Sparse code multiple access(SCMA)is a non-orthogonal multiple access(NOMA)scheme based on joint modulation and spread spectrum coding.It is ideal for future communication networks with a massive number of nodes due to...Sparse code multiple access(SCMA)is a non-orthogonal multiple access(NOMA)scheme based on joint modulation and spread spectrum coding.It is ideal for future communication networks with a massive number of nodes due to its ability to handle user overload.Introducing SCMA into visible light communication(VLC)systems can improve the data transmission capability of the system.However,designing a suitable codebook becomes a challenging problem when addressing the demands of massive connectivity scenarios.Therefore,this paper proposes a low-complexity design method for high-overload codebooks based on the minimum bit error rate(BER)criterion.Firstly,this paper constructs a new codebook with parameters based on the symmetric mother codebook structure by allocating the codeword power so that the power of each user codebook is unbalanced;then,the BER performance in the visible light communication system is optimized to obtain specific parameters;finally,the successive interference cancellation(SIC)detection algorithm is used at the receiver side.Simulation results show that the method proposed in this paper can converge quickly by utilizing a relatively small number of detection iterations.This can simultaneously reduce the complexity of design and detection,outperforming existing design methods for massive SCMA codebooks.展开更多
Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which ent...Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.展开更多
Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)systems.However,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complex...Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)systems.However,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complexity,resulting in slow convergence or high complexity.To address this issue,a low-complexity Approximate Message Passing(AMP)detection algorithm with Deep Neural Network(DNN)(denoted as AMP-DNN)is investigated in this paper.Firstly,an efficient AMP detection algorithm is derived by scalarizing the simplification of Belief Propagation(BP)algorithm.Secondly,by unfolding the obtained AMP detection algorithm,a DNN is specifically designed for the optimal performance gain.For the proposed AMP-DNN,the number of trainable parameters is only related to that of layers,regardless of modulation scheme,antenna number and matrix calculation,thus facilitating fast and stable training of the network.In addition,the AMP-DNN can detect different channels under the same distribution with only one training.The superior performance of the AMP-DNN is also verified by theoretical analysis and experiments.It is found that the proposed algorithm enables the reduction of BER without signal prior information,especially in the spatially correlated channel,and has a lower computational complexity compared with existing state-of-the-art methods.展开更多
Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multi...Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multiple-input multiple-output(MIMO)systems,attributable to inter-cell interference for channel state information.Apart from that,a higher number of radio frequency(RF)chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers.Therefore,antenna selection,user selection,optimal transmission power,and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems.This work aims to investigate joint antenna selection,optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE,with complete knowledge of large-scale fading with maximum ratio transmission.It also accounts for channel estimation and eliminating pilot contamination as antennas M→∞.This formulates the optimization problem of joint optimal antenna selection,transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm(LCA)for Newton’s methods and Lagrange multipliers.To analyze the precise power consumption,a novel power consumption scheme is proposed for each individual antenna,based on the transmit power amplifier and CPC.Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power,in the case the noise power is less than the received power pilot.The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse,in the case the transmit power allocationρd=40 dBm,and the optimal EE=71.232 Mb/j.展开更多
Massive MIMO is a promising technology to improve spectral efficiency, cell coverage, and system capacity for 5G. However, these benefits take place at great cost of computational complexity, especially in systems wit...Massive MIMO is a promising technology to improve spectral efficiency, cell coverage, and system capacity for 5G. However, these benefits take place at great cost of computational complexity, especially in systems with hundreds of antennas at the base station. This paper aims to address the minimum mean square error(MMSE) detection in uplink massive MIMO systems utilizing the symmetric complex bi-conjugate gradients(SCBiCG) and the Lanczos method. Both the proposed methods can avoid the large scale matrix inversion which is necessary for MMSE, thus, reducing the computational complexity by an order of magnitude with respect to the number of user equipment. To enable the proposed methods for soft-output detection, we also derive an approximating calculation scheme for the log-likelihood ratios(LLRs), which further reduces the complexity. We compare the proposed methods with existing exact and approximate detection methods. Simulation results demonstrate that the proposed methods can achieve near-optimal performance of MMSE detection with relatively low computational complexity.展开更多
