In this paper, a tensor-based joint channel parameter estimation and information symbol detection scheme is developed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems. At the base station (BS), the information symbols are encoded according to the Khatri-Rao space-time (KRST) method and transmitted through time-varying channels. The received signals at the mobile station (MS) are constructed into a nested complex-valued parallel factor (PARAFAC) model, which contains an outer model and an inner model, respectively. With outer model, we estimate the compound channel matrix and detect the information symbols considering the sparse scattering nature of mmWave channels. With inner model, we extract physical parameters, including angles of arrival/departure (AoAs/AoDs), Doppler shifts and complex path gains from the estimated compound channel matrix. These physical parameters can be used to significantly reduce feedback overhead. A tricky way here is that we convert complex inner-submodel into a real one such that the computational complexity is reduced. Compared with existing schemes, the proposed one improves the estimation accuracy with low computational complexity, and is applicable for both uniform linear arrays (ULAs) and uniform planar arrays (UPAs). Simulation results demonstrate the effectiveness of the proposed joint channel estimation and information symbol detection scheme.

In this letter, we propose an efficient off-grid sparse Bayesian learning (SBL) algorithm to estimate the near-field (NF) channel in ultra-scale massive multiple-input multiple-output (MIMO) systems. We first exploit the sparsity of the NF channel in the polar domain and perform an off-grid sampling of angles and distances. Then, an effective SBL method is proposed for sparse channel recovery and off-grid refinement. The sampled grid points are set to adjustable parameters, which undergo iterative updates by resorting to an accurate block majorization-minimization (MM) algorithm and gradient calculation. The proposed method demonstrates efficient channel estimation capabilities even in the absence of prior knowledge of the noise variance, leveraging the sparse characteristics of the NF channel. Simulation results demonstrate that our approach significantly improves the sparsity and accuracy of the NF channel representation. Furthermore, it outperforms the existing state-of-the-art algorithms in terms of estimation performance while requiring less pilot overhead.

In this paper, we consider the decode-and-forward (DF) relay system in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, and propose a hybrid beamforming design method for the mixed structure, which contains the fully-connected and sub-connected structures. To satisfy constant-modulus and block-diagonalization (BD) constraints, the analog beamforming is designed by the idea of sorted successive interference cancellation (SSIC). More specifically, the proposed method first sorts the capacities of different analog sub-channels in descending order, and then designs the analog beamforming serially according to the order of the capacities. To efficiently mitigate the inner-user and inter-user interference, we propose a modified baseband BD technology to reduce the information loss in digital beamforming design, thereby improving the system capacity. In addition, the proposed hybrid beamforming algorithm is designed by considering both uniform linear arrays (ULAs) and uniform planar arrays (UPAs). Simulation results demonstrate that the proposed hybrid beamforming design scheme can obtain good performance in terms of the achievable sum-rate and power efficiency in ULAs and UPAs.

Integrated sensing and communications (ISAC) is one of the potential key technologies of the sixth-generation (6G) that could enhance the information interaction ability among network nodes. Since tensor technology can excellently store, process, and analyze a large amount of multi-dimensional data generated by modern wireless systems, it has aroused great research interest in both academia and industry. In this article, we present a general review on tensor decompositions for ISAC. We first describe the commonly used tensors and their extended models. Next, we introduce the recent development and appealing advantages of tensor decompositions for communication, sensing, and ISAC, respectively. Moreover, the superiority of tensor decompositions to facilitate ISAC is demonstrated through a case study, and some promising research directions of tensor decompositions in ISAC are discussed to promote future works. We envision that the tensor technology will play an indispensable role in ISAC systems by virtue of its excellence in handling abundant multi-dimensional data.

In this paper, we investigate the hybrid beamforming design with sub-connected structure for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) amplify-and-forward (AF) relay systems. Considering the constant-modulus and block-diagonal constraints, an iterative algorithm is proposed to sequentially design the analog beamforming of the base station, relay, and users. Next, the relay baseband combiner is designed by transforming the highly complicated non-convex mutual information maximization problem into an easily tractable weighted minimum mean squared error one. To mitigate the inter-user interference, a successive serial interference cancellation based and a piecewise successive approximation based methods are developed for the uniform sparse distribution and dense distribution scenarios, respectively. Simulation results demonstrate that the two proposed hybrid beamforming schemes can achieve good performance in terms of sum-rate and energy efficiency, and outperform benchmarks significantly. In addition, the two proposed schemes are shown to be robust to imperfect channel state information (CSI), even though they are studied based on perfect CSI.

For multiple-antenna systems, the technologies of joint symbol and channel parameter estimation have been developed in recent works. However, existing technologies have a number of problems, such as performance degradation and the large cost of prior information. In this paper, a tensor space-time coding scheme in multiple-antenna systems was considered. This scheme allowed spreading, multiplexing, and allocating information symbols associated with multiple transmitted data streams. We showed that the received signal was formulated as a third-order tensor satisfying a Tucker-2 model, and then a robust semi-blind receiver was developed based on the optimized Levenberg–Marquardt (LM) algorithm. Under the assumption that the instantaneous channel state information (CSI) is unknown at the receiving end, the proposed semi-blind receiver jointly estimates the information symbol and channel parameters efficiently. The proposed receiver had a better estimation performance compared with existing semi-blind receivers, and still performed well when the channel became strongly correlated. Moreover, the proposed semi-blind receiver could be extended to the multi-user massive multiple-input multiple-output (MIMO) system for joint symbol and channel estimation. Computer simulation results were shown to demonstrate the effectiveness of the proposed receiver.