A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output (MIMO) - orthogonal frequency division multiplexing (OFDM) system using linear minimum mean square error (LMMSE...A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output (MIMO) - orthogonal frequency division multiplexing (OFDM) system using linear minimum mean square error (LMMSE)- based space-alternating generalized expectation-maximization (SAGE) algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood (ML) detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate (BER) performance.展开更多
This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The spec...This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.展开更多
This paper develops a Cyclic Prefix(CP)based joint Maximum-Likelihood(ML)estima-tion algorithm of Carrier Frequency Offset(CFO)and Power Delay Profile(PDP)for Multi-InputMulti-Output Orthogonal Frequency Division Mult...This paper develops a Cyclic Prefix(CP)based joint Maximum-Likelihood(ML)estima-tion algorithm of Carrier Frequency Offset(CFO)and Power Delay Profile(PDP)for Multi-InputMulti-Output Orthogonal Frequency Division Multiplexing(MIMO-OFDM)systems.However,theexact solution of the joint ML estimation is very complex since it needs a search over amulti-dimensional domain.Thus a simplified method is proposed to estimate the CFO and the PDPiteratively via the alternating-projection method which could induce the multidimensional searchproblem to a sequence of simple one-dimensional searches.Simulations show that the proposed algo-rithm is more accurate and robust than the existing algorithms.展开更多
In this paper, a novel robust precoder with imperfect channel state information(CSI)is proposed for multi-input multi-output(MIMO)cognitive multiuser networks equipped with relays. In the proposed model, the secondary...In this paper, a novel robust precoder with imperfect channel state information(CSI)is proposed for multi-input multi-output(MIMO)cognitive multiuser networks equipped with relays. In the proposed model, the secondary users(SUs)are allowed to share the spectrum with the primary users(PUs)when the interference temperature(IT)is below a specific threshold. The transmitting strategy of relays is amplify-and-forward(AF), and the CSI error is characterized in terms of spherical uncertainty region. A minmax problem for the transmit power of the relays is considered when the mean square error(MSE)of SUs and the IT of PU meet their corresponding thresholds, and it is transformed into a semi-definite programming(SDP)problem to search for the solution. Numerical simulations demonstrate the effectiveness of the proposed precoder.展开更多
Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to mod...Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.展开更多
The presented scheme named M-CAP (Maximum CAPacity) uses the CSI (Channel State Information) and its statistics to deduce an equivalent channel according to which the transmit power is allocated to the subchannels. An...The presented scheme named M-CAP (Maximum CAPacity) uses the CSI (Channel State Information) and its statistics to deduce an equivalent channel according to which the transmit power is allocated to the subchannels. And then modulation scheme is determined adaptively according to the power allocated to each subchannel. The advantage of the M-CAP scheme is that it combines power allocation and adaptive modulation while maintaining a large capacity. We demonstrate by computer simulations that the proposed M-CAP scheme can significantly improve system performance compared with the traditional schemes.展开更多
We address the problem of adaptive modulation and coding scheme(AMCS) for a multi-input multioutput(MIMO) system in presence of time-varying transmitting correlation.Antenna subset selection and quasiorthogonal space-...We address the problem of adaptive modulation and coding scheme(AMCS) for a multi-input multioutput(MIMO) system in presence of time-varying transmitting correlation.Antenna subset selection and quasiorthogonal space-time block code(QOSTBC) have different error performances with different signal-to-noise ratios(SNRs) and in different spatial correlation scenarios.The error performance can be improved by selecting an appropriate transmission scheme to adapt to various channel conditions.The maximum distance criterion is the simplest and very effective algorithm for the antenna subset selection without needs of complex calculation and channel state information at transmitter(CSIT).The minimum error performance criteria and the simplified linear decision strategy are developed for constant transmission rate traffic to select the optimal transmission scheme.It can dramatically decrease algorithm complexity for obtaining error probability according to the known quantities comparing with using instant CSIT.Simulation results show that,remarkable performances including low SNR and weak spatial correlation at the expense of simple calculation and almost no bandwidth loss by adopting AMCS can be achieved.The proposed AMCS improves robustness of slowly varying spatial correlated channels.展开更多
基金Supported by the National Natural Science Foundation of China (No. 61001105), the National Science and Technology Major Projects (No. 2011ZX03001- 007- 03) and Beijing Natural Science Foundation (No. 4102043).
