Low earth orbit (LEO) satellite-enabled orthogonal frequency division multiple access (OFDMA) systems will play a pivotal role in future integrated satellite-terrestrial networks to realize ubiquitous high-throughput communication. However, the high mobility of LEO satellites and the utilization of Ku-Ka and millimeter wave (mmWave) bands introduce wide-range Doppler shifts, which are especially detrimental to OFDMA-based systems. Existing Doppler shift compensation methods are limited by the requirement for prior user location information and/or high computational complexities associated with searching across broad Doppler shift ranges. In this work, we propose a multi-stage Doppler shift compensation method aimed at compensating for wide-range Doppler shifts in downlink LEO satellite OFDMA systems over Ku-Ka to mmWave bands. The proposed method consists of three stages: incorporating the phase-differential (PD) operation into the extended Kalman filter (EKF) to widen the estimation range, enhancing compensation using a repetition training sequence, and utilizing the cyclic prefix (CP) for fine estimation. Simulation results demonstrate the proposed method's effectiveness in handling Doppler shifts in LEO SatCom over different channels and frequency bands. Moreover, the proposed method attains the maximum estimation range and exhibits high accuracy with low complexity, irrespective of the Doppler shift range, making it an effective, practical, and easily implementable solution in LEO satellite communication.
With the advances of machine learning, edge computing, and wireless communications, split inference has tracked more and more attention as a versatile inference paradigm. Split inference is essential to accelerate large-scale deep neural network (DNN) inference on resource-limited edge devices through partitioning a DNN between the edge device and the cloud server with advanced wireless communications such as B5G/6G and WiFi 6. We investigate the U-shape partitioning inference system, where both the input raw data and output inference results are kept on the edge device. We use image semantic segmentation as an exemplary application in our experiments. The experiment results showed that an honest-but-curious (HbC) server can launch the bidirectional privacy attack to reconstruct the raw data and steal the inference results, even when only the middle-end partition of the model is visible. To ensure bidirectional privacy and user experience on the U-shape partitioning inference system, a privacy and latency-aware partitioning strategy is needed to balance the trade-off between service latency and data privacy. We compared our proposed framework to other inference paradigms, including conventional split inference and inferencing entirely on the edge device or the server. We analyzed their inference latencies in various wireless technologies and quantitatively measured their level of privacy protection. The experiment results show that the U-shape partitioning inference system is advantageous over inference entirely on the edge device or the server.
This letter explores energy efficiency (EE) maximization in a downlink multiple-input single-output (MISO) reconfigurable intelligent surface (RIS)-aided multiuser system employing rate-splitting multiple access (RSMA). The optimization task entails base station (BS) and RIS beamforming and RSMA common rate allocation with constraints. We propose a graph neural network (GNN) model that learns beamforming and rate allocation directly from the channel information using a unique graph representation derived from the communication system. The GNN model outperforms existing deep neural network (DNN) and model-based methods in terms of EE, demonstrating low complexity, resilience to imperfect channel information, and effective generalization across varying user numbers.
Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading decision and resource allocation need to be jointly handled to optimize the service provision efficiency within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision in the MEC system. M-DRL is composed of two parts: glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a “glimpse” of the future, and a DRL specialized in supporting offloading decisions and resource allocation in MEC. By integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the MEC service provision with average 70% performance improvements.
Reconfigurable intelligent surface (RIS) is a revolutionary passive radio technique to facilitate capacity enhancement beyond the current massive multiple-input multiple-output (MIMO) transmission. However, the potential hardware impairment (HWI) of the RIS usually causes inevitable performance degradation and the amplification of imperfect CSI. These impacts still lack full investigation in the RIS-assisted wireless network. This paper developed a robust joint RIS and transceiver design algorithm to minimize the worst-case mean square error (MSE) of the received signal under the HWI effect and imperfect channel state information (CSI) in the RIS-assisted multi-user MIMO (MU-MIMO) wireless network. Specifically, since the proposed robust joint RIS and transceiver design problem yields non-convex characteristics under severe HWI, an iterative three-step convex algorithm is developed to approach the optimality by relaxation and convex transformation. Compared with the state-of-the-art baselines that ignore the HWI, the proposed robust algorithm inhibits the destruction of HWI while raising the worst-case MSE effectively in several numerical simulations. Moreover, due to the properties of the HWI, the performance loss is notable under the magnification of the number of reflected elements in the RIS-assisted MU-MIMO wireless network.
