Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems With Joint Transmit and Reflective Beamforming

This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) network with a set of multi-antenna base stations (BSs) each communicating multiple single-antenna users, in which IRS is dedicatedly deployed for assisting the wireless transmission and suppressing inter-cell interference. Under this setup, we jointly optimize coordinated transmit beamforming vectors at BSs reflective vector (with both phases amplitudes) IRS, purpose maximizing minimum weighted signal-to-interference-plus-noise ratio (SINR) subject to individual maximum power constraints reflection IRS. To solve non-convex min-weighted-SINR maximization problem, first present exact-alternating-optimization approach alternating manner, optimization subproblems are solved exactly iteration by using techniques second-order-cone program (SOCP) semi-definite relaxation (SDR), respectively. However, has high computational complexity, may lead compromised performance due uncertainty randomization SDR. avoid these drawbacks, further propose inexact-alternating-optimization approach, inexactly based on principle successive convex approximation (SCA). In addition, reduce low-complexity design, subproblem more inexactly. Via numerical results, it shown that proposed three designs achieve significantly increased values, as compared benchmark schemes without or random beamforming. It also design outperforms one terms achieved value while much lower complexity slightly performance. Furthermore, show our can be applied scenario unit-amplitude constraints, negligible loss.