Acoustic scene classification(ASC)is a method of recognizing and classifying environments that employ acoustic signals.Various ASC approaches based on deep learning have been developed,with convolutional neural networ...Acoustic scene classification(ASC)is a method of recognizing and classifying environments that employ acoustic signals.Various ASC approaches based on deep learning have been developed,with convolutional neural networks(CNNs)proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models.When using ASC systems in the real world,model complexity and device robustness are essential considerations.In this paper,we propose a two-pass mobile network for low-complexity classification of the acoustic scene,named TP-MobNet.With inverse residuals and linear bottlenecks,TPMobNet is based on MobileNetV2,and following mobile blocks,coordinate attention and two-pass fusion approaches are utilized.The log-range dependencies and precise position information in feature maps can be trained via coordinate attention.By capturing more diverse feature resolutions at the network’s end sides,two-pass fusions can also train generalization.Also,the model size is reduced by applying weight quantization to the trained model.By adding weight quantization to the trained model,the model size is also lowered.The TAU Urban Acoustic Scenes 2020 Mobile development set was used for all of the experiments.It has been confirmed that the proposed model,with a model size of 219.6 kB,achieves an accuracy of 73.94%.展开更多
In mobile environment, a low-complexity is the significant feature because the mobile device has very limited resources due to power consumption. In this paper, we propose a low-complexity watermarking scheme for mobi...In mobile environment, a low-complexity is the significant feature because the mobile device has very limited resources due to power consumption. In this paper, we propose a low-complexity watermarking scheme for mobile device. We apply the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter to watermark detection. By the scale tolerance property of MACE-MRH correlation filter, the proposed watermark detector can be robust to scaling attacks. Empirical evidence from a large database of test images indicates outperforming performance of the proposed method.展开更多
Millimeter-wave(mmWave) and massive multiple-input multiple-output(MIMO) are broadly recognized as key enabling technologies for the fifth generation(5G) communication systems. In this paper, a low-complexity angle-de...Millimeter-wave(mmWave) and massive multiple-input multiple-output(MIMO) are broadly recognized as key enabling technologies for the fifth generation(5G) communication systems. In this paper, a low-complexity angle-delay parameters estimation(ADPE) algorithm was put forward for wideband mmWave systems with uniform planar arrays(UPAs). In particular, the ADPE algorithm effectively decouples the angle-delay parameters and converts the angle-delay estimation problem into three independent subproblems. Accordingly, the ability to devise an off-grid method based on discrete Fourier transform(DFT) with a closed-form solution for angle-delay estimation and potential path number acquisition can be realized. In actuality, only a limited number of potential paths are close to the true paths influenced by noise. Consequently, the removal of noise paths to acquire the corresponding true path gains through a sparsity adaptive path gains estimation(APGE) algorithm is postulated. Finally, the simulation results substantiate the effectiveness of ADPE and APGE algorithms.展开更多
In order to solve the problem of high computational complexity in demodulation for multi-h continuous phase modulation(CPM) signal, a maximum cumulative measure combing with the Laurent decomposition(MCM-LD) scheme is...In order to solve the problem of high computational complexity in demodulation for multi-h continuous phase modulation(CPM) signal, a maximum cumulative measure combing with the Laurent decomposition(MCM-LD) scheme is proposed to reduce the number of the grid states and the required number of matched filters, which degrades the demodulation complexity at the receiver.The advanced range telemetry(ARTM) Tier Ⅱ CPM signal is adopted to evaluate the performance in simulation. The results show that, compared with the traditional maximum likelihood sequence detection(MLSD), MCM-LD can respectively reduce the numbers of grid states and matched filters from 256 to 32 and 128 to 48 with negligible performance loss, which effectively degrades the computational complexity for multi-h CPM signal.展开更多
Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a locatio...Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.展开更多
A novel hybrid model combining a convolutional neural network(CNN)and a low-complexity Transformer network is introduced for predicting lung cancer response to neoadjuvant chemoimmunotherapy using computed tomography ...A novel hybrid model combining a convolutional neural network(CNN)and a low-complexity Transformer network is introduced for predicting lung cancer response to neoadjuvant chemoimmunotherapy using computed tomography scans.This approach is crucial as it assists clinicians in identifying patients likely to benefit from treatment and in assessing their prognosis.The model employs channel splitting to minimize parameter count.It then leverages both CNN for local feature extraction and a streamlined Transformer for global feature comprehension.To enhance efficiency,a novel self-attention mechanism is implemented,focusing on feature aggregation and element-wise multiplication.To address the different semantic meanings of features,an attention-based module is designed to seamlessly integrate features from both networks,employing a process of coarse fusion,attention computation,and fine fusion.When evaluated with data from 232 lung cancer patients who have undergone neoadjuvant chemoimmunotherapy,the model demonstrates exceptional performance,achieving a Dice score of 47.04%and a 95.00%Hausdorff distance of 25.12 mm,outperforming existing methods.Additionally,it has only 2.91×106 parameters and 52.95×109 floating point operations.Moreover,the model’s predictive accuracy in tumor diameter estimation is beneficial for treatment planning.Its robustness is further validated through its application in stroke lesion prediction,indicating its broad applicability.展开更多
Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error performance.In CIM, at least two different IM ope...Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error performance.In CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital algorithm.Then, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. Computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.展开更多
Due to the high complexity of the pairwise decoding algorithm and the poor performance of zero forcing（ ZF） /minimum mean square error（ MMSE） decoding algorithm, two low-complexity suboptimal decoding algorithms, ...Due to the high complexity of the pairwise decoding algorithm and the poor performance of zero forcing（ ZF） /minimum mean square error（ MMSE） decoding algorithm, two low-complexity suboptimal decoding algorithms, called pairwisequasi-ZF and pairwise-quasi-MMSE decoders, are proposed. First,two transmit signals are detected by the quasi-ZF or the quasiMMSE algorithm at the receiver. Then, the two detected signals as the decoding results are substituted into the two pairwise decoding algorithm expressions to detect the other two transmit signals. The bit error rate（ BER） performance of the proposed algorithms is compared with that of the current known decoding algorithms.Also, the number of calculations of ZF, MMSE, quasi-ZF and quasi-MMSE algorithms is compared with each other. Simulation results showthat the BER performance of the proposed algorithms is substantially improved in comparison to the quasi-ZF and quasiMMSE algorithms. The BER performance of the pairwise-quasiZF（ pairwise-quasi-MMSE） decoder is equivalent to the pairwiseZF（ pairwise-MMSE） decoder, while the computational complexity is significantly reduced.展开更多
Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmissio...Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.展开更多
For multi-user cooperative Distributed MIMO (D-MIMO) systems, a low-complexity Remote Radio Unit (RRU) selection and adaptive bit partition algorithm is proposed to maximize the transmission Signal-to-Interference-Noi...For multi-user cooperative Distributed MIMO (D-MIMO) systems, a low-complexity Remote Radio Unit (RRU) selection and adaptive bit partition algorithm is proposed to maximize the transmission Signal-to-Interference-Noise Ratio (SINR). Considering limited feedback, each user can adaptively select an RRU cluster to maintain the best communication quality. Under this condition, only one codebook is utilized for quantizing the Channel State Information (CSI) with variable dimensions, which effectively reduces the codebook storage amount. Furthermore, we propose an adaptive bit partition algorithm, which separately allocates bits to quantize the desired channels and interference channels. The optimal solution is achieved through an optimization theory to minimize the effect of inter-cell interference. Simulation results show that the proposed algorithm substantially improves the performance compared to other non-adaptive schemes.展开更多
We formulate the subcarrier and power allocation problem in cognitive radio networks employing orthogonal frequency division multiplexing （OFDM） as a non-linear optimization problem with the objective of maximizing ...We formulate the subcarrier and power allocation problem in cognitive radio networks employing orthogonal frequency division multiplexing （OFDM） as a non-linear optimization problem with the objective of maximizing sum capacity under constraints of available subcarriers, interference temperature, power budget, etc. A close-to-optimal solution with much reduced complexity is proposed to separate the problem into two steps, which also considers fairness among secondary users. A fair al- gorithm for subcarrier allocation （FA_SA） is firstly presented. Secondly, a fast iterative water-filling algorithm for power allocation （FIWFA_PA） is also proposed to maximize the sum capacity. Exten- sive simulation results show that sum capacity performance of our low-complexity solution is very close to the optimal one, while significantly improving fairness and reducing computation complexity compared with the existing solutions.展开更多
Achievable rate (AR) is significant to communications. As to multi-input multi-output (MIMO) digital transmissions with finite alphabets inputs, which greatly improve the performance of communications, it seems rather...Achievable rate (AR) is significant to communications. As to multi-input multi-output (MIMO) digital transmissions with finite alphabets inputs, which greatly improve the performance of communications, it seems rather difficult to calculate accurate AR. Here we propose an estimation of con-siderable accuracy and low complexity, based on Euclidean measure matrix for given channel states and constellations. The main contribution is explicit expression, non-constraints to MIMO schemes and channel states and constellations, and controllable estimating gap. Numerical results show that the proposition is able to achieve enough accurate AR computation. In addition the estimating gap given by theoretical deduction is well agreed.展开更多
