In this paper, we propose a nested tensor-based algorithm for integrated sensing and communication (ISAC) in reconfigurable intelligent surface (RIS)-assisted downlink terahertz (THz) multiple-input multiple-output (MIMO) systems. By exploiting the multi-group Khatri-Rao space-time (KRST) coding scheme at the base station (BS) and the structure of RIS phase shifts, we formulate the received signal at the vehicle terminal (VT) as a nested tensor that is composed of multiple outer parallel factor (PARAFAC) tensors and an inner PARATUCK tensor. With the outer PARAFAC tensors, a low-complexity least squares Khatri-Rao factorization (LS-KRF) algorithm is developed for joint channel estimation and symbol detection. Then, with the inner PARATUCK tensor, an optimized five-linear alternating least squares (OF-ALS) algorithm is designed to precisely estimate the sensing parameters such as angles of departure (AoDs), angles of arrival (AoAs), and time delays for VT and scatterer positioning. The proposed algorithm can be effectively applied to the scenario where the number of paths is unknown. Simulation results show that the proposed algorithm has more relaxed parameter conditions and provides superior ISAC performance compared with the existing state-of-the-art algorithms.

The burgeoning technology of deep learning-based semantic communications has significantly enhanced the efficiency and reliability of wireless communication systems by facilitating the transmission of semantic features. However, security threats, notably the interception of sensitive data, remain a significant challenge for secure communications. To safeguard the confidentiality of transmitted semantics and effectively counteract eavesdropping threats, this letter proposes a secure deep learning-based semantic communication system, SecureDSC. It comprises semantic encoder/decoder, channel encoder/decoder, and encryption/decryption modules with a key processing network. By incorporating a symmetric encryption module and an attacker-oriented adversarial network, SecureDSC guarantees the secure transmission between legitimate users in the semantic communications. Besides, experiments are conducted to evaluate the effectiveness and feasibility of the proposed scheme.

In order to support colourful multimedia services with strict quality-of-service (QoS) requirements of user equipments (UEs), the space-air-ground integrated networks (SAGIN) can be taken as a promising approach to enhance network capacity. Among them, millimeter wave (mmWave) and edge caching promise to significantly improve the SAGIN performance due to the advantage in rich bandwidth resource and low latency, respectively. In this paper, we investigate the joint caching and resource allocation for multimedia services in SAGIN, where multimedia content requests can be simultaneously served by multiple access points (APs). Considering the delay-constraint of multimedia services, we then formulate a mixed-integer non-linear programming (MINLP) problem aiming at minimizing the service delay, which involves jointly optimizing coded caching (CC), power allocation (PA) and UEs-to-APs association (UA). We propose to find the optimal solution by employing an alternating iteration optimization framework. The optimal CC and PA problems are firstly addressed by utilizing convex optimization technology. Then, two many-to-many swap matching algorithms are developed to slove the UA subproblem effectively. Numerical results demonstrate that our proposed algorithms can substantially reduce the service delay over other benchmarks.

Multiple-Input Multiple-Output Non-Orthogonal Multiple Access (MIMO-NOMA) is considered a promising multiple access technology in fifth generation (5G) networks, which can improve system capacity and spectral efficiency. In this paper, we proposed two methods of user grouping and proposed a dynamic power allocation solution for MIMO-NOMA system. Then we proposed an algorithm to maximize energy harvest for MIMO-NOMA system by integrating Simultaneous Wireless Information and Power Transfer (SWIPT), known as maximizing energy harvesting. Specifically, we added a power splitter at the receiver and found the optimal power splitting factor for each user. The harvested power of the user is maximized under the premise of satisfying the minimum communication rate. The simulation results show that the proposed method is effective.

Sparse Code Multiple Access (SCMA) is capable of ameliorating the performance of wireless communication systems for massive connectivity. The sparsity of the codeword allows SCMA to employ the message-passing algorithm (MPA) at the receiver to address the connection overload. The detection complexity of traditional MPA, on the other hand, is exponentially exploding with the increasing number of connected devices. In this paper, a low-complexity MPA scheme is developed with the threshold and dynamical user removal algorithm (TB-RMPA) based on shuffled MPA (SMPA). By introducing the concept of Iterative Elimination of Dominated Strategies (IEDS) in game theory, a novel method of assigning different dominance levels to users is proposed hereby, and a threshold criterion is discussed to judge the reliability of user decoding. Inspired by this, users with reliable decoding in the iterative process will be dynamically eliminated in each iteration. The numerical analysis revealed that the new TB-RMPA detector’s implementation complexity decreases significantly compared with SMPA, while the symbol error ratio performance degrades unnoticeably.

To provide reliable and high-quality services in the sixth-generation (6G) systems, movable antennas (MAs) have attracted much attention since they can use the spatial degree of freedom adequately. Compared to the traditional fixed position arrays, MAs give much better performance in multi-user and multi-antenna scenarios, which implement efficient beamforming and interference suppression in various communication cases. However, the MA array design strategy and the associated channel estimation problems require high-complexity iterative computation algorithms, making it difficult to be exploited in practical applications. In this work, a novel channel estimation method with the MA arrays is proposed based on the convolutional neural network (CNN), which considers the complexity of the algorithm and time consumption while accomplishing the optimal channel estimation. By comparing it with different benchmarks, especially for the orthogonal matching tracking, the CNN-based channel estimation method implements a better trade-off between the mean square error and the computational complexity and the designed examples are provided to verify the effectiveness of the proposed approaches.