Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base stations (BSs). In conventional cell-centric TS strategies, BSs make TS decisions for all user equipment (UEs) in a centralized manner, which focuses more on the overall performance of the whole cell, disregarding specific requirements of individual UE. The flourishing machine learning technologies and evolving UE-centric 5G network architecture have prompted the emergence of new TS technologies. In this paper, we propose a knowledge transfer and federated learning-enabled UE-centric (KT-FLUC) TS framework for highly dynamic 5G radio access networks (RAN). Specifically, first, we propose an attention-weighted group federated learning scheme. It enables intelligent UEs to make TS decisions autonomously using local models and observations, and a global model is defined to coordinate local TS decisions and share experiences among UEs. Secondly, considering the individual UE's limited computation and energy resources, a growing and pruning-based model compression method is introduced, mitigating the computation burden of UEs and reducing the communication overhead of federated learning. In addition, we propose a Q-value-based knowledge transfer method to initialize newcomer UEs, achieving a jump start for their training efficiency. Finally, the simulations show that our proposed KT-FLUC algorithm can effectively improve the service quality, achieving 65\% and 38\% lower delay and 52% and 57% higher throughput compared with cell-based TS and other UE-centric TS strategies, respectively.
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Swin-Transformer has demonstrated remarkable success in computer vision by leveraging its hierarchical feature representation based on Transformer. In speech signals, emotional information is distributed across different scales of speech features, e.\,g., word, phrase, and utterance. Drawing above inspiration, this paper presents a hierarchical speech Transformer with shifted windows to aggregate multi-scale emotion features for speech emotion recognition (SER), called Speech Swin-Transformer. Specifically, we first divide the speech spectrogram into segment-level patches in the time domain, composed of multiple frame patches. These segment-level patches are then encoded using a stack of Swin blocks, in which a local window Transformer is utilized to explore local inter-frame emotional information across frame patches of each segment patch. After that, we also design a shifted window Transformer to compensate for patch correlations near the boundaries of segment patches. Finally, we employ a patch merging operation to aggregate segment-level emotional features for hierarchical speech representation by expanding the receptive field of Transformer from frame-level to segment-level. Experimental results demonstrate that our proposed Speech Swin-Transformer outperforms the state-of-the-art methods.
Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.
On-device learning remains a formidable challenge, especially when dealing with resource-constrained devices that have limited computational capabilities. This challenge is primarily rooted in two key issues: first, the memory available on embedded devices is typically insufficient to accommodate the memory-intensive back-propagation algorithm, which often relies on floating-point precision. Second, the development of learning algorithms on models with extreme quantization levels, such as Binary Neural Networks (BNNs), is critical due to the drastic reduction in bit representation. In this study, we propose a solution that combines recent advancements in the field of Continual Learning (CL) and Binary Neural Networks to enable on-device training while maintaining competitive performance. Specifically, our approach leverages binary latent replay (LR) activations and a novel quantization scheme that significantly reduces the number of bits required for gradient computation. The experimental validation demonstrates a significant accuracy improvement in combination with a noticeable reduction in memory requirement, confirming the suitability of our approach in expanding the practical applications of deep learning in real-world scenarios.
Movable antenna (MA) has emerged as a promising technology to enhance wireless communication performance by enabling the local movement of antennas at the transmitter (Tx) and/or receiver (Rx) for achieving more favorable channel conditions. As the existing studies on MA-aided wireless communications have mainly considered narrow-band transmission in flat fading channels, we investigate in this paper the MA-aided wideband communications employing orthogonal frequency division multiplexing (OFDM) in frequency-selective fading channels. Under the general multi-tap field-response channel model, the wireless channel variations in both space and frequency are characterized with different positions of the MAs. Unlike the narrow-band transmission where the optimal MA position at the Tx/Rx simply maximizes the single-tap channel amplitude, the MA position in the wideband case needs to balance the amplitudes and phases over multiple channel taps in order to maximize the OFDM transmission rate over multiple frequency subcarriers. First, we derive an upper bound on the OFDM achievable rate in closed form when the size of the Tx/Rx region for antenna movement is arbitrarily large. Next, we develop a parallel greedy ascent (PGA) algorithm to obtain locally optimal solutions to the MAs' positions for OFDM rate maximization subject to finite-size Tx/Rx regions. To reduce computational complexity, a simplified PGA algorithm is also provided to optimize the MAs' positions more efficiently. Simulation results demonstrate that the proposed PGA algorithms can approach the OFDM rate upper bound closely with the increase of Tx/Rx region sizes and outperform conventional systems with fixed-position antennas (FPAs) under the wideband channel setup.
