In this letter, we consider an unmanned aerial vehicle (UAV)-aided information monitoring scheme in a multiple input multiple output (MIMO) communication system. According to the tensor structure of the communication links, an information monitoring receiver is proposed. The receiver is based on the generalized tensor contraction (GTC), which exploits a nested parallel factor (PARAFAC) model for the received signals, via an iterative least squares (LS) algorithm to jointly monitor the data symbols transmitted from the users via the base stations. Compared with some competing receivers, our simulation results demonstrate the superiority of the proposed GTC-LS receiver in terms of normalized mean square error and bit error rate.

In this letter, an enhanced alternating least squares (EALS) algorithm for joint channel and symbol estimation is proposed in a dual-hop multiple input multiple output (MIMO) relaying communication system based on a simplified Khatri-Rao space-time (KRST) coding at the source node. The proposed method exploits the parallel factor (PARAFAC) and PARATUCK2 models at the relay node and the destination node, respectively. Exploiting the structure of these tensor models, we present an iterative alternating estimation scheme using optimized step size factors for accelerated convergence and lower overall complexity compared to conventional approaches. Identifiability conditions and computational complexity are analyzed, and simulation results are provided to corroborate the effectiveness of the proposed algorithm.

In this letter, we consider the dual-hop and three-hop multiple-input multiple-output (MIMO) sensor systems using multiple Khatri-Rao space-time (M-KRST) coding at the unmanned aerial vehicles and sensor nodes. According to the tensor structure of the communication links, the signals received at the fusion center form two separate nested tensors, which satisfy a nested parallel factor (PARAFAC) model and a nested parallel Tucker2 (PARATUCK2) model. Exploiting this nested structure, two semi-blind receivers based on an alternating least squares algorithm are formulated for a joint estimation of symbols and channels. Compared with the two-stage training method, simulation results demonstrate the superiority of the proposed semi-blind receivers in terms of normalized mean square error and bit error rate.

This study proposes a semi‐blind receivers for two‐way multiple‐input multiple‐output (MIMO) relaying systems capable of jointly estimating the channels and symbols in a direct‐data approach. Resorting to a Khatri‐Rao coding scheme applied at both uplink and downlink transmission phases, the authors show that the signals received at the relay and user node are third‐order tensors satisfying the parallel factor (PARAFAC) and PARATUCK2 models, respectively. Combining these two tensor models, a semi‐blind receiver based on an integrated alternating least squares algorithm is proposed to estimate the channels and symbols transmitted by the user nodes without training sequences. The effectiveness of the proposed semi‐blind receiver is corroborated with numerical results, which show that the proposed semi‐blind receiver outperforms state‐of‐the‐art two‐stage training sequence estimator, while operating close to the tensor‐based channel estimator.