication network; spectrum allocation; enhanced dual deep Q-Net; online network; target network; and zebra optimization algorithm.
  DOI: 10.1504/IJAACS.2026.10075893
   
  
• Reversible Data Hiding in Encrypted Image via Block Classification and Adaptive Coding  
  by Yuanyuan Fu, Shiliang He, Jiaohua Qin 
  Abstract: Reversible data hiding in encrypted images (RDHEI) enables secret information embedding while ensuring error-free data extraction and perfect image recovery. However, the fixed vacating room method used to divide encrypted images into blocks results in limited embedding space. To address this limitation, this paper proposes a block classification and adaptive coding-based RDHEI scheme. Specifically, block-level encryption and permutation are first applied to the original image. Then, adaptive block classification is conducted, and block labels are recorded in a block label location map to achieve first-stage room vacating. For blocks with insufficient available space, adaptive coding and block label map compression are further employed to realise second-stage room vacating. Experimental results demonstrate that the proposed method achieves reversibility and separability while providing a higher embedding rate than existing approaches.
  Keywords: Reversible data hiding in encrypted image; Adaptive block classification; Adaptive coding strategy.
  DOI: 10.1504/IJAACS.2026.10076297
   
  
• Design of Energy-Efficient 6G Communication System using Two-Block Resource Algorithm  
  by Anil Kumar R, Vatsala Anand, Naziya Hussain, A. Arunkumar Gudivada, Gummarekula Sattibabu, Ramesh Adireddy 
  Abstract: Energy efficiency (EE) is a critical metric in wireless communication systems. It measures the balance between data transmission and energy consumption. An optimal EE is key objective due to demand for higher data rates and reliable connections. Modern communication systems use advanced techniques such as Massive multiple-input multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM) and hybrid methods to improve efficiency. The present work focuses on power, two block resource algorithm (TBRA) and signal-to-noise ratio (SNR). Here, the transmitter section used a block structure to transmit data symbols. The resource block is divided into sub channels and sub symbols for efficient data transmission. The proposed method is evaluated against dynamic resource optimisation, DRL-Based Resource Allocation, and Federated DRL-Based D2D scheduling. The proposed TBRA demonstrates superior energy efficiency across multiple performance metrics. Additionally, focussed on the factors influences maximises the energy efficiency in wireless systems. Evaluated the impact of SNR, power and resource allocation on EE. Compare EE across different transmission massive MIMO, MIMO-OFDM and hybrid approaches.
  Keywords: Energy Efficiency; Massive MIMO; SNR; OFDM; Resource Block.
  DOI: 10.1504/IJAACS.2026.10076308
   
  
• A Review on Advanced Nanostructured Thin Films for High-Capacity and Long-Lasting Battery Technologies  
  by A. Rani Sangeetha, K. Visalakshi 
  Abstract: Nanostructured thin films represent a promising pathway for next-generation high-performance batteries due to their enhanced ion transport, large surface area, and improved electrochemical stability. This review summarises key advances reported between 2019 and 2025 in thin-film materials used for battery electrodes, electrolytes, and separators. Emphasis is placed on advanced fabrication techniques, including atomic layer deposition and solgel synthesis, together with modern characterisation methods. The influence of nanostructured morphology on electrochemical performance is critically examined, particularly in terms of chargedischarge efficiency, interface stability, and cycling durability. In addition, hybrid and multilayer architectures, such as composite and interface-engineered systems, are discussed for their ability to improve conductivity and energy density. Major challenges related to large-scale manufacturing, cost, and long-term structural integrity are identified, and potential mitigation strategies are outlined. Overall, the review highlights how integrating materials science, nanoengineering, and AI-driven design can accelerate the commercialisation of nanostructured thin-film battery technologies.
  Keywords: Nanostructured Thin Films; High-Capacity; Battery Technologies; Electrochemical Processes; Polyanionic Compound.
  DOI: 10.1504/IJAACS.2026.10076356
   
  
• Cross-Modal Attention for Fake News Detection: Integrating Text and Image Features with Multi-Modal Fusion and Advanced Methods  
  by Yamini Devi Jonnala, J. Sirisha Devi 
  Abstract: The rise of digital media accelerates the spread of fake news, impacting public opinion, politics, and health. Traditional detection methods analyse text and images separately, failing to capture cross-modal relationships. This study proposes a multimodal framework that processes text and image to address these challenges. The text data is first processed using bidirectional long short-term memory (BiLSTM) and CapsNet, where BiLSTM captures sequential dependencies and CapsNet with self-attention enhances spatial feature extraction. Simultaneously, image data is processed using ResNet and vision transformer (ViT) to extract meaningful features, creating a single representation. The extracted text and image features are then refined through multi-head self-attention. These refined features are fed into a cross-modal attention mechanism, which integrates and fuses the enhanced representations from both modalities the fused representation passes through a fully connected layer for classification. The specified models achieves a high accuracy of 96.2% and 95.2% for text and image inputs.
  Keywords: BiLSTM; CapsNet; ResNet,ViT; Fully connected Layer; Multiple Multi-Model; Multi-head self-attention and Cross-Modal Attention Network.
  DOI: 10.1504/IJAACS.2026.10077164
   
  
• Semantic Compensation-Driven Low-Distortion Linguistic Steganography  
  by Lingyun Xiang, Songjun Liang, Song Jiang 
  Abstract: Existing modification-based linguistic steganography (MLS) often suffers from semantic distortion, compromising imperceptibility and security. To address this issue, we propose Low-Distortion Linguistic Steganography(LDStega), which enhances semantic fidelity via semantic compensation substitutions. LDStega utilises an augmented masked language model (MLM) with a dynamic masking strategy to generate contextually coherent candidate words. To ensure substitution integrity, we introduce a BART-based evaluation to quantitatively assess contextual appropriateness. Furthermore, a semantic compensation strategy is proposed to refine subsequent word choices based on semantic fidelity feedback rather than secret messages, effectively mitigating collocation mismatches and unintended distortions. Extensive experiments demonstrate that LDStega outperforms baseline methods in text quality, semantic similarity, and resistance to steganalysis.
  Keywords: Linguistic steganography; word substitution; semantic distortion; semantic fidelity; masked language model.
  DOI: 10.1504/IJAACS.2026.10077172