This work considers a system where a dual-function radar transmitter (source) performs direct communication with a reader while simultaneously enabling ambient backscatter communication from a tag. The source embeds its message into a coded pulse repeatedly transmitted over a frame, whereas the tag exploits the resulting environmental reverberation (clutter) as an ambient carrier to convey its own message. By leveraging the structure induced by the radar waveforms, we develop two signaling schemes. In the pilot-free scheme, the source and tag messages are conveyed through nonlinear vector modulation; the induced subspace structure enables both joint decoding, where all unknown quantities are simultaneously estimated, and disjoint decoding, where the tag codeword is recovered first, followed by the estimation of the source codeword and the channel vectors. In the pilot-aided scheme, pilot symbols and linearly modulated data symbols are embedded within each frame, enabling both non-iterative decoding based on pilot-derived channel estimates and iterative decoding via alternating channel estimation and data detection. We establish sufficient conditions on the source and tag codebooks that guarantee noiseless identifiability of the involved messages and channels. Finally, performance is evaluated in terms of source/tag error probabilities and channel-estimation accuracy, and the resulting system-level tradeoffs are discussed.

This paper studies covert communication of positive rate, i.e., reliable and covert communication on the order of N bits in N channel uses, over a channel with Action-Dependent State Information (ADSI) when the ADSI is known either non-causally, causally, or strictly causally by both the transmitter and the receiver, and when the ADSI is only known by the receiver. We derive the covert capacity for these problems, which subsumes the capacity of channels with random states at both the transmitter and the receiver. As an application of our problem setup, we study and solve the problem of covert communication over state-dependent Multiple-Access Channels (MACs) with a common message and states known at one of the transmitters and the receiver. Unlike state-dependent point-to-point channels, we show that strictly causal Channel State Information (CSI) can help achieve a positive covert rate in state-dependent MACs.

We study covert classical communication over quantum multiple-access channels (MACs) with general message sets. Specifically, we consider a fully quantum MAC with arbitrary message sets and an arbitrary number of transmitters. We demonstrate the feasibility of achieving a positive covert rate over this channel and establish general one-shot and asymptotic achievable rate regions. For classical-quantum MACs with general message sets, we establish the covert capacity, when the transmitters are restricted to deterministic encoding. Our result recovers, as a special case, known results for classical communication over classical MACs with general message sets, covert communication of a classical message over a classical channel with two transmitters, and classical communication over quantum MACs. We provide three examples of MACs to which our results can be applied, either directly or indirectly, to achieve positive covert rates. Specifically, we first study covert communication over a finite-dimensional MAC with a helper. We then analyze a classical Gaussian MAC with a helper and derive its covert capacity. Finally, we extend the analysis to a single-mode bosonic MAC with a helper and show that positive covert rates can also be achieved in this setting. To the best of our knowledge, this is the first work to achieve positive-rate covert communication over both classical and quantum MACs.

Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants. Ledger-assisted federated learning (FL) improves auditability, yet blockchains, shards, or settlement committees can reintroduce global coordination costs that conflict with DFL locality. This paper proposes gspDAG-FL, a secure DFL framework that derives consensus from the same gossip history used to disseminate models. Nodes exchange model payloads only with neighbors, while full nodes collect event certificates and receiver-endorsed accepted gossip proofs, reconstruct a compact Topology directed acyclic graph (DAG), and run Hashgraph-style virtual voting followed by compact full-node certificates. Finality is over unique model-origin tuples, not identical local parameter states. To improve resilience, gspDAG-FL combines payload validation, accepted-proof validation, and private semantic audit before aggregation. We formalize the adversarial setting, prove safety and conditional liveness of the control plane, and give a convergence guarantee for certified perturbed gossip under time-varying effective mixing. Experiments on MNIST classification and Penn Treebank language modeling, using fair held-out validation/audit data and networks up to N=100, show that gspDAG-FL achieves learning quality close to validation-based ledger FL while reducing coordination bottlenecks, improving throughput, and maintaining high invalid-origin detection under mixed Byzantine and lazy participation.

