This paper proposes a Transformer-basedshared embedding (SE)scheme as an artificial intelligence (AI)-native multiple access for semantic communications. In SE, multiple users jointly occupy a high-dimensional embedding space and are separated by learnable user-specific positional masks together with attention-driven demultiplexing, so that inter-user interference is suppressed directly in the embedding domain. The resulting interference structure closely resembles interference alignment (IA), wherein residual multiuser energy is compressed into a low-rank subspace that the decoder can effectively attenuate. We develop closed-form expressions for signal power, effective signal-to-interference-plus-noise ratio (SINR), symbol error rate (SER), and per-user capacity, explicitly characterizing the roles of intra-user coherence and an alignment coefficient that quantifies attention-induced interference suppression. These formulas yield theoretical baselines that situate SE performance between a no-alignment lower bound and an ideal non-orthogonal multiple access (NOMA)-inspired upper bound and explain capacity gains in dense regimes via statistical multiplexing and alignment. Experimental evaluations are carried out under Rayleigh fading channels, and the results show that SE consistently outperforms the dedicated embedding (DE) scheme, which assigns each user an exclusive portion of the embedding space, across different channel conditions and user densities. The empirical SE curves match the theoretical predictions with fitted alignment factors and approach the NOMA-inspired bound as the embedding dimension increases, thereby showing that the embedding space can function as a new axis of multiple access complementary to time, frequency, and code domains. Finally, we outline adaptive and generalized training strategies that extend beyond symbol-level recovery to semantic-level classification and reconstruction tasks for next generation networks.

In this paper, we propose a new cooperative infer-ence method between the end device and the edge server for intelligent surveillance services. In this method, the end device with a small neural network (NN) model operates with dual confidence thresholds to filter ambiguous input images, which are then forwarded to the edge server and reevaluated by a large NN model. We numerically analyze the performance of the proposed method in terms of accuracy and end-to-end latency, taking into account the confidence scores derived from both positive images and negative images that induce false alarms. Subsequently, we identify the optimal confidence thresholds for both the end device and the edge server to minimize the end-to-end latency while ensuring the required accuracy. The simulation and analysis results show that a tradeoff exists between accuracy and latency according to the confidence thresholds, and the selection of optimal confidence thresholds significantly reduces the latency while satisfying the required accuracy. Accordingly, the proposed method achieves higher accuracy than the device-only inference and lower latency than the server-only inference. This highlights the importance of employing cooperative inference with optimal confidence thresholds in surveillance systems to avoid the fate of ‘The Boy Who Cried Wolf.’

To overcome the limitations of standalone inference that relies on either an edge device or a server, this study proposes a new cooperative inference method between the edge device and the edge server for intelligent surveillance services. In this method, the edge device equipped with a small neural network (NN) model operates with dual confidence thresholds to filter ambiguous input images, which are then forwarded to the edge server and reevaluated by a large NN model. We numerically analyze the performance of the proposed method in terms of inference accuracy and end-to-end latency, taking into account the distribution of confidence scores resulting from positive images as well as negative images that may induce false alarms. Subsequently, we formulate an optimization problem to minimize the end-to-end latency while ensuring the required accuracy, and propose a greedy search algorithm to find the optimal confidence thresholds with low complexity in a nonconvex problem. We also present an operational framework to utilize the proposed cooperative inference method in a practical on-site environment. The simulation and analysis results show that a tradeoff exists between accuracy and latency according to the confidence thresholds, and the selection of optimal confidence thresholds significantly reduces the latency while satisfying the required accuracy. Therefore, the proposed cooperative inference achieves higher accuracy than the device-only inference and much lower latency than the server-only inference across various system parameters. This verifies the importance of optimizing confidence thresholds when applying a cooperative inference method to mobile edge networks.

Accurate and real-time three-dimensional (3D) pose estimation is challenging in resource-constrained and dynamic environments owing to its high computational complexity. To address this issue, this study proposes a novel cooperative inference method for real-time 3D human pose estimation in mobile edge computing (MEC) networks. In the proposed method, multiple end devices equipped with lightweight inference models employ dual confidence thresholds to filter ambiguous images. Only the filtered images are offloaded to an edge server with a more powerful inference model for re-evaluation, thereby improving the estimation accuracy under computational and communication constraints. We numerically analyze the performance of the proposed inference method in terms of the inference accuracy and end-to-end delay and formulate a joint optimization problem to derive the optimal confidence thresholds and transmission time for each device, with the objective of minimizing the mean per-joint position error (MPJPE) while satisfying the required end-to-end delay constraint. To solve this problem, we demonstrate that minimizing the MPJPE is equivalent to maximizing the sum of the inference accuracies for all devices, decompose the problem into manageable subproblems, and present a low-complexity optimization algorithm to obtain a near-optimal solution. The experimental results show that a trade-off exists between the MPJPE and end-to-end delay depending on the confidence thresholds. Furthermore, the results confirm that the proposed cooperative inference method achieves a significant reduction in the MPJPE through the optimal selection of confidence thresholds and transmission times, while consistently satisfying the end-to-end delay requirement in various MEC environments.

In this paper, we analyze the effect of Cell_FACH and Cell_PCH in the view point of delay and power consumption. By putting the Cell_FACH and Cell_PCH states in practice to Smartphone Web surfing, we verify that the user experience can be enhanced significantly while the power consumption does not increase much.

The previous approach to the multicast problem for ad hoc networks is to resolve at networks layer. As a result, there are many multicast routing protocols based on multicast delivery structure in mobile ad hoc networks. In order to improve cross-layer optimization, we propose a collision free multiple access scheme by combining RTS/CTS with scheduling algorithms to support the multicast routing protocol. The benefit of proposed scheme, such as power saving, reliable data transmission and higher channel utilization compared with CSMA or multiple unicast, enables the efficient support of multicast services in mobile ad hoc networks.

In satellite communication systems, channel capacity is limited because of the limited number of satellites. In particular, uplink channel capacity is relatively low due to low-power handheld terminals, collision of many terminals, and fading by long propagation time. Also long propagation delay limits the number of data-retransmissions for real time traffic, and small capacity limits the number of data-retransmissions for non real time traffic. For these reasons, comparative studies are required in order to identify the most appropriate satellite-based RLC (radio link control) protocol for global mobile multimedia communication via LEO satellite networks. In this paper we consider two kinds of traffic, real time traffic and non real time traffic. For real time traffic, a class of adaptive error-control schemes using both the code rate of FEC and frame length based on the estimated channel state/condition are used as an RLC protocol with transparent mode or unacknowledged mode. Also, for non real time traffic, a hybrid ARQ type II/III protocol is used as an RLC protocol with acknowledged mode.