Currently the state of the art network models are based or depend on Discrete Event Simulation (DES). While DES is highly accurate, it is also computationally costly and cumbersome to parallelize, making it unpractical to simulate high performance networks. Additionally, simulated scenarios fail to capture all of the complexities present in real network scenarios. While there exists network models based on Machine Learning (ML) techniques to minimize these issues, these models are also trained with simulated data and hence vulnerable to the same pitfalls. Consequently, the Graph Neural Networking Challenge 2023 introduces a dataset of captured traffic traces that can be used to build a ML-based network model without these limitations. In this paper we propose a Graph Neural Network (GNN)-based solution specifically designed to better capture the complexities of real network scenarios. This is done through a novel encoding method to capture information from the sequence of captured packets, and an improved message passing algorithm to better represent the dependencies present in physical networks. We show that the proposed solution it is able to learn and generalize to unseen captured network scenarios. less

Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better coverage in challenging environments. In this paper, we investigate the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable. To deal with this problem, quantum computing that can provide faster and more efficient solutions to large-scale combinatorial optimization is considered. MBS is formulated in a quadratic unbounded binary optimization form and solved with Coherent Ising Machine (CIM) physical machine. We compare the performance of our solution with two classic heuristics, simulated annealing and Tabu search. The results demonstrate an average performance improvement by a factor of 261.23 and 20.6, respectively, which shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams. This work shows great promise for practical 5G operation and promotes the application of quantum computing in solving computationally hard problems in communication. less

The advent of reconfigurable intelligent surfaces(RISs) brings along significant improvements for wireless technology on the verge of beyond-fifth-generation networks (B5G).The proven flexibility in influencing the propagation environment opens up the possibility of programmatically altering the wireless channel to the advantage of network designers, enabling the exploitation of higher-frequency bands for superior throughput overcoming the challenging electromagnetic (EM) propagation properties at these frequency bands. However, RISs are not magic bullets. Their employment comes with significant complexity, requiring ad-hoc deployments and management operations to come to fruition. In this paper, we tackle the open problem of bringing RISs to the field, focusing on areas with little or no coverage. In fact, we present a first-of-its-kind deep reinforcement learning (DRL) solution, dubbed as D-RISA, which trains a DRL agent and, in turn, obtain san optimal RIS deployment. We validate our framework in the indoor scenario of the Rennes railway station in France, assessing the performance of our algorithm against state-of-the-art (SOA) approaches. Our benchmarks showcase better coverage, i.e., 10-dB increase in minimum signal-to-noise ratio (SNR), at lower computational time (up to -25 percent) while improving scalability towards denser network deployments. less

The emergence of new services and applications in emerging wireless networks (e.g., beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) in the Internet of Things (IoT). However, the proliferation of massive IoT connections and the availability of computing resources distributed across future IoT systems have strongly demanded the development of distributed AI for better IoT services and applications. Therefore, existing AI-enabled IoT systems can be enhanced by implementing distributed machine learning (aka distributed learning) approaches. This work aims to provide a comprehensive survey on distributed learning for IoT services and applications in emerging networks. In particular, we first provide a background of machine learning and present a preliminary to typical distributed learning approaches, such as federated learning, multi-agent reinforcement learning, and distributed inference. Then, we provide an extensive review of distributed learning for critical IoT services (e.g., data sharing and computation offloading, localization, mobile crowdsensing, and security and privacy) and IoT applications (e.g., smart healthcare, smart grid, autonomous vehicle, aerial IoT networks, and smart industry). From the reviewed literature, we also present critical challenges of distributed learning for IoT and propose several promising solutions and research directions in this emerging area. less

P2P trading of energy can be a good alternative to incentivize distributed non-conventional energy production and meet the burgeoning energy demand. For efficient P2P trading, a free market for trading needs to be established while ensuring the information reliability, security, and privacy. Blockchain has been used to provide this framework, but it consumes very high energy and is slow. Further, until now, no blockchain model has considered the role of conventional electric utility companies in P2P trading. In this paper, we have introduced a credit blockchain that reduces energy consumption by employing a new mechanism to update transactions and increases speed by providing interest free loans to buyers. This model also integrates the electric utility companies within the P2P trading framework, thereby increasing members trading options. We have also discussed the pricing strategies for trading. All the above assertions have been verified through simulations, demonstrating that this model will promote P2P trading by providing enhanced security, speed, and greater trading options. The proposed model will also help trade energy at prices beneficial for both sellers and buyers. less

