Energy Minimization for Active RIS-Aided UAV-Enabled SWIPT Systems

In this paper, we consider an active reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle(UAV)-enabled simultaneous wireless information and power transfer(SWIPT) system with multiple ground users. Compared with the conventional passive RIS, the active RIS deploying the internally integrated amplifiers can offset part of the multiplicative fading. In this system, we deal with an optimization problem of minimizing the total energy cost of the UAV. Specifically, we alternately optimize the trajectories, the hovering time, and the reflection vectors at the active RIS by using the successive convex approximation (SCA) method. Simulation results show that the active RIS performs better in energy saving than the conventional passive RIS.Comment: Keywords:Reconfigurable intelligent surface (RIS), active RIS, unmanned aerial vehicle (UAV), simultaneous wireless information and power transfer (SWIPT), successive convex approximation (SCA

Two-Timescale Transmission Design for Wireless Communication Systems Aided by Active RIS

This paper considers an active reconfigurable intelligent surface (RIS)-aided communication system, where an M-antenna base station (BS) transmits data symbols to a single-antenna user via an N-element active RIS. We use two-timescale channel state information (CSI) in our system, so that the channel estimation overhead and feedback overhead can be decreased dramatically. A closed-form approximate expression of the achievable rate (AR) is derived and the phase shift at the active RIS is optimized. In addition, we compare the performance of the active RIS system with that of the passive RIS system. The conclusion shows that the active RIS system achieves a lager AR than the passive RIS system

Multiuser Full-Duplex Two-Way Communications via Intelligent Reflecting Surface

Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). Based on information exchanged by the base station (BS) and all users, this approach can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unit-modulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas.Comment: Accepted by IEEE Transactions on Signal Processin

CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well

Solving a Class of Modular Polynomial Equations and its Relation to Modular Inversion Hidden Number Problem and Inversive Congruential Generator

In this paper we revisit the modular inversion hidden number problem (MIHNP) and the inversive congruential generator (ICG) and consider how to attack them more efficiently. We consider systems of modular polynomial equations of the form a_{ij}+b_{ij}x_i+c_{ij}x_j+x_ix_j=0 (mod p) and show the relation between solving such equations and attacking MIHNP and ICG. We present three heuristic strategies using Coppersmith\u27s lattice-based root-finding technique for solving the above modular equations. In the first strategy, we use the polynomial number of samples and get the same asymptotic bound on attacking ICG proposed in PKC 2012, which is the best result so far. However, exponential number of samples is required in the work of PKC 2012. In the second strategy, a part of polynomials chosen for the involved lattice are linear combinations of some polynomials and this enables us to achieve a larger upper bound for the desired root. Corresponding to the analysis of MIHNP we give an explicit lattice construction of the second attack method proposed by Boneh, Halevi and Howgrave-Graham in Asiacrypt 2001. We provide better bound than that in the work of PKC 2012 for attacking ICG. Moreover, we propose the third strategy in order to give a further improvement in the involved lattice construction in the sense of requiring fewer samples

Secure Wireless Communication in RIS-Aided MISO System With Hardware Impairments

In practice, residual transceiver hardware impairments inevitably lead to distortion noise which causes the performance loss. In this letter, we study the robust transmission design for a reconfigurable intelligent surface (RIS)-aided secure communication system in the presence of transceiver hardware impairments. We aim for maximizing the secrecy rate while ensuring the transmit power constraint on the active beamforming at the base station and the unit-modulus constraint on the passive beamforming at the RIS. To address this problem, we adopt the alternate optimization method to iteratively optimize one set of variables while keeping the other set fixed. Specifically, the successive convex approximation (SCA) method is used to solve the active beamforming optimization subproblem, while the passive beamforming is obtained by using the semidefinite program (SDP) method. Numerical results illustrate that the proposed transmission design scheme is more robust to the hardware impairments than the conventional non-robust scheme that ignores the impact of the hardware impairments