Due to high spectral efficiency and power efficiency, the continuous phase modulation(CPM) technique with constant envelop is widely used in range telemetry. How to improve the bit error rate(BER) performance of CPM a...Due to high spectral efficiency and power efficiency, the continuous phase modulation(CPM) technique with constant envelop is widely used in range telemetry. How to improve the bit error rate(BER) performance of CPM and keep a reasonable computational complexity is the key of the entire telemetry system and the focus of research and engineering design. In this paper, a reduced-state noncoherent maximum likelihood sequence detection(MLSD) method for CPM is proposed. In the proposed method, the criterion of noncoherent MLSD is derived for CPM when the carrier phase is unknown. A novel Viterbi algorithm(VA) with modified state vector is designed to simplify the implementation of noncoherent MLSD. Both analysis and numerical results show that the proposed method reduces the computational complexity significantly and does not need accurate carrier phase recovery, which overcomes the shortage of traditional MLSD method. Additionally, the proposed method exceeds the traditional MLSD method when carrier phase deviation exists.展开更多
A novel adaptively iterative list decoding(ILD) approach using for Reed-Solomon(RS) codes was investigated. The proposed scheme is exploited to reduce the complexity of RS Chase algorithm(CA) via an iterative decoding...A novel adaptively iterative list decoding(ILD) approach using for Reed-Solomon(RS) codes was investigated. The proposed scheme is exploited to reduce the complexity of RS Chase algorithm(CA) via an iterative decoding attempt mode. In each decoding attempt process, a test pattern is generated by flipping the bits of the least reliable positions(LRPs) within the received hard-decision(HD) vector. The ILD algorithm continues until a test pattern is successfully decoded by the underlying Berlekamp-Massey algorithm(BMA) of RS codes. Flipping within the same bits, the ILD algorithm provides the same test pattern set as the conventional RS CA, thus there is no degradation in error-rate performance. Without decoding all test patterns, the ILD algorithm can simplify the decoding complexity by its early termination. Simulation results show that the average complexity of the ILD algorithm is much lower than that of the conventional RS CA(and is similar to that of BMA decoding) at high signal-to-noise ratio(SNR) region with no less to the RS CA decoding error-rate performance.展开更多
Since the different characteristics of various network services determine that their requirements for network are also disparate, the performance of one network varies according to the services running on it. However,...Since the different characteristics of various network services determine that their requirements for network are also disparate, the performance of one network varies according to the services running on it. However, most of previous network performance evaluation （NPE） researches conduct evaluations based on the network parameters, but without considering from the perspective of specific service running on the network. In view of this issue, a novel service-oriented NPE framework is proposed. First, the characteristics discrepancy among different types of services are investigated. Next, in order to conduct comprehensive evaluation of multiple services, an enhanced low-complexity adaptive （LA）-fuzzy analytical hierarchy process （FAHP） is introduced; meanwhile by applying the experts-construct-directly （ECD） algorithm proposed later, the consistency check required in previous studies can be omitted, thereby significantly reducing the computation complexity and assessment workload for experts. Then, in accordance with the features of each service, corrections are made to their respective membership functions, thus making the proposed LA-FAHP adaptive to various service evaluation scenarios. The subsequent comparison with other NPE methods well proves the effectiveness and high sensitivity of proposed framework, and the analysis verifies the low computation complexity of the proposed algorithms as well.展开更多
基金supported in part by the National Science Foundation of China(NSFC)under Grant 62161024Jiangxi Provincial Natural Science Foundation under Grant 20224BAB212002+3 种基金Jiangxi Provincial Talent Project for Academic and Technical Leaders of Major Disciplines under Grant 20232BCJ23085,China Postdoctoral Science Foundation under Grant 2021TQ0136 and 2022M711463the State Key Laboratory of Computer Architecture(ICT,CAS)Open Project under Grant CARCHB202019supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62061030supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62161023。
文摘Sparse code multiple access(SCMA)is a non-orthogonal multiple access(NOMA)scheme based on joint modulation and spread spectrum coding.It is ideal for future communication networks with a massive number of nodes due to its ability to handle user overload.Introducing SCMA into visible light communication(VLC)systems can improve the data transmission capability of the system.However,designing a suitable codebook becomes a challenging problem when addressing the demands of massive connectivity scenarios.Therefore,this paper proposes a low-complexity design method for high-overload codebooks based on the minimum bit error rate(BER)criterion.Firstly,this paper constructs a new codebook with parameters based on the symmetric mother codebook structure by allocating the codeword power so that the power of each user codebook is unbalanced;then,the BER performance in the visible light communication system is optimized to obtain specific parameters;finally,the successive interference cancellation(SIC)detection algorithm is used at the receiver side.Simulation results show that the method proposed in this paper can converge quickly by utilizing a relatively small number of detection iterations.This can simultaneously reduce the complexity of design and detection,outperforming existing design methods for massive SCMA codebooks.
