The powerful phase modulation capability of reconfigurable intelligent surfaces (RIS) endows them with the potential to replace future base station (BS) antenna arrays. However, current research on planar transmissive RIS-enabled BSs (PT-RIS-BSs) suffers from angle-sensitive limitations, failing to meet the demands of dynamic low-altitude communications. This paper proposes a spherical transmissive RIS-enabled BS (ST-RIS-BS) architecture, where traditional array antennas in BS are replaced by a combination of an omnidirectional antenna and a ST-RIS, achieving stable gain across all spatial directions. Specifically, considering angle-sensitive gain characteristics, three-dimensional (3D) signal incidence/departure angles, and the effective responsive elements of ST-RIS, we establish a ST-RIS-BS model tailored for dynamic low-altitude communications. Furthermore, through spatial integration methods, we derive the spatial average gain for both PT-RIS and ST-RIS, theoretically demonstrating a performance enhancement of up to 36.6% for the spherical configuration. Additionally, addressing a typical application scenario in the future low-altitude economy where uplink user communications coexist with unmanned aerial vehicle (UAV) data collection, we verify the performance of proposed ST-RIS-BS by the joint optimization of RIS phase shifts, transmit power, user scheduling, and UAV 3D trajectory, which is solved by using block coordinate descent and successive convex approximation techniques. This approach maximizes data collection while guaranteeing uplink user rates. Simulation results validate the effectiveness of the proposed scheme, showing that the ST-RIS solution achieves a 45% increase in data collection compared to the PT-RIS configuration.

In comparison to the unmanned aerial vehicle-mounted base station (BS), the unmanned aerial vehicle-mounted reconfigurable intelligent surface (UAV-RIS) is a potential technology for future high-quality communications due to its high gain and flexibility. At the same time, the inaccuracy of channel state information (CSI) estimation can cause fluctuations in rates. This poses a challenge for the deployment optimization of UAV-RIS. In this paper, we propose an outage-aware transmission scheme in multi-UAV-RIS assisted networks, where the outage probability of all users is constrained and the three-dimensional (3D) position optimization of UAV-RIS is taken into consideration. Specifically, by utilizing the Bernstein-Type inequality, the challenging outage probability constraint is approximated into a manageable explicit expression. We then decouple the resulting total power minimization problem and solve three sub-problems separately: beamforming at the BS, phase shifts at RISs, and 3D positions of UAV-RISs. Furthermore, we first consider the imperfect cascaded channels and assume perfect direct channels. By solving three sub-problems through techniques such as equivalent matrix transformation, semi-definite relaxation, and first-order Taylor expansion, an alternating optimization algorithm is formed. Subsequently, we extend the proposed algorithm to the case where direct channels are also inaccurate, and the complexity and convergence of the proposed algorithm are rigorously proven. Simulation results show that a reasonable configuration of UAV-RIS can effectively improve system performance. Compared to the baseline scheme, 3D position optimization of UAV-RIS and outage awareness can reduce the transmit power by 9.6 dB.

Intelligent reflecting surfaces (IRSs) have been proven to be high gain from the validation perspective of commercial mobile networks. However, the rotation of the IRS is ignored by most works, and real-time adjustment of IRS parameters for dynamic discrete user locations is difficult to achieve in practice due to the difficulty of the instantaneous cascaded channel estimation. In this paper, a new deployment design and experimental measurement for IRS-aided indoor communications is carried out, where the rotation of the IRS is considered to maximize the worst signal to noise ratio (SNR) over all locations in a target area, and the sub-optimal closed solution is obtained and verified by experiment. Specifically, the size of the target coverage area is modeled into the objective problem and an accurate IRS-aided wireless communication network model with IRS rotation is established. Furthermore, by dividing the IRS/area into an equal number of sub-IRS/sub-areas, theoretical closed-form formulas for sub-optimal IRS phase shift and rotation angle for area coverage are given by using the beam-broadening technique and the approximation of regional center alignment. Besides, a physical demonstration system is constructed and a large number of experimental measurements are carried out, which shows that the optimal rotation angle is very close to the theoretical formula given by us. Additionally, numerical comparison of simulation results show that our proposed scheme can obtain 14.1 dB gain for the worst SNR compared with the well-known baseline.

The complex indoor environment makes the deployment of indoor communication networks difficult and expensive. Thanks to the emergence of intelligent surfaces (ISs), including intelligent reflecting surfaces (IRSs), intelligent transmission surfaces (ITSs), and intelligent omni-surfaces (IOSs), a new technology has been brought to indoor communications. However, due to the irregularly scattered distribution of indoor walls, it is difficult to achieve splendid indoor communications using only one kind of ISs. Moreover, most existing works do not consider the location optimization of ISs, resulting in poor performance. This paper proposes a new hybrid IRS/ITS/IOS assisted indoor communication scheme, where the cooperative multi-hop is considered and IRSs/ITSs/IOSs are optimally deployed in specific areas for enhancing the minimum signal-to-interference-plus-noise ratio. Specifically, we first consider the case of hybrid deployment of IRSs/ITSs, the blockage factors between the base station (BS)/users and IRSs/ITSs are modeled, and the multi-hop cooperative channel model is expressed as an explicit function of the phase shifts of IRSs/ITSs by matrix transformation. Furthermore, to deal with the non-convexity and the strong coupling between BS beamforming, phase shifts and positions of IRSs/ITSs, an efficient iterative algorithm is developed based on the successive convex approximation and semi-definite relaxation. Additionally, we analyze and illustrate that our proposed scheme can be extended to the case of IRSs/ITSs/IOSs hybrid deployment. Simulation results prove the advantages of the proposed scheme and show a 45.9% performance enhancement compared with the well-known graph-based deployment.