In quantum state discrimination, the design of measurement operators and probe states is typically formulated under the assumption that the set of possible states is perfectly known, but this may yield designs that are sensitive to deviations in the realized set of states. For example, the channel through which a transmitted state is sent may not be deterministic, but instead may be characterized by a classical distribution over quantum channels. In this article, we consider the design of measurement schemes and probe states for quantum detection over an uncertain quantum channel. We present stochastic-gradient-based algorithms to maximize the expected performance over the channel distribution under two design scenarios: joint design and two-stage design. We consider various design objectives, including detection probability and mutual information, with the latter leading to a hybrid scheme consisting of a von Neumann measurement and a classical hypothesis test. Furthermore, we introduce a channel discrimination scheme that leverages the isometric extension of a quantum channel, which increases channel distinguishability while simultaneously reducing the effective dimensionality and optimization complexity. In addition, we apply amortized optimization techniques to train a recurrent neural network in order to improve the convergence speed of the proposed algorithms. Finally, we apply the proposed algorithms to multicopy channel discrimination as well as to a novel joint channel–state discrimination scenario.

We develop a semi-amortized learning framework for downlink beamforming in large-scale sparse multiple-input multiple-output (MIMO) channels. The core of the proposed approach is a deep semi-amortized encoder-decoder network (SA-EDN) architecture composed of three modules: 1) An encoder neural network (NN), deployed at each user, compresses the estimated downlink channel into a low-dimensional latent representation, which is then fed back to the base station (BS); 2) A beamformer decoder NN at the BS, first maps the recovered latent vectors to transmit beamformers and then performs a few steps of gradient ascent to refine the beamformers; and 3) A channel decoder NN, also located at the BS, reconstructs the downlink channels from the recovered latent vectors. The training of SA-EDN leverages two key strategies: 1) A two-phase training scheme, in which the encoder NN and beamformer decoder NN are alternately trained in the first phase, followed by supervised training of the channel decoder NN in the second phase; and 2) Knowledge distillation, where the first training phase starts from supervised training with linear minimum mean-square error (LMMSE) beamformers as labels, and gradually shifts toward unsupervised training using the sum-rate objective. The proposed SA-EDN beamforming framework is extended to both far-field and near-field hybrid beamforming scenarios. Extensive simulation results demonstrate its effectiveness across diverse network and channel conditions, as well as its superiority over several baseline methods.

In quantum state discrimination, the design of measurement operators and probe states is typically formulated under the assumption that the set of possible states is perfectly known, but this may yield designs that are sensitive to deviations in the realized set of states. For example, the channel through which a transmitted state is sent may not be deterministic, but instead may be characterized by a classical distribution over quantum channels. In this article, we consider the design of measurement schemes and probe states for quantum detection over an uncertain quantum channel. We present stochastic-gradient-based algorithms to maximize the expected performance over the channel distribution under two design scenarios: joint design and two-stage design. We consider various design objectives, including detection probability and mutual information, with the latter leading to a hybrid scheme consisting of a von Neumann measurement and a classical hypothesis test. Furthermore, we introduce a channel discrimination scheme that leverages the isometric extension of a quantum channel, which increases channel distinguishability while simultaneously reducing the effective dimensionality and optimization complexity. In addition, we apply amortized optimization techniques to train a recurrent neural network in order to improve the convergence speed of the proposed algorithms. Finally, we apply the proposed algorithms to multicopy channel discrimination as well as to a novel joint channel-state discrimination scenario.

Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts (EDSCs) and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.

We develop an enhanced in-context learning (ICL) framework to improve the performance of pilot-based beamforming in multi-user multiple-input single-output (MU-MISO) systems. The proposed scheme integrates the ICL-Transformer backbone with the pilot encoder-decoder network (EDN) and the beamformer EDN. A crucial feature of our ICL network is that it can handle multiple channel models without retraining, enabled by the construction of model-specific context datasets. To improve convergence and robustness, we introduce three key innovations: (a) a curriculum learning (CL) strategy that smoothly transitions from supervised LMMSE-labeled imitation to unsupervised sum-rate maximization, (b) a self-evolving mechanism that dynamically expands and refines the context datasets for all channel models during CL-based training, and (c) a mismatch-aware extension that incorporates several mismatches into the general ICL framework and bypasses explicit channel calibrations. Ablation studies validate the effectiveness of the in-context architecture and enhanced training strategies. Simulation results over diverse communication environments show that the proposed scheme is able to rapidly adapt to both seen and unseen channel models without gradient-based parameter updates, and can mitigate the mismatch issues via intelligent context constructions. Furthermore, our scheme consistently outperforms the existing beamforming schemes under pilot-based settings, including the WMMSE benchmark and the recent Transformer-based methods.

This paper studies a symbiotic system in which a reconfigurable intelligent surface (RIS) assists a radar transmitter while conveying information to a reader via backscattering. The RIS is partitioned into subarrays that redirect the radar signal toward the angular sector under inspection and superimpose a slow-time modulation using orthogonal phase codes, thereby implementing MIMO radar functionalities. Communication is achieved by encoding information in the selection of an unordered subset of orthogonal codewords, without altering the RIS transmit beampattern. At the reader, the proposed index modulation scheme enables low-complexity detection without requiring channel state information. Numerical results demonstrate the effectiveness of the proposed backscatter communication approach.

