The asymmetrical uplink and downlink transceiver architecture has emerged as a promising solution to reduce hardware cost and complexity in massive multiple-input multiple-output (MIMO) systems, especially under scenarios with dense antenna deployments, such as uniform planar arrays (UPAs). However, accurate full-dimensional channel state information (CSI) recovery becomes more challenging than uniform linear array (ULA) scenarios due to the significantly reduced number of radio frequency (RF) chains. Directly extending the ULA channel recovery method into UPAs will not only result in high computational complexity but also introduce angle estimation ambiguity owing to the extra vertical array dimension. To address these challenges, we propose a channel recovery framework for UPA-assisted massive MIMO systems with asymmetrical transceiver architectures. First, we introduce the concept of the mixed angle to deal with the low elevation angular resolution originating from the compact array form, and a virtual array is then constructed based on the mixed angle via the spatial correlation matrix. After that, an antenna selection algorithm is designed to maximize the virtual array aperture with a minimal number of RF chains, and a low-complexity UPA-based modified newtonized orthogonal matching pursuit (UPA-based mNOMP) channel recovery algorithm is developed to enable accurate full-dimensional CSI reconstruction. Finally, the imperfect spatial correlation matrix is considered and a orthogonal rank-one matrix pursuit-based spatial correlation matrix recovery algorithm is proposed to recover the spatial correlation matrix from its spatial sparse measurements by exploiting the low-rank property of massive MIMO channels. Simulation results validate the superiority of the proposed algorithms in achieving excellent full-dimensional channel recovery performance for asymmetrical transceiver-based massive MIMO systems with UPAs.

The asymmetrical transceiver architecture has shown potential in reducing hardware complexity and cost in massive multiple-input multiple-output (MIMO) systems. However, channel recovery is necessary for the asymmetrical transceiver to acquire excellent transmission performance. Although the uniform planar array (UPA) is widely deployed in practical systems, the compact form of UPAs will result in high computational complexity due to the inherent high-dimensional nature of the array. The angle ambiguity problem also arises when directly extending the uniform linear array channel recovery method into the UPAs. To address these challenges, we propose a low-complexity gridless channel recovery method in this paper. First, the concept of the mixed angle for UPAs is introduced to deal with the low elevation angular resolution originating from the compact array form. After that, an antenna selection algorithm is designed to construct a virtual array based on the mixed angle to maximize the virtual array aperture with a minimal number of antennas. Finally, a low-complexity UPAbased modified newtonized orthogonal matching pursuit channel recovery algorithm is developed to mitigate angle ambiguity, thus enabling accurate reconstruction of the full downlink channel state information. Numerical results demonstrate the superiority of the proposed method in significantly reducing the number of receive uplink radio frequency chains while ensuring satisfactory channel recovery performance.

In this article, the Bayesian estimator and detector are proposed in the scenario of massive communication. The ultra massive multiple-input-multiple-output (MIMO) is established at the base station (BS), which is communicated with a huge number of online devices in the near field. In order to estimate the uplink channel responses, the novel nonorthogonal pilot sequences are designed and the principle of turbo decoding is applied. Then, the sparse estimation of extra large-scale channel state information (CSI) is performed depending on the extrinsic information transferring in the spatial domain and angular domain. Besides, the mixed analog-to-digital converter (ADC) architecture is considered to accomplish the linear and nonlinear measurements. Based on this framework, the tradeoff between the system performance and hardware overhead can be achieved. Additionally, a submodule-based segmentation technique is addressed to eliminate the energy spreading phenomenon caused by the near filed effects of the ultra massive MIMO. Specifically, we also analyze the theoretical statistical result of sparse channel estimation and device activity detection using the state evolution method. Furthermore, several engineering implementation strategies are provided to enhance the efficiency improvements in the practical system of massive communication. Numerical simulation results demonstrate that the satisfactory performance of estimation/detection is beyond other methods in terms of hardware costs and computational complexity in the extra large Internet of Things (IoT) network.

