Numerical results show that the CKM empowered FBL RSMA outperforms space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA), particularly in high-mobility scenarios, and its performance is improved by a data-based CKM, which provides more accurate large-scale channel information than model-based approaches and enables more precise common-stream allocation.
Abstract
To meet the extended ultra-low latency and high reliability (xURLLC) requirements for autonomous driving systems, multiple access schemes must operate reliably in high-mobility and complex propagation environments. Recently, rate-splitting multiple access (RSMA) has emerged as a promising multi-user transmission framework, showing robustness in dynamic situations where imperfect and outdated channel state information (CSI) is prevalent.Moreover, the advanced sensing, localization, and on-board computation capabilities of autonomous driving vehicles facilitate the construction of a channel knowledge map (CKM), which is a key enabler for environment-aware communications in future 6G networks.In this work, we propose a CKM empowered finite-blocklength (FBL) RSMA for downlink autonomous driving system. The location-dependent large-scale channel information provided by CKM is exploited in RSMA to develop a refined rate-splitting design. The min-rate performance of FBL rate splitting is analyzed explicitly to ensure user fairness. We derive a new and tight closed-form bound for the private-stream ergodic rate. Combined with the closed-form common-stream expression, an efficient optimization design of rate-splitting ratios has been formulated. Numerical results show that the CKM empowered FBL RSMA outperforms space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA), particularly in high-mobility scenarios. Its performance is improved by a data-based CKM, which provides more accurate large-scale channel information than model-based approaches and enables more precise common-stream allocation. The results also reveal that RSMA is sensitive to errors in large-scale channel knowledge, emphasizing the importance of accurate CKM information for optimal rate-splitting.
To address the high mobility impacts and the ultra-reliable and low-latency communications (URLLC) requirements in autonomous driving scenarios, rate-splitting multiple access (RSMA) combined with short-packet communication (SPC) emerges as a promising solution. Autonomous vehicles rely on real-time information exchange to ensure safety and coordination, making information freshness essential. By jointly capturing transmission delays and packet errors, age of information (AoI) serves as a comprehensive metric for freshness. In this paper, we investigate short-packet rate splitting to enhance information freshness measured by the AoI. By splitting the unicast messages into common and private parts, encoding all common parts together with the multicast message into a common stream, and encoding each private part into a private stream, RSMA effectively manages interference and enables achieving lower AoI. By considering critical factors such as transmit power, vehicle velocity, blocklength, and the number of transmit antennas, we derive closed-form expressions for the average AoI (AAoI) of the common stream under partial decoding and the overall AAoI under complete decoding. To enhance the AAoI performance, we propose the multi-start two-step successive convex approximation (SCA) algorithm. This algorithm first optimizes the power allocation and subsequently optimizes the rate splitting under the quality of service (QoS) trade-off constraint. Simulation results demonstrate that our short-packet rate-splitting scheme significantly improves the AAoI performance while ensuring system fairness and enabling ultra-low AAoI through the common stream, meeting the requirements of autonomous driving applications. Moreover, the trade-off between the common and overall performance is revealed, indicating that the overall performance can be further enhanced while maintaining the advantages of the common stream.
Zi-Ru Zheng, Yingyang Chen, Xinyue Pei et al.· IEEE Transactions on Wireles...· 0 citations
Conventional wireless protocols such as Hybrid Automatic Repeat Request (HARQ) rely on frequent and timely feedback, which becomes impractical in low-feedback regimes including non-terrestrial networks and massive IoT. This limitation is particularly critical in heterogeneous multi-user systems with unknown and asymmetric channels, where a single weak user can dominate the overall completion latency. We propose Overhearing-driven Non-Orthogonal Multiple Access (ONOMA), a novel cross-layer transmission scheme that minimizes latency without requiring instantaneous or statistical CSI at the transmitter. ONOMA integrates Random Linear Network Coding (RLNC) with symbol-aware NOMA and explicitly exploits overhearing and acknowledgment timing. In the first phase, users overhear RLNC transmissions, and the relative timing of acknowledgments is used to implicitly infer channel strength ordering. In the second phase, symbol reconstruction enables interference-free decoding for strong users, effectively decoupling user latencies. An adaptive power allocation policy is derived from acknowledgment timing-based channel estimates. Analytical and simulation results show that ONOMA outperforms TDMA, multicast, FDMA, inter-session, and classical NOMA, reducing completion time by up to 34% in two-user and 50% in larger asymmetric networks.
