Table 3.
Summary of the advantages, prospects, and drawbacks of conventional reconfigurable antennas, DMAs, and AI/ML-based smart antennas.
| Smart antenna type | Design philosophy | Advantages/Prospects | Drawbacks/Challenges |
|---|---|---|---|
| Dynamic reconfigurable antennas using conventional antenna elements | Based on an array of antenna elements, each of which serves as a radiator | Relatively easier to design and model | Relatively large size as compared to DMA with the same physical area |
| Each antenna element is given active RF feed through a power divider (or an active feed network) | Feed is provided to each radiating antenna element; therefore, radiation efficiency is usually high | Design of efficient power division is difficult and usually lossy | |
| Each antenna element is always radiating, as it gets direct feed. Switching the state of the PIN diode simply changes the phase in each element | Availability of different types of feed networks, such as corporate feed, series feed, or corporate-series feed network | Phase arrays with bulky phase shifters consume more power and are costly | |
| Well-established literature and experimental verifications | |||
| Dynamic metasurface antennas (DMAs) | Based on the waveguide structure, such as the microstrip waveguide structure | Small size of meta-element as compared to the conventional antenna element | Hardware design challenges to accurately model waveguide structure for electromagnetic coupling of the wave with meta-elements |
| Basic radiator is a meta-element, which is excited when it is coupled to the waveguide structure | No need for active phase shifters, or complicated corporate feed network | Difficult to provide sufficient coupling of signal to each meta-element, thus needs careful waveguide design considerations | |
| Switching ON the PIN diode (forward bias state) results in loss of radiation characteristics of the meta-element. Switching OFF the PIN diode results in the radiation of the meta-element, as it couples with the waveguide structure | Low cost and low power as compared to dynamic conventional antenna arrays (e.g., those based on patch elements and phase shifters) | Challenging to handle insertion loss of meta-elements in the ON state when coupled with a waveguide aperture | |
| Lower number of RF chains required in case of digital beam processing with DMAs with enhanced performance, as compared to conventional dynamic antennas where the number of RF chains = the number of antenna elements. Therefore, a DMA manifests inherent so-called hybrid beamforming | Overall reduced radiation efficiency due to increased dielectric and conduction losses inside the waveguide structure as compared to that of conventional reconfigurable antennas | ||
| Has the potential to be used at base station/access point as a multitude of radiating elements in massive MIMO communication even at sub-6-GHz bands | The system model and signal processing of multi-port DMA (or 2-D DMA) is complex | ||
| Analysis of 1-D waveguide DMAs is easier with single mode as the wave propagates in one direction | Less established experimental research work | ||
| Less established literature | |||
| AI/ML-based dynamic reconfigurable antennas | AI/ML antenna technique can be used with both types of antennas stated above, for real-time beam-switching | A trained AI-based model can steer the radiation pattern of antenna toward the required angular direction in real time | Challenging to train the AI model for a large number of possible antenna states and obtain accurate results. For instance, for 16 antenna elements/meta-elements, the possible coding combinations (or thus the number of radiation beams) will be 216 = 65,536, and so on |
| Very efficient for real-time dynamic reconfigurable antennas to achieve agility in industrial settings to ensure URLLC | Little established work in the literature on antenna radiation pattern reconfigurability using AI/ML |