With the accelerated progression of future-generation technologies, the network is anticipated to achieve enhanced reliability, adaptability, and energy efficiency. High-frequency transmission is prone to coverage issues due to blockages, compromising the reliability of real-time healthcare data transfer, including patient monitoring and remote diagnostics. Additionally, inefficient resource management leads to delays and limits the scalability of IoT-based healthcare systems. This work proposes a novel RIS-assisted NOMA-based resource allocation framework tailored for healthcare IoT applications to address these challenges. RIS mitigates signal blockage by improving coverage, while NOMA enhances resource utilization, ensuring seamless data transmission in dense environments. The RIS deployment is done for the indoor scenario serving only blocked users/low signal strength users, while, the rest of the users form NOMA pairs. The results are analyzed in terms of throughput, coverage, and fairness index. Through numerical analysis, the proposed approach shows a considerable improvement in throughput of approximately 58%. A trade-off is observed among the downlink scenario’s throughput, coverage, and fairness.

The evolution of Internet-of-Vehicles (IoV) from IoT has revolutionized Smart cities, enabling vehicle communication for safety and traffic information dissemination. However, fulfilling time-sensitive applications like safety alerts via Cellular Vehicle-to-Everything (C-V2X) faces resource constraints. This study presents a Non-Orthogonal Multiple Access (NOMA) based resource allocation for C-V2X in Ultra-dense networks (UDN). This paper has also discussed the role of TinyML in unmanned aerial vehicle (UAV) and it is demonstrated with use case scenario. Additionally, a generalized expression for scheduling time fraction is derived for the proposed scheme. The proposed framework optimizes power allocation, accommodating high-speed users and UAV scenarios to improve performance in obstructed regions. Numerical analysis demonstrates an approximate 85% throughput increase over conventional schemes, affirming the efficiency of the NOMA-based approach for enhanced C-V2X performance.

Non-orthogonal multiple access (NOMA) is a next-generation multiple access technology to improve users’ throughput and spectral efficiency for 5G and beyond cellular networks. Similarly, coordinated multi-point transmission and reception (CoMP) is an existing technology to improve the coverage of cell-edge users. Hence, NOMA with CoMP can potentially enhance the throughput and coverage of the users. However, the order of implementation of CoMP and NOMA can significantly impact the system performance of Ultra-dense networks (UDNs). Motivated by this, we study the performance of the CoMP and NOMA-based UDN by proposing two kinds of user grouping and pairing schemes that differ in the order in which CoMP and NOMA are performed for a group of users. Detailed simulation results are presented, comparing the proposed schemes with the state-of-the-art systems with varying user and base station densities. Through numerical results, we show that the proposed schemes can be used to achieve a suitable coverage-throughout trade-off in UDNs.

The cellular Internet-of-Things has resulted in the deployment of millions of machine type communication (MTC) devices under the coverage of a single gNodeB (gNB). These massive number of devices should connect to the gNodeB (gNB) via the random access channel (RACH) mechanism. Moreover, the existing RACH mechanisms are inefficient when dealing with such large number of devices. To address this issue, we propose the rate-splitting random access (RSRA) mechanism, which uses rate splitting and decoding in rate-splitting multiple access (RSMA), to improve the RACH success rate. The proposed mechanism divides the message into common and private messages and enhances the decoding performance. We demonstrate, using extensive simulations, that the proposed RSRA mechanism significantly improves the success rate of MTC in cellular IoT networks. We also evaluate the performance of the proposed mechanism with increasing number of devices and received power difference.

With an unprecedented amount of sensitive data and private information generated by mobile devices, security becomes critical for the emerging ultradense network. The conventional approach of encryption is not suitable to provide desired level of security as a result of increased scalability. Thus, physical layer protection is stressed to ensure secure transmission to users in dense environment. In this paper, we examine the security challenges of high speed users for ultradense network under dense picocells deployment. Initially, we consider a dense condition where users are randomly distributed within the picocell. Targeting the problem of security for vehicular users, we propose a beam broadening (BB) and beam merging (BM) techniques that ensure reliable transmission between source and destination. We prove the effectiveness of proposed approach through mathematical and simulation analysis that guarantees high QoS and secure communication. Based on the above formulations, we examine the reliability and potential capability of BB and BM with the measurement of fairness security assessment (FSA). Numerical results demonstrate that BM and BB can achieve higher value of secrecy rate and FSA, in comparison to conventional beamforming. To end this, the complexity analysis is performed in terms of beam generations as a function of speed of user.

Geometric wavelet is a recent development in the field of multivariate nonlinear piecewise polynomials approximation. The present study improves the geometric wavelet (GW) image coding method by using the slope intercept representation of the straight line in the binary space partition scheme. The performance of the proposed algorithm is compared with the wavelet transform-based compression methods such as the embedded zerotree wavelet (EZW), the set partitioning in hierarchical trees (SPIHT) and the embedded block coding with optimized truncation (EBCOT), and other recently developed "sparse geometric representation" based compression algorithms. The proposed image compression algorithm outperforms the EZW, the Bandelets and the GW algorithm. The presented algorithm reports a gain of 0.22 dB over the GW method at the compression ratio of 64 for the Cameraman test image.