Hiding in Plain Sight: RIS-Aided Target Obfuscation in ISAC

In this article, a cooperative non-orthogonal multiple access (NOMA) mobile edge computing (MEC) network aided by reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT) is considered, where the transmitters offload parts of their computing tasks to the MEC server via a relay and an RIS for enhancing the transmission environment. The goal is to minimize the task delay and energy consumption for the considered networks by jointly designing the RIS's reflection beamforming vector, the power splitting (PS) ratio, the power allocation factors of the relay, as well as the offloading transmit power and the offloading task ratio. To solve the proposed problems, an iterative optimization algorithm based on the successive convex approximation (SCA) method and the penalty method is proposed to decompose the formulated problems into two sub-problems. In particular, we jointly optimize the PS ratio, the power allocation coefficients and the edge computing resource based on the SCA technology, and then the penalty method is used to optimize the RIS's reflecting beamforming vector. Simulation results illustrate that the proposed NOMA-MEC networks aided by RIS and SWIPT are superior to the conventional orthogonal multiple access MEC networks with SWIPT aided by RIS in terms of task delay and energy consumption.

With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is a promising technology for improving signal quality by controlling the scattering of electromagnetic waves in a nearly passive manner. Heterogeneous networks (HetNets) are another promising technology that is designed to meet the capacity requirements of the network. RIS technology can be used to improve system performance in the context of HetNets. This study investigates the applications of reconfigurable intelligent surfaces (RISs) in heterogeneous downlink networks (HetNets). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUEs). In this paper, we utilise RIS to mitigate cross-tier interference in a HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider RIS-assisted heterogeneous networks consisting of multiple SBS nodes and MUEs that utilise both direct paths and reflected paths. Therefore, the aim of this study is to maximise the sum rate of all MUEs by jointly optimising the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficient-based optimisation (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

In this paper, we propose a novel computer vision-based approach to aid reconfigurable intelligent surface (RIS) for dynamic beam tracking and implement the corresponding prototype verification system. A camera is attached at the RIS to obtain the visual information about the surroundings, with which RIS identifies the incident beam direction and the desired reflected beam direction, and then adjusts the reflection coefficients according to the pre-designed codebooks. We build a 20-by-20 RIS running at 5.4 GHz and develop a high-speed control board to ensure the real-time refresh of the reflection coefficients. Meanwhile we implement an independent peer-to-peer communications system to simulate the physical link between the base station and the user equipment. The vision-aided RIS prototype system is tested in two mobile scenarios: RIS works in near-field conditions as a passive array antenna of the base station; RIS works in far-field conditions to assist the communication between the base station and the user equipment. The experimental results show that RIS can quickly adjust the reflection coefficients for dynamic beam tracking with the help of visual information.

In this paper, we propose a location-aware channel estimation based on the atomic norm minimization (ANM) for the reconfigurable intelligent surface (RIS)-aided millimeter-wave multiple-input-multiple-output (MIMO) systems. The beam training overhead at the base station (BS) is reduced by the direct beam steering towards the RIS with the location of the BS and the RIS. The RIS beamwidth adaptation is proposed to reduce the beam training overhead at the RIS, and also it enables accurate channel estimation by ensuring the user equipment receives all the multipath components from the RIS. After the beam training, the cascaded effective channel of the RIS-aided MIMO systems is estimated by ANM. Depending on whether the beam training overhead at the BS or at the RIS is reduced or not, the channel is represented as a linear combination of either 1D atoms, 2D atoms, or 3D atoms, and the ANM is applied to estimate the channel. Simulation results show that the proposed location-aware channel estimation via 2D ANM and 3D ANM achieves superior estimation accuracy to benchmarks.

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

Recently, a reconfigurable intelligent surface (RIS) that provides alternative wireless propagation paths (by reflecting an electromagnetic wave from a base station in desired directions) has attracted attention as a key facilitating technology for Beyond 5G and 6G wireless communications. This paper presents an improved path-loss model suitable for RIS-aided wireless communications. Unlike previously reported RIS path-loss models, the proposed model considers more practical electromagnetic phenomena (e.g., the incident and reflected gain patterns of the RIS unit cell, the effective received power at the RIS, the reflection phase error of each unit cell, and the specular reflection loss), by applying rigorous analysis. Furthermore, the accuracy of the proposed path-loss model is validated by extensive experimental measurements using our developed RIS prototype. The prototype consists of 576 phase-reconfigurable unit cells operating at 29 GHz, each of which features two PIN diodes. Each PIN diode is independently controlled to produce the desired phase distributions on the RIS. It is shown that the calculated received power in the proposed path-loss model matches the measurement results within a 1 dB margin across various practical test environments.

