Noise-driven communication fornext-generation wireless networks and iotsystems

Current advancements in the wireless communications have opened new opportunities for low-data-rate Internet-of-Things (IoT) systems. Legacy communication frameworks which rely on high-complexity operations on signal processing and strict synchronization requirements, often fail to meet the low bit error rate (BER), energy harvesting capability, and ability to operate reliably at low data rates, key requirements of modern and future IoT networks. To address these limitations, noise-domain modulation techniques have emerged, encoding data not into the deterministic parameters like frequency or phase, but into the statistical properties such as mean, variance and the correlation of the artificially generated Gaussian noise. These schemes eliminate the need for power allocation and also the successive interference cancellation (SIC), which reduces the complexity and energy consumption. To clarify, by eliminating the need for synchronization and equalization, these techniques enable suitable communication for dynamic and unpredictable wireless environments. Chapter 3, presents an innovative joint energy harvesting (EH) and communication scheme for IoT devices by leveraging the emerging noise modulation (Noise-Mod) technique. The proposed approach embeds information into the mean value of real Gaussian noise samples to enable wireless energy and information harvesting. We propose a mean-based detector to decode the information and derive the analytical bit error probability (BEP) of the proposed scheme under Rician fading channels. We utilize a nonlinear rectenna model to demonstrate the feasibility of the proposed scheme in terms of energy harvesting capability. Simulation results demonstrate that the proposed method outperforms conventional modulation techniques in terms of EH performance across various channel conditions while maintaining reliable communication performance for next-generation IoT networks. In Chapter 4, noise-domain non-orthogonal multiple access (ND-NOMA), an innovative communication scheme that utilizes the modulation of artificial noise mean and variance to convey information, is presented. Distinct from traditional methods such as power-domain non-orthogonal multiple access (PD-NOMA) that heavily rely on SIC, ND-NOMA utilizes the noise domain, considerably reducing power consumption and system complexity. Inspired by noise modulation, ND-NOMA enhances energy efficiency and provides lower BEP, making it highly suitable for next-generation IoT networks. Our theoretical analyses and computer simulations reveal that ND-NOMA can achieve exceptionally low bit error rates in both uplink and downlink scenarios, in the presence of Rician fading channels. The proposed multi-user system is supported by a minimum distance detector for mean detection and a threshold-based detector for variance detection, ensuring robust communication in low-power environments. By leveraging the inherent properties of noise, ND-NOMA offers a promising platform for long-term deployments of low-cost and low-complexity devices. Ultimately, in the Chapter 5, a novel three-user noise-domain non-orthogonal multiple access ND-NOMA scheme by introducing the correlation as a new dimension besides mean and variance quantities used in two-user ND-NOMA is proposed. The new three-user ND-NOMA scheme includes both uplink and downlink scenarios, with detectors designed to decode the information embedded in mean, variance, and correlation. Our theoretical analysis and simulation results under Rician fading channels show that the proposed system is capable of achieving promising BER performance while preserving the low power and low complexity advantages of ND-NOMA. This new ND-NOMA design enables simultaneous communication among three users using different dimensions, paving the way for scalable multi-user communication in noise-domain systems and in the IoT environments.