By enabling spectrum sharing between radar and communication operations, the cell-free dual-functional radar–communication (CF-DFRC) system is a promising candidate to significantly improve spectrum efficiency in future sixth-generation (6G) wireless networks. However, in wideband scenarios, synchronization errors caused by carrier frequency offset (CFO) can severely reduce both communication capacity and sensing accuracy, especially when multiple geographically distributed full-duplex (FD) access points (APs) are jointly coordinated. In this paper, we consider a wideband FD CF-DFRC system where each AP is equipped with movable antennas (MAs). This setting is fundamentally different from existing DFRC or MA-aided designs that typically assume fixed-position antennas, half-duplex operation, or perfect synchronization. First, we develop a field-response-based channel model and derive a worst-case weighted communication–sensing rate (WCSR) that explicitly captures the impact of inter-AP CFO on both the uplink communication signal-to-interference-plus-noise ratio (SINR) and the radar echo SINR. Our analysis reveals that CFO increases the Cramér–Rao lower bound (CRLB) of target position estimation, thereby degrading sensing accuracy. Based on this characterization, we formulate a robust worst-case WCSR maximization problem that jointly optimizes MA positions, transmit beamforming vectors, receive filters, and CFO-related parameters under transmit power and MA-position constraints. To tackle the resulting highly non-convex problem, we propose a two-stage robust optimization framework. In the first stage, we employ fractional programming together with manifold optimization (MO) and penalty dual decomposition (PDD) to solve the worst-case CFO subproblem on the complex unit-modulus manifold, thus obtaining a CFO-robust closed-form structure for the WCSR. In the second stage, we design a meta–reinforcement learning (MRL) based resource allocation scheme that jointly optimizes the MA positions and beamforming vectors in a data-driven manner for dynamic wireless environments. Unlike conventional deep reinforcement learning (DRL) methods, the proposed MRL framework learns a meta-policy that can rapidly adapt to varying channel and CFO realizations, substantially improving convergence speed and scalability. Simulation results show that the proposed robust MO–PDD–MRL framework significantly outperforms existing DRL-based and non-robust CF-DFRC schemes in terms of both communication and sensing performance under CFO impairments. Furthermore, compared to fixed-position antenna (FPA) architectures, the MA-aided CF-DFRC system exhibits markedly enhanced robustness and adaptability to CFO effects and target mobility.

This paper addresses the joint beamforming and data stream allocation (DSA) optimization problem for non-coherent joint transmission (NCJT). A critical yet neglected issue in NCJT beamforming is the tightly related DSA, which involves determining the number of streams transmitted from access points (APs) to their serving user equipments (UEs) according to the channel quality, so that the weighted sum-rate (WSR) can be maximized. However, since the integer number of streams directly determines the dimensions of beamformers, the joint optimization problem is mixed-integer and nonconvex with tightly coupled decision variables, making it NP-hard. To solve this problem, we first fix the DSA variables and propose a distributed and reduced weighted minimum mean square error (WMMSE) beamforming algorithm, called distributed RWMMSE, by leveraging the low-dimensional subspace property of the beamformer obtained by the traditional WMMSE. The distributed RWMMSE achieves the same WSR as the traditional WMMSE but with significantly lower computational complexity (scaling linearly with the number of AP antennas) and reduced interaction cost. Building on this, a joint beamforming and DSA optimization algorithm, named RWMMSE-LSA, is proposed by decoupling the decision variables through introduced stream indicator matrices. The RWMMSE-LSA optimizes beamformers and DSA via the distributed RWMMSE and linear programming, respectively, both of which have closed-form solutions. Simulations validate substantial performance gain of our proposed algorithms over existing alternatives in computational and interaction costs.

Abstract Offset spatial modulation (OSM) is an attractive variant in the family of spatial modulation technology, which is capable of reducing the frequency of radio frequency chain switching in practical implementations. This paper elaborates on the benefits of adaptive power allocation (PA) conceived for OSM systems. First, a closed‐form solution of the optimal PA algorithm is derived for the case of two transmit antennas, which maximizes the minimum Euclidean distance (ED) between the received signal points. Moreover, based on the above solutions, a low‐complexity iterative PA algorithm is proposed for larger numbers of transmit antennas, by exploiting the EDs of a few dominant error vectors. Furthermore, for striking a flexible trade‐off between the bit error performance and complexity, an iterative convex approximation based PA algorithm is also developed. Finally, simulation results are presented to show that the proposed PA algorithms provide considerable improvements on the original OSM in terms of the bit error rate.

This paper explores the application of spatial modulation to a cross-media communication system. Specifically, based on the theoretical expression of the mutual information of spatial modulation, an optimization model to maximize the minimum mutual information in the uplink and downlink of the relay is proposed, along with a developed power allocation (PA) scheme. In addition, the simulation is operated under two cross-media communication scenarios to confirm the superiority and robustness of the proposed PA scheme. Finally, the simulation results confirm that the proposed PA scheme has better mutual information performance than that of the average PA scheme.

Artificial noise (AN) is considered as a new physical layer technology to improve the security of wireless systems. In this paper, we investigate secure transmission of AN-aided generalized spatial modulation (GSM), which maintains the same hardware requirements at the transmitter as the conventional GSM. In order to further improve the jamming intensity of conventional AN scheme, we propose an Euclidean distance optimized AN (ED-AN) scheme by minimizing the Euclidean distance between the transmit signal and the jamming signal, which also avoids the power waste of conventional AN scheme. The secrecy capacities of both the AN-GSM and EDAN-GSM schemes are analyzed, and the optimal power allocation of AN-GSM is further investigated by maximizing the secrecy capacity. Furthermore, the upper bounds of the theoretical bit error rates (BERs) of both the legitimate receiver and the illegal eavesdropper over the Rayleigh fading channel are derived. Simulation results validate our derived analysis and demonstrate that the ED-AN scheme offers better secrecy and BER performance.

In this paper, we consider the secure generalised precoding aided spatial modulation (GPSM) scheme, which is combined with artificial noise (AN) to resist unknown malicious eavesdropping. Given a strict power constraint for the transmit signal and a wiretap Rayleigh fading channel, the BER performances of both the desired receiver and malicious eavesdropper are derived. Simulation results validate the accuracy of the theoretical analysis and demonstrate the secrecy performance of the secure GPSM system.

Hybrid analog-digital precoding is a class of efficient techniques for improving the performance of massive multiple-input multiple-output (MIMO) systems with reduced number of radio frequency (RF) chains. Meanwhile, for combating the frequency selective fading, orthogonal frequency-division multiplexing (OFDM) should be also combined in current wireless standards such as 5G. Therefore, for massive MIMO-OFDM, it is difficult to decide the best precoding matrix in the context of frequency selective fading. In this paper, for alleviating this problem, a novel hybrid analog-digital precoding algorithm is proposed for multi-user large-scale MIMO-OFDM systems when the frequency selective fading channel is considered. We show that the proposed algorithm offers a flexible precoding design as well as reducing the requirement of the number of RF chains. Finally, the performance results demonstrate that the proposed algorithm is capable of approaching the performance of optimal full-digital precoding with a low implemention complexity.