基金supported by National Natural Science Foundation of China(62371225,62371227)。
文摘Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
基金supported by Major Project of Science and Technology Research Program of Chongqing Education Commission of China(Grant No.KJZD-M201900601)China Postdoctoral Science Foundation(Grant No.2021MD703932)Project Supported by Engineering Research Center of Mobile Communications,Ministry of Education,China(Grant No.cqupt-mct-202006)。
文摘Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)systems.However,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complexity,resulting in slow convergence or high complexity.To address this issue,a low-complexity Approximate Message Passing(AMP)detection algorithm with Deep Neural Network(DNN)(denoted as AMP-DNN)is investigated in this paper.Firstly,an efficient AMP detection algorithm is derived by scalarizing the simplification of Belief Propagation(BP)algorithm.Secondly,by unfolding the obtained AMP detection algorithm,a DNN is specifically designed for the optimal performance gain.For the proposed AMP-DNN,the number of trainable parameters is only related to that of layers,regardless of modulation scheme,antenna number and matrix calculation,thus facilitating fast and stable training of the network.In addition,the AMP-DNN can detect different channels under the same distribution with only one training.The superior performance of the AMP-DNN is also verified by theoretical analysis and experiments.It is found that the proposed algorithm enables the reduction of BER without signal prior information,especially in the spatially correlated channel,and has a lower computational complexity compared with existing state-of-the-art methods.
基金support under the Multi-Disciplinary Research(MDR)Grant(H470)the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme(FRGS/1/2019/TK04/UTHM/02/8).
文摘Energy efficiency(EE)is a critical design when taking into account circuit power consumption(CPC)in fifth-generation cellular networks.These problems arise because of the increasing number of antennas in massive multiple-input multiple-output(MIMO)systems,attributable to inter-cell interference for channel state information.Apart from that,a higher number of radio frequency(RF)chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers.Therefore,antenna selection,user selection,optimal transmission power,and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems.This work aims to investigate joint antenna selection,optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE,with complete knowledge of large-scale fading with maximum ratio transmission.It also accounts for channel estimation and eliminating pilot contamination as antennas M→∞.This formulates the optimization problem of joint optimal antenna selection,transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massive MIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm(LCA)for Newton’s methods and Lagrange multipliers.To analyze the precise power consumption,a novel power consumption scheme is proposed for each individual antenna,based on the transmit power amplifier and CPC.Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power,in the case the noise power is less than the received power pilot.The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse,in the case the transmit power allocationρd=40 dBm,and the optimal EE=71.232 Mb/j.
基金supported by Chinas 863 Project NO.2015AA01A706the National S&T Major Project NO.2014ZX03001011+1 种基金the Science and Technology Program of Beijing NO.D151100000115003the Scientific and Technological Cooperation Projects NO.2015DFT10160B
文摘Massive MIMO is a promising technology to improve spectral efficiency, cell coverage, and system capacity for 5G. However, these benefits take place at great cost of computational complexity, especially in systems with hundreds of antennas at the base station. This paper aims to address the minimum mean square error(MMSE) detection in uplink massive MIMO systems utilizing the symmetric complex bi-conjugate gradients(SCBiCG) and the Lanczos method. Both the proposed methods can avoid the large scale matrix inversion which is necessary for MMSE, thus, reducing the computational complexity by an order of magnitude with respect to the number of user equipment. To enable the proposed methods for soft-output detection, we also derive an approximating calculation scheme for the log-likelihood ratios(LLRs), which further reduces the complexity. We compare the proposed methods with existing exact and approximate detection methods. Simulation results demonstrate that the proposed methods can achieve near-optimal performance of MMSE detection with relatively low computational complexity.
