Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing-based channel estimators under the STCS framework. Several STCS-based algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting the structured sparsity. We show that, compared with other existing algorithms, the proposed algorithms have a much faster convergence speed and achieve competitive error performance under a wide range of simulation settings.
Compared with the recent work @cite_29 , the novelty of our work consists of the following aspects. First, we employ a Markov model to efficiently exploit the clustered sparsity of the massive MIMO channel in the angle-frequency domain and angle-delay domain, while @cite_29 uses the nearest neighbor sparsity pattern learning (NNSPL) algorithm first proposed in @cite_12 to exploit the sparsity structure. Note that @cite_19 uses the NNSPL algorithm to exploit the angle domain sparsity of the channel, while @cite_8 uses the NNSPL algorithm to exploit the delay domain sparsity. @cite_29 presents a comprehensive version of the NNSPL algorithm to jointly handle the angle-delay domain sparsity. Second, STCS-FS achieves a considerably lower mean square error (MSE) performance than the NNSPL algorithm with frequency support, while STCS-DS performs slightly better than the NNSPL algorithm with delay support. Third, the computational complexity of STCS is much lower than that of NNSPL. In this regard, we show that the per-iteration complexity of STCS is lower than that of NNSPL; we further show that STCS converges much faster than NNSPL.
@cite_19 @cite_29 @cite_12 @cite_8
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We consider the problem of recovering clustered sparse signals with no prior knowledge of the sparsity pattern. Beyond simple sparsity, signals of interest often exhibits an underlying sparsity pattern which, if leveraged, can improve the reconstruction performance. However, the sparsity pattern is usually unknown a priori. Inspired by the idea of k-nearest neighbor (k-NN) algorithm, we propose an efficient algorithm termed approximate message passing with nearest neighbor sparsity pattern learning (AMP-NNSPL), which learns the sparsity pattern adaptively. AMP-NNSPL specifies a flexible spike and slab prior on the unknown signal and, after each AMP iteration, sets the sparse ratios as the average of the nearest neighbor estimates via expectation maximization (EM). Experimental results on both synthetic and real data demonstrate the superiority of our proposed algorithm both in terms of reconstruction performance and computational complexity. In this letter, we reveal that in the massive multiple-input multiple-output system with large bandwidth, sub-channels of orthogonal frequency division multiplexing share approximately sparse common support due to the frequency difference of subcarriers. We use the approximate message passing with nearest neighbor sparsity pattern learning (AMP-NNSPL) algorithm to adaptively learn the underlying structure for improving the accuracy of channel estimation, where the learning strategy is newly derived by solving an optimization problem. In addition, the performance of the AMP-NNSPL is well predicted by the state evolution. Simulation results demonstrate the superiority of the algorithm in systems with large bandwidth. The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7 on VOC07, 80.4 on VOC12, and 34.4 on COCO. Codes will be made publicly available. Recent development of compressive sensing has greatly benefited radar imaging problems. In this paper, we investigate the problem of obtaining enhanced targets such as ships and airplanes, where targets often exhibit structured sparsity. A novel structured sparsity-driven autofocus algorithm is proposed based on sparse Bayesian framework.The structured sparse prior is imposed on the target scene in a statistical manner. Based on a statistical framework, the proposed algorithm can simultaneously cope with structured sparse recovery and phase error correction problem. The focused high-resolution radar image can be obtained by iteratively estimating scattering coefficients and phase. Due to the structured sparse constraint, the proposed algorithm can desirably preserve the target region and alleviate over-shrinkage problem, compared to previous sparsity-driven auto-focus approaches.Moreover, to accelerate convergence rate of the algorithm, we propose to adaptively eliminate noise-only range cells in estimating phase errors. The selection is conveniently conducted based on the parameters controlling sparsity degree of the signal in the proposed hierarchical model.The simulated and real data experimental results demonstrate that the proposed algorithm can obtain more concentrated images with much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios. HighlightsA novel structured sparsity-driven autofocus algorithm is proposed based on sparse Bayesian framework.To accelerate convergence rate of the algorithm, we propose to adaptively eliminate noise-only range cells in phase error estimation stage.The proposed algorithm can obtain more concentrated images with much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios.