Wireless-powered communications (WPCs) are increasingly crucial for extending the lifespan of low-power Internet of Things (IoT) devices. Furthermore, reconfigurable intelligent surfaces (RISs) can create favorable electromagnetic environments by providing alternative signal paths to counteract blockages. The strategic integration of WPC and RIS technologies can significantly enhance energy transfer and data transmission efficiency. However, passive RISs suffer from double-fading attenuation over RIS-aided cascaded links. In this article, we propose the application of an active RIS within WPC-enabled IoT networks. The enhanced flexibility of the active RIS in terms of energy transfer and information transmission is investigated using adjustable parameters. We derive novel closed-form expressions for the ergodic rate and outage probability by incorporating key parameters, including signal amplification, active noise, power consumption, and phase quantization errors. Additionally, we explore the optimization of WPC scenarios, focusing on the time-switching factor and power consumption of the active RIS. The results validate our analysis, demonstrating that an active RIS significantly enhances WPC performance compared to a passive RIS.

This paper uses the theory of point processes and stochastic geometry to quantify the sky visibility experienced by users located in an outdoor environment. The general idea is to represent the buildings of this environment as a stationary marked point process, where the points represent the building locations and the marks their heights. The point process framework is first used to characterize the distribution of the blockage angle, which limits the visibility of a typical user into the sky due to the obstruction by buildings. In the context of communications, this distribution is useful when users try to connect to the nodes of an aerial or non-terrestrial network in a Line-of-Sight way. Within this context, the point process framework can also be used to investigate the gain of connectivity obtained thanks to Reconfigurable Intelligent Surfaces. Assuming that such surfaces are installed on the top of buildings to extend the user’s sky visibility, this point process approach allows one to quantify the gain in visibility and hence the gain in connectivity obtained by the typical user. The distributional properties of visibility-related metrics are cross-validated by comparison to simulation results and 3GPP measurements.

Satellite communications have been promising to guarantee global coverage with high capacity. In this paper, we analyze coverage performance of satellite networks with a distance-dependent line-of-sight (LOS) and non-LOS (NLOS) channel propagation probability to incorporate shadowing effect. Extending the stochastic geometry-based network analysis for terrestrial networks, we model the satellite network and users as a Poisson point process and derive an theoretical coverage probability expression to provide analytical understanding of the satellite network. Simulation results verify the exactness of the derived expression. The derived expression includes network parameters for satellite density and altitude, channel fading, pathloss, and the LOS probability, and provides insights on satel-lite networks. Our key finding is that NLOS channel propagation benefits the coverage performance by reducing the interference from non-associated satellites, and the higher NLOS probability is desirable to improve the coverage performance as the network becomes denser.

Reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) communication is a promising technology for enlarging the coverage area of millimeter wave systems. Unfortunately, realizing the full potential of these systems requires addressing numerous challenges in channel estimation. In this paper, channel estimation for RIS-assisted mmWave communications is considered. Under the assumption that the array manifolds of the base station antennas and the RIS reflecting elements are given by uniform arrays, an efficient two-stage channel estimation method based on array signal processing techniques is proposed. In the proposed algorithm, the direct and indirect channels are jointly estimated by space-time processing that exploits the sparsity in RIS-assisted mmWave channels and the features associated with uniform arrays. Then, several practical issues, including detection of the number of channel paths, imperfect RIS hardware, and complexity, are addressed. Extensions to the cases of uniform planar array-based RIS, wideband communication, and multiple users are also discussed. Numerical results validate the effectiveness of the proposed method.

This paper proposes a new systematic analysis framework for the LEO satellite networks by focusing on the orbit geometry. By modeling LEO satellite orbits as a Poisson line process, we characterize the distribution of the total length of the visible orbit region observed by the typical user.

We propose and analyze a new shadowing field model meant to capture spatial correlations. The interference field associated with this new model is compared to that of the widely used independent shadowing model. Independent shadowing over links is adopted because of the resulting closed forms for performance metrics, and in spite of the well-known fact that the shadowing fields of networks are spatially correlated. The main purpose of this paper is to challenge this independent shadowing approximation. For this, we analyze the interference measured at the origin in networks where 1) nodes which are in the same cell of some random shadowing tessellation share the same shadow, or 2) nodes which share a common mother point in some cluster process share the same shadow. By leveraging stochastic comparison techniques, we give the order relation of the three main user performance metrics, namely coverage probability, Shannon throughput and local delay, under both the correlated and the independent shadowing assumptions. We show that the evaluation of the considered metrics under the independent approximation is systematically pessimistic compared to the correlated shadowing model. The improvement in each metric when adopting the correlated shadow model is quantified and shown to be quite significant.

