Method for joint active and passive beamforming and received signal optimization in isac system assisted by dual irss

A method for joint active and passive beamforming and received signal optimization in an integrated sensing and communication (ISAC) system assisted by dual Intelligent Reflecting Surfaces (IRSs), comprising:

• S1, model establishing:
• establishing an ISAC system model assisted by the dual IRSs, wherein the ISAC system model comprises a multi-antenna Base Station (BS) configured to simultaneously provide communication services for a plurality of users and perform sensing for targets, wherein the dual IRSs assist the multi-antenna BS in operation, and reliable channels between the multi-antenna BS and communication users and sensing targets are established by dispersing the dual IRSs on a surface of a building;
• and
• S2, alternating optimization:
• S21:
• deriving a signal-to-interference-plus-noise ratio (SINR) for the communication users and a signal-to-noise ratio (SNR) lower bound for the sensing targets based on the reliable channels for communication and sensing, and formulating an optimization problem based on the SINR and the SNR lower bound, the optimization problem jointly designing active beamforming w at a BS end, received signal processing u at the multi-antenna BS for a sensing signal, and passive beamforming θ1,θ2 at the dual IRSs, under an objective of maximizing a communication sum-rate of the communication users, while ensuring that a sensing SNR satisfies a minimum requirement, and satisfying a power constraint at the BS end and a constant modulus constraint at IRSs;
• S22:
• introducing auxiliary variables β and ρ, and decoupling the optimization problem using a fractional programming method;
• S23:
• updating the auxiliary variables β and ρ by obtaining partial derivatives, and updating the received signal processing u using Rayleigh quotient under given premise conditions w,θ1,θ2,ρ,β;
• S24:
• representing an original optimization problem as a second-order cone programming (SOCP) problem under given conditions u,ρ,β,θ1,θ2, and solving and updating the active beamforming w using a convex optimization (CVX) toolbox;
• S25:
• introducing an auxiliary variable φ as well as a dual variable μ, successively updating first reflection coefficient vectors θ1,φ, μ using an alternating direction method of multipliers (ADMM) under given conditions u,ρ,β,w,θ2, and updating a second reflection coefficient vector θ2 using a same method;
• S26:
• repeating S25 until values of θ1,θ2 converges;
• and
• S27:
• repeating S23, S24, S25, and S26 until an algorithm converges, thereby obtaining an optimal solution wopt,uopt,
• θ
• 1
• opt
• ,
• θ
• 2
• opt
• .