Mohsen Abedi, A. Badawy, Amr Mohamed· arXiv.org· 0 citations
A versatile DS2D system that supports cooperative task offloading and non-cooperative access monitoring, and Transformer-based models to enable blind signal detection and automatic modulation classification (AMC) are proposed.
Sai Huang, Wanli Ni, Ke Lv et al.· IEEE Vehicular Technology Ma...· 0 citations
This paper presents a novel 6G-Enabled vehicle-to-everything (V2X) communication framework designed for the Intelligent Internet of Vehicles (IoV). The proposed system integrates the multi-user access and interference suppression capabilities of multi-carrier code division multiple access (MC-CDMA) with the delay–Doppler domain robustness of orthogonal time frequency space (OTFS) modulation. This hybrid design ensures reliable transmission in high-mobility and dense vehicular environments. Vehicle mobility is modeled using a combination of car-following dynamics and Markov chain-based lane-changing behavior, capturing both longitudinal and lateral motion patterns. The communication system further employs minimum mean square error (MMSE) and zero-forcing (ZF) techniques for multi-user detection at the receiver. To cope with rapid channel variations caused by mobility, we introduce a Multi-Agent Deep Q-learning (DQL) framework for mobility-aware channel tracking. Each agent learns adaptive channel update strategies based on historical channel estimates and mobility context. Simulation results show that the proposed MC-CDMA–OTFS system with DQL tracking achieves up to a 2.5 dB SNR gain at BER = 10⁻³ and improves throughput compared to a conventional OFTS baseline. These findings highlight the potential of the proposed hybrid design as a robust physical-layer foundation for next-generation 6G V2X networks.
T. H. Nguyen, Anh Thuy Nguyen· Vietnam Journal of Science a...· 0 citations
Passive overlay communication for batteryless devices is an important enabling capability for next-generation vehicle-to-everything (V2X) networks. However, enabling reliable passive payload delivery without occupying additional spectrum remains challenging, since overlay signaling must be embedded into short and time-varying vehicular packets while preserving the decodability of the legacy host transmission. This paper investigates a packetized batteryless V2X overlay architecture in which a dedicated short-range communications (DSRC)-based packet simultaneously carries conventional V2X data and a passive overlay payload. A compact PHY-layer model is developed to characterize the coupled effects of attenuation depth, embedded-bit rate, and legacy modulation and coding scheme (MCS) on host-link and passive-link reliability, as well as packet-level embedding feasibility. We then formulate a sum-throughput maximization problem that jointly accounts for the legacy packet error rate and passive decoding error rate. We further propose a multi-agent reinforcement learning (MARL)-based adaptive parameter-selection method. Simulation results show that the proposed MARL controller achieves stable convergence and improves the average throughput by 15\%, demonstrating the effectiveness of throughput-driven PHY adaptation for batteryless V2X overlay communications.
Zhao-Yu Liu, Ruikang Li, Liu Cao et al.· 0 citations
Uncrewed aerial vehicles (UAVs) that use reconfigurable intelligent surfaces (RIS) offer exceptional spatial flexibility for 6G networks. However, their real-world application faces significant challenges due to fast-changing channel conditions and limitations in hardware performance. Most generative channel estimation models incorrectly assume perfect, zero-power phase shifts and static conditions, which leads to significant failures when dealing with strong Doppler effects and real-world 1/2-bit RLC circuit limitations. To address this issue, this letter proposes a dynamic Transformer-Diffusion (TransDiff) framework that takes into account hardware considerations. By smoothly combining a generative Vision Transformer (ViT) with temporal Kalman tracking, the proposed method successfully captures the quick changes in both spatial and temporal channel behavior. Furthermore, a generative coding mechanism is integrated to physically penalize the diffusion reverse process using exact RLC impedance mismatches. Numerical evaluations confirm that at a high mobility of 120 km/h, the proposed framework achieves an unprecedented normalized mean square error (NMSE) of -21.87 dB, yielding an effective pilot overhead reduction of 29.3% and maximizing the energy efficiency to 19.15 bits/J, substantially outperforming state-of-the-art CNN-based denoisers.
Ammar M. Raheema· IEEE Wireless Communications...· 0 citations
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