In this paper, we examine the benefits of multiple antenna communication in random wireless networks, the topology of which is modeled by stochastic geometry. The setting is the Poisson bipolar model introduced in [1], which is a natural model for ad-hoc and device-to-device networks. The primary finding is that, with the knowledge of channel state information between a receiver and its associated transmitter, by zero-forcing successive interference cancellation, and for appropriate antenna configurations, the ergodic spectral efficiency can be made to scale linearly with both: 1) the minimum of the number of transmit and receive antennas and 2) the density of nodes. This scaling law is achieved by using the multiple transmit antennas to send multiple data streams (e.g., through an openloop transmission method) and by exploiting the receive antennas to cancel interference. Furthermore, when a receiver is able to learn channel state information from a certain number of near interferers, higher scaling gains can be achieved when a successive interference cancellation method is used. Both results require rich scattering environments. A major implication of the derived scaling laws is that, under this scattering assumption, spatial multiplexing transmission methods are essential for obtaining better and eventually optimal scaling laws in random wireless networks with multiple antennas.

This paper considers a multi-antenna wireless net- work where the locations of transmitters are distributed as a Poisson point process, which is a natural model for ad-hoc and device-to-device networks. We show that, in such a network, the ergodic spectral efficiency scales linearly with respect to the network density under appropriate multiple antenna configurations and diversity assumptions. This scaling law is achieved by a simple zero-forcing decoder, which eliminates inter-stream interference using spatial multiplexing transmissions. We also show that when each receiver knows channel state information from some interferers, a higher scaling law holds for ergodic spectral efficiency than that without channel state information when using a partial zero-forcing method which eliminates dominant interference signals while boosting the desired signal power. Further, we show that spatial multiplexing transmission methods are essential for obtaining better scaling laws in certain regions of network parameters.

The Poisson building features a Poisson collection of random planes orthogonal to the axes of the 3-D Euclidean space. It divides the space into rectangular rooms of random sizes. The addition of wireless small cell base stations, deployed as Poisson point processes along the ceiling and corner lines of these rooms, provides a first stochastic geometric model representing in-building 3-D wireless networks. The main challenge for analyzing interference in such an environment is the fact that electromagnetic signals originating from different locations are blocked by common obstacles like walls and floors, which makes the path loss highly correlated in space. We propose a natural propagation model taking this phenomenon into account. We give analytical expressions for the interference field and its correlation within this framework. We illustrate the tractability of this model by providing analytical expressions for the spectral efficiency of the downlink in such indoor cellular networks and for D2D communications in such an environment. We combine the model and spatial simulations to show that classical 2-D and distance based attenuation models cannot be used in this context and to argue for the need of such 3-D models to assess the performance of this type of indoor communications.

Consider orthogonal planes in the 3-D space representing floors and walls in a large building. These planes divide the space into rooms, where a wireless infrastructure is deployed. This paper is focused on the analysis of the correlated shadowing field created by this wireless infrastructure through the set of walls and floors. When the locations of the planes and wireless nodes are governed by Poisson processes, we obtain a simple stochastic model which captures the non-uniform nature of node deployment and room sizes. This model, which we propose to call the Poisson building, captures the complex in-building shadowing correlations, is scalable in the number of dimensions, and can be used for network performance analysis. It allows an exact mathematical characterization of the interference distribution in both infinite and finite buildings, which further leads to closed-form expressions for the coverage probabilities in in-building cellular networks and the success probability of in-building underlay D2D transmissions.

Based on a linear formulation to interference alignment, an adaptive algorithm for interference-aligning beam tracking in time-varying MIMO interference channels is proposed. It is shown that obtaining the interference-aligning beam vector is equivalent to minimizing a certain Rayleigh quotient, and the conjugate gradient approach is adopted to construct an adaptive algorithm. The convergence and stability of the proposed algorithm are established in static channel case, and numerical results show that the proposed algorithm performs well compared with other existing methods with much less complexity.