Energy efficiency (EE) has become a key performance indicator in designing future wireless networks. In this regard, we propose a novel two reconfigurable intelligent surfaces (RISs) assisted coordinated multipoint (CoMP) network, where two RISs cooperate to reduce energy consumption by constructing an intelligent radio environment. Aiming to maximize this system’s energy efficiency (EE), the joint optimization problem on the cooperative beamforming among base stations (BSs) and passive beamforming among RISs, subject to each user’s data rate requirement, is formulated. An alternative optimization algorithm is proposed by decoupling it into a cooperative beamforming subproblem among BSs and a cooperative reflective coefficients design subproblem among RISs. Dinkelbach’s transform and complex fractional programming algorithms are also invoked to transform these two subproblems into convex optimization problems and obtain the optimal solution for each sub-problem. Numerical results validate the proposed algorithm’s feasibility, fast convergence, and flexibility. It also shows that the proposed scheme with cooperative beamforming design for both BSs and RISs outperforms that without RIS and that with randomly initialized RISs.

Reconfigurable intelligent surfaces (RIS) have the ability to steer the electromagnetic (EM) waves to a desired direction. This enables the improvement of the wireless link performance by allowing the illumination of receivers otherwise shadowed by buildings or hills. In this paper, a standards-compliant link-level simulator is developed to study the performance improvement offered by a RIS in 5G New Radio (NR) uplink operating at sub-6 GHz bands. At these frequencies the direct channel between the user and base station is rarely completely blocked, but given the stringent power restrictions of devices, the RIS is utilized for enhancing the coverage performance in the uplink direction. In the studied cases, the transmitter (TX) is close to the RIS and a line-of-sight (LoS) path between the TX and RIS is assumed. The channel between the TX and receiver (RX) is modeled as a non-line-of-sight (NLoS) channel with 5G NR clustered delay line A (CDL-A) profile. Both LoS and NLoS channels between the RIS and RX are considered. Under state-of-the-art system settings, the RIS is shown to increase the symbol error rate link performance by 6 dB. When the performance is measured with coded bit error rate, the performance gain in simulated cases is 1 dB. The coverage enhancement is measured with the throughput as a function of the distance between the TX and RX. The distance at which the maximum possible throughput can be achieved is increased about 5%. The coverage can be further extended if a lower than the maximum throughput is accepted.

This letter investigates an active reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system. Compared with passive RIS, the active RIS is equipped with active reflective amplifier, which can effectively circumvent the “double path loss” attenuation. We propose a joint computing and communication design to minimize the maximum computational latency (MCL), subject to both the phase shift constraints and the edge computing capability constraints. Specifically, the original problem is decoupled into four subproblems, and then the block coordinate descent (BCD) method and the successive convex approximation (SCA) method are applied to alternately optimize the subproblems. The simulation results show that with the same power budget, the performance gain achieved by the active RIS is much larger than that by the passive RIS.

The widespread deployment of the Internet of Things (IoT) is promoting interest in simultaneous wireless information and power transfer (SWIPT), the performance of which can be further improved by employing a reconfigurable intelligent surface (RIS). In this article, we propose a novel RIS-enhanced SWIPT system built on an electromagnetic-compliant framework. The mutual-coupling effects in the whole system are presented explicitly. Moreover, the reconfigurability of RIS is no longer expressed by the reflection-coefficient matrix but by the impedances of the tunable circuit. For comparison, both the no-coupling and the coupling-awareness cases are discussed. In particular, the energy efficiency (EE) is maximized by cooperatively optimizing the impedance parameters of the RIS elements as well as the active beamforming vectors at the base station (BS). For the coupling-awareness case, the considered problem is split into several subproblems and solved alternatively due to its nonconvexity. First, it is transformed into a more solvable form by applying the Neuman series approximation, which can be resolved iteratively. Then, an alternative optimization (AO) framework and semidefinite relaxation (SDR), successive convex approximation (SCA), and Dinkelbach's algorithm are applied to solve each subproblem decomposed from it. Owning to the similarity between the two cases, the no-coupling one can be viewed as a reduced form of the coupling case and, thus, solved through a similar approach. Numerical results reveal the influence of mutual-coupling effects on the EE, especially in the RIS with closely spaced elements. In addition, physical beam designs are presented to demonstrate how the RIS assists SWIPT through various reflecting states in different conditions.

