Quantum Multi-User Detection Based on Coherent State Signals

Multi-user detection is one of the important technical problems for moderncommunications. In the field of quantum communication, the multi-access channel onwhich we apply the technology of quantum information processing is still an openquestion. In this work, we investigate the multi-user detection problem based on thebinary coherent-state signals whose communication way is supposed to be seen as aquantum channel. A binary phase shift keying model of this multi-access channel isstudied and a novel method of quantum detection proposed according to the conclusionof the quantum measurement theory. As a result, the average interference betweendeferent users is presented and the average error probability of the quantum detection isderived theoretically. Finally, we show the maximum channel capacity of this effectivedetection for a two-access quantum channel.

Multi-user quantum key distribution with collective eavesdropping detection over collective-noise channels

A multi-user quantum key distribution protocol is proposed with single particles and the collective eavesdropping detection strategy on a star network. By utilizing this protocol, any two users of the network can accomplish quantum key distribution with the help of a serving center. Due to the utilization of the collective eavesdropping detection strategy, the users of the protocol just need to have the ability of performing certain unitary operations. Furthermore, we present three fault-tolerant versions of the proposed protocol, which can combat with the errors over different collective-noise channels.The security of all the proposed protocols is guaranteed by the theorems on quantum operation discrimination.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

A highly sensitive ratiometric near-infrared nanosensor based on erbium-hyperdoped silicon quantum dots for iron(Ⅲ) detection

Ratiometric fluorescent detection of iron(Ⅲ)(Fe^(3+))offers inherent self-calibration and contactless analytic capabilities.However,realizing a dual-emission near-infrared(NIR)nanosensor with a low limit of detection(LOD)is rather challenging.In this work,we report the synthesis of water-dispersible erbium-hyperdoped silicon quantum dots(Si QDs:Er),which emit NIR light at the wavelengths of 810 and 1540 nm.A dual-emission NIR nanosensor based on water-dispersible Si QDs:Er enables ratiometric Fe^(3+)detection with a very low LOD(0.06μM).The effects of pH,recyclability,and the interplay between static and dynamic quenching mechanisms for Fe^(3+)detection have been systematically studied.In addition,we demonstrate that the nanosensor may be used to construct a sequential logic circuit with memory functions.

Complex field network-coded cooperation based on multi-user detection in wireless networks

Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.

Wavelet Packet Domain LMS Based Multi-User Detection

An improved wavelet packet domain least mean square （IWPD-LMS） based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the best wavelet packet basis according to a novel convergence contribution function rather than the conventional Shannon entropy. The theoretic analyses show that the inadequacy of the eigenvalue spread of the tap-input correlation matrix is ameliorated, thus the convergence performance is improved greatly. The simulation result of convergence performance and bit error rate（BER） performance as a function of the signal power to noise power ratio（SNR） are presented finally to prove the validity of the proposed algorithm.

RLS and LMS blind adaptive multi-user detection method and comparison in acoustic communication

RLS and LMS blind adaptive multi-user detection algorithm and multi-user detector was proposed to solve the problem of multi-user signal detection problem encountered in underwater acoustic communication networks.In simulation analysis,RLS and the LMS blind adaptive multi-user detector were designed and tested for synchronous and asynchronous multi-user communication process.The results of SIR comparison and MMSE comparison show that,both of the two methods can realize blind adaptive detection when any user change in multi-user communication,during this process,the training communication sequences are not needed.The RLS algorithm has about 5 dB higher in SIR compared with LMS algorithm,and the convergence velocity of RLS algorithm is also higher than LMS algorithm when the communication users change.RLS algorithm has better ability in multi-user detection than that of LMS algorithm,and it has great attraction and guiding significance for solving the problem of multiple access interference(MAI) in multi-user communication.

Study of multi-rate multi-user detection based on supervision decision

Multi-user detection (MUD) based on multirate transmission in code division multiple access (CDMA) system is discussed. Under the requirement of signal interference ratio (SIR) detection at base station and framework with parallel interference cancellation, a supervision decision algorithm based on pre-decision of probabilistic data association (PDA) and hard decision is proposed. The detection performance is analyzed and simulation is implemented to show that the supervision decision algorithm improves the detection performance effectively.

ROLS-AWS algorithm used in RBF neural network for multi-user detection

To improve the computational speed, the ROLS-AWS algorithm was employed in the RBF based MUD receiver. The radial basis function was introduced into the multi-user detection （MUD） firstly. Then a three-layer neural network demodulation spread-spectrum signal model in the synchronous Gauss channel was given and the multi-user detection receiver was analyzed intensively. Simulations by computer illustrate that the proposed RBF based MUD receiver employing the ROKS-AWS algorithm is better than conventional detectors and common BP neural network based MUD receivers on suppressing multiple access interference and near-far resistance.

A New Approach to Multi-user Detection in DS-CDMA

A graph model is constructed for the Multi-user Detection of DS-CDMA system. Based on it, a Hopfield-like algorithm is put forward for the implementation of optimum receiver. Compared with the Hopfield approach, it has a higher computational complexity but better performance.

