The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWa...The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.Then accurate channel inference can be efficiently implemented using the trained network.The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas.It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.展开更多
In a millimeter-wave(mmWave)Massive multiple-input multiple-output(MIMO)systems,full-digital beamforming(i.e.,connecting each antenna with a specific radio-frequency(RF)chain)becomes inefficient due to the hardware co...In a millimeter-wave(mmWave)Massive multiple-input multiple-output(MIMO)systems,full-digital beamforming(i.e.,connecting each antenna with a specific radio-frequency(RF)chain)becomes inefficient due to the hardware cost and power consumption.Therefore,hybrid analog and digital transceiver where the number of RF chains are much smaller than that of the antennas has drawn great research interest.In this work,we investigate the use of low-resolution analog-to-digital converters(ADCs)in the uplink of multi-user hybrid and full-digital mmWave Massive MIMO systems.To be specific,we compare the performance of full-digital minimum mean square error(MMSE)and hybrid MMSE beamforming in both sum rates and energy efficiency.Accurate approximations of sum rates and energy efficiency are provided for both schemes,which captures the dominant factors.The analytical results show that full-digital beamforming outperforms hybrid beamforming in terms of sum rates and requires only a small portion(γ)of antennas used by hybrid beamforming to achieve the same sum rates.We given sufficient condition for full-digital beamforming to outperform hybrid beamforming in terms of energy efficiency.Moreover,an algorithm is proposed to search for the optimal ADC resolution bits.Numerical results demonstrate the correctness of the analysis.展开更多
基金This work is supported in part by the National Natural Science Foundation of China under grants 61901403,61971366 and 61971365in part by the Youth Innovation Fund of Xiamen under grant 3502Z20206039in part by the Natural Science Foundation of Fujian Province of China under grant 2019J05001.
文摘The deep convolutional neural network(CNN)is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output(MIMO)systems.The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training.Then accurate channel inference can be efficiently implemented using the trained network.The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas.It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.
基金supported in part by the Key Research&Development Plan of Jiangsu Province(No.BE2018108)National Nature Science Foundation of China(Nos.61701198&61772243)+3 种基金Nature Science Foundation of Jiangsu Province(No.BK20170557)Nature Science Foundation for Higher Education Institutions of Jiangsu Province of China(No.17KJB510009)the open research fund of National Mobile Communications Research Laboratory,Southeast University(No.2018D13)Young Talent Project of Jiangsu University and Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX18_0742)
文摘In a millimeter-wave(mmWave)Massive multiple-input multiple-output(MIMO)systems,full-digital beamforming(i.e.,connecting each antenna with a specific radio-frequency(RF)chain)becomes inefficient due to the hardware cost and power consumption.Therefore,hybrid analog and digital transceiver where the number of RF chains are much smaller than that of the antennas has drawn great research interest.In this work,we investigate the use of low-resolution analog-to-digital converters(ADCs)in the uplink of multi-user hybrid and full-digital mmWave Massive MIMO systems.To be specific,we compare the performance of full-digital minimum mean square error(MMSE)and hybrid MMSE beamforming in both sum rates and energy efficiency.Accurate approximations of sum rates and energy efficiency are provided for both schemes,which captures the dominant factors.The analytical results show that full-digital beamforming outperforms hybrid beamforming in terms of sum rates and requires only a small portion(γ)of antennas used by hybrid beamforming to achieve the same sum rates.We given sufficient condition for full-digital beamforming to outperform hybrid beamforming in terms of energy efficiency.Moreover,an algorithm is proposed to search for the optimal ADC resolution bits.Numerical results demonstrate the correctness of the analysis.
文摘为改善智能反射表面(Intelligent reflective surface,IRS)辅助的毫米波多输入多输出(Multiple⁃input multiple⁃output,MIMO)级联信道的估计精度和收敛速度,基于平行因子(Parallel factor,PARAFAC)分解模型,把常规的双线性交替最小二乘(Bilinear alternating least squares,BALS)算法改进为带松弛因子的ω⁃BALS算法和正则化的T⁃BALS,加快了收敛速度和算法稳定性。当基站、IRS元件或用户侧的阵列天线数目较大时,提出改进的奇异值(Singular value decomposition,svd)⁃BALS算法。该算法通过奇异值分解压缩张量,再利用低维度的核心张量来重构模式n矩阵。仿真结果表明,该算法的归一化均方误差性能有所提高,并且加快了收敛速度。
