Integrated sensing and communication (ISAC) systems with cell-free massive multiple-input-multiple-output (CFMM) frameworks can offer significant performance improvements for both sensing and communication functions. However, there is an obstacle to the system supporting an extremely large number of users in explosive terminal access scenarios. In this article, we propose a novel nonorthogonal multiple access (NOMA)-aided CFMM-ISAC architecture to enhance the connectivity and efficiency of the system. First, we consider a commonly used single-radar sensing scenario and jointly design user pairing and beamforming to maximize the minimum achievable communication rate while ensuring the sensing requirement. To solve the nonconvexity of the formulated optimization problem, the binary variables denoting the feasible pairing of users are first relaxed into continuous variables. An iterative optimization programming algorithm is then developed using the semidefinite relaxation (SDR), rank-one constraint omission (RCO), and successive convex approximation (SCA) approaches. To obtain feasible initial points of the iterative algorithm, we propose an initial point construction algorithm. Based on the obtained user pairing strategy, a corrective beamforming scheme is devised. We further extend our proposed algorithms to a generalized multiradar sensing scenario. Simulation results validate that the proposed joint user pairing and beamforming design achieves noticeable performance improvements compared to the existing methods.

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology to address user service inequity and frequent cell re-selection. However, most of the existing distributed detectors are based on the assumption that all users are served by each access point (AP) with simple linear detection methods, resulting in extremely high fronthaul overhead and performance degradation. In this paper, we propose a novel hybrid distributed detector for the user-centric cell-free massive MIMO system. Each AP pre-defines a load threshold to determine whether to use the low-complexity maximum ratio combining (MRC) algorithm or the high-performance expectation propagation (EP) algorithm to ensure detection accuracy while reducing the computational complexity. Moreover, we propose a sparsified EP (SEP) algorithm to further improve the detection performance, from the perspective of user interference suppression via channel sparsified transformation. Based on the properties of the distributed MRC-EP/MRCSEP detection scheme, an efficient hybrid fusion approach is proposed. Numerical results illustrate that the proposed hybrid distributed detectors achieve a good performance-complexity trade-off compared to the existing distributed detectors.

This paper investigates semantic energy efficiency in a non-orthogonal multiple access (NOMA) enabled integrated sensing and semantic communication (ISSC) system. The model involves the base station (BS) transmitting information to multiple users while performing target sensing using dedicated beamforming. In the considered model, the BS needs to transmit substantial text data to each user using text semantic communication techniques while sensing the targets with certain constraints. Our goal is to maximize semantic energy efficiency and meet semantic communication and sensing accuracy requirements. We formulate an optimization problem for the beamforming matrix and semantic parameter, employing the Dinkelbach's algorithm for simplification and proposing an iterative solution. Numerical results validate the efficacy of the NOMA-ISSC scheme.

The integration of unmanned aerial vehicles (UAVs) and terahertz (THz) technology can provide high data rate for air-to-ground (A2G) communications. In this paper, a non-stationary geometric sub-THz multiple-input multiple-output (MIMO) channel model is proposed for UAV A2G communication based on a three-dimensional (3D) semi-spherical model. The detailed channel modeling of reflection fading and scattering fading on the rough surface in sub-THz band is considered. According to the proposed geometry-based stochastic model (GBSM), the space-time correlation function (STCF) has been derived and the impact of several important UAV-related parameters on the STCF has been investigated and compared to the millimeter-wave (mm-wave) bands. The results indicate that the stationary time separation of channel and the dependency of spatial cross-correlation function (S-CCF) on antenna element spacing in sub-THz band of 140GHz are both smaller than those in mm-wave band of 28GHz.

With the proliferation of emerging Internet of Things (IoT) applications, massive multiuser multiple-input–multiple-output (MU-MIMO) is a promising technology to support the massive connectivity requirement with limited spectral resources. The existing detection algorithms are mainly designed based on a centralized baseband processing architecture, which requires extremely high raw baseband data rates transferred between base-station antennas and the central processing unit (CPU). Decentralized baseband processing (DBP) architecture has recently been proposed to alleviate high interconnect data rates and chip input/output bandwidth bottlenecks. Since the information is not fully shared among each antenna cluster, conventional decentralized detectors suffer from significant performance loss, especially for systems with high overload ratios and/or spatially correlated channels. In this work, we first propose a decentralized groupwise detection paradigm for the star architecture by dividing users into multiple user groups, which can be effectively exploited by factor graph-based message passing algorithms, such as the expectation propagation (EP) algorithm. Then, an efficient message fusion rule is devised based on the product principle of the multivariate Gaussian probability density function at the CPU. To reduce the computational complexity, an approximate groupwise EP (AGW-EP) method is proposed by judiciously selecting reliable constellation vectors during the symbol belief calculation phase. In addition, we extend the proposed methods to the daisy-chain architecture, which requires constant interconnect data rates. Simulation results demonstrate that the proposed groupwise paradigm greatly enhances the performance of the conventional EP detector. Moreover, the proposed decentralized groupwise EP and AGW-EP detection schemes outperform their counterparts, particularly in correlated MIMO channels.

In this paper, we propose a joint signal detection algorithm under the framework of the expectation propagation (EP) algorithm for uplink massive multiuser multiple-input multiple-output (MIMO) systems with generalized spatial modulation (GSM). By projecting the discrete probability distribution into a multivariate complex Gaussian function, the symbol beliefs of the tridimensional GSM constellation are calculated via the iterative propagation of the mean vectors and covariance matrices. To reduce the computational complexity, an efficient separate signal detection called two-stage EP (TS-EP) algorithm is designed. In the first stage, the active transmit antenna indices are determined via the EP. Each vector-valued variable node (VN) in the factor graph is decomposed into multiple sub-VNs, and the invalid sub-VNs of the determined silent transmit antennas are pruned from the factor graph. In the second stage, since the modulation symbols are independent conditioned on the active transmit antennas, the symbols are independently detected by adopting the EP with univariate complex Gaussian approximations, and the number of probability calculations for each symbol belief is significantly reduced in the subsequent EP update. Simulation results illustrate that the proposed EP and TS-EP signal detection schemes outperform the recently proposed counterparts. Moreover, the proposed TS-EP algorithm strikes a desirable and flexible performance-complexity tradeoff.