High-order multiple-input multiple-output (MIMO) detection requires efficient search over a large discrete symbol space while producing reliable soft information for channel decoding. This paper develops a learning-to-transition (L2T) framework that formulates MIMO detection as a stochastic sequence of complete-vector transitions. At each transition, a channel-coupled Transformer updates both the instance embedding and the sampling policy, while a blockwise autoregressive factorization captures inter-stream dependence with moderate sequential complexity. For hard-output detection, a transition network is applied recursively and trained through a residual-to-BER curriculum, which first learns the MIMO search geometry from the exact residual metric and then aligns the policy with transmitted-bit accuracy. For soft-output reception, the well-trained hard policy is cloned at the parameter level into every layer of an untied soft-input soft-output iterative detection and decoding (IDD) receiver. This tied-to-untied transfer preserves the learned zero-prior search dynamics while enabling layer- and round-specific specialization under decoder feedback. Within each IDD round, decoder priors tilt candidate generation according to Bayes' rule, and likelihood-weighted terminal hypotheses produce posterior and extrinsic log-likelihood ratios for LDPC decoding. A multi-stage training strategy further stabilizes the hard-to-soft transfer by progressively exposing the receiver to synthetic and in-loop decoder-generated priors.

Personalized sequential recommendation aims to predict the appropriate items to users from their behavioral sequences. To alleviate the data sparsity and interest drift issues, conventional approaches typically utilize the additional behaviors from other domains via cross-domain transition. However, existing cross-domain sequential recommendation (CDSR) algorithms follow the align-then-fusion paradigm which conducts the representation-level alignment across multiple domains and mechanically combine them for recommendation, overlooking the fine-grained multi-domain fusion. Inspired by the advancements of diffusion models (DMs) in distribution matching, we propose an align-for-fusion framework for CDSR to Harmonize triple preferences utilizing Dual-oriented DMs (HorizonRec). Specifically, we first investigate the uncertainty injection of DMs and attribute the fundamental factor of the instability in existing DMs recommenders to the stochastic noise and propose a Mixed-conditioned Distribution Retrieval strategy which leverages the retrieved distribution from users' authentic behavioral logic as a bridge across the triple domains, enabling consistent multi-domain preference modeling. To suppress the potential noise and emphasize target-relevant interests during multi-domain user representation fusion, we further propose a Dual-oriented Preference Diffusion method to guide the extraction of preferences aligned with users' authentic interests from each domain under the supervision of the mixed representation. We conduct extensive experiments and analyses on four CDSR datasets from two distinct platforms to verify the effectiveness and robustness of our HorizonRec and its effective mechanism in fine-grained fusion of triple domains. Our code and datasets are available in https://github.com/YongfuZha/HorizonRec.

We study covert communication and covert secret key generation with positive rates over quantum state-dependent channels. Specifically, we consider fully quantum state-dependent channels when the transmitter shares an entangled state with the channel. We study this problem setting under two security metrics. For the first security metric, the transmitter aims to communicate covertly with the receiver while simultaneously generating a covert secret key, and for the second security metric, the transmitter aims to transmit a secure message covertly and generate a covert secret key with the receiver simultaneously. Our main results include one-shot and asymptotic achievable positive covert-secret key rate pairs for both security metrics. Our results recover as a special case the best-known results for covert communication over state-dependent classical channels. To the best of our knowledge, our results are the first instance of achieving a positive rate for covert secret key generation and the first instance of achieving a positive covert rate over a quantum channel. Additionally, we show that our results are optimal when the channel is classical and the state is available non-causally at both the transmitter and the receiver.

We propose a new coded blockchain scheme suitable for the Internet-of-Things (IoT) network. In contrast to existing works for coded blockchains, especially blockchain-of-things, the proposed scheme is more realistic, practical, and secure while achieving high throughput. This is accomplished by: 1) modeling the variety of transactions using a reward model, based on which an optimization problem is solved to select transactions that are more accessible and cheaper computational-wise to be processed together; 2) a transaction-based and lightweight consensus algorithm that emphasizes on using the minimum possible number of miners for processing the transactions; and 3) employing the raptor codes with linear-time encoding and decoding which results in requiring lower storage to maintain the blockchain and having a higher throughput. We provide detailed analysis and simulation results on the proposed scheme and compare it with the state-of-the-art coded IoT blockchain schemes including Polyshard and LCB, to show the advantages of our proposed scheme in terms of security, storage, decentralization, and throughput.

