R has one positive eigenvalue and one negative eigenvalue. Therefore, it is not hermitian negative semidefinite, as is required for obj to be (real) concave.
I.e., your problem is not convex. I have no idea whether you made a mistake in implementing your intended (convex) optimization problem; or your CVX formulation is the problem you really want to solve, in which case it is non-convex, and you need to use a different tool, such as YALMIP.
Dear Mark, thank you for your prompt response. You’re absolutely correct, and I have since revised my approach accordingly. However, I’ve encountered a new error. The objective function is indeed a quadratic convex expression, and maximizing it doesn’t logically hold. The model I’m simulating, according to my references, maximizes a quadratic function subject to modulus constraints. Could you possibly provide any further guidance or recommendations on how to tackle this?