Joint Symbol-Level Precoding and Reflecting Design for Heterogeneous Networks with Intelligent Reflecting Surface

Recently, intelligent reflecting surfaces (IRS) emerge as an effective technique in saving the power consumption by customizing the wireless propagation environment. On the other hand, the symbol level precoding (SLP) technique provides a clever solution to interference exploitation by converting the multiuser interference (MUI) into a beneficial part of the desired signal. In this paper, we propose to jointly exploit IRS and SLP to cope with the power control and interference management issues in a heterogeneous network (HetNet). To this end, the IRS mainly assists the communication link from a macro base station (MBS) to macro users, and the SLP is employed by both the MBS and pico base station (PBS) to process MUI and intra-cell interference. We formulate a multi-objective optimization (MOO) problem to minimize the transmit power of MBS and PBS by jointly optimizing the precoding matrices at the MBS and PBS as well as reflecting coefficients at the IRS. Due to the non-convexity of this problem, the precoding matrices and reflecting coefficients are optimized alternately. In the precoding design, the MOO problem is transformed into a single-objective optimization problem via the weighted Tchebycheff method and then solved by standard optimization solvers with fixed reflecting coefficients. A multiple gradient descent on the Riemannian manifold based algorithm is proposed to obtain the local optimal solution for the reflecting design. Simulation results manifest a significant performance gain achieved by our proposed HetNet over the benchmarks.