Joint Modulation Identification and IQ Impairment Handling In Two-Path Relayed Multiple Users OFDMA Over Unknown Channels

The design of effective modulation identification (MI) schemes in orthogonal frequency division multiple access (OFDMA) transmissions poses a considerable challenge for autonomous radio systems, owing to system complexity and dynamic channel conditions. While prior research has addressed this problem, it has predominantly considered non-cooperative single-user scenarios and overlooked practical transmission constraints. In this study, we develop an advanced maximum-likelihood (ML) method to perform MI in the presence of in-phase and quadrature-phase (IQ) impairments, which are commonly introduced by fabrication flaws in transceiver components. The focus is placed on a multiple users OFDMA uplink configuration that employs amplify-and-forward relays within a two-path consecutive relayed (TCR) system. To better capture realistic circumstances, we represent IQ impairments as impacting each node individually, mirroring variances seen in practical installations. We further assume that the channel parameters across all links are unknown and must be implicitly handled during processing. The proposed solution is reinforced by utilizing the data detection mechanism included in TCR systems. Theoretical analysis confirms that the cumulative IQ distortions from each node can be effectively embedded within the link parameters, leading to the formulation of aggregated link parameters (ALPs). The conceptual analysis indicates that the direct ML solution for combining MI with ALPs is computationally prohibitive for practical implementation. To address this challenge, we adopt a lightweight estimation scheme derived from the space-alternating generalized expectation-maximization (SAGE) strategy. The proposed scheme includes a feedback-driven cycle that enables dynamic interaction among the MI, ALPS estimator, and data discovery components to optimize their collective efficacy. Simulation results substantiate the effectiveness of the proposed approach, indicating its performance exceeds that of existing methods.