Diffusion Model-based Reinforcement Learning for Version Age of Information Scheduling: Average and Tail-Risk-Sensitive Control

Ensuring timely and semantically accurate information delivery is critical in real-time wireless systems. While Age of Information (AoI) quantifies temporal freshness, Version Age of Information (VAoI) captures semantic staleness by accounting for version evolution between transmitters and receivers. Existing VAoI scheduling approaches primarily focus on minimizing average VAoI, overlooking rare but severe staleness events that can compromise reliability under stochastic packet arrivals and unreliable channels. This paper investigates both average-oriented and tail-risk-sensitive VAoI scheduling in a multi-user status update system with long-term transmission cost constraints. We first formulate the average VAoI minimization problem as a constrained Markov decision process and introduce a deep diffusion-based Soft Actor-Critic (D2SAC) algorithm. By generating actions through a diffusion-based denoising process, D2SAC enhances policy expressiveness and establishes a strong baseline for mean performance. Building on this foundation, we put forth RS-D3SAC, a risk-sensitive deep distributional diffusion-based Soft Actor-Critic algorithm. RS-D3SAC integrates a diffusion-based actor with a quantile-based distributional critic, explicitly modeling the full VAoI return distribution. This enables principled tail-risk optimization via Conditional Value-at-Risk (CVaR) while satisfying long-term transmission cost constraints. Extensive simulations show that, while D2SAC reduces average VAoI, RS-D3SAC consistently achieves substantial reductions in CVaR without sacrificing mean performance. The dominant gain in tail-risk reduction stems from the distributional critic, with the diffusion-based actor providing complementary refinement to stabilize and enrich policy decisions, highlighting their effectiveness for robust and risk-aware VAoI scheduling in multi-user wireless systems.

User Centric Semantic Communications

Current studies on semantic communications mainly focus on efficiently extracting semantic information to reduce bandwidth usage between a transmitter and a user. Although significant process has been made in the semantic communications, a fundamental design problem is that the semantic information is extracted based on certain criteria at the transmitter side along, without considering the user's actual requirements. As a result, critical information that is of primary concern to the user may be lost. In such cases, the semantic transmission becomes meaningless to the user, as all received information is irrelevant to the user's interests. To solve this problem, this paper presents a user centric semantic communication system, where the user sends its request for the desired semantic information to the transmitter at the start of each transmission. Then, the transmitter extracts the required semantic information accordingly. A key challenge is how the transmitter can understand the user's requests for semantic information and extract the required semantic information in a reasonable and robust manner. We solve this challenge by designing a well-structured framework and leveraging off-the-shelf products, such as GPT-4, along with several specialized tools for detection and estimation. Evaluation results demonstrate the feasibility and effectiveness of the proposed user centric semantic communication system.

Reconfigurable Intelligent Surface Assisted Semantic Communication Systems

Semantic communication, which focuses on conveying the meaning of information rather than exact bit reconstruction, has gained considerable attention in recent years. Meanwhile, reconfigurable intelligent surface (RIS) is a promising technology that can achieve high spectral and energy efficiency by dynamically reflecting incident signals through programmable passive components. In this paper, we put forth a semantic communication scheme aided by RIS. Using text transmission as an example, experimental results demonstrate that the RIS-assisted semantic communication system outperforms the point-to-point semantic communication system in terms of bilingual evaluation understudy (BLEU) scores in Rayleigh fading channels, especially at low signal-to-noise ratio (SNR) regimes. In addition, the RIS-assisted semantic communication system exhibits superior robustness against channel estimation errors compared to its point-to-point counterpart. RIS can improve performance as it provides extra line-of-sight (LoS) paths and enhances signal propagation conditions compared to point-to-point systems.

Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices $K$. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of $K$ is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for $K$ and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of $K$ are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of $K$ and the design of the ADC quantizer; 2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol.