The propagation environment was uncontrollable in first-generation to fifth-generation (5G) wireless technologies. This behavior of the wireless propagation environment is one of the prime constraints in harnessing the performance of wireless networks. This problem can be addressed in sixth-generation (6G) wireless networks by deploying intelligent reflecting surfaces (IRSs). IRS’s amplitude and phase reflecting coefficient of reflecting units (RUs) can be adjusted via a programmable controller to meet the network requirements. On the other hand, in 5G and 6G wireless communication networks, non-orthogonal multiple access (NOMA) is a robust and well-admired multiple access scheme among the other multiple access counterparts in terms of spectrum efficiency and link capacity. NOMA allows many user equipment (UE) by utilizing non-orthogonal distribution of resources. Therefore, the combination of IRS and NOMA is one of the dominant technologies for 6G wireless networks. Based upon the importance of NOMA and IRS in the initial development of 6G wireless networks, this paper presents a comprehensive survey on IRS-assisted NOMA-based networks, considering their designs and challenges. In this work, the concept and structure of IRS-assisted NOMA have been explained with an in-depth analysis of the frameworks. It also includes some challenges of IRS-assisted NOMA in wireless communication networks. Further, applications and future research directions of IRSassisted NOMA networks are discussed.

In sixth-generation (6G) wireless networks, non-orthogonal multiple access (NOMA) technology is widely used to improve the spectrum efficiency. Along with NOMA, hybrid automatic repeat request (HARQ) technique is used to enhance the reliability of communication system. Recently, intelligent reflecting surfaces (IRS) are a futuristic transmission technology that can achieve high spectral and energy efficiency at low cost. In this paper, an IRS aided downlink NOMA-HARQ based Internet of Things (IoT) framework is proposed to enhance the IoT devices' performance in non-line-of-sight (NLoS) region. Further, to enhance the performance of NLoS IoT device/users, two NOMA-HARQ aided IRS schemes are proposed namely: NOMA-selection combining (SC) with IRS and NOMA-maximum ratio combining (MRC) with IRS. This paper formulates and examines the IoT user/device performance in terms of the achievable data rates, outage probability, bit error rate (BER) and diversity order in IRS aided downlink NOMA-HARQ environment with variable signal-to-noise ratio (SNR). With the help of the simulation results, it is demonstrated that in dynamic wireless channels, the proposed schemes outperform the conventional NOMA with HARQ (NOMA-HARQ) in terms of achievable data rates, outage probability, BER and diversity order.

Battery lifetime maximization of Wireless Sensor Network (WSN) nodes has been an area of prime research interest, and clustering of WSN nodes for realizing the goal of network lifetime maximization has been extensively addressed in the literature. Various clustering schemes for partitioning WSN nodes into equal, unequal or random cluster sizes have been prevalent, each having their own set of pros and cons. This paper proposes a Hierarchical Layer-Balanced Clustering (HLBC) scheme that partitions the WSN into a number of hierarchical layers of varied sizes while maintaining equal sized clusters at every layer. The proposed HLBC architecture is presented along with the proposed Frame Forwarding Algorithm (FFA) and the proposed TDMA Schedule for eliminating the synchronization problem. Extensive simulation experiments have been carried out under varying network scenarios to assess the performance of the proposed scheme, and the results demonstrate its efficacy. In particular, the proposed HLBC was found to outperform state of the art approaches LEACH, UCR and EMUC by 77.71%, 54.23% and 27.93%, respectively, in terms of Network Lifetime.

Clustering is an approach exploited in Wireless Sensor Networks (WSNs) to enhance their performance and energy efficiency. Therefore, cluster formation and proper cluster head (CH) selection are of utmost importance in such scenarios. The proposed method, game theory-based fuzzy routing (GTFR) protocol, optimizes both of these phases of network operation in WSN. In the proposed work, fuzzy clustering is utilized for forming clusters with the desired quality of having low intra-cluster communication distance (IACD). In addition, CH selection is based on game theory and a fitness function that ensures optimal CH selection during two sub-phases of CH selection (tentative CH selection and final CH selection). In conjunction with using a penalty function for the clustering game, the proposed work also considers the nodes’ history of acting as a CH as a fitness criterion to enhance performance by impartially rotating the role among all the nodes. The proposed protocol outperforms LEACH, HSCR LEACH, FCM, and CT-RPL protocols in terms of stability by 238.27%, 183.94%, 432.04%, and 47.31%, respectively. Rigorous simulations prove that the proposed protocol outperforms LEACH, HSCR LEACH, FCM, and CT-RPL in all three considered network scenarios.

In wireless networks, automatic modulation classification (AMC) is crucial for enabling intelligent signal demodulation, thereby enhancing the system’s adaptability across various applications. Concurrently, the rapid expansion of the Internet of Things (IoT) necessitates scalable network solutions with limited power consumption. Moreover, addressing the non-line-of-sight (NLoS) effects in IoT networks, intelligent reflecting surface (IRS) emerges as a promising, cost-effective technology. This paper introduces a novel transformer-based deep recurrent architecture (TDRA) for AMC, tailored for IRS-assisted IoT networks, which significantly improves IoT device performance in NLoS scenarios. In TDRA, the existing recurrent models, long-short-term memory (LSTM), and gated-recurrent-unit (GRU) are suitably revamped with a transformer-based approach and termed as transformer-based LSTM (T-LSTM) and transformer-based GRU (T-GRU). Numerical datasets are generated for IoT applications considering the seven widely used modulation types to train and test the proposed models. Comparative analysis with seven state-of-the-art deep learning models and five machine learning models for AMC demonstrates the superior performance of the proposed models across multiple metrics, including accuracy, R-squared-score, mean-squared-error, mean-absolute-error, precision, recall, and F1-score. Further, the proposed models exhibit notable improvements under various conditions, such as optimized and random IRS phase shifts, with and without IRS-assisted IoT networks, different modulation sequence lengths, and fading channels. Additionally, the time complexity and processing time of the proposed models have been studied to test their suitability for IoT devices. The simulation results indicate that the TDRA for AMC in IRS-assisted IoT networks achieves up to 87% higher accuracy compared to without IRS-assisted IoT networks. This significant enhancement underscores the potential of TDRA to revolutionize IoT networks by providing robust, efficient, and scalable solutions for real-world applications.