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This work reports the development of a dual‐band switched‐beam antenna array for multiple‐input multiple‐output (MIMO) fifth‐generation systems, operating in millimetre waves. The proposed antenna array consists of applying four slotted‐waveguide antenna arrays (SWAAs), rotated by 30°, as the final array element. The SWAA elements were designed to simultaneously operate in the 28 and 38 GHz bands, by using two groups of slots with different electrical lengths on the opposite faces of the waveguide. The measured array fractional bandwidth was ∼25.6 and 10% at 28 and 38 GHz, respectively. Its applicability for MIMO systems was evaluated as a function of mutual coupling, active reflection coefficient, total active reflection coefficient and envelope correlation coefficient. Experimental results demonstrate low mutual coupling as low as −36 dB within the 28 and 38 GHz frequency bands.

This work reports the concept and development of two mechanically frequency-tunable horn filtennas for microwave and millimeter-waves. Our design approach relies on the integration of a horn antenna with a mechanically tunable filter based on dual-post resonators. The proposed filtennas have been manufactured and experimentally characterized, by means of reflection coefficient, radiation pattern, and gain. Measurements demonstrate that both filtennas have a tuning ratio of approximately 1.37 with continuous adjustment. The first prototype operates from 2.56 to 3.50 GHz, whereas in the second one the bandwidth is from 17.4 to 24.0 GHz. In addition, the higher-frequency filtenna has been implemented in a 5.0-meter-reach indoor environment, using a 16-QAM signal at 24 GHz. The best configuration in terms of performance resulted in a root mean square error vector magnitude (EVMRMS) and antenna radiation efficiency of 3.69% and 97.0%, respectively.

Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

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