One of the key enablers of future wireless communications is constituted by massive multiple-input multiple-output (MIMO) systems, which can improve the spectral efficiency by orders of magnitude. In existing massive MIMO systems, however, conventional phased arrays are used for beamforming. This method results in excessive power consumption and high hardware costs. Recently, reconfigurable intelligent surface (RIS) has been considered as one of the revolutionary technologies to enable energy-efficient and smart wireless communications, which is a two-dimensional structure with a large number of passive elements. In this paper, we develop a new type of high-gain yet low-cost RIS that bears 256 elements. The proposed RIS combines the functions of phase shift and radiation together on an electromagnetic surface, where positive intrinsic-negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming. This radical design forms the basis for the world's first wireless communication prototype using RIS having 256 two-bit elements. The prototype consists of modular hardware and flexible software that encompass the following: the hosts for parameter setting and data exchange, the universal software radio peripherals (USRPs) for baseband and radio frequency (RF) signal processing, as well as the RIS for signal transmission and reception. Our performance evaluation confirms the feasibility and efficiency of RISs in wireless communications. We show that, at 2.3 GHz, the proposed RIS can achieve a 21.7 dBi antenna gain. At the millimeter wave (mmWave) frequency, that is, 28.5 GHz, it attains a 19.1 dBi antenna gain. Furthermore, it has been shown that the RIS-based wireless communication prototype developed is capable of significantly reducing the power consumption.

To overcome the “multiplicative fading effect” introduced by passive reconfigurable intelligent surface (RIS), the concept of active RIS has been recently proposed to amplify the radiated signals. However, the existing fully-connected architecture of active RIS consumes high power due to the additionally integrated active components. To address this issue, we propose the sub-connected architecture of active RIS. Different from fully-connected architecture, where each element integrates a dedicated power amplifier, in the sub-connected architecture, multiple elements control their phase shifts independently but share a same power amplifier, which significantly reduces the number of power amplifiers for power saving at the cost of fewer degrees of freedom (DoFs) for beamforming design. Fortunately, our analysis reveals that performance loss introduced by the sub-connected architecture is slight, indicating that it can achieve much higher energy efficiency (EE). Furthermore, we formulate the EE maximization problem in the active RIS-aided system for both architectures and develop a corresponding joint beamforming design. Simulation results verify the proposed sub-connected architecture as an energy-efficient realization of active RIS.

Design of a circularly polarized reconfigurable reflectarray antenna (RRA) at X band is presented. The RRA element employs concentric dual split rings, and can be mechanically rotated to obtain continuous 360° phase coverage with negligible element loss. A prototype with 15 × 15 manually rotated elements is first computed, simulated, and measured. Full-wave simulations show that the gain of the beam focused in (8 = 20°, φ = 0°) direction is 25.8 dB and the 1-dB gain bandwidth is over 28.6%. The measured results show that the beam at (8 = 20°, φ = 0°) direction achieves the maximum gain of 25.6 dB, corresponding to an aperture efficiency of 51.8%. The beam-steering capability of the RRA is measured within ±60° angular range, and well-defined scanned beams are obtained with maximum scan loss of 3.7 dB. The versatile beam-forming capability of the RRA is also verified by synthesizing a square shaped beam and a cosecant shaped beam. Another micromotor-controlled prototype with 756 elements on an octagonal aperture is fabricated and its measured radiation performance validates the feasibility of the proposed design as well.

Combining a reflectarray and a transmitarray together, a novel array antenna with bidirectional high-gain beams is proposed in this paper. A novel single-layer metalonly defected square-ring slot element, which has a near-zero thickness, is introduced as the phasing element. Full 360° phase shift range is achieved by utilizing the cross-polarized field of the phasing element. As a bidirectional high-gain antenna (bi-HGA), a transmit-reflect-array using the proposed elements is then designed, fabricated, and measured. It is only composed of a thin metallic sheet with 420 mm side length, spatially fed by a corrugated horn. Well-defined pencil beams are formed in broadside directions on both sides. The measured gains of the transmitted and reflected beams at 10 GHz are 25.5 dBi with 15% 1 dB gain bandwidth and 25 dBi with 14% 1 dB gain bandwidth, respectively. The proposed bi-HGA exhibits distinctive advantages of being ultrathin (0.0067λ ₀ ), lightweight, and low cost without using dielectric substrates, and is a promising candidate for bidirectional wireless communication applications.

Programmable metasurfaces have recently been proposed to dynamically manipulate electromagnetic (EM) waves in both temporal and spatial dimensions. With active components integrated into unit cells of the metasurface, states of the unit cells can be adjusted by digital codes. The metasurface can then construct complex spatial and temporal electromagnetic beams. Given the main parameters of the beam, the optimal codes can be computed by nonlinear optimization algorithms, such as genetic algorithm, particle swarm optimization, etc. The high computational complexity of these algorithms makes it very challenging to compute the codes in real time. In this study, we applied deep learning techniques to compute the codes. A deep convolutional neural network is designed and trained to compute the required element codes in milliseconds, given the requirement of the waveform. The average accuracy of the prediction reaches more than 94 percent. This scheme is validated on a 1-bit programmable metasurface and both experimental and numerical results agree with each other well. This study shows that machines may ”learn” the physics of modulating electromagnetic waves with the help of the good generalization ability in deep convolutional neural networks. The proposed scheme may provide us with a possible solution for real-time complex beam forming in antenna arrays, such as the programmable metasurface.