An improved LDA approach

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Characterization of soluble microbial products as precursors of disinfection byproducts in drinking water supply

Water pollution by wastewater discharge can cause the problem of disinfection byproducts (DBPs) in drinking water supply. In this study, DBP formation characteristics of soluble microbial products (SMPs) as the main products of wastewater organic biodegradation were investigated. The results show that SMPs can act as DBP precursors in simulated wastewater biodegradation process. Under the experimental conditions, stabilized SMPs had DBPFP (DBP formation potential) yield of around 5.6 mumol mmol(-1)-DOC (dissolved organic carbon) and DBP speciation profile different from that of the conventional precursor, natural organic matter (NOM). SMPs contained polysaccharides, proteins, and humic-like substances, and the latter two groups can act as reactive DBP precursors. SMP fraction with molecular weight of <1 kDa accounted for 85% of the organic carbon and 65% of the DBP formation. As small SMP molecules are more difficult to remove by conventional water treatment processes, more efforts are needed to control wastewater-derived DBP problem in water resource management.postprin

Offset Learning based Channel Estimation for IRS-Assisted Indoor Communication

The system capacity can be remarkably enhanced with the help of intelligent reflecting surface (IRS) which has been recognized as a advanced breaking point for the beyond fifth-generation (B5G) communications. However, the accuracy of IRS channel estimation restricts the potential of IRS-assisted multiple input multiple output (MIMO) systems. Especially, for the resource-limited indoor applications which typically contains lots of parameters estimation calculation and is limited by the rare pilots, the practical applications encountered severe obstacles. Previous works takes the advantages of mathematical-based statistical approaches to associate the optimization issue, but the increasing of scatterers number reduces the practicality of statistical approaches in more complex situations. To obtain the accurate estimation of indoor channels with appropriate piloting overhead, an offset learning (OL)-based neural network method is proposed. The proposed estimation method can trace the channel state information (CSI) dynamically with non-prior information, which get rid of the IRS-assisted channel structure as well as indoor statistics. Moreover, a convolution neural network (CNN)-based inversion is investigated. The CNN, which owns powerful information extraction capability, is deployed to estimate the offset, it works as an offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes

Recurrent chromosome changes in 31 primary ovarian carcinomas detected by comparative genomic hybridization

published_or_final_versio

Joint Location and Channel Error Optimization for Beamforming Design for Multi-RIS Assisted MIMO System

Reconfigurable intelligent surface (RIS) has been proved to be a promising approach to enhance the performance of wireless communication because of its intelligently reconfiguring the passive reflecting elements. Previous works only consider the beamforming design for a fixed RIS location/deployment and perfect channel state information (CSI). While RIS's location and perfect CSI can be further optimized to enhance the system performance. In this paper, the robust beamforming design is investigated for multi-RIS assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization (WSM) problem is formulated. The considered WSM maximization problem includes channel estimation error, bandwidth as well as RIS placement variables, which results in a complicated nonconvex optimization problem. To handle this problem, we decouple the original problem into a series of subproblems, where the location, bandwidth, transmit beamforming and passive beamforming are optimized iteratively. Then, we develop an alternating optimization algorithm based on the penalty and gradient projection (GP) methods to alleviate the performance loss caused by the effect of imperfect CSI. Simulations validate that the proposed scheme can bring significant performance gains, especially considering its high spectral efficiency, when designing the location of RIS and imperfect CSI

Robust Hybrid Beamforming Design for Multi-RIS Assisted MIMO System with Imperfect CSI

Reconfigurable intelligent surface (RIS) has been developed as a promising approach to enhance the performance of fifth-generation (5G) systems through intelligently reconfiguring the reflection elements. However, RIS-assisted beamforming design highly depends on the channel state information (CSI) and RIS&#x2019;s location, which could have a significant impact on system performance. In this paper, the robust beamforming design is investigated for a RIS-assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization problem (WSM) is formulated to jointly optimize transmit beamforming of the BS, RIS placement and reflect beamforming of the RIS. The considered WSM maximization problem includes CSI error, phase shifts matrices, transmit beamforming as well as RIS placement variables, which results in a complicated nonconvex problem. To handle this problem, the original problem is divided into a series of subproblems, where the location of RIS, transmit/reflect beamforming and CSI error are optimized iteratively. Then, a multiobjective evolutionary algorithm is introduced to gradient projection-based alternating optimization, which can alleviate the performance loss caused by the effect of imperfect CSI. Simulation results reveal that the proposed scheme can potentially enhance the performance of existing wireless communication, especially considering a desirable trade-off among beamforming gain, user priority and error factor

Hybrid Evolutionary-based Sparse Channel Estimation for IRS-assisted mmWave MIMO Systems

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods

Identification of a minimal overlapping amplified region at 19q13.1-q13.2 in four ovarian cancer cell lines

Program no. 361postprin

Cross-Layer Optimization for Industrial Internet of Things in NOMA-based C-RANs

This paper investigates non-orthogonal multiple access (NOMA)-based cloud radio access networks (C-RANs), where edge caching is adopted to cut down the crowdedness of the fronthaul links. We aim to maximize the energy efficency (EE) by jointly optimizing the power allocation, analog and digital precoding, which turns out to be an intractable non-convex optimization problem. To tackle this problem, we first select cluster heads using the selecting cluster-head (SCH) algorithm, where the analog precoding matrix can be resolved by means of maximizing the array gains. Then, the device grouping algorithm is proposed to group devices according to the equivalent channel correlations, and thus the NOMA devices in the same beam are capable of sharing the same digital precoding vector. Finally, joint digital precoding design and power allocation algorithm is proposed to decompose the resultant optimization problem into two subproblems and solve them iteratively by applying Taylor expansion operation and the minimum mean square error (MMSE) detection. Simulation results validate that the proposed NOMA-based C-RANs with hybrid precoding (HP) scheme can achieve higher SE and EE than traditional orthogonal multiple access (OMA)-based approach and two-stage HP scheme

Zero-Forcing Beamforming for RIS-Enhanced Secure Transmission

This article considers a reconfigurable intelligent surface (RIS) enhanced multi-antenna secure transmission system in the presence of both active eavesdroppers (AEves) and passive eavesdroppers (PEves). We propose a zero-forcing (ZF) beamforming strategy that can steer transmit beam to the null space of AEves&#x0027; channel, while simultaneously enhancing the SNRs for a legitimate user equipment (UE) and PEves without perfect channel state information (CSI). The design goal is to maximize the SNR of UE subject to the transmit power constraint at the BS, SNR limitations on PEves, and reflection constraints at RIS. Due to the complexity of modeling, we first introduce a homogeneous Poisson point process (HPPP) to imitate the distribution of spatially random PEves, which derives a complicated non-convex problem. We then develop an efficient alternating algorithm where the transmit beamforming vector and the reflective beamforming vector are obtained by convex-concave procedure (CCP) and semi-definite relaxation (SDR) technique, respectively. Simulation results validate the performance advantages of the proposed optimized design