Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)systems.In this paper,Gaussian Mixture lear...Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)systems.In this paper,Gaussian Mixture learned approximate message passing(GM-LAMP)network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems.Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit(OMP)and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high.This drawback can be addressed using classical iterative algorithms such as approximate message passing(AMP),which has comparatively low computational complexity.The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders.In this paper,the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP(LAMP)network,and is further enhanced as GMLAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders.The simula-tion results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates,better accuracy and low computational complexity compared to the existing algorithms.展开更多
This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst inte...This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.展开更多
The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,th...The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.展开更多
In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)syst...In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.展开更多
文摘Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)systems.In this paper,Gaussian Mixture learned approximate message passing(GM-LAMP)network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems.Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit(OMP)and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high.This drawback can be addressed using classical iterative algorithms such as approximate message passing(AMP),which has comparatively low computational complexity.The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders.In this paper,the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP(LAMP)network,and is further enhanced as GMLAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders.The simula-tion results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates,better accuracy and low computational complexity compared to the existing algorithms.
基金supported by the National Key Laboratory of Wireless Communications Foundation,China (IFN20230204)。
文摘This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.
基金supported by National Key Research and Development Program of China under Grants 2021YFB1600500,2021YFB3201502,and 2022YFB3207704Natural Science Foundation of China(NSFC)under Grants U2233216,62071044,61827901,62088101 and 62201056+1 种基金supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62supported by Beijing Nova Program,Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009.
文摘The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.
基金supported by the National Natural Science Foundation of China under Grant U19B2014the Sichuan Science and Technology Program under Grant 2023NSFSC0457the Fundamental Research Funds for the Central Universities under Grant 2242022k60006.
文摘In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.
