This paper focuses on non-orthogonal multiple ac-cess (NOMA) scenarios with emerging sensing targets, where beamforming is utilized to integrate sensing and NOMA communications (NOMA-ISAC). This scheme does not require ded-icated sensing signals, avoids further degradation of system performance in the overloaded regime, and facilitates massive device connectivity in future wireless networks. We investigate the performance region of NOMA-ISAC from an information-theoretic perspective and propose a learning-based design to approach the given performance bound. Specifically, the upper bounds for the sensing estimation rate and communication information rate are derived to evaluate the achievable rate region of NOMA-ISAC. Guided by mutual information, the proposed end-to-end learning architecture for NOMA-ISAC enables joint transmitter-receiver optimization to achieve bound-approaching performance. Furthermore, we design a sensing mutual information neural estimation unit (SMEU) for the proposed architecture to evaluate the available sensing mutual information. Simulation results show that the proposed scheme can approach the theoretical bounds of NOMA-ISAC.

Deep learning-based semantic communication is a promising research direction for next-generation communication systems. The emergence of large language models(LLMs) with remarkable semantic comprehension abilities leads us to consider whether LLMs can be used in semantic communication to enhance model's performance. In this paper, we discuss the main implementing details of the idea by proposing a general end-to-end learning semantic communication model with LLM, including subword-Ievel tokenization, a rate adapter based on gradients for matching the rate requirements of any channel codec and fine-tuning for possessing private background knowl-edge. By taking Bidirectional and Auto-Regressive Transformers (BART) and Generative Pre-trained Transformer 2 (GPT2) as examples, we demonstrate how we can utilize various structures of LLMs to design semantic codecs. In terms of semantic fidelity, generalizability to cross-scenario, and complexity, results reveal that the LLM-based semantic communication system achieves exciting performance. We hope this initial work can inspire more research devoted to this field.

Visual inspection is an effective approach for anomaly detection in the industrial Internet of Things. The inpainting-based strategy is widely adopted for industrial visual inspection. However, the training process of the semantic-based inpainting model requires heavy resource consumption due to its complex structure. Additionally, the inspection performance of the model degrades when it encounters an out-of-scope image. Image can be naturally deemed as a tensor with the global low-rank property. Therefore, we propose a data structure-based strategy to implement effective inpainting in this paper. Inspired by the tensor-tensor product, a patch-masked deep tensor factorization model is constructed to reconstruct the intentionally masked region. This model has a simplified structure with three feed-forward neural subnets sharing one trainable low-dimensional input. Besides, Laplacian regularization is imposed to improve the recovery accuracy based on the local smoothness in images. Above all, we apply a parallel approach to implement patch-masked visual inspection by comparing the structural similarity between the recovered and original patches. It is independent of the dataset and works as a sample-related pattern. Experiments conducted on the MVTec dataset demonstrate that, although our model has lower inspection resolution than semantic-based inpainting models, it has much higher training efficiency, which makes it more beneficial for practical deployment.

Beam alignment is essential to compensate for the high path loss in the millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated sensing and communication (ISAC) technology has been envisioned as a promising solution to enable efficient beam alignment in the dynamic UAV network. However, since the digital identity (DID) is not contained in the reflected echoes, the conventional ISAC solution has to either periodically feed back the D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by the separation of D-ID and physical identity (P-ID). This paper presents a novel dual identity association (DIA)-based ISAC approach, the first solution that enables specific, fast, and accurate beamforming towards multiple UAVs. In particular, the P-IDs extracted from echo signals are distinguished dynamically by calculating the feature similarity according to their prevalence, and thus the DIA is accurately achieved. We also present the extended Kalman filtering scheme to track and predict P-IDs, and the specific beam is thereby effectively aligned toward the intended UAVs in dynamic networks. Numerical results show that the proposed DIA-based ISAC solution significantly outperforms the conventional methods in association accuracy and communication performance.

End-to-end learning communication provides an exciting new approach to physical layer design, which inspired us to design the Non-orthogonal multiple access (NOMA) system with superposition coding guided by information theory. Unlike assuming continuous Gaussian inputs in most NOMA systems, we consider actual finite-alphabet inputs at two transmitters. Due to the lack of capacity region under finite cardinality, optimizing design is challenging. In this work, we first give a closed-form expression for the conditional mutual information to define the capacity region of NOMA with finite-alphabet inputs. To simplify the conditional mutual information, we then derive a tight lower bound on mutual information, which is necessary to achieve an accurate and consistent mutual information estimator. Finally, we propose an end-to-end learning model for NOMA that considers maximizing mutual information and bit error rate (BER) constraints to achieve optimal encoders and decoders. Simulation results show that the proposed scheme achieves the capacity region of NOMA with finite-alphabet inputs. It also significantly improves the bit error performance of the system than prior work.

The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in the B5G/6G network thanks to its appealing features, such as wide coverage and on-demand deployment. Emergency communication (EC) is essential to promptly inform UAVs of potential danger to avoid accidents, whereas the conventional communication-only feedback-based methods, which separate the digital and physical identities (DPI), bring intolerable latency and disturb the unintended receivers. In this paper, we present a novel DPI-Mapping solution to match the identities (IDs) of UAVs from dual domains for visual networking, which is the first solution that enables UAVs to communicate promptly with what they see without the tedious exchange of beacons. The IDs are distinguished dynamically by defining feature similarity, and the asymmetric IDs from different domains are matched via the proposed bio-inspired matching algorithm. We also consider Kalman filtering to combine the IDs and predict the states for accurate mapping. Experiment results show that the DPI-Mapping reduces individual inaccuracy of features and significantly outperforms the conventional broadcast-based and feedback-based methods in EC latency. Furthermore, it also reduces the disturbing messages without sacrificing the hit rate.