This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts that cause high bandwidth consumption, more latency, and experience with bad quality of services (QoS). In particular, these challenges hinder applications like intelligent transportation systems (ITS), metaverse, mixed reality, and the Internet of Everything, where real-time and efficient data transmission is paramount. Therefore, to reduce communication overhead and maintain the QoS of emerging applications such as metaverse, ITS, and digital twin creation, this work proposes a novel semantic communication framework. First, an intelligent semantic transmitter is designed to capture the meaningful information (e.g., the rode-side image in ITS) by designing a domain-specific Mobile Segment Anything Model (MSAM)-based mechanism to reduce the potential communication traffic while QoS remains intact. Second, the concept of generative AI is introduced for building the SemCom to reconstruct and denoise the received semantic data frame at the receiver end. In particular, the Generative Adversarial Network (GAN) mechanism is designed to maintain a superior quality reconstruction under different signal-to-noise (SNR) channel conditions. Finally, we have tested and evaluated the proposed semantic communication (SemCom) framework with the real-world 6G scenario of ITS; in particular, the base station equipped with an RGB camera and a mmWave phased array. Experimental results demonstrate the efficacy of the proposed SemCom framework by achieving high-quality reconstruction across various SNR channel conditions, resulting in 93.45% data reduction in communication. less

Near-space information networks (NSIN) composed of high-altitude platforms (HAPs), high- and low-altitude unmanned aerial vehicles (UAVs) are a new regime for providing quickly, robustly, and cost-efficiently sensing and communication services. Precipitated by innovations and breakthroughs in manufacturing, materials, communications, electronics, and control technologies, NSIN have emerged as an essential component of the emerging sixth-generation of mobile communication systems. This article aims at providing and discussing the latest advances in NSIN in the research areas of channel modeling, networking, and transmission from a forward-looking, comparative, and technological evolutionary perspective. In this article, we highlight the characteristics of NSIN and present the promising use-cases of NSIN. The impact of airborne platforms' unstable movements on the phase delays of onboard antenna arrays with diverse structures is mathematically analyzed. The recent advancements in HAP channel modeling are elaborated on, along with the significant differences between HAP and UAV channel modeling. A comprehensive review of the networking technologies of NSIN in network deployment, handoff management, and network management aspects is provided. Besides, the promising technologies and communication protocols of the physical layer, medium access control (MAC) layer, network layer, and transport layer of NSIN for achieving efficient transmission over NSIN are overviewed. Finally, we outline some open issues and promising directions of NSIN deserved for future study and discuss the corresponding challenges. less

Current serverless solutions are primarily designed to run on cloud centric environments. While bringing these solutions to the edge is its further evolution, it introduces new challenges, due to resource constraints, different CPU architectures, network topology and latency. Specifically, when clustering devices at the edge, inter-node latency plays an important role. In this paper, we experimentally examine the impact that latency has on scalablity by measuring the throughput of distributed serverless applications. We deploy Knative over a Kubernetes cluster of nodes and emulate latencies between them to compare the performance of serverless functions when deployed over centralized versus distributed computing sites. The results show how scaling over edge achieves half the throughput as compared to a centralized deployment in the cloud when the processing times are low, but more than two thirds the improved performance of cloud with increased processing delays. less

With the emergence of 5G, the Internet of Things (IoT) will bring about the next industrial revolution in the name of Industry 4.0. The communication aspect of IoT devices is one of the most important factors in choosing the right device for the right usage. So far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. And most of them are wireless protocols due to the sheer number of device requirements for IoT. In this paper, we summarize the network architectures of some of the most popular IoT wireless communications protocols. We also present them side by side and provide a comparative analysis revolving around some key features, including power consumption, coverage, data rate, security, cost, and Quality of Service (QoS). This comparative study shows that LTE-based protocols like NB-IoT and LTE-M can offer better QoS and robustness, while the Industrial, Scientific, and Medical (ISM) Band based protocols like LoRa, Sigfox, and Z-wave claim their place in usage where lower power consumption and lesser device complexity are desired. Based on their respective strengths and weaknesses, the study also presents an application perspective of the suitability of each protocol in a certain type of scenario and addresses some open issues that need to be researched in the future. Thus, this study can assist in the decision making regarding choosing the most suitable protocol for a certain field. less

Recent research works have proposed machine learning models for classifying IoT devices connected to a network. However, there is still a practical challenge of not having all devices (and hence their traffic) available during the training of a model. This essentially means, during the operational phase, we need to classify new devices not seen during the training phase. To address this challenge, we propose ZEST -- a ZSL (zero-shot learning) framework based on self-attention for classifying both seen and unseen devices. ZEST consists of i) a self-attention based network feature extractor, termed SANE, for extracting latent space representations of IoT traffic, ii) a generative model that trains a decoder using latent features to generate pseudo data, and iii) a supervised model that is trained on the generated pseudo data for classifying devices. We carry out extensive experiments on real IoT traffic data; our experiments demonstrate i) ZEST achieves significant improvement (in terms of accuracy) over the baselines; ii) ZEST is able to better extract meaningful representations than LSTM which has been commonly used for modeling network traffic. less