Active reconfigurable intelligent surface (RIS) is deemed a prospective candidate to compensate the double fading attenuation caused by the passive RIS, where each element can reflect and amplify the received signals through its low power integrating amplifiers. This paper investigates the physical layer security (PLS) for a non-orthogonal multiple access (NOMA) system through a deployed active RIS to defend multiple eavesdroppers (Eves), where the practical discrete RIS phase shift designs are taken into account. To characterize the secrecy performance, analytical expressions of secrecy outage probability (SOP) and effective secrecy throughput (EST) for an active RIS assisted NOMA (RIS-NOMA) system are derived based on heuristic approximations, which is in consideration of two scenarios with and without direct links. Furthermore, secrecy diversity orders are attained based on theoretical results, of which the convergence rates and gaps between the discrete and continuous phase shifting are further evaluated. The results reveal that a 4-bit quantization can achieve the secrecy diversity order of continuous phase shifts. Numerical results are furnished to corroborate the analyses, and illustrate that the security performance for active RIS-NOMA outperforms the passive RIS-NOMA and conventional cooperation communications under the same total power consumption. The impacts of phase quantization bit, amplification factor and the number of Eves as well as reflective elements on security performance are also substantiated by simulations.

The sixth-generation (6G) network is considered a promising enabler of the Internet of Things (IoT), with potential applications spanning from terrestrial to non-terrestrial terminals. In this paper, a novel orthogonal time frequency space (OTFS)-based pattern division multiple access (PDMA) scheme, shortened as OTFS-PDMA, is proposed to meet the low-latency, high-reliability, and massive connection requirements of 6G networks in high-mobility scenarios. A combined detection approach is proposed for the OTFS-PDMA scheme based on the expectation propagation algorithm (EPA). To mitigate the detrimental effect of base station antenna correlation, we design a vector EPA (V-EPA) receiver by combining different receive antennas at each delay-Doppler domain resource element. A pattern-aware serial mechanism is proposed to facilitate the convergence of the proposed EPA and V-EPA receivers by exploiting theconvergence-amenableproperty of the PDMA scheme. An iterative detection and decoding (IDD) structure with parallel interference cancellation (PIC) is integrated into the proposed receivers to further improve the system performance. Simulation results illustrate that the proposed OTFS-PDMA scheme with EPA-based receivers achieves significant performance gains over the conventional OTFS-OMA scheme. Moreover, the proposed V-EPA receiver provides a good performance-complexity trade-off, particularly in spatially correlated multiple-input multiple-output (MIMO) channels.

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.