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Recently, reconfigurable intelligent surfaces (RIS) have attracted a lot of attention due to their capability of extending cell coverage by reflecting signals toward the receiver. In this letter, we analyze the coverage of a downlink RIS-assisted network with one base station (BS) and one user equipment (UE). Since the RIS orientation and the horizontal distance between the RIS and the BS have a significant influence on the cell coverage, we formulate an RIS placement optimization problem to maximize the cell coverage by optimizing the RIS orientation and horizontal distance. To solve the formulated problem, a coverage maximization algorithm (CMA) is proposed, where a closed-form optimal RIS orientation is obtained. Numerical results verify our analysis.
The received signal strength (RSS) fingerprinting based localization is a widely used technique to locate mobile devices in cellular networks. However, traditional RSS finger-printing based techniques require a set of RSS values from multiple base stations (BSs) at each location, and the RSS from remote BSs is weak and easily affected by the noise and other variations of the propagation channel, which limits the localization accuracy. In this paper, we propose to utilize a reconfigurable intelligent surface (RIS) to improve the accuracy for the RSS fingerprinting based multi-user outdoor localization, which requires the RSS from only one BS. By modifying phase shifts of signals reflected by the RIS, we can create different RSS values at the same location using the signal from the nearest BS for localization. To optimize the RIS phase shifts, we formulate the localization error minimization (LEM) problem and propose an LEM algorithm. Simulation results validate the effectiveness of the proposed scheme.
Due to its ability to precisely control wireless beams, holographic multiple-input multiple-output (HMIMO) is expected to be a promising solution to achieve high-accuracy localization. However, as the scale of HMIMO increases to improve beam control capability, the corresponding near-field (NF) region expands, indicating that users may exist in both NF and far-field (FF) regions with different electromagnetic transmission characteristics. As a result, existing methods for pure NF or FF localization are no longer applicable. We consider a hybrid NF and FF localization scenario in this paper, where a base station (BS) locates multiple users in both NF and FF regions with the aid of a reconfigurable intelligent surface (RIS), which is a low-cost implementation of HMIMO. In such a scenario, it is difficult to locate the users and optimize the RIS phase shifts because whether the location of the user is in the NF or FF region is unknown, and the channels of different users are coupled. To tackle this challenge, we propose a RIS-enabled localization method that searches the users in both NF and FF regions and tackles the coupling issue by jointly estimating all user locations. We derive the localization error bound by considering the channel coupling and propose an RIS phase shift optimization algorithm that minimizes the derived bound. Simulations show the effectiveness of the proposed method and demonstrate the performance gain compared to pure NF and FF techniques.
Intelligent reflecting surface (IRS), which is capable to adjust propagation conditions by controlling phase shifts of the reflected waves that impinge on the surface, has been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, IOS is an important instance of reconfigurable intelligent surface (RIS) that is capable to provide service coverage to the mobile users (MUs) in a reflective and a transmissive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS perform beamforming jointly, to improve the received power of multiple MUs on both sides of the IOS, through different reflective/transmissive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to solve the resulting non-convex program efficiently. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS.
The Internet of Meta-material Things (Meta-IoT) has shown its potential in future IoT applications as they are battery-free and easy to maintain. Through a Meta-IoT sensor, we can obtain environmental information by detecting the frequency response from its reflected signals. Due to the reflection property, we can utilize Meta-IoT sensors to provide additional reflection paths to improve the communication performance of existing wireless systems while performing sensing tasks. In this letter, we propose the concept of dual-functional Meta-IoT sensor, which is able to sense environmental conditions and facilitate communication at the same time. Nevertheless, it remains challenging to simultaneously guarantee the quality of sensing and communication. To address these challenges, we optimize the sensor structure and the subcarrier power allocation successively. Simulation results show that the communication performance can be significantly improved with the proposed scheme while the sensing performance guaranteed.
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