Joint Channel and Multi-User Detection Empowered with Machine Learning

The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

Dynamic multi-user detection scheme based on CVA-SSAOMP algorithm in uplink grant-free NOMA

In the uplink grant-free non-orthogonal multiple access(NOMA)scenario,since the active user at the sender has a structured sparsity transmission characteristic,the compressive sensing recovery algorithm is initially applied to the joint detection of the active user and the transmitted data.However,the existing compressed sensing recovery algorithms with unknown sparsity often require noise power or signal-to-noise ratio(SNR)as the priori conditions,which greatly reduces the algorithm adaptability in multi-user detection.Therefore,an algorithm based on cross validation aided structured sparsity adaptive orthogonal matching pursuit(CVA-SSAOMP)is proposed to realize multi-user detection in dynamic change communication scenario of channel state information(CSI).The proposed algorithm transforms the structured sparsity model into a block sparse model,and without the priori conditions above,the cross validation method in the field of statistics and machine learning is used to adaptively estimate the sparsity of active user through the residual update of cross validation.The simulation results show that,compared with the traditional orthogonal matching pursuit(OMP)algorithm,subspace pursuit(SP)algorithm and cross validation aided block sparsity adaptive subspace pursuit(CVA-BSASP)algorithm,the proposed algorithm can effectively improve the accurate estimation of the sparsity of active user and the performance of system bit error ratio(BER),and has the advantage of low-complexity.

Joint Multi-User Detection with Weighting Factors for Unsourced Multiple Access

Multi-user detection techniques are currently being studied as highly promising technologies for improving the performance of unsourced multiple access systems. In this paper, we propose joint multi-user detection schemes with weighting factors for unsourced multiple access. First, we introduce bidirectional weighting factors in the extrinsic information passing process between the multi-user detector based on belief propagation (BP) and the LDPC decoder. Second, we incorporate bidirectional weighting factors in the message passing process between the MAC nodes and the user variable nodes in BP- based multi-user detector. The proposed schemes select the optimal weighting factors through simulations. The simulation results demonstrate that the proposed schemes exhibit significant performance improvements in terms of block error rate (BLER) compared to traditional schemes. .

Rapid Isolation and Multiplexed Detection of Exosome Tumor Markers Via Queued Beads Combined with Quantum Dots in a Microarray

Tumor-derived exosomes are actively involved in cancer progression and metastasis and have emerged as a promising marker for cancer diagnosis in liquid biopsy.Because of their nanoscale size,complex biogenesis,and methodological limitations related to exosome isolation and detection,advancements in their analysis remain slow.Microfluidic technology offers a better analytic approach compared with conventional methods.Here,we developed a bead-based microarray for exosome isolation and multiplexed tumor marker detection.Using this method,exosomes are isolated by binding to antibodies on the bead surface,and tumor markers on the exosomes are detected through quantum dot(QD)probes.The beads are then uniformly trapped and queued among micropillars in the chip.This design benefits fluorescence observation by dispersing the signals into every single bead,thereby avoiding optical interference and enabling more accurate test results.We analyzed exosomes in the cell culture supernatant of lung cancer and endothelial cell lines,and different lung cancer markers labeled with three QD probes were used to conduct multiplexed detection of exosome surface protein markers.Lung cancer-derived samples showed much higher(~sixfold-tenfold)fluorescence intensity than endothelial cell samples,and different types of lung cancer samples showed distinctive marker expression levels.Additionally,using the chip to detect clinical plasma samples from cancer patients showed good diagnostic power and revealed a well consistency with conventional tests for serological markers.These results provide insight into a promising method for exosome tumor marker detection and early-stage cancer diagnosis.

Improved Security Detection Strategy for Quantum "Ping-Pong" Protocol and Its Security Analysis

In order to transmit the secure messages,a deterministic secure quantum direct communication protocol,called the "Ping-pong"protocol was proposed by Bostrm and Felbinger [Phys.Rev.Lett.89,187902(2002) ].But the protocol was proved to have many vulnerabilities,and can be attacked by eavesdroppers.To overcome the problem,an improved security detection strategy which inserts the | 0〉,| 1〉,|+〉and |-〉particles into the messages as the decoy particles randomly in the"Ping-pong"protocol is presented.During the security analysis,the method of the entropy theory is introduced,and three detection strategies are compared quantitatively by using the constraint between the information which eavesdroppers can obtain and the interference introduced.Because of the presence of the trap particles |+〉and |-〉,the detection rate will be no less than 25% when Eve attacks the communication.The security analysis result shows that the efficiency of eavesdropping detection in the presented protocol is higher than the other two,so the detection strategy in the protocol can ensure that the "Ping-pong"protocol is more secure.

