Weighted Sum-Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Cell MU-MIMO Communications
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In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-cell downlink communication system, where the IRS is dedicatedly deployed at cells boundary for mitigating the intra-cell and crosscell interference. We assume that each base station (BS) equipped with multiple antennas serves multiple users with multiple antennas in each cell, and an IRS consists of a large number of reflective elements to reflect the incident electromagnetic wave by changing phase shift and amplitude controlled by all BSs cooperatively. In other words, the IRS can adjust the propagation conditions near-instantaneously. For this configuration, the weighted sum-rate (WSR) of all the users is maximized under the individual maximum transmit power constraint at each BS and the phase shift constraint at each reflection element by jointly optimizing the active transmit beamforming at BSs and passive reflection coefficient matrix at IRS. Since the original problem is expressed as a sum-of-logarithmic function of signal-to-inerference-plus-noise (SINR) and the optimizing variables of SINR are coupled in fractional form, it is non-convex and hard to solve. To address the problem, we propose an efficient jointly optimizing algorithm based on Lagrangian Dual Transform and Fractional Transform and Successive Convex Approximation (SCA) technique is used to reduce complexity. Extensive simulations results indicate that the proposed jointly optimizing algorithm offers a substantial improvement performance gain over baseline schemes.
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