Mobile edge computing effectively reduces delay and improves the system efficiency by offloading computing tasks. However, the standard communication channel, Orthogonal Frequency Division Multiple Access (OFDMA), is difficult to support large-scale connections in dense networks. Meanwhile, existing task offloading methods fail to fully exploit the structured semantics of the system state in task offloading domain. Therefore, this paper proposes an efficiency strategy: 1) designing an adaptive hybrid communication framework that integrates OFDMA and Non-Orthogonal Multiple Access modes, and dynamically select the optimal communication method based on the task sensitivity entropy; 2) modeling the offloading decision and communication mode selection as a hybrid decision-making process, and adopting reinforcement learning methods to minimize the weighted sum of energy consumption and delay; 3) introducing the large model-assisted proximal policy optimization (LMA-PPO) to encode semantic information of structured data. We conduct high-density experiments in different scenarios and compare them with widely adopted methods. The experimental results show that LMA-PPO performs better in performance, converges faster, and reduces the average cost by 22%. LMA-PPO significantly outperforms existing methods in terms of delay and energy consumption.
Yang Xia, Min-Cong Chen, Qiang He et al.· IEEE Transactions on Mobile...· 0 citations
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
As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service providers (ESPs) to offer computation offloading services to MDs. As such, a CEC resource trading market is essential for efficient interactions between MDs and ESPs. However, jointly determining the offloading ratios, allocating combinatorial computation and communication resources, and designing appropriate pricing strategies in a dynamic market remains a significant challenge. To this end, we propose a truthful online double auction-based resource allocation mechanism for partial computation offloading (TRAPO) that explicitly accounts for the stochastic nature of both MDs and ESPs. TRAPO first leverages spatial diversity to construct a set of bids for each MD by mapping their task requirements into resource demands through considering MDs’ preferences and partial offloading. Next, we match resource-demanding MDs with resource-supplying ESPs based on adaptive valid price thresholds to maximize social welfare, and calculate the payments of MDs and the rewards of ESPs. Theoretical analyses demonstrate that TRAPO satisfies truthfulness, budget balance, individual rationality, and computational tractability. Simulation experiments further verify the effectiveness and efficiency of TRAPO.
Dongkuo Wu, Xing-Wei Wang, Xue-Yi Wang et al.· IEEE Transactions on Mobile...· 0 citations
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