The emerging integrated sensing and backscatter communication (ISABC), is expected to provide a new paradigm for Internet of Things (IoT) applications. In this paper, we propose a novel radar-enabled ISABC (R-ISABC) system design, where the signal processing center (SPC) can simultaneously perform localization for multiple targets and symbol detection for multiple backscatter devices (BDs) without requiring knowledge of the exact waveform of the radar signal. In particular, we first characterize the received signal models using the angle of arrivals (AOAs) of targets, symbol vectors transmitted by BDs, and the environment radar reverberation, and then an explicit third-order tensor model is elaborated by leveraging the periodicity of the radar reverberation and rearranging the sampled received signals. Then, we propose a novel CANDECOMP/PARAFAC decomposition (CPD)-assisted joint angle estimation and BD symbol detection algorithm based on the formulated third-order tensor model with the differential coding adopted at each BD. Numerous simulation results verify the feasibility and effectiveness of the R-ISABC system design.

In this paper, we develop a novel receiver design for massive MIMO-OFDM systems in time-varying scenarios. Due to the Doppler shift effects, the received subcarriers measurements suffer from severely inter-carrier interference (ICI), which degrades the system performance. Motivated by this, we proposed a novel joint channel estimation and signal detection method with ICI suppression. By exploiting the channel sparsity in the delay-angle-doppler domain, the proposed method utilizes variational Expectation Maximization (EM) framework for iterative channel and signal updates. Concretely, in E-step, the turbo message passing algorithm is performed for calculating the mariginal posteriors. In M-step, the unknown parameters are updated. Numerical results show that this proposed receiver design outperforms the counterpart methods.

Ambient backscatter communication (AmBC) has emerged as a promising technology for passive Internet of Things (IoT), which enables backscatter devices (BDs) to transmit information over ambient radio frequency (RF) signals. This paper investigates joint localization and signal detection for an AmBC system. The BDs modulate information over ambient orthogonal frequency division multiplexing (OFDM) signals, and the receiver realizes joint localization and signal detection for the BDs. A two-stage receiver algorithm is proposed to realize the joint localization and signal detection. In the first stage, the receiver estimates the delays and angles-of-arrival (AoAs) of the BDs through the orthogonal matching pursuit (OMP) based algorithm. Then attenuation coefficients of the backscatter link are estimated through the least squares (LS) method, based on which the information symbols transmitted by the BDs can be detected in the second stage. Further, to address the discretization error in the estimation of AoAs and delays through the OMP algorithm, we propose an sparse Bayesian learning (SBL) based algorithm to achieve off-grid estimation. Finally, the Cramér-Rao lower bound (CRLB) is derived to evaluate the performance of the algorithms. Simulation results have verified the effectiveness of the system model and the superiority of the SBL-based algorithm, and it is shown that larger bandwidth and antenna arrays are beneficial to improving the estimation accuracy.

Multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) systems have gained increasing attention due to their superior performance in double-selective channel scenarios. However, MIMO-OTFS systems typically suffer from high pilot overhead and complex channel estimation (CE) when the number of antennas is large. To tackle these issues, in this paper, we propose a novel transceiver design, consisting of a new transceiver architecture, a low-overhead pilot pattern, and the corresponding low-complexity CE algorithm. Specifically, firstly, we apply the Inverse Discrete Fourier Transformation (IDFT) module at the transmitter (TX) and the corresponding Discrete Fourier Transformation (DFT) module at the receiver (RX), by resorting to which the received signals can be separated effectively in the time-delay-angular (TDA) domain to greatly reduce the inter-path and inter-antenna interferences. Secondly, at the TX, we design a new pilot pattern that removes the guard region and the length of which does not increase with the number of transmit antennas (TAs), leading to significantly reduced pilot overhead and increased spectral efficiency. Thirdly, at the RX, we utilize the three-dimensional (3D) sparsity of the MIMO-OTFS channel in the delay-Doppler-angular (DDA) domain to correspondingly achieve a low-complexity CE algorithm. Extensive numerical results demonstrate the effectiveness of our proposed transceiver design.