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

Reconfigurable intelligent surface (RIS)-aided wireless communications systems are one the promising wireless communication system where the wave can be guided by the RIS. It is envisioned that beyond-5G/6G communication will have a low-cost, high spectral efficiency, high energy efficiency, and smart wireless environment. In this paper, initially, different measurement techniques of the RIS have been discussed, which are available in the literature. Then, a new type of RIS has been proposed. Finally, a different parameter measurement technique for our proposed RIS has been presented. A low-cost FR4 substrate with a height of 1.6 mm was considered to design the RIS in the sub-6 GHz frequency band. Another important thing is that our proposed IRS is a single-layer substrate backed by a copper plate. The area of each unit cell was 42 mm × 42 mm. The RIS was designed to operate at the central frequency of the 3.5 GHz frequency band. The novelty of the proposed RIS is that it is a polarization-independent structure. Thus, polarization-related losses can be overcome using this structure. A 10×10-unit cell array was designed to check the radiation performance. The magnitude of the reflection coefficients was measured in our laboratory for the proposed configuration.

Millimeter wave communications are sensitive to complex scattering environments (e.g., to the presence of obstacles), which can be mitigated by reconfigurable intelligent surfaces (RISs). Traditional nearly passive RISs lack the ability to perform signal processing operations, which makes channel estimation in RIS-aided communications more challenging. Hence, in this paper, we focus on a hybrid RIS architecture, which is equipped with a small number of active elements. These active elements can be connected with baseband processing units through radio frequency (RF) chains. We estimate the whole channel, including the channel between the base station (BS) and the RIS, that between the users and the RIS, and that between the BS and the users. The whole channel is acquired at the hybrid RIS through the transmission of several segments of training pilots. In order to decrease the hardware cost, the BS and the hybrid RIS are equipped with RF chains with low-resolution analog-to-digital converters (ADCs). Since the numbers of BS antennas and RIS elements are very large, the estimation of the full-space channels is not straightforward. To tackle this problem, we propose a channel extrapolation scheme based on a joint selection model. Specifically, we select a BS antenna subset and an RIS element subset to be connected to the RF chains and to estimate the partial-space channels related to these subsets. Then, a reference-based variational auto-encoder model is used to implement the extrapolation from the partial-space channels to the full-space channels. Besides, the optimal joint selection pattern is acquired through a selection network to improve the channel extrapolation performance. Moreover, to overcome the quantization error caused by the use of low-resolution ADCs, we propose a two-stage repair scheme for channel estimation. Simulation results are provided to demonstrate the effectiveness of the designed channel extrapolation scheme.

Active reconfigurable intelligent surface (RIS) emerges as an effective technique to resist the double-fading attenuation of passive RIS. By embedding with power harvesting function, it further evolves to zero-power active RIS, which can effectively enhance the flexibility of RIS deployment without external power demand. Nevertheless, existing works neglected the inherent difficulty of channel estimation (CE) for RIS-assisted systems, and the discrete phase shift constraint in practical deployment. In this paper we design a new element-wise RIS architecture and propose a distributed location-aided transmission scheme with low complexity to enhance the reflected gain for channel state information (CSI)-limited RIS-assisted near-field communications. Specifically, the new element-wise RIS provides dynamic element selection capability with low hardware resources. Based on Fresnel diffraction theory, we construct the mapping from locations in space-domain to phase distributions of waves in phase-domain and reveal the priority of elements for harvesting and reflecting. Then, the distributed beamforming design with the phase of determine-then-align is proposed, where the estimation overhead reduction stems from exempted requirements of RIS-associated CE at base station (BS). The asymptotic analysis indicates that the proposed scheme can achieve the optimal gain with a fixed proportion of reflective elements when RIS is large, followed by simulations to verify its superiority to other protocols.

Reconfigurable Intelligent Surface (RIS) is one of the promising techniques for 6G wireless communications, and recently has also been shown to be able to improve secure communications. However, there is a “double fading” effect in the reflection link between base station and user, thus passive RIS only achieves a negligible secrecy gain in typical communications scenarios. In this letter, we propose an active RIS-aided multi-antenna physical layer secrecy transmission scheme, where the active RIS can amplify the signal actively. Our aim is to minimize the transmit power subject to the constraint of secrecy rate. To solve the non-convex optimization problem, a penalty-based alternating minimization (AltMin) algorithm is proposed to optimize both the beamformer at the transmitter and the reflection matrix at RIS. Simulation results show that active RIS can resist the impact of “double fading” effect effectively, and is more energy efficient than passive RIS.