## 3 Emerging and State-of-the-Art Technologies
### Multiple-Input Multiple-Output (MIMO)
The complex interrelation between beamforming and MIMO at mmWave frequencies is elaborated by NTT-DOCMO [87], which refers to \"user and spatial multiplexing of massive-element antennas\". An indicative diagram (Figure 3) illustrates how the number of elements increases with the square of the frequency ratio, while the overall array size remains approximately constant. For instance, at a range of approximately 490 m, a square array of size \(20\,\mathrm{cm}\times 20\,\mathrm{cm}\) comprises \(10\times 10=100\) elements at 10 GHz, or \(20\times 20=400\) elements at 20 GHz. Although the physical array size is retained, the dramatic increase in element count introduces major challenges in beamforming or antenna feeding network design. A practical solution lies in _hybrid beamforming_. In the example above, each \(20\times 20\) cm\({}^{2}\) subarray could employ an analog beamforming circuit, e.g., switched-beam Butler, Nolen, or Blass matrices, while MIMO-based user multiplexing could be handled by an equal number of subarrays, each driven by a separate RF chain. Another major challenge in implementing massive MIMO systems is the enormous complexity introduced by managing the envisioned millions of antenna elements. Controlling such a vast number of elements requires numerous fully controllable RF transceiver chains, each of which must be monitored and dynamically adjusted in real-time. Essentially, the challenge lies in managing and optimizing the individual radiated beams, typically achieved by controlling the excitation current of each antenna element in terms of amplitude and phase. However, some analog beamforming techniques, such as switched-beam systems, offer a more practical approach by simplifying control through the selection of the appropriate transmit/receive (Tx/Rx) beamforming network port. To address these issues, various research groups have proposed hybrid MIMO architectures that combine analog and digital signal processing, aiming to minimize the number of required RF chains without compromising performance. Despite these theoretical advancements, practical implementations of massive MIMO systems for mmWave frequencies remain scarce. The majority of published work focuses on simulations, with emphasis on developing and testing advanced algorithms for MIMO system optimization. The introduction of artificial intelligence (AI) and machine learning (ML) is expected to play a crucial role in addressing beam management challenges, offering enhanced adaptability and real-time optimization. However, the physical fabrication and experimental validation of such systems present significant challenges. These stem from intricate design requirements, complex manufacturing processes, and the demanding testing environments needed to ensure accurate performance validation. Traditional microwave testing methods become impractical due to the extremely high number of ports. Thus, conductive path approaches involve high complexity and require excessive effort. In the sub-7 GHz band, these issues are reliably addressed through over-the-air (OTA) testing approaches. However, in the mmWave bands, the number of ports is expected to remain relatively small, making testing methods involving conductive techniques (e.g., cables or waveguides) potentially more practical and accurate.