Wireless channels are inherently vulnerable to malicious jamming. Recently, end-to-end (E2E) learning has emerged as a promising paradigm for intelligent physical-layer design, particularly in multiple-input multiple-output (MIMO) load modulation array (LMA) systems, due to its channel-adaptive capability. By treating jamming as part of the channel state, we investigate robust E2E communication under adversarial conditions. However, two major challenges hinder practical deployment: (1) the high training and communication overhead caused by frequent retraining under dynamic channels and jamming, and (2) the unrealistic reliance on perfect channel state information (CSI) and feedback in resource-constrained systems. To address these issues, we propose Channel-Adaptive Hierarchical End-to-End learning (CAHE), a framework that reformulates E2E learning under stochastic channels as a dynamic mapping from channel realizations to optimal network parameters. CAHE consists of a channel-adaptive generator layer that dynamically produces encoder and decoder parameters, and an E2E learning layer that deploys these parameters for adaptive transceiver design. To reduce CSI feedback, we introduce a compact channel embedding mechanism for efficient representation. For jamming scenarios, we further propose a differential block-averaged channel estimation method to enable accurate anti-jamming channel estimation. In addition, we design a jamming-resilient network that embeds convolutional neural network (CNN)-learned jamming features into the decoder generator, enhancing perception of the communication state and improving robustness to random and dynamic jamming. Extensive analysis demonstrates the interpretability and feasibility of CAHE, and simulations confirm its significant performance gains over existing schemes.

End-to-end (E2E) learning has emerged as a key technology for intelligent physical-layer communications, demonstrating significant potential in multiple-input multiple-output (MIMO) load-modulation array (LMA) systems. However, practical deployment faces two major challenges: high online training overhead due to retraining on new channel realizations and impractical reliance on perfect channel state information (CSI) and feedback in resource-constrained systems. To address these issues, we propose Channel-Adaptive Hierarchical End-to-End Learning (CAHE), a novel framework that reformulates E2E learning under stochastic channels as a dynamic mapping problem between channel realizations and optimal E2E network parameters. CAHE comprises a channel-adaptive generator layer, which dynamically generates encoder and decoder parameters tailored to each channel, and an E2E learning layer, which deploys these parameters for adaptive encoding and decoding. Moreover, to reduce channel feedback overhead, we introduce an efficient channel embedding mechanism, achieving up to 75% lossless compression of channel data. Furthermore, we develop a pilot-assisted alternative scheme to address scenarios where perfect CSI is unavailable. Simulation results demonstrate that CAHE significantly outperforms existing methods, achieving up to 9 dB gain at a bit error rate of 3×10−5, while the pilot-based scheme exhibits robust performance in imperfect CSI scenarios.

The reconfigurable intelligent surface (RIS)-based carrier modulation technology, which operates without conventional radio-frequency chains, offers hardware simplicity and cost efficiency. In particular, most current RIS-based amplitude modulation schemes rely on the ON/OFF state switching or amplifier gain control of RIS elements, and ambiguity-free high-order phase modulation requires correspondingly highresolution phase shifts. However, practical hardware limitations often impose a low-resolution phase shift and near-constant reflecting amplitude of RIS elements, complicating high-order modulation. To address this, we propose an efficient composite phase shift keying (CPSK) scheme to generate virtual highorder rectangular quadrature amplitude modulation (RQAM) signals at the receiver, referred to as CPSK-RQAM. By decomposing high-order RQAM signals into multiple low-order quadrature phase shift keying signal components, with each component transmitted by a distinct RIS group, CPSK-RQAM eliminates the need for additional amplitude control and ensures robustness to phase-shift quantization errors. Unlike the similar schemes based on conventional I/Q decomposition and equal grouping, CPSK-RQAM achieves a significantly larger minimum Euclidean distance by employing a tailored grouping criterion. Furthermore, we derive the closed-form expressions for the approximate symbol error probability (SEP) of CPSK-RQAM under maximum likelihood detection. Simulation results validate the theoretical analysis and demonstrate the superiority of CPSKRQAM over the state-of-the-art schemes.

Hierarchical federated learning (HFL) is a key technology enabling distributed learning with reduced communication overhead. However, practical HFL systems encounter two major challenges: limited resources and data heterogeneity. In particular, limited resources can result in intolerable system latency, while heterogeneous data across clients can significantly degrade model accuracy and convergence rates. To address these issues and fully leverage the potential of HFL, we propose a novel framework called graph-based joint client and resource orchestration. This framework addresses the challenges of practical networks through joint client clustering and resource allocation. First, we propose a learning process where edge servers employ hypernetworks to achieve edge aggregation. This method can generate personalized client models and extract data distributions without directly exposing data distributions. Then, to characterize the joint effects of limited resources and data heterogeneity, we propose a graph-based modeling method and formulate a joint optimization problem that aims to balance data distributions and minimize latency. Subsequently, we propose a graph neural network-based algorithm to tackle the formulated problem with low-complexity optimization. Numerical results demonstrate significant benefits over existing algorithms in terms of convergence latency, model accuracy, scalability, and adaptability to new distributions.

This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. Furthermore, a conventional LMA codebook with codewords uniformly distributed on a hypersphere may not be channel-adaptive and may lead to increased signal detection complexity. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly. The proposed FAS-based system addresses the SAS structural problems and can support larger numbers of users. For LMA-imposed constant-power downlink precoding, we propose an FAS-based normalized block diagonalization (FAS-NBD) algorithm. However, the forced normalization may result in performance degradation. This degradation, together with the aforementioned codebook design problems, is difficult to solve analytically. This motivates us to propose a Deep Learning-enhanced (FAS-DL-NBD) algorithm for adaptive codebook design and codebook-independent decoding. It is shown that the proposed algorithms are robust to imperfect knowledge of channel state information and yield excellent error performance. Moreover, the FAS-DL-NBD algorithm enables signal detection with low complexity as the number of bits per codeword increases.