Ishtiaq Ahmad

Physical Layer Security for STAR-RIS-Assisted NOMA Backscatter Communications Against Multiple Eavesdroppers

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology for achieving omnidirectional coverage in smart radio environments, enabling energy-efficient and spectrally efficient wireless communications. When integrated with ambient backscatter communication (AmBC) and non-orthogonal multiple access (NOMA), STAR-RIS facilitates the concurrent exploitation of ambient radio frequency signals and spectrum resources, offering significant potential for scalable Internet of Things (IoT) networks. This paper conducts a comprehensive analysis of the physical layer security performance of a STAR-RIS-assisted AmBC system employing NOMA in the presence of multiple eavesdroppers. Specifically, the STAR-RIS serves as an active backscatter device to enhance the backscatter link by mitigating direct link interference, while NOMA optimizes spectrum utilization through power-domain multiplexing. We derive closed-form expressions for critical performance metrics, including outage probability (OP), intercept probability, throughput, and energy efficiency, under realistic channel fading models. Asymptotic analysis of the OP is provided to reveal insights into high signal-to-noise ratio regimes. Furthermore, we investigate the impact of key system parameters. Numerical results validate that the proposed STAR-RIS-assisted AmBC-NOMA framework significantly enhances secrecy performance compared to conventional AmBC systems, demonstrating its robustness against eavesdropping threats and its suitability for secure IoT applications.

Efficient Federated LLM Framework for Intelligent IoMT Management

Generalized Coordinated Learning for Radio Resource Management in Dense D2D Networks

Dense device-to-device (D2D) communication networks with a huge number of directly communicating devices necessitate an efficient radio resource management to maximize network performance. To this end, we first propose a novel intelligent channel reuse based on deep deterministic policy gradient (DDPG) to maximize efficiency of the radio resource usage. In the real-world, any channel reuse inevitably imposes additional interference requiring to i) allocate properly transmission power at individual reused channels and ii) knowledge of a high number of interference channels among devices. Such practical challenges are addressed in existing literature commonly via deep neural networks (DNNs). However, simple coexistence of multiple naturally sub-optimal machine learning models, such as DNN for channel quality prediction, DNN for transmission power allocation, and DDPG for channel reuse leads to a problem with a propagation of inevitable small errors in the prediction by individual models. Consequently, even a small error in the decision taken by one model can mislead decisions taken by other models. To solve this challenge, we further propose a low-complexity Generalized Coordinated Learning (GCL) allowing a coordination of multiple light-weight machine learning models. The GCL employs feedback loops among machine learning models to jointly optimize multiple radio resource management parameters in a coordinated way with awareness of decisions taken by other models. Simulation results demonstrate that the proposed GCL reaches near-optimal performance even in dense D2D networks and improves sum capacity and ratio of users meeting their required communication capacity by up to 69.6% and 22.1%, respectively, compared to state-of-the-art works.