This paper explores the use of intelligent reflecting surfaces (IRSs) to enhance cell-edge performance in multicell MIMO communication systems. The authors propose to deploy an IRS at the cell boundary to assist downlink transmission to cell-edge users while mitigating inter-cell interference. The main contributions include: 1. **Problem Formulation**: The authors formulate a problem to maximize the weighted sum rate (WSR) of all users by jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS, subject to power constraints and unit modulus constraints. 2. **Reformulation and Algorithm Development**: To solve the non-convex optimization problem, the authors reformulate it into a more tractable form using the equivalence between data rate and weighted minimum mean-square error (WMMSE). They propose a block coordinate descent (BCD) algorithm to alternately optimize the precoding matrices and phase shifts. 3. **Optimization of Precoding Matrices**: For a given set of phase shifts, the optimal precoding matrices are derived in closed form using the Lagrangian multiplier method. For the phase shift optimization problem, they propose two efficient algorithms: the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. 4. **Extension to Multiple-IRS and Network MIMO**: The proposed algorithms are extended to more general scenarios involving multiple IRSs and network MIMO systems. 5. **Simulation Results**: The simulation results demonstrate that the introduction of IRSs significantly enhances cell-edge performance compared to conventional multicell systems. The performance gain is attributed to the improved BS-IRS and IRS-user links, with optimal IRS deployment at the cell boundary and near user clusters being crucial. The paper provides a comprehensive approach to leveraging IRSs in multicell MIMO systems, offering insights into the optimization of precoding and phase shifts for enhanced network performance.This paper explores the use of intelligent reflecting surfaces (IRSs) to enhance cell-edge performance in multicell MIMO communication systems. The authors propose to deploy an IRS at the cell boundary to assist downlink transmission to cell-edge users while mitigating inter-cell interference. The main contributions include: 1. **Problem Formulation**: The authors formulate a problem to maximize the weighted sum rate (WSR) of all users by jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS, subject to power constraints and unit modulus constraints. 2. **Reformulation and Algorithm Development**: To solve the non-convex optimization problem, the authors reformulate it into a more tractable form using the equivalence between data rate and weighted minimum mean-square error (WMMSE). They propose a block coordinate descent (BCD) algorithm to alternately optimize the precoding matrices and phase shifts. 3. **Optimization of Precoding Matrices**: For a given set of phase shifts, the optimal precoding matrices are derived in closed form using the Lagrangian multiplier method. For the phase shift optimization problem, they propose two efficient algorithms: the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. 4. **Extension to Multiple-IRS and Network MIMO**: The proposed algorithms are extended to more general scenarios involving multiple IRSs and network MIMO systems. 5. **Simulation Results**: The simulation results demonstrate that the introduction of IRSs significantly enhances cell-edge performance compared to conventional multicell systems. The performance gain is attributed to the improved BS-IRS and IRS-user links, with optimal IRS deployment at the cell boundary and near user clusters being crucial. The paper provides a comprehensive approach to leveraging IRSs in multicell MIMO systems, offering insights into the optimization of precoding and phase shifts for enhanced network performance.