Downlink and uplink decoupling access (DUDA) has recently been shown to significantly improve uplink transmission performance in wireless networks, such as in terrestrial and unmanned aerial vehicle (UAV) networks. In this paper, we propose a novel DUDA scheme for non-geostationary orbit (NGEO) heterogeneous satellite networks (HSN) with the avoidance of in-line interference for spectral coexistence of geostationary Earth orbit (GEO) and NGEO satellites. Concretely, the in-line interference caused by the high main lobe gain of the directional antenna in NGEO satellites explicitly described by utilizing geocentric angle-based spherical surface model. For both uplink and downlink transmission of the proposed DUDA scheme, each NGEO user equipment (UE) is allowed to access the NGEO satellite with the largest average received signal power under the different NGEO satellites exclusion angles constraint. The performance is evaluated in terms of transmission rate. Simulation results show notable enhancement of DUDA compared with counterpart access schemes.

This paper investigates the physical layer security (PLS) problems of satellite-terrestrial links for LEO satellite communication systems, ultimately aiming to improve LEO satellite communications’ security. A scheme derived from the handoff process of LEO satellite communication system has been proposed to adapt special characteristics of LEO satellite channel, especially the time-varying and block fading in Land Mobile Satellite (LMS) channel. Through a series of simulations, the superiority of the proposed scheme compared with the traditional model could be illustrated.

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi- backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time-frequency resource block. Due to the presence of inter-user and inter-BD interference, multiuser and multi-BD detection in the receiver design become much more challenging. Concretely, the detection problem involves several key components: direct-link channel estimation, backscatter-link channel estimation, user signal decoding, and BD symbol detection. A conventional way is to realise these components in two separate phases, in which a channel estimation phase is followed by a data decoding phase. However, channel state information (CSI) acquisition is very difficult for the multiuser multi-BD SR communication, as compared to the case of orthogonal multiple access. In addition, the backscatter-link is relatively weak, which further increases the difficulty of CSI acquisition. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Based on the factor graph representation of the joint estimation problem, we design a message-passing receiver for the multiuser multi-BD SR system to iteratively refine the estimation outputs. Extensive simulation results demonstrate the effectiveness of the proposed receiver design.

Symbiotic radio (SR) emerges as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi-backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time and frequency resource block. However, the receiver design becomes much more complicated due to the presence of inter-user and inter-BD interference. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Specifically, motivated by the idea of approximate message passing, we develop a computationally efficient iterative algorithm under the Bayesian inference framework to resolve the joint estimation problem. Simulation results demonstrate the effectiveness of the proposed receiver design.

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services.

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

This paper investigates the passive beamforming and information transfer (PBIT) technique for the large intelligent surface (LIS) aided multiuser multiple-input multiple-output (MuMIMO) systems, where the LIS is deployed to enhance the primary communication via passive beamforming and simultaneously deliver its private data via spatial modulation. For the passive beamforming design, we propose to maximize the sum channel capacity of the LIS-aided Mu-MIMO systems and formulate the maximization problem as a two-step stochastic program. An efficient sample average approximation based iterative algorithm is developed for the passive beamforming design. For the receiver design, the signal detection at the receiver is a bilinear estimation problem since the LIS data are multiplicatively modulated onto the reflected signals of the reflecting elements. To solve this problem, we develop a turbo message passing algorithm in which the bilinear estimation problem is divided into two subproblems: one for the estimation of the user signals and the other for the estimation of the LIS data. Extensive simulation results are provided to demonstrate the advantages of our passive beamforming and receiver designs.