In intelligent reflecting surface (IRS) assisted networks, most works select collaborative IRS only based on the nearest distance without considering the joint influence of double path loss. This paper proposes a tractable model of product-distance association in IRS-assisted networks, where the Cassini oval is leveraged to characterize the IRS association mechanism theoretically, and the system coverage probability is analyzed by stochastic geometry. Specifically, modeling the product-distance of IRS by the mathematical model of the Cassini curve and considering the physical limitations of the half-space reflection in IRS, the probability density function of the minimum product-distance of IRS that can successfully reflect the signal is derived by approximating the area of Cassini oval. Furthermore, a semi-closed expression of the coverage probability is obtained under Rician fading through the Gamma approximation of expected signal power and the Laplace transform of interference power. In addition, the analysis results indicate a trade-off between the number of IRS elements deployed in the network and the density of base stations, and the tendency of this trade-off changes with IRS density. The numerical results reveal the optimal coverage performance based on product-distance association is 28.8% higher than that of nearest-distance-based association.

In aerial reconfigurable intelligent surface (RIS) aided networks, high line-of-sight (LoS) and agility characteristics provide favorable conditions for RIS reflection. However, this also introduces extra LoS RIS reflection interference, especially when aerial RIS is fixedly deployed. This paper proposes a dynamic unmanned aerial vehicle (UAV)-mounted RIS aided multi-cell multi-user communication scheme, where the minimum ergodic rate of randomly mobile users is maximized through joint optimization of BS beamforming, RIS phases, user scheduling and UAV trajectory under the assumption of only statistical channel state information. Specifically, a closed-form ergodic rate approximation is derived, based on which the block coordinate descent framework is leveraged to solve the above four variables due to the non-convexity of the problem. Furthermore, the optimal beamforming is acquired in closed form utilizing fractional programming and quadratic transformation, RIS phases are optimized by the complex circle manifold, user scheduling is obtained by the reconstruction-based relaxation, and UAV trajectory is solved through the first-order Taylor expansion and successive convex optimization techniques. Moreover, the complexity of the proposed algorithm is analyzed and its convergence is carefully proved. The numerical results demonstrate our proposed scheme achieves a better ergodic rate than traditional methods and other aerial schemes with heuristic trajectories.

Wireless sensor nodes (SNs) are usually energy-limited, a low energy efficiency of SN may be caused by the occlusion of transmission link in the unmanned aerial vehicle (UAV)-enabled data collection. Reconfigurable intelligent surfaces (RISs) provide potential communication opportunities by reflecting and enhancing the signal, thus increasing SNs’ energy efficiency. This paper investigates energy-efficient data collection aided by$N$RIS elements, where a height-fixed and flight time-constrained UAV is considered as the collector, a tractable air-to-ground channel model is established considering the cascaded RIS channel and time-varying trajectory of UAV, and maximum energy consumption of SNs is minimized. Specifically, coupling two intractable fourth-order moment variables, the air-to-ground channel model is processed into an easily computable expression through the Gamma function. Furthermore, based on the proposed two RIS deployment schemes, maximum energy consumption of SNs minimization problem is formulated as a non-convex mixed-integer nonlinear programming (NMNP) problem with optimization of UAV trajectory and SNs wake-up schedules. Additionally, through the slack technique and Taylor expansion, the NMNP problem is decoupled as linear programming and quadratically constrained quadratic programming, and an effective algorithm is designed to obtain the sub-optimal solution. Simulations prove the validity of our algorithm and RIS deployment insights are given for SNs energy saving.

Existing deep learning-based beamforming models’ input sizes are fixed, and they can only be used in multiple-input multiple-output (MIMO) networks containing a fixed number of users and base stations. Therefore, they cannot work and need retraining when the number of users is dynamic. In this letter, a fully convolutional beamforming model named FC-BFNet is proposed, where data of different dimensions can be addressed to adapt to the dynamic environment without retraining the model. Specifically, FC-BFNet has two computational parts which are mainly made up of convolution layers because the convolution operation is not sensitive to the size of the input data. Differently and importantly, the first part extra uses max-pooling to calculate the underlying features of the input and stores max-pooling indices, while the second part extra leverages up-pooling based on stored indices to obtain the expected results from the underlying features and ensure the size consistency of input and output. Extensive experiments demonstrate FC-BFNet has better dynamic environment adaptability and sum-rate performance than other baselines, while maintaining great computing efficiency.