This paper studies the reconfigurable intelligent surface (RIS)-enhanced channel estimation and device activity detection technique for the next generation massive internet of things (IoT) networks. Thanks to its low cost, RIS can be introduced into massive IoT networks to extend the area coverage and support more online devices. However, introducing RIS also brings new challenges in channel estimation and device detection for massive connectivity systems owning to the resulting cascaded channel and its inherent passive characteristics. To address this issue, we first formulate the RIS-aided channel estimation and device activity detection as a joint sparse signal recovery problem by simultaneously exploring the sparsity of sporadic transmission and RIS-aided channel links. After that, an RIS-aided generalized Turbo multiple measurement vector algorithm to estimate the channels between the devices and the base station, and detect the active devices jointly under different channel distributions, i.e., the Bernoulli Gaussian scale mixture distribution and the Bernoulli Gaussian approximation distribution. Furthermore, we analyze the state evolution equations of the proposed channel estimation technique and the theoretical detection results from the perspective of missing detection and false alarm probabilities are also provided. Numerical results confirm the correctness of the theoretical analysis, and show that RIS is beneficial for improving the mean square error performance of the channel estimators, as well as the active device detection performance of detectors in massive connectivity systems.

We propose a learned factor graph based on convolutional neural network (CNN) and Bi-directional Long Short Term Memory (BiLSTM) to realize signal detection under the scenario of no channel model. It can solve the inevitable over-reliance on channel state information (CSI) of model-based signal detection methods and avoid the shortcomings of large training scale of general data-driven methods by using relatively small training samples. The proposed method uses a network of CNN-BiLSTM structure with strong learning capabilities to determine the statistical relationship of the channel model which is what traditional model-based methods rely on. Based on above, the parameter estimation (Gaussian mixture model considering Akaike information criterion) and non-parametric estimation (adaptive kernel density) are adopted to learn a factor node together. The simulations show that, the proposed method can guarantee the accuracy of signal detection and robustness to the training of imperfect CSI.

In this paper, a model-driven signal detection method with deep transfer learning (DTL) is proposed for downlink multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Specifically, we first introduce some learnable parameters to an unfolded iterative algorithm for MIMO detection and improve it through a preconditioned process to speed up its convergence. Then we combine this modified algorithm with the successive interference cancellation (SIC) structure in NOMA detection to propose our learned preconditioned conjugate gradient descent network with SIC (LPCG-SIC). Furthermore, to improve the reusability of the trained network, a DTL-based detection algorithm and three model-driven transfer strategies are proposed for our LPCG-SIC detector. Simulation results show that the proposed detection network outperforms conventional detectors, and the transfer strategies can obtain significant performance gain compared to no-transfer methods.

This paper aims to provide a partial discrete Fourier transform (DFT) pilot sequence assisted joint channel estimation and user activity detection scheme for massive connectivity, in which a large number of devices with sporadic transmission communicate with a base station (BS) in the uplink. The joint channel estimation and device detection problem can be formulated as a compressed sensing single measurement vector or multiple measurement vector (MMV) problem depending on whether the BS is equipped with single or large number of antennas. Due to high hardware cost and power consumption in massive multiple-input multiple-output (MIMO) systems, a mixed analog-to-digital converter (ADC) architecture is considered. In order to accommodate a large number of simultaneously transmitting devices, the joint channel estimation and active user detection are formulated as an MMV problem for the massive connectivity scenario; and the proposed GTurbo-MMV algorithm can precisely estimate the channel state information and detect active devices with relatively low overhead. Furthermore, we study the state evolution (SE) for the MMV problem to obtain achievable bounds on channel estimation and device detection performance, in which both the missing and false detection probabilities can be made tend to zero in the massive MIMO regime. The simulation results confirm the theoretical accuracy of our analysis.

In this study, we propose a generalized turbo signal recovery algorithm to estimate a signal from quantized measurements, in which the sensing matrix is a row-orthogonal matrix, such as the partial discrete Fourier transform matrix. The state evolution of the proposed algorithm is derived and is shown to be consistent with that obtained with the replica method. Numerical experiments illustrate the excellent agreement of the proposed algorithm with theoretical state evolution.