Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems (1909.04606v4)

Abstract: Intelligent reflecting surface (IRS) has recently been envisioned to offer unprecedented massive multiple-input multiple-output (MIMO)-like gains by deploying large-scale and low-cost passive reflection elements. By adjusting the reflection coefficients, the IRS can change the phase shifts on the impinging electromagnetic waves so that it can smartly reconfigure the signal propagation environment and enhance the power of the desired received signal or suppress the interference signal. In this paper, we consider downlink multigroup multicast communication systems assisted by an IRS. We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint. To tackle this non-convex problem, we propose two efficient algorithms under the majorization--minimization (MM) algorithm framework. Specifically, a concave lower bound surrogate objective function of each user's rate has been derived firstly, based on which two sets of variables can be updated alternately by solving two corresponding second-order cone programming (SOCP) problems. Then, in order to reduce the computational complexity, we derive another concave lower bound function of each group's rate for each set of variables at every iteration, and obtain the closed-form solutions under these loose surrogate objective functions. Finally, the simulation results demonstrate the benefits in terms of the spectral and energy efficiency of the introduced IRS and the effectiveness in terms of the convergence and complexity of our proposed algorithms.

• The paper introduces an IRS-based multigroup multicast framework that jointly optimizes the precoding matrix and IRS reflection coefficients to maximize the sum rate under power and unit-modulus constraints.
• It employs two majorization-minimization approaches—one via quadratic subproblems and a reduced-complexity method—to efficiently solve the non-convex optimization challenges.
• Numerical simulations demonstrate that the IRS-assisted system achieves superior spectral and energy efficiency compared to conventional MISO and relay systems, highlighting its potential for green communications.

Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

The paper investigates the utilization of Intelligent Reflecting Surfaces (IRS) in enhancing the performance of multigroup multicast Multiple-Input Single-Output (MISO) communication systems. In advancing the concept of IRS, the authors have proposed a system where large-scale passive reflection elements are deployed to tailor the electromagnetic signal environment in a manner akin to massive MIMO systems but with lower cost and energy consumption. By implementing IRS, the authors aim to influence the phase and propagation of signals to simultaneously maximize the desired received signal's power and mitigate interference for multigroup multicast systems.

The paper focuses on the maximization of the sum rate of all the multicasting groups, which is pursued through the joint optimization of the precoding matrix at the base station and the reflection coefficients at the IRS. This optimization is constrained by both power limitations and unit-modulus constraints on the IRS. Given the complexity introduced by the non-convex, non-differentiable nature of the formulated optimization task, the authors employ the majorization-minimization (MM) algorithm to solve these challenges.

Two different algorithmic approaches under the MM framework are proposed. The first method reformulates the primary problem into a series of quadratic subproblems addressed through well-established second-order cone programming techniques. This approach ensures that convergence to a KKT point is achieved efficiently, albeit with significant computational complexity. To alleviate this computational burden, the second approach uses a reduced complexity MM algorithm, deriving surrogate objective functions and closed-form solutions that streamline resource allocation. This low-complexity approach achieves similar spectral and energy efficiency gains compared to traditional methods but with much less computational demand, as evidenced by numerical simulations.

The IRS-enhanced system significantly surpasses conventional MISO systems in spectral efficiency, as demonstrated in simulation scenarios with varying system parameters such as the number of reflecting elements and transmit antennas. Additionally, the paper pits the IRS system against a full-duplex AF relay scenario. Results indicate that while the relay system demonstrates higher sum rates due to its active nature, the IRS system excels in energy efficiency, offering a promising solution for green communications.

The paper's contributions are multi-faceted, providing insights into the applicability of IRS in complex communication settings, highlighting its potential over traditional massive MIMO configurations especially in scenarios of high spectral and energy efficiency demand. The research lays the groundwork for future exploration into practically feasible and cost-effective IRS-aided communication architectures.

The research bears implications for the development of future wireless networks that will need agile, power-efficient solutions capable of catering to an ever-growing demand for data. While the statistical and theoretical formulations point to the validity of IRS in fulfilling these roles, further exploration into real-world deployment scenarios and hardware limitations would be prudent. There is also potential for refining the IRS configurations using advanced AI methods for even more efficient signal management and optimization in complex environments.

In summary, this paper delineates a comprehensive framework for realizing IRS-assisted multigroup multicast systems, offering valuable algorithmic insights and benchmark analyses, ultimately paving the way for the practical implementation of IRS technology in next-generation wireless communications.