基金This work was supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[No.2021-0-0268,Artificial Intelligence Innovation Hub(Artificial Intelligence Institute,Seoul National University)]。
文摘Acoustic scene classification(ASC)is a method of recognizing and classifying environments that employ acoustic signals.Various ASC approaches based on deep learning have been developed,with convolutional neural networks(CNNs)proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models.When using ASC systems in the real world,model complexity and device robustness are essential considerations.In this paper,we propose a two-pass mobile network for low-complexity classification of the acoustic scene,named TP-MobNet.With inverse residuals and linear bottlenecks,TPMobNet is based on MobileNetV2,and following mobile blocks,coordinate attention and two-pass fusion approaches are utilized.The log-range dependencies and precise position information in feature maps can be trained via coordinate attention.By capturing more diverse feature resolutions at the network’s end sides,two-pass fusions can also train generalization.Also,the model size is reduced by applying weight quantization to the trained model.By adding weight quantization to the trained model,the model size is also lowered.The TAU Urban Acoustic Scenes 2020 Mobile development set was used for all of the experiments.It has been confirmed that the proposed model,with a model size of 219.6 kB,achieves an accuracy of 73.94%.
文摘In mobile environment, a low-complexity is the significant feature because the mobile device has very limited resources due to power consumption. In this paper, we propose a low-complexity watermarking scheme for mobile device. We apply the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter to watermark detection. By the scale tolerance property of MACE-MRH correlation filter, the proposed watermark detector can be robust to scaling attacks. Empirical evidence from a large database of test images indicates outperforming performance of the proposed method.
基金supported by the National Key Technology R&D Program of China (2022YFB2902302)。
文摘Millimeter-wave(mmWave) and massive multiple-input multiple-output(MIMO) are broadly recognized as key enabling technologies for the fifth generation(5G) communication systems. In this paper, a low-complexity angle-delay parameters estimation(ADPE) algorithm was put forward for wideband mmWave systems with uniform planar arrays(UPAs). In particular, the ADPE algorithm effectively decouples the angle-delay parameters and converts the angle-delay estimation problem into three independent subproblems. Accordingly, the ability to devise an off-grid method based on discrete Fourier transform(DFT) with a closed-form solution for angle-delay estimation and potential path number acquisition can be realized. In actuality, only a limited number of potential paths are close to the true paths influenced by noise. Consequently, the removal of noise paths to acquire the corresponding true path gains through a sparsity adaptive path gains estimation(APGE) algorithm is postulated. Finally, the simulation results substantiate the effectiveness of ADPE and APGE algorithms.
文摘In order to solve the problem of high computational complexity in demodulation for multi-h continuous phase modulation(CPM) signal, a maximum cumulative measure combing with the Laurent decomposition(MCM-LD) scheme is proposed to reduce the number of the grid states and the required number of matched filters, which degrades the demodulation complexity at the receiver.The advanced range telemetry(ARTM) Tier Ⅱ CPM signal is adopted to evaluate the performance in simulation. The results show that, compared with the traditional maximum likelihood sequence detection(MLSD), MCM-LD can respectively reduce the numbers of grid states and matched filters from 256 to 32 and 128 to 48 with negligible performance loss, which effectively degrades the computational complexity for multi-h CPM signal.
基金supported by the National Natural Science Foundation of China(61901341).
文摘Using the existing positioning technology can easily obtain high-precision positioning information,which can save resources and reduce complexity when used in the communication field.In this paper,we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system.Specifically,we combine an analog outer beamformer with a digital inner beamformer.An outer beamformer can be selected from a codebook formed by antenna steering vectors,and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices(QR)decomposition is applied to cancel interuser interference.Then,we propose a low-complexity user selection algorithm using location information in this paper.We first derive the geometric angle between channel matrices,which represent the correlation between users.Furthermore,we derive the asymptotic signal to interference-plus-noise ratio(SINR)of the system in the context of two-stage beamforming using random matrix theory(RMT),taking into account inter-channel correlations and energies.Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.
基金supported in part by the National Key Research and Development Program of China(No.2021YFF1201200)the Science and Technology Innovation Program of Hunan Province(No.2022RC1031),the Natural Science Foundation of Hunan Province(No.2023JJ50354)+1 种基金the Scientific Research Project of Hunan Education Department(No.24A0575)the High Performance Computing Center of Central South University.