Vehicle-to-everything (V2X) communication is one of the key technologies of 5G New Radio to support emerging applications such as autonomous driving. Due to the high density of vehicles, Remote Radio Heads (RRHs) will be deployed as Road Side Units to support V2X. Nevertheless, activation of all RRHs during low-traffic off-peak hours may cause energy wasting. The proper activation of RRH and association between vehicles and RRHs while maintaining the required service quality are the keys to reducing energy consumption. In this work, we first formulate the problem as an Integer Linear Programming optimization problem and prove that the problem is NP-hard. Then, we propose two novel algorithms, referred to as “Least Delete (LD)” and ”Largest-First Rounding with Capacity Constraints (LFRCC).” The simulation results show that the proposed algorithms can achieve significantly better performance compared with existing solutions and are competitive with the optimal solution. Specifically, the LD and LFRCC algorithms can reduce the number of activated RRHs by 86 % and 89 % in low-density scenarios. In high-density scenarios, the LD algorithm can reduce the number of activated RRHs by 90 % . In addition, the solution of LFRCC is larger than that of the optimal solution within 7 % on average.
Designing intelligent, tiny devices with limited memory is immensely challenging, exacerbated by the additional memory requirement of residual connections in deep neural networks. In contrast to existing approaches that eliminate residuals to reduce peak memory usage at the cost of significant accuracy degradation, this paper presents DERO, which reorganizes residual connections by leveraging insights into the types and interdependencies of operations across residual connections. Evaluations were conducted across diverse model architectures designed for common computer vision applications. DERO consistently achieves peak memory usage comparable to plain-style models without residuals, while closely matching the accuracy of the original models with residuals.
This study examines a downlink multiple-input single-output (MISO) system, where a base station (BS) with multiple antennas sends data to multiple single-antenna users with the help of a reconfigurable intelligent surface (RIS) and a half-duplex decode-and-forward (DF) relay. The system's sum rate is maximized through joint optimization of active beamforming at the BS and DF relay and passive beamforming at the RIS. The conventional alternating optimization algorithm for handling this complex design problem is suboptimal and computationally intensive. To overcome these challenges, this letter proposes a two-phase graph neural network (GNN) model that learns the joint beamforming strategy by exchanging and updating relevant relational information embedded in the graph representation of the transmission system. The proposed method demonstrates superior performance compared to existing approaches, robustness against channel imperfections and variations, generalizability across varying user numbers, and notable complexity advantages.
Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-based services and applications, and personalized federated learning (PFL) evolves to alleviate the issue of data heterogeneity. However, FL and PFL usually rest on two assumptions: the users’ data is well-labeled, or the personalized goals align with sufficient local data. Unfortunately, the two assumptions may not hold in most cases, where data labeling is costly, or most users have no sufficient local data to satisfy their personalized needs. To this end, we first formulate the problem, DoLP, that studies the issue of insufficient and partially-labeled data on FL-based services. DoLP aims to maximize two service objectives: 1) personalized classification objective and 2) the personalized labeling objective for each user within the constraint of training time over wireless networks. Then, we propose a PFL-based service system DoFed-SPP to solve DoLP. The DoFed-SPP's novelty is two-fold. First, we devise an inference-based first-order approximation metric, similarity ratio, to identify the similarity between users’ local data. Second, we design an approximation algorithm to determine the appropriate size and set of users for uploading in each round. Extensive experiments show DoFed-SPP outperforms the state-of-the-art in final accuracy and time-to-accuracy performance on CIFAR10/100 and DBPedia.
Intermittent deep neural network (DNN) inference is a promising technique to enable intelligent applications on tiny devices powered by ambient energy sources. Nonetheless, intermittent execution presents inherent challenges, primarily involving accumulating progress across power cycles and having to refetch volatile data lost due to power loss in each power cycle. Existing approaches typically optimize the inference configuration to maximize data reuse. However, we observe that such a fixed configuration may be significantly inefficient due to the fluctuating balance point between data reuse and data refetch caused by the dynamic nature of ambient energy. This work proposes DynBal, an approach to dynamically reconfigure the inference engine at runtime. DynBal is realized as a middleware plugin that improves inference performance by exploring the interplay between data reuse and data refetch to maintain their balance with respect to the changing level of intermittency. An indirect metric is developed to easily evaluate an inference configuration considering the variability in intermittency, and a lightweight reconfiguration algorithm is employed to efficiently optimize the configuration at runtime. We evaluate the improvement brought by integrating DynBal into a recent intermittent inference approach that uses a fixed configuration. Evaluations were conducted on a Texas Instruments device with various network models and under varied intermittent power strengths. Our experimental results demonstrate that DynBal can speed up intermittent inference by 3.26 times, achieving a greater improvement for a large network under high intermittency and a large gap between memory and computation performance.