On the Design of NOMA-Based Integrated Sensing and Communications (ISAC) in Near-Field Extremely Large-Scale MIMO Systems

Integrated sensing and communications (ISAC) has been widely applied in Internet of Things (IoT) networks for its capability to simultaneously support high-performance communication and sensing, and non-orthogonal multiple access (NOMA) is introduced to further improve spectral efficiency and connection density. However, with the deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO) and high-frequency (HF) technologies, the near-field (NF) paradigm replaces the conventional far-field (FF) paradigm and becomes dominant, necessitating a reassessment of NOMA-based ISAC system performance in the NF region. To address this, a novel NOMA-based ISAC scheme in NF XL-MIMO systems is proposed in this paper, wherein a multi-beam design based on subarray partitioning is employed to realize a communication-and-sensing coexistence ISAC system, while the additional distance-based degree of freedom (DoF) provided by the unique NF beamfocusing is utilized to improve NOMA performance gains. To balance the optimal performance tradeoff between communication and sensing, an optimization problem for joint device scheduling, subarray partitioning, and power allocation is formulated to maximize the ISAC joint rate under various constraints. Based on alternating optimization (AO) and fractional programming (FP) techniques, an efficient joint optimization algorithm is developed to solve the complex non-convex problem with coupled variables. In particular, the original problem is decoupled into three subproblems and solved by applying the linearization of 0-1 polynomial programming, Lagrangian dual reformulation, and quadratic transform techniques. Numerical results validate that our proposed NOMA-based ISAC scheme and joint optimization algorithm significantly enhance the ISAC joint rate performance in NF XL-MIMO systems for IoT networks.