This paper investigates the passive beamforming and information transfer (PBIT) technique for multiuser multiple-input multiple-output (Mu-MIMO) systems with the aid of reconfigurable intelligent surfaces (RISs), where the RISs enhance the primary communication via passive beamforming (P-BF) and at the same time deliver additional information by the on-off reflecting modulation (in which the RIS information is carried by the on/off state of each reflecting element). For the P-BF design, we propose to maximize the achievable user sum rate of the RIS-aided Mu-MIMO channel and formulate the problem as a two-step stochastic program. A sample average approximation (SAA) based iterative algorithm is developed for the efficient P-BF design of the considered scheme. To strike a balance between complexity and performance, we further propose a simplified P-BF algorithm by approximating the stochastic program as a deterministic alternating optimization problem. For the receiver design, the signal detection at the receiver is a bilinear estimation problem since the RIS information is multiplicatively modulated onto the reflected signals of the reflecting elements. To solve this bilinear estimation problem, we develop a turbo message passing (TMP) algorithm in which the factor graph associated with the problem is divided into two modules: one for the estimation of the user signals and the other for the estimation of the on-off state of each RIS element. The two modules are executed iteratively to yield a near-optimal low-complexity solution. Furthermore, we extend the design of the Mu-MIMO PBIT scheme from single-RIS to multi-RIS, by leveraging the similarity between the single-RIS and multi-RIS system models. Extensive simulation results are provided to demonstrate the advantages of our P-BF and receiver designs.

In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity massive MIMO model by considering the channel sparsity of the massive MIMO channel and the signal sparsity of GSM. Based on the double-sparsity model, we formulate a blind detection problem. To solve the blind detection problem, we develop message-passing based blind channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically follows the affine sparse matrix factorization technique, but with critical modifications to handle the double-sparsity property of the model. We show that the BCSE algorithm significantly outperforms the existing blind and training-based algorithms, and is able to closely approach the genie bounds (with either known channel or known signal). In the BCSE algorithm, short pilots are employed to remove the phase and permutation ambiguities after sparse matrix factorization. To utilize the short pilots more efficiently, we further develop the semi-blind channel-and-signal estimation (SBCSE) algorithm to incorporate the estimation of the phase and permutation ambiguities into the iterative message-passing process. We show that the SBCSE algorithm substantially outperforms the counterpart algorithms including the BCSE algorithm in the short-pilot regime.

This paper is concerned with a machine learning approach to cancel the interference for cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model, where the C-IoT system is noncooperative and has very limited knowledge on the interference. Our transceiver design uses an iterative processing structure, which consists of a linear estimator, a demodulation-and-decoding module, and a clustering module. In the clustering module, we employ modified expectation-maximization (EM)-based algorithms to estimate the interference under the knowledge of the modulation constraint (MC) of the interference. We show that this modified EM algorithm-based receiver outperforms the original EM-based receiver, since the former is able to generate a more accurate clustering result by reducing the dimension of the parameter space. We further improve the performance of the iterative receiver by introducing the extrinsic information technique, with the resulting algorithm referred to as the extrinsic modulation constrained EM (Ext-MC-EM) algorithm. We show that the Ext-MC-EM algorithm-based receiver considerably outperforms the counterpart iterative receivers, including the MC-EM algorithm.

In this paper, we study time-varying orthogonal frequency division multiplexing (OFDM) systems, and propose a joint channel-and-signal estimation receiver based on turbo message passing (TMP) to efficiently suppress inter-carrier interference (ICI). We establish a factor graph representation of the problem and divide the whole factor graph into two parts, one for channel estimation and the other for signal detection. For the first part, we use Gaussian message passing (GMP) for channel estimation; for the second part, a discrete state space (DSS) model is employed to describe the transition of signal states, and a forward-backward algorithm is adopted for message passing over the transition trellis in signal detection. The resulting algorithm is referred to as DSS-GMP. The complexity of DSS-GMP quickly becomes the bottleneck as the increase of the signal constellation size and the ICI width. To address this issue, we further develop a continuous-state-space (CSS) model based turbo message passing algorithm, where the messages of modulated signals are approximated as continuous Gaussian messages. Numerical results demonstrate that the TMP based scheme significantly outperforms the state-of-the-art schemes.

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.