owever, the SSA source code is available from the authors in MATLAB, Python, and R. As stated earlier, an additional advantage is the fact that SSA is parameter free. On the other hand, if we consider the SSA disadvantages, we will include the fact that the algorithm may lead to stagnation to local optima. The SSA has been employed for several optimization problems in wireless communications. These include the combined power allocation and user association problem in non-orthogonal multiple access networks (NOMA) [8]. This type of problem is non-convex and very difficult to solve with any other method. Additionally, SSA was also employed in [6] for 5G antenna design. The authors applied SSA for a bow-tie MIMO antenna design that resonates at 28 GHz for use in Frequency Range 2 (FR2) in 5G cellular networks. S.K. Goudos and M. A. Matin Algorithm 1: SSA algorithm for antenna design 1: Initialize the maximum number of generations Gmax 2: Initialize the random initial population of size NP 3: Calculate antenna fitness value for population 4: Set the antenna design with the best fitness to be the initial food Food 5: k = 1 6: while k G max do Calculate new C1 value using (10.2) for r=1 to NP do if <= NP/2 then Update the leaders positions using (10.1) Update the followers positions using (10.3) end if end for Sort all the antenna design vectors according to fitness value in descending order If a better antenna design is found, update the food vector 18: end while 10.4 6G Patch Antenna Design The E-shaped patch antennas are linearly polarized and wide band (LP). Patch antennas can be used for a variety of purposes, including high-speed short-distance communication. Furthermore, circular polarization is frequently a prerequisite for such communication systems (CP). The fundamental advantage of CP is that it frees the receiver from the direction of arrival dependency in an incoming electromagnetic wave. Misalignment causes a mismatch between the transmitter and receiver, which is eliminated by CP. Thus, CP potentially in