Hydroxylated graphene quantum dots as fluorescent probes for sensitive detection of metal ions

Highly sensitive methods are important for monitoring the concentration of metal ions in industrial wastewater.Here,we developed a new probe for the determination of metal ions by fluorescence quenching.The probe consists of hydroxylated graphene quantum dots(H-GQDs),prepared from GQDs by electrochemical method followed by surface hydroxylation.It is a non-reactive indicator with high sensitivity and detection limits of 0.01μM for Cu2+,0.005μM for Al3+,0.04μM for Fe3+,and 0.02μM for Cr3+.In addition,the low biotoxicity and excellent solubility of H-GQDs make them promising for application in wastewater metal ion detection.

Establishment and performance analysis of a new multiplex detection method for influenza an and B virus antigen

BACKGROUND Influenza A and B virus detection is pivotal in epidemiological surveillance and disease management.Rapid and accurate diagnostic techniques are crucial for timely clinical intervention and outbreak prevention.Quantum dot-encoded microspheres have been widely used in immunodetection.The integration of quantum dot-encoded microspheres with flow cytometry is a well-established technique that enables rapid analysis.Thus,establishing a multiplex detection method for influenza A and B virus antigens based on flow cytometry quantum dot microspheres will help in disease diagnosis.AIM To establish a codetection method of influenza A and B virus antigens based on flow cytometry quantum dot-encoded microsphere technology,which forms the foundation for the assays of multiple respiratory virus biomarkers.METHODS Different quantum dot-encoded microspheres were used to couple the monoclonal antibodies against influenza A and B.The known influenza A and B antigens were detected both separately and simultaneously on a flow cytometer,and the detection conditions were optimized to establish the influenza A and B antigen codetection method,which was utilized for their detection in clinical samples.The results were compared with the fluorescence quantitative polymerase chain reaction(PCR)method to validate the clinical performance of this method.RESULTS The limits of detection of this method were 26.1 and 10.7 pg/mL for influenza A and B antigens,respectively,which both ranged from 15.6 to 250000 pg/mL.In the clinical sample evaluation,the proposed method well correlated with the fluorescent quantitative PCR method,with positive,negative,and overall compliance rates of 57.4%,100%,and 71.6%,respectively.CONCLUSION A multiplex assay for quantitative detection of influenza A and B virus antigens has been established,which is characterized by high sensitivity,good specificity,and a wide detection range and is promising for clinical applications.

Fluorescent aptasensor for detection of live foodborne pathogens based on multicolor perovskite-quantum-dot-encoded DNA probes and dual-stirring-bar-assisted signal amplification

In this study,a fluorescent(FL)aptasensor was developed for on-site detection of live Salmonella typhimurium(S.T.)and Vibrio parahaemolyticus(V.P.).Complementary DNA(cDNA)of aptamer(Apt)-functionalized multicolor polyhedral oligomeric silsesquioxane-perovskite quantum dots(cDNA-POSSPQDs)were used as encoded probes and combined with dual-stirring-bar-assisted signal amplification for pathogen quantification.In this system,bar 1 was labeled with the S.T.and V.P.Apts,and then bar 2 was functionalized with cDNA-POSS-PQDs.When S.T.and V.P.were introduced,pathogen-Apt complexes would form and be released into the supernatant from bar 1.Under agitation,the two complexes reached bar 2 and subsequently reacted with cDNA-POSS-PQDs,which were immobilized on MXene.Then,the encoded probes would be detached from bar 2 to generate FL signals in the supernatant.Notably,the pathogens can resume their free state and initiate next cycle.They swim between the two bars,and the FL signals can be gradually enhanced to maximum after several cycles.The FL signals from released encoded probes can be used to detect the analytes.In particular,live pathogens can be distinguished from dead ones by using an assay.The detection limits and linear range for S.T.and V.P.were 30 and 10 CFU/mL and 10^(2) -10^(6) CFU/mL,respectively.Therefore,this assay has broad application potential for simultaneous on-site detection of various live pathogenic bacteria in water.

A long-distance quantum key distribution scheme based on pre-detection of optical pulse with auxiliary state

We construct a circuit based on PBS and CNOT gates, which can be used to determine whether the input pulse is empty or not according to the detection result of the auxiliary state, while the input state will not be changed. The circuit can be treated as a pre-detection device. Equipping the pre-detection device in the front of the receiver of the quantum key distribution （QKD） can reduce the influence of the dark count of the detector, hence increasing the secure communication distance significantly. Simulation results show that the secure communication distance can reach 516 km and 479 km for QKD with perfect single photon source and decoy-state QKD with weak coherent photon source, respectively.

Quantum information procession with fermions based on charge detection

This paper proposes a fermionic linear optical scheme for the teleportation and entanglement concentration via entanglement swapping based on charge detection. It also proves that this method is useful in generating entangled states such as GHZ states, W states, and cluster states by using fermionic polarizing beam splitters and single spin rotations assisted by a parity check on the fermionic qubits. This scheme is nearly deterministic （i.e., with 100% successful probability） and does not need the joint Bell state measurement required in the previous schemes.