For a required gain with corresponding HPBW, the size of the antenna needed to assure the communication link strongly goes down with frequency, i.e., the number of radiating elements being \(\lambda/2\) apart in case of an array. However, when the hub or user terminal is moving, then these antennas have to be steered by heavy and bulky mechanical systems, which are impractical to deploy wide-scale applications. Therefore, smart antennas will be a major element in future to alter the narrow beam continuously toward the desired communication partner for a reliable link and for suppressing interference effectively as well as to increase the capabilities of wireless platforms, i.e., the overall capacity and spectrum efficiency. An example of an antenna with multiple beams covering different areas is illustrated in Figure 2 (right). This requires advanced antenna technologies such as analog, digital, or hybrid 3D-beam-steering/beam-forming, massive Multiple Input Multiple Output (MIMO) and network MIMO, where a significantly more advanced baseband computation and Radio Frequency (RF)- and antenna hardware is required to meet the complex requirements [3]. Implementing smart antenna concepts is very challenging at mm-waves, where complexity, technological constrains, and cost increase. At present, there are different approaches to enable cost-efficient, low-profile Electronically Steerable Antennas (ESAs), using different technologies to improve the performance while reducing the manufacturing cost to an economical price point [2]. Beyond smart antennas, reconfigurable hardware concepts and technologies for smart user devices and terminals as well as for the base stations and satellites are crucial for the deployment of the new platforms above, following to some extent the Software-Controlled Radio (SCR) or even  
Figure 3: (**Left**) Free-space propagation loss \(a_{fs}=20\cdot\lg(4\pi\frac{d}{A})\), where \(d\) is the distance between the communication partners, \(\lambda=\frac{c_{0}}{f_{0}}\) is the wavelength, \(c_{0}\) is the speed of light, and \(f_{0}\) is the operating frequency as a parameter for two 5G sub-6 GHz bands as well as some micro- and millimeter wave frequencies allocated for future 5G and satellite platforms (see Appendix A). (**Right**) Calculated gain \(G=\epsilon_{ap}\frac{4\pi}{A}A^{2}\) and half-power beamwidth \(HPBW=k\frac{\lambda}{A}\) in degrees of a quadratic antenna array with size \(A=B\) and with radiating element distances of \(\lambda/2\), assuming an aperture efficiency of \(\epsilon_{ap}=0.4\) and \(-10\) dB-taper of the aperture field with \(k=1.15\). the Cognitive Radio (CR) approaches, which would enable the user handset to look at the radio landscape in which it is located and choose the optimum radio access network, modulation scheme, and other parameters to configure itself to gain the best connection and optimum performance. Hence, to enable computationally intensive, adaptive air interfaces, programmable digital baseband and reconfigurable analog RF-frontend components such as tunable filter, phase shifters, and adaptive matching networks require control by software [2]. Figure 4 illustrates various applications and platforms with some important characteristics. In particular for mm-wave systems, the bottleneck of this approach is the reconfigurable/tunable components in the analog Radio Frequency (RF) frontends of the transceiver hardware and the antennas, to provide smart system functionalities, i.e., to dynamically change/tune the characteristics of these components electronically (instead mechanically) to enable (1) adaptive impedance and power matching, (2) dynamic frequency tuning (center frequency and bandwidth), (3) polarization tuning and converting, (4) electronically reconfigurable multi-beams as well as electronically beam-steering and beam-forming of single or multi-beams [2].