- The paper introduces an alternating optimization algorithm to jointly design BS transmit and IRS reflecting beamforming for maximizing energy efficiency.
- It utilizes Successive Convex Approximation and Semi-Definite Relaxation techniques to transform non-convex constraints into tractable optimization problems.
- Simulations demonstrate superior performance over conventional OMA and random IRS configurations, emphasizing the benefits of increased reflecting elements and optimized hardware characteristics.
Energy-Efficient Design of IRS-NOMA Networks
The manuscript explores the integration of Intelligent Reflecting Surfaces (IRS) with Non-Orthogonal Multiple Access (NOMA) systems, aimed at enhancing communication coverage and achieving energy efficiency. The paper specifically targets the downlink transmission scenario in a Multiple-Input-Single-Output (MISO) IRS-assisted NOMA network. The core objective is to strike an optimal balance between maximizing the sum rate and minimizing power consumption, ultimately maximizing overall energy efficiency.
The system model includes a base station (BS) equipped with multiple antennas, relying on an IRS composed of passive reflecting elements to serve single-antenna users positioned in the dead zones. The model assumes a perfectly known channel state information environment, laying out the channel configurations and reflecting element parameters to solidify the framework for the algorithmic approach.
Central to the proposed solution is an alternating optimization algorithm that independently optimizes the transmit beamforming at the BS and the reflecting beamforming at the IRS. This bifurcation into subproblems allows the researchers to employ advanced techniques such as Successive Convex Approximation (SCA) and Semi-Definite Relaxation (SDR) to achieve convergence and enhance efficiency. The transmit beamforming problem is initially approached using a series of slack variables and sequential convex programming to transform the non-convex constraints into more manageable forms. Meanwhile, the phase optimization problem is formulated as a sum-rate maximization task, resolving non-convexity through Rician fading channel assumptions and translating it into a feasible semi-definite programming problem.
Simulations validate the proposed scheme's superior performance over conventional Orthogonal Multiple Access (OMA) schemes and NOMA systems with random phase IRS configurations. The results depict how increased numbers of reflecting elements enhance energy efficiency, reinforcing the advantage of the IRS-NOMA configuration. Furthermore, variations in the circuit power of the BS impact efficiency, highlighting the influence of hardware characteristics on system performance.
The practical implications of this research could notably influence the design of next-generation wireless networks by promoting methods to optimize energy consumption without compromising data transmission rates. The theoretical grounding provided can stimulate further refinements in IRS designs and their integration with multi-access technologies. Future developments may involve extending the current findings to multi-user cases, expanding the practical reach of IRS in dense network environments. This exploration reflects a robust stride toward sustainable and energy-efficient wireless communication solutions, pivotal for advancing beyond fifth-generation (B5G) wireless networks.