文摘A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output (MIMO) - orthogonal frequency division multiplexing (OFDM) system using linear minimum mean square error (LMMSE)- based space-alternating generalized expectation-maximization (SAGE) algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood (ML) detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate (BER) performance.
基金Supported by the National Natural Science Foundation of China(No.60496311)
文摘This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.
基金the National Natural Science Foundation of China(No.60496311).
文摘This paper develops a Cyclic Prefix(CP)based joint Maximum-Likelihood(ML)estima-tion algorithm of Carrier Frequency Offset(CFO)and Power Delay Profile(PDP)for Multi-InputMulti-Output Orthogonal Frequency Division Multiplexing(MIMO-OFDM)systems.However,theexact solution of the joint ML estimation is very complex since it needs a search over amulti-dimensional domain.Thus a simplified method is proposed to estimate the CFO and the PDPiteratively via the alternating-projection method which could induce the multidimensional searchproblem to a sequence of simple one-dimensional searches.Simulations show that the proposed algo-rithm is more accurate and robust than the existing algorithms.
基金Supported by the Beijing Key Laboratory of Work Safety Intelligent Monitoring(Beijing University of Posts and Telecommunications)
文摘In this paper, a novel robust precoder with imperfect channel state information(CSI)is proposed for multi-input multi-output(MIMO)cognitive multiuser networks equipped with relays. In the proposed model, the secondary users(SUs)are allowed to share the spectrum with the primary users(PUs)when the interference temperature(IT)is below a specific threshold. The transmitting strategy of relays is amplify-and-forward(AF), and the CSI error is characterized in terms of spherical uncertainty region. A minmax problem for the transmit power of the relays is considered when the mean square error(MSE)of SUs and the IT of PU meet their corresponding thresholds, and it is transformed into a semi-definite programming(SDP)problem to search for the solution. Numerical simulations demonstrate the effectiveness of the proposed precoder.
基金supported in part by the National Natural Science Foundation of China under Grants 62125101 and 62341101in part by the New Cornerstone Science Foundation through the XPLORER PRIZE+2 种基金in part by Guangdong Provincial Key Lab of Integrated Communication,Sensing and Computation for Ubiquitous Internet of Things under Grant 2023B1212010007in part by Guangzhou Municipal Science and Technology Project under Grant 2023A03J0011in part by Guangdong Provincial Department of Education Major Research Project under Grant 2023ZDZX1037.
文摘Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.
基金the National Natural Science Foundation of China (No.90104019).
文摘The presented scheme named M-CAP (Maximum CAPacity) uses the CSI (Channel State Information) and its statistics to deduce an equivalent channel according to which the transmit power is allocated to the subchannels. And then modulation scheme is determined adaptively according to the power allocated to each subchannel. The advantage of the M-CAP scheme is that it combines power allocation and adaptive modulation while maintaining a large capacity. We demonstrate by computer simulations that the proposed M-CAP scheme can significantly improve system performance compared with the traditional schemes.
基金the Chinese Scholarship Council for the financial support
文摘We address the problem of adaptive modulation and coding scheme(AMCS) for a multi-input multioutput(MIMO) system in presence of time-varying transmitting correlation.Antenna subset selection and quasiorthogonal space-time block code(QOSTBC) have different error performances with different signal-to-noise ratios(SNRs) and in different spatial correlation scenarios.The error performance can be improved by selecting an appropriate transmission scheme to adapt to various channel conditions.The maximum distance criterion is the simplest and very effective algorithm for the antenna subset selection without needs of complex calculation and channel state information at transmitter(CSIT).The minimum error performance criteria and the simplified linear decision strategy are developed for constant transmission rate traffic to select the optimal transmission scheme.It can dramatically decrease algorithm complexity for obtaining error probability according to the known quantities comparing with using instant CSIT.Simulation results show that,remarkable performances including low SNR and weak spatial correlation at the expense of simple calculation and almost no bandwidth loss by adopting AMCS can be achieved.The proposed AMCS improves robustness of slowly varying spatial correlated channels.