文摘A novel hybrid model combining a convolutional neural network(CNN)and a low-complexity Transformer network is introduced for predicting lung cancer response to neoadjuvant chemoimmunotherapy using computed tomography scans.This approach is crucial as it assists clinicians in identifying patients likely to benefit from treatment and in assessing their prognosis.The model employs channel splitting to minimize parameter count.It then leverages both CNN for local feature extraction and a streamlined Transformer for global feature comprehension.To enhance efficiency,a novel self-attention mechanism is implemented,focusing on feature aggregation and element-wise multiplication.To address the different semantic meanings of features,an attention-based module is designed to seamlessly integrate features from both networks,employing a process of coarse fusion,attention computation,and fine fusion.When evaluated with data from 232 lung cancer patients who have undergone neoadjuvant chemoimmunotherapy,the model demonstrates exceptional performance,achieving a Dice score of 47.04%and a 95.00%Hausdorff distance of 25.12 mm,outperforming existing methods.Additionally,it has only 2.91×106 parameters and 52.95×109 floating point operations.Moreover,the model’s predictive accuracy in tumor diameter estimation is beneficial for treatment planning.Its robustness is further validated through its application in stroke lesion prediction,indicating its broad applicability.
基金supported by National Natural Science Foundation of China (No. 61971149, 62071504, 62271208)in part by the Special Projects in Key Fields for General Universities of Guangdong Province (No. 2020ZDZX3025, 2021ZDZX056)+1 种基金in part by the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515011657)in part by the Featured Innovation Projects of Guangdong Province of China (No. 2021KTSCX049)。
文摘Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error performance.In CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital algorithm.Then, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. Computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.
基金The National Natural Science Foundation of China(No.6157110861201248)+1 种基金the Open Research Fund of National Mobile Communications Research Laboratory of China(No.2011D18)China Postdoctoral Science Foundation(No.2012M511175)
文摘Due to the high complexity of the pairwise decoding algorithm and the poor performance of zero forcing（ ZF） /minimum mean square error（ MMSE） decoding algorithm, two low-complexity suboptimal decoding algorithms, called pairwisequasi-ZF and pairwise-quasi-MMSE decoders, are proposed. First,two transmit signals are detected by the quasi-ZF or the quasiMMSE algorithm at the receiver. Then, the two detected signals as the decoding results are substituted into the two pairwise decoding algorithm expressions to detect the other two transmit signals. The bit error rate（ BER） performance of the proposed algorithms is compared with that of the current known decoding algorithms.Also, the number of calculations of ZF, MMSE, quasi-ZF and quasi-MMSE algorithms is compared with each other. Simulation results showthat the BER performance of the proposed algorithms is substantially improved in comparison to the quasi-ZF and quasiMMSE algorithms. The BER performance of the pairwise-quasiZF（ pairwise-quasi-MMSE） decoder is equivalent to the pairwiseZF（ pairwise-MMSE） decoder, while the computational complexity is significantly reduced.
基金supported by the 2011 China Aerospace Science and Technology Foundationthe Certain Ministry Foundation under Grant No.20212HK03010
文摘Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.
基金supported partially by Important National Science&Technology Specific Projects under Grant No.2010ZX03005-001-0National High Technology Research and Development of China(863 Program)under Grant No.2006AA01Z272New Century Excellent Talents in University (NCET) under Grant No.NCET-11-0593
文摘For multi-user cooperative Distributed MIMO (D-MIMO) systems, a low-complexity Remote Radio Unit (RRU) selection and adaptive bit partition algorithm is proposed to maximize the transmission Signal-to-Interference-Noise Ratio (SINR). Considering limited feedback, each user can adaptively select an RRU cluster to maintain the best communication quality. Under this condition, only one codebook is utilized for quantizing the Channel State Information (CSI) with variable dimensions, which effectively reduces the codebook storage amount. Furthermore, we propose an adaptive bit partition algorithm, which separately allocates bits to quantize the desired channels and interference channels. The optimal solution is achieved through an optimization theory to minimize the effect of inter-cell interference. Simulation results show that the proposed algorithm substantially improves the performance compared to other non-adaptive schemes.
基金Supported by the National High Technology Research and Development Programme of China（ No. 2007AA01Z221, No. 2009AA01Z246） , and the National Natural Science Foundation of China（ No. 60672124, 60832009）.