We study covert communication and covert secret key generation with positive rates over channels with causal Channel State Information (CSI) at the transmitter. Specifically, we consider a state-dependent Discrete Memoryless Channel (DMC) where the transmitter has causal access to the CSI, and aims to communicate covertly with the receiver while simultaneously generating a covert secret key shared with the receiver. We derive an achievable rate region for this problem, which recovers as a special case the best-known results for covert communication over channels with CSI. To the best of our knowledge, our results are the first instance of achieving a positive rate for covert secret key generation.

We study covert classical communication over a quantum multiple-access channel (MAC) with a helper. Specifically, we consider three transmitters, where one transmitter helps the other two transmitters communicate covertly with a receiver. We demonstrate the feasibility of achieving a positive covert rate over this channel and establish an achievable rate region. Our result recovers as a special case known results for classical communication over classical MACs with a degraded message set, classical communication over quantum MACs, and classical communication over MACs with a helper. To the best of our knowledge, our result is the first to achieve covert communication with positive rates over both classical and quantum MACs.

Noiseless private side information does not reduce the download cost in Symmetric Private Information Retrieval (SPIR) unless the client knows all but one file. While this is a pessimistic result, we explore in this paper whether noisy client side information available at the client helps decrease the download cost in the context of SPIR with colluding and replicated servers. Specifically, we assume that the client possesses noisy side information about each stored file, which is obtained by passing each file through one of D possible discrete memoryless test channels. The statistics of the test channels are known by the client and by all the servers, but the mapping M between the files and the test channels is unknown to the servers. We study this problem under two privacy metrics. Under the first metric, the client wants to preserve the privacy of its file selection and the mapping M, and the servers want to preserve the privacy of all the non-selected files. Under the second metric, the client is willing to reveal the index of the test channel that is associated with its desired file. For both privacy metrics, we derive the optimal common randomness and download cost. Our setup generalizes SPIR with colluding servers and SPIR with private noiseless side information. Unlike noiseless side information, our results demonstrate that noisy side information can reduce the download cost, even when the client does not have noiseless knowledge of all but one file.

Next-generation wireless technologies such as rate-splitting multiple access (RSMA) and massive MIMO are characterized by optimization problems too complex to solve in real-time, hence suboptimal heuristics are adopted in practice. As we explore in this paper, machine learning techniques have the potential to upend this paradigm, offering new algorithms customized for a particular distribution of problems. We consider MISO downlink beamforming optimization for NOMA, SDMA, and RSMA with sum rate and min rate criteria. We apply the framework of learning to optimize to learn an RNN optimizer that produces beamformers with much less computation than existing optimization algorithms such as weighted-MMSE. The RNN inference complexity scales linearly with the size of the antenna array and therefore is suitable for massive MIMO. We show that the learned optimizer is also compatible with a distributed beamforming scenario such as cell-free massive MIMO with information exchange facilitated by a central processor. Our simulation results show that the learned optimizer is competitive with state-of-the-art optimization methods, but requires a fraction of the computational cost.

We develop an unsupervised deep learning framework for real-time scalable and generalizable downlink beamforming in multi-user multiple-input single-output (MU-MISO) systems. The proposed semi-amortized lifted learning-to-optimize (SALLO) framework employs a multi-layer Transformer to iteratively refine an auxiliary variable and the beamformer solution, with a few projected gradient ascent steps at each layer. A key feature of our SALLO Transformer model is that it can handle varying numbers of users and antennas, enabled by a user-antenna dual tokenization and a structured sample/attention masking scheme, leading to generalization across different configurations without retraining. To improve convergence and robustness, we introduce three training strategies: (a) sliding-window training to stabilize gradient propagation, (b) curriculum learning with random masking to enable user-antenna configuration generalization and prevent poor early-stage convergence, and (c) sample replay to mitigate catastrophic forgetting during multi-stage training. Ablation studies validate several key architecture designs and show that the enhanced training scheme improves both generalizability and solution quality. Simulation results over both Gaussian and sparse channels show that the proposed scheme consistently outperforms existing deep learning baselines across diverse system configurations and channel conditions. The performance gain becomes more pronounced in overloaded regimes, highlighting improved robustness under challenging scenarios. Furthermore, our scheme surpasses the WMMSE benchmark in underloaded systems and even in overloaded systems when the overloading factor is below certain threshold. These gains are achieved with fast inference and a substantially more lightweight model than wireless foundation models.