Table 3.

Steps for the CAGII Algorithm.

Step 0: Set ${\left\{x_{\mathrm{H}}\left(n\right)\right\}}_{n=1}^N$ and ${\left\{x_{\mathrm{V}}\left(n\right)\right\}}_{n=1}^N$ to initial sequences (e.g., ${\left\{x_p\left(n\right)\right\}}_{n=1}^N$ can be set to ${\left\{e^{j{\varphi }_p(n)}\right\}}_{n=1}^N$, where ${\left\{{\varphi }_p(n)\right\}}_{n=1}^N$ are independent random variables distributed in $\left[0,2\pi \right])$. Fix the set of motion states, which means the matrix L should be given. Step 1: Calculate the gradient ${\overline{\nabla }}_p$ according to Equations (28) and (37). Step 2: Renew the phases of the sequences using ${\mathbf{x}}_p^{i+1}={\mathbf{x}}_p^i·e^{-j{\beta }^i{\overline{\nabla }}_p}$, where the step length ${\beta }^i$ is computed according to the line search algorithm [27]. Step 3: Repeat $\mathbf{Steps}\mathbf{1}$ and $\mathbf{2}$ until a stop criterion is satisfied, e.g., $\left|{\overline{\psi }}^{i+1}-{\overline{\psi }}^i\right|\le \epsilon$, where ${\overline{\psi }}^i$ is the objective function at the ith iteration and $\epsilon$ is a predefined threshold.