Independent Validation of the SWMM Green Roof Module

Green roofs are a popular Sustainable Drainage Systems (SuDS) technology. They provide multiple benefits, amongst which the retention of rainfall and detention of runoff are of particular interest to stormwater engineers. The hydrological performance of green roofs has been represented in various models, including the Storm Water Management Model (SWMM). The latest version of SWMM includes a new LID green roof module, which makes it possible to model the hydrological performance of a green roof by directly defining the physical parameters of a green roof’s three layers. However, to date, no study has validated the capability of this module for representing the hydrological performance of an extensive green roof in response to actual rainfall events. In this study, data from a previously-monitored extensive green roof test bed has been utilised to validate the SWMM green roof module for both long-term (173 events over a year) and short-term (per-event) simulations. With only 0.357% difference between measured and modelled annual retention, the uncalibrated model provided good estimates of total annual retention, but the modelled runoff depths deviated significantly from the measured data at certain times (particularly during summer) in the year. Retention results improved (with the difference between modelled and measured annual retention decreasing to 0.169% and the Nash-Sutcliffe Model Efficiency (NSME) coefficient for per-event rainfall depth reaching 0.948) when reductions in actual evapotranspiration due to reduced substrate moisture availability during prolonged dry conditions were used to provide revised estimates of monthly ET. However, this aspect of the model’s performance is ultimately limited by the failure to account for the influence of substrate moisture on actual ET rates. With significant differences existing between measured and simulated runoff and NSME coefficients of below 0.5, the uncalibrated model failed to provide reasonable predictions of the green roof’s detention performance, although this was significantly improved through calibration. To precisely model the hydrological behaviour of an extensive green roof with a plastic board drainage layer, some of the modelling structures in SWMM green roof module require further refinement

Detention Performance of Green Roof Systems: Experimental Characterisation and Numerical Modelling

Green roofs, as an example of Sustainable Drainage Systems (SuDS), can benefit stormwater management through retention and detention processes. Retention in a green roof refers to the rainwater that is retained in the system, and detention is the process that leads to lag and attenuation effects in the system runoff hydrograph. The understanding of retention in green roof systems has been well-established. However, the understanding and modelling of green roof detention processes are less developed. The physical properties of green roof substrates that contribute to detention performance have not been fully characterised to date. Current research in detention modelling lacks a generic physically-based model capable of modelling the detention processes in a complete green roof system. In this study, the physical properties of green roof substrates were characterised, and a physically-based detention model capable of representing the detention processes in a complete green roof system was developed. The Soil Water Release Curves (SWRC) for four representative green roof substrates were determined using the hanging column method, and the Hydraulic Conductivity Function (HCF) for the substrates was characterised in an infiltration column using steady-state and transient techniques. The conventional natural soil-derived HCF model — Durner-Mualem model, for which the model parameters were from the measured SWRC data, was shown to provide a poor fit to the measured HCF data. A new three-segment HCF curve was, therefore, proposed to fit measured HCF data for the green roof substrates. Detention tests were carried out on 100 mm and 200 mm deep substrates using four simulated design storms. The runoff and moisture content data collected during the detention tests were used to validate the HCFs using the Richards Equation. The results showed that the new HCF provides a better estimation of the runoff and moisture content profiles than the Durner-Mualem model. A two-stage physically-based detention model was developed for complete green roof systems in this study. In the model, the vertical flow in the substrate is represented by the Richards Equation, and the horizontal flow in the underlying drainage layer is modelled by the Saint Venant equation. This two-stage physically-based model, together with the green roof model in SWMM (Storm Water Management Model) were validated using measured runoff profiles from two contrasting green roof systems: a conventional green roof system; and an innovative system. Both models showed a reasonable estimation of the runoff profiles from the green roof systems. However, due to the limitation that the models are not capable of representing the flow conditions in the innovative green roof system’s detention layer, the model results were less accurate than for the conventional green roof system