Serverless computing relieves developers from the burden of resource management, thus providing ease-of-use to the users and the opportunity to optimize resource utilization for the providers. However, today's serverless systems lack performance guarantees for function invocations, thus limiting support for performance-critical applications: we observed severe performance variability (up to 6x). Providers lack visibility into user functions and hence find it challenging to right-size them: we observed heavy resource underutilization (up to 80%). To understand the causes behind the performance variability and underutilization, we conducted a measurement study of commonly deployed serverless functions and learned that the function performance and resource utilization depend crucially on function semantics and inputs. Our key insight is to delay making resource allocation decisions until after the function inputs are available. We introduce Shabari, a resource management framework for serverless systems that makes decisions as late as possible to right-size each invocation to meet functions' performance objectives (SLOs) and improve resource utilization. Shabari uses an online learning agent to right-size each function invocation based on the features of the function input and makes cold-start-aware scheduling decisions. For a range of serverless functions and inputs, Shabari reduces SLO violations by 11-73% while not wasting any vCPUs and reducing wasted memory by 64-94% in the median case, compared to state-of-the-art systems, including Aquatope, Parrotfish, and Cypress.
Beyond 5G and 6G networks are expected to support new and challenging use cases and applications that depend on a certain level of Quality of Service (QoS) to operate smoothly. Predicting the QoS in a timely manner is of high importance, especially for safety-critical applications as in the case of vehicular communications. Although until recent years the QoS prediction has been carried out by centralized Artificial Intelligence (AI) solutions, a number of privacy, computational, and operational concerns have emerged. Alternative solutions have been surfaced (e.g. Split Learning, Federated Learning), distributing AI tasks of reduced complexity across nodes, while preserving the privacy of the data. However, new challenges rise when it comes to scalable distributed learning approaches, taking into account the heterogeneous nature of future wireless networks. The current work proposes DISTINQT, a privacy-aware distributed learning framework for QoS prediction. Our framework supports multiple heterogeneous nodes, in terms of data types and model architectures, by sharing computations across them. This, enables the incorporation of diverse knowledge into a sole learning process that will enhance the robustness and generalization capabilities of the final QoS prediction model. DISTINQT also contributes to data privacy preservation by encoding any raw input data into a non-linear latent representation before any transmission. Evaluation results showcase that our framework achieves a statistically identical performance compared to its centralized version and an average performance improvement of up to 65% against six state-of-the-art centralized baseline solutions in the Tele-Operated Driving use case.
Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.
Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands. However, in most of the existing research, the graph convolution was implemented on a heuristically generated adjacency matrix, which could neither reflect the real spatial relationships of stations accurately, nor capture the multi-level spatial dependence of demands adaptively. To cope with the above problems, this paper provides a novel graph convolutional network for transportation demand prediction. Firstly, a novel graph convolution architecture is proposed, which has different adjacency matrices in different layers and all the adjacency matrices are self-learned during the training process. Secondly, a layer-wise coupling mechanism is provided, which associates the upper-level adjacency matrix with the lower-level one. It also reduces the scale of parameters in our model. Lastly, a unitary network is constructed to give the final prediction result by integrating the hidden spatial states with gated recurrent unit, which could capture the multi-level spatial dependence and temporal dynamics simultaneously. Experiments have been conducted on two real-world datasets, NYC Citi Bike and NYC Taxi, and the results demonstrate the superiority of our model over the state-of-the-art ones.
Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory. In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). For training a 3-layer GCN on this data, Cluster-GCN is faster than the previous state-of-the-art VR-GCN (1523 seconds vs 1961 seconds) and using much less memory (2.2GB vs 11.2GB). Furthermore, for training 4 layer GCN on this data, our algorithm can finish in around 36 minutes while all the existing GCN training algorithms fail to train due to the out-of-memory issue. Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.36 on the PPI dataset, while the previous best result was 98.71 by [16]. Our codes are publicly available at //github.com/google-research/google-research/tree/master/cluster_gcn.
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