文摘We formulate the subcarrier and power allocation problem in cognitive radio networks employing orthogonal frequency division multiplexing （OFDM） as a non-linear optimization problem with the objective of maximizing sum capacity under constraints of available subcarriers, interference temperature, power budget, etc. A close-to-optimal solution with much reduced complexity is proposed to separate the problem into two steps, which also considers fairness among secondary users. A fair al- gorithm for subcarrier allocation （FA_SA） is firstly presented. Secondly, a fast iterative water-filling algorithm for power allocation （FIWFA_PA） is also proposed to maximize the sum capacity. Exten- sive simulation results show that sum capacity performance of our low-complexity solution is very close to the optimal one, while significantly improving fairness and reducing computation complexity compared with the existing solutions.
文摘Achievable rate (AR) is significant to communications. As to multi-input multi-output (MIMO) digital transmissions with finite alphabets inputs, which greatly improve the performance of communications, it seems rather difficult to calculate accurate AR. Here we propose an estimation of con-siderable accuracy and low complexity, based on Euclidean measure matrix for given channel states and constellations. The main contribution is explicit expression, non-constraints to MIMO schemes and channel states and constellations, and controllable estimating gap. Numerical results show that the proposition is able to achieve enough accurate AR computation. In addition the estimating gap given by theoretical deduction is well agreed.
基金supported by the Fundamental Research Funds for the Central Universities ( BLX201623 )the National Natural Science Foundation of China ( 31700479)。
文摘Due to high spectral efficiency and power efficiency, the continuous phase modulation(CPM) technique with constant envelop is widely used in range telemetry. How to improve the bit error rate(BER) performance of CPM and keep a reasonable computational complexity is the key of the entire telemetry system and the focus of research and engineering design. In this paper, a reduced-state noncoherent maximum likelihood sequence detection(MLSD) method for CPM is proposed. In the proposed method, the criterion of noncoherent MLSD is derived for CPM when the carrier phase is unknown. A novel Viterbi algorithm(VA) with modified state vector is designed to simplify the implementation of noncoherent MLSD. Both analysis and numerical results show that the proposed method reduces the computational complexity significantly and does not need accurate carrier phase recovery, which overcomes the shortage of traditional MLSD method. Additionally, the proposed method exceeds the traditional MLSD method when carrier phase deviation exists.
基金supported by the National Natural Science Foundation of China (61671080,61601047)
文摘A novel adaptively iterative list decoding(ILD) approach using for Reed-Solomon(RS) codes was investigated. The proposed scheme is exploited to reduce the complexity of RS Chase algorithm(CA) via an iterative decoding attempt mode. In each decoding attempt process, a test pattern is generated by flipping the bits of the least reliable positions(LRPs) within the received hard-decision(HD) vector. The ILD algorithm continues until a test pattern is successfully decoded by the underlying Berlekamp-Massey algorithm(BMA) of RS codes. Flipping within the same bits, the ILD algorithm provides the same test pattern set as the conventional RS CA, thus there is no degradation in error-rate performance. Without decoding all test patterns, the ILD algorithm can simplify the decoding complexity by its early termination. Simulation results show that the average complexity of the ILD algorithm is much lower than that of the conventional RS CA(and is similar to that of BMA decoding) at high signal-to-noise ratio(SNR) region with no less to the RS CA decoding error-rate performance.
基金supported by the Hi-Tech Research and Development Program of China (2014AA01A701)the Ministry of Education-CMCC research fund (MCM 20120132)Beijing Municipal Science and technology Commission research fund project "The Design of Radio Access Network Architecture in 5G communication system"
文摘Since the different characteristics of various network services determine that their requirements for network are also disparate, the performance of one network varies according to the services running on it. However, most of previous network performance evaluation （NPE） researches conduct evaluations based on the network parameters, but without considering from the perspective of specific service running on the network. In view of this issue, a novel service-oriented NPE framework is proposed. First, the characteristics discrepancy among different types of services are investigated. Next, in order to conduct comprehensive evaluation of multiple services, an enhanced low-complexity adaptive （LA）-fuzzy analytical hierarchy process （FAHP） is introduced; meanwhile by applying the experts-construct-directly （ECD） algorithm proposed later, the consistency check required in previous studies can be omitted, thereby significantly reducing the computation complexity and assessment workload for experts. Then, in accordance with the features of each service, corrections are made to their respective membership functions, thus making the proposed LA-FAHP adaptive to various service evaluation scenarios. The subsequent comparison with other NPE methods well proves the effectiveness and high sensitivity of proposed framework, and the analysis verifies the low computation complexity of the proposed algorithms as well.
