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The upcoming Sixth Generation (6G) mobile communications system envisions supporting a variety of use cases with differing characteristics, e.g., very low to extremely high data rates, diverse latency needs, ultra massive connectivity, sustainable communications, ultra-wide coverage etc. To accommodate these diverse use cases, the 6G system architecture needs to be scalable, modular, and flexible; both in its user plane and the control plane. In this paper, we identify some limitations of the existing Fifth Generation System (5GS) architecture, especially that of its control plane. Further, we propose a novel architecture for the 6G System (6GS) employing Software Defined Networking (SDN) technology to address these limitations of the control plane. The control plane in existing 5GS supports two different categories of functionalities handling end user signalling (e.g., user registration, authentication) and control of user plane functions. We propose to move the end-user signalling functionality out of the mobile network control plane and treat it as user service, i.e., as payload or data. This proposal results in an evolved service-driven architecture for mobile networks bringing increased simplicity, modularity, scalability, flexibility and security to its control plane. The proposed architecture can also support service specific signalling support, if needed, making it better suited for diverse 6GS use cases. To demonstrate the advantages of the proposed architecture, we also compare its performance with the 5GS using a process algebra-based simulation tool.

Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to the model become much longer which slows down decoding time directly translating to the time a user has to wait for an answer. We address this challenge by presenting COCOM, an effective context compression method, reducing long contexts to only a handful of Context Embeddings speeding up the generation time by a large margin. Our method allows for different compression rates trading off decoding time for answer quality. Compared to earlier methods, COCOM allows for handling multiple contexts more effectively, significantly reducing decoding time for long inputs. Our method demonstrates a speed-up of up to 5.69 $\times$ while achieving higher performance compared to existing efficient context compression methods.

Space-air-ground integrated networks (SAGINs) will play a pivotal role in 6G communication systems. They are considered a promising technology for enhancing network capacity in densely populated urban areas and extending connectivity to rural regions. However, the complex, multi-layered, and heterogeneous nature of SAGINs demands an innovative approach to designing their multi-tier associations. In this context, we propose a modeling of the SAGINs association problem using multi-sided matching theory. Our objective is to devise a reliable, asynchronous, and fully distributed approach that associates nodes across the layers to maximize the total end-to-end rate of the assigned agents. To achieve this, our problem is formulated as a multi-sided many-to-one matching game. We introduce a randomized matching algorithm with minimal information exchange. The algorithm is shown to reach an efficient and stable association between nodes in adjacent layers. Simulation results show that our proposed approach yields significant gains compared to both greedy and distance-based algorithms.

It looks very attractive to coordinate racetrack-memory(RM) and stochastic-computing (SC) jointly to build an ultra-low power neuron-architecture.However,the above combination has always been questioned in a fatal weakness that the narrow bit-view of the RM-MTJ structure,a.k.a.shift-and-access pattern,cannot physically match the great throughput of direct-stored stochastic sequences.Fortunately,a recently developed Transverse-Read(TR) provides a wider segment-view to RM via detecting the resistance of domain-walls between a couple of MTJs on single nanowire,therefore RM can be enhanced with a faster access to the sequences without any substantial domain-shift.To utilize TR for a power-efficient SC-DNNs, in this work, we propose a segment-based compression to leverage one-cycle TR to only read those kernel segments of stochastic sequences,meanwhile,remove a large number of redundant segments for ultra-high storage density.In decompression stage,the low-discrepancy stochastic sequences can be quickly reassembled by a select-and-output loop using kernel segments rather than slowly regenerated by costly SNGs.Since TR can provide an ideal in-memory acceleration in one-counting, counter-free SC-MACs are designed and deployed near RMs to form a power-efficient neuron-architecture,in which,the binary results of TR are activated straightforward without sluggish APCs.The results show that under the TR aided RM model,the power efficiency,speed,and stochastic accuracy of Seed-based Fast Stochastic Computing significantly enhance the performance of DNNs.The speed of computation is 2.88x faster in Lenet-5 and 4.40x faster in VGG-19 compared to the CORUSCANT model.The integration of TR with RTM is deployed near the memory to create a power-efficient neuron architecture, eliminating the need for slow Accumulative Parallel Counters (APCs) and improving access speed to stochastic sequences.

