## 3 Emerging and State-of-the-Art Technologies
### Beamforming Networks at mmWaves and THz  
The demand for high-gain antennas operating at mmWave frequencies has surged, driven by the need to extend the range of communication links in these bands, while meeting the performance requirements of 6G networks. As 6G aims to deliver ultra-high data rates, extreme coverage and reliability, and massive connectivity, beamforming has emerged as the most effective method for achieving these goals: not only enhancing signal gain, but also enabling dynamic real-time beam steering, thus optimizing link quality, reducing interference, and supporting massive connectivity. Additionally, 6G networks require highly adaptive and intelligent beamforming solutions that leverage AI and ML to predict user movement, optimize spectral efficiency, and manage complex multi-antennadeployments. This adaptability makes beamforming a cornerstone technology in mmWave communication systems, ensuring robust and energy-efficient wireless transmission in next-generation networks [88; 89]. Beamforming relies on two key components: a phased array (linear, conformal cylindrical, or planar) and a feeding network [23]. The phased array consists of multiple antenna elements, while the beamforming feeding network provides each element with the necessary signal characteristics, specifically, the amplitude and phase of the excitation current, to shape and direct the beam. Historically, fully passive beamforming networks were used (see Figure 5a). Switched beam networks, such as Butler matrices, Nolen matrices, Blass matrices, and Rotman lenses, introduced predefined phase shifts or time delays through fixed excitation ports [90; 91; 92; 93; 94; 95]. While these systems were effective in early applications, their inherent inflexibility and lack of adaptability limited their functionality in dynamic environments. Particularly in the sub-7 GHz band, where multipath dominates and is exploited through MIMO, digital beamforming is preferably utilized, while matrix-switched beams are rarely adopted. However, these matrix-based beamforming networks (BFNs) have the potential to be effectively utilized in mmWave networks, particularly when modified to function as hybrid digital-analog beamformers, as indicatively presented in Figure 5d. By integrating multiple matrices, it is possible to construct three-dimensional beamforming networks capable of optimally steering highly directional radiation beams [96; 97]. To understand the beamforming effect, we conduct the following study. Assuming a total transmission power of \(P_{t}=40\) dBm from a 5G base station [98], and a received power of \(P_{r}=-50\) dBm for a conventional mobile phone with full reception [99], we consider communication in the 3.5 GHz, 10 GHz, and 20 GHz bands. The mobile phone antenna gain is assumed to be \(G_{r}=0\) dBi. To maintain communication at the same distances across these frequency bands, we analyze different planar antenna array configurations at the base station: (1) at 3.5 GHz, we consider \(4\times 4\), \(8\times 8\), and \(16\times 16\) arrays; (2) at 10 GHz, we consider \(8\times 8\), \(16\times 16\), and \(64\times 64\) arrays; and (3) at 20 GHz, we consider \(16\times 16\), \(64\times 64\), and \(128\times 128\) arrays. The coverage estimation is based on the Friis transmission formula, \(P_{r}=P_{t}+G_{t}+G_{r}-L_{m}-L_{p}-L_{s}\), where \(P_{r}\) is the received power at the mobile phone in dBm, \(P_{t}\) is the transmit power from the base station in dBm, \(G_{t}\) is the base station antenna gain in dBi, \(G_{r}\) is the mobile phone antenna gain in dBi, \(L_{m}\) represents penetration losses in dB, \(L_{s}\) accounts for shadowing and fading losses in dB, and \(L_{p}\) denotes path losses in dB, given by \(L_{p}=32.4+20\log_{10}(d)+20\log_{10}(f)\), where \(d\) is the distance in km, and \(f\) is the signal frequency in MHz.