Vehicular communications integrated with the Radio Access Network (RAN) are envisioned as a breakthrough application for the 6th generation (6G) cellular systems. However, traditional RANs lack the flexibility to enable sophisticated control mechanisms that are demanded by the strict performance requirements of the vehicle-to-everything (V2X) environment. In contrast, the features of Open RAN (O-RAN) can be exploited to support advanced use cases, as its core paradigms represent an ideal framework for orchestrating vehicular communication. Although the high potential stemming from their integration can be easily seen and recognized, the effective combination of the two ecosystems is an open issue. Conceptual and architectural advances are required for O-RAN to be capable of facilitating network intelligence in V2X. This article pioneers the integration of the two strategies for seamlessly incorporating V2X control within O-RAN ecosystem. First, an enabling architecture that tightly integrates V2X and O-RAN is proposed and discussed. Then, a set of key V2X challenges is identified, and O-RAN-based solutions are proposed, paired with extensive numerical analysis to support their effectiveness. Results showcase the superior performance of such an approach in terms of raw throughput, network resilience, and control overhead. Finally, these results validate the proposed enabling architecture and confirm the potential of O-RAN in support of V2X communications.

Implementing Decentralized Gradient Descent (DGD) in wireless systems is challenging due to noise, fading, and limited bandwidth, necessitating topology awareness, transmission scheduling, and the acquisition of channel state information (CSI) to mitigate interference and maintain reliable communications. These operations may result in substantial signaling overhead and scalability challenges in large networks lacking central coordination. This paper introduces a scalable DGD algorithm that eliminates the need for scheduling, topology information, or CSI (both average and instantaneous). At its core is a Non-Coherent Over-The-Air (NCOTA) consensus scheme that exploits a noisy energy superposition property of wireless channels. Nodes encode their local optimization signals into energy levels within an OFDM frame and transmit simultaneously, without coordination. The key insight is that the received energy equals, on average, the sum of the energies of the transmitted signals, scaled by their respective average channel gains, akin to a consensus step. This property enables unbiased consensus estimation, utilizing average channel gains as mixing weights, thereby removing the need for their explicit design or for CSI. Introducing a consensus stepsize mitigates consensus estimation errors due to energy fluctuations around their expected values. For strongly-convex problems, it is shown that the expected squared distance between the local and globally optimum models vanishes at a rate of $\mathcal O(1/\sqrt{k})$ after $k$ iterations, with suitable decreasing learning and consensus stepsizes. Extensions accommodate a broad class of fading models and frequency-selective channels. Numerical experiments on image classification demonstrate faster convergence in terms of running time compared to state-of-the-art schemes, especially in dense network scenarios.

Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues. While federated learning (FL) has emerged as a framework for model training without requiring direct data sharing and exchange, effectively modeling the complex spatio-temporal dependencies to improve forecasting capabilities still remains an open problem. On the other hand, state-of-the-art spatio-temporal forecasting models assume unfettered access to the data, neglecting constraints on data sharing. To bridge this gap, we propose a federated spatio-temporal model -- Cross-Node Federated Graph Neural Network (CNFGNN) -- which explicitly encodes the underlying graph structure using graph neural network (GNN)-based architecture under the constraint of cross-node federated learning, which requires that data in a network of nodes is generated locally on each node and remains decentralized. CNFGNN operates by disentangling the temporal dynamics modeling on devices and spatial dynamics on the server, utilizing alternating optimization to reduce the communication cost, facilitating computations on the edge devices. Experiments on the traffic flow forecasting task show that CNFGNN achieves the best forecasting performance in both transductive and inductive learning settings with no extra computation cost on edge devices, while incurring modest communication cost.

Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently introduced batch ensembles provide a drop-in replacement that is more parameter efficient. In this paper, we design ensembles not only over weights, but over hyperparameters to improve the state of the art in both settings. For best performance independent of budget, we propose hyper-deep ensembles, a simple procedure that involves a random search over different hyperparameters, themselves stratified across multiple random initializations. Its strong performance highlights the benefit of combining models with both weight and hyperparameter diversity. We further propose a parameter efficient version, hyper-batch ensembles, which builds on the layer structure of batch ensembles and self-tuning networks. The computational and memory costs of our method are notably lower than typical ensembles. On image classification tasks, with MLP, LeNet, and Wide ResNet 28-10 architectures, our methodology improves upon both deep and batch ensembles.

We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a global activation module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.7 on VOC2007 test and an mAP of 32.8 on COCO test-dev with an inference speed of 31.5 milliseconds per image on a Titan Xp GPU. With a lower resolution version, we achieve an mAP of 79.7 on VOC2007 with an inference speed of 13.0 milliseconds per image.

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.