Annals of Telecommunications最新文献

In the present paper, we propose a method for controlling the interval of safety message transmissions in a fully distributed manner that maximizes the number of successful transmissions to vehicles located a set target distance away. In the proposed method, each vehicle estimates the density of vehicles in its vicinity, and, based on the estimated vehicle density, each vehicle calculates an optimal message transmission interval in order to maximize the number of successful message transmissions to vehicles located a set target distance away. The optimal message transmission interval can be analytically obtained as a simple expression when it is assumed that the vehicles are positioned according to a two-dimensional Poisson point process, which is appropriate for downtown scenarios. In addition, we propose two different methods for a vehicle by which to estimate the density of other vehicles in its vicinity. The first method is based on the measured channel busy ratio, and the second method relies on counting the number of distinct IDs of vehicles in the vicinity. We validate the effectiveness of the proposed methods using several simulations.

Renewable energy sources were introduced as an alternative to fossil fuel sources to make electricity generation cleaner. However, today’s renewable energy markets face a number of limitations, such as inflexible pricing models and inaccurate consumption information. These limitations can be addressed with a decentralized marketplace architecture. Such architecture requires a mechanism to guarantee that all marketplace operations are executed according to predefined rules and regulations. One of the ways to establish such a mechanism is blockchain technology. This work defines a decentralized blockchain-based peer-to-peer (P2P) energy marketplace which addresses actors’ privacy and the performance of consensus mechanisms. The defined marketplace utilizes private permissioned Ethereum-based blockchain client Hyperledger Besu (HB) and its smart contracts to automate the P2P trade settlement process. Also, to make the marketplace compliant with energy trade regulations, it includes the regulator actor, which manages the issue and consumption of guarantees of origin and certifies the renewable energy sources used to generate traded electricity. Finally, the proposed marketplace incorporates privacy-preserving features, allowing it to generate private transactions and store them within a designated group of actors. Performance evaluation results of HB-based marketplace with three main consensus mechanisms for private networks, i.e., Clique, IBFT 2.0, and QBFT, demonstrate a lower throughput than another popular private permissioned blockchain platform Hyperledger Fabric (HF). However, the lower throughput is a side effect of the Byzantine Fault Tolerant characteristics of HB’s consensus mechanisms, i.e., IBFT 2.0 and QBFT, which provide increased security compared to HF’s Crash Fault Tolerant consensus RAFT.

The diversity of services and infrastructure in metropolitan edge-to-cloud network(s) is rising to unprecedented levels. This is causing a rising threat of a wider range of cyber attacks coupled with a growing integration of a constrained range of infrastructure, particularly seen at the network edge. Deep reinforcement-based learning is an attractive approach to detecting attacks, as it allows less dependency on labeled data with better ability to classify different attacks. However, current approaches to learning are known to be computationally expensive (cost), and the learning experience can be negatively impacted by the presence of outliers and noise (quality). This work tackles both the cost and quality challenges with a novel service-based federated deep reinforcement learning solution, enabling anomaly detection and attack classification at a reduced data cost and with better quality. The federated settings in the proposed approach enable multiple edge units to create clusters that follow a bottom-up learning approach. The proposed solution adapts a deep Q-learning network (DQN) for service-tunable flow classification and introduces a novel federated DQN (FDQN) for federated learning. Through such targeted training and validation, variation in data patterns and noise is reduced. This leads to improved performance per service with lower training cost. Performance and cost of the solution, along with sensitivity to exploration parameters, are evaluated using examples of publicly available datasets (UNSW-NB15 and CIC-IDS2018). Evaluation results show the proposed solution to maintain detection accuracy in the range of ≈75–85% with lower data supply while improving the classification rate by a factor of ≈2.

Network troubleshooting usually requires packet level traffic capturing and analyzing. Indeed, the observation of emission patterns sheds some light on the kind of degradation experienced by a connection. In the case of reliable transport traffic where congestion control is performed, such as TCP and QUIC traffic, these patterns are the fruit of decisions made by the congestion control algorithm (CCA), according to its own perception of network conditions. The CCA estimates the bottleneck’s capacity via an exponential probing, during the so-called “Slow-Start” (SS) state. The bottleneck is considered reached upon reception of congestion signs, typically lost packets or abnormal packet delays depending on the version of CCA used. The SS state duration is thus a key indicator for the diagnosis of faults; this indicator is estimated empirically by human experts today, which is time-consuming and a cumbersome task with large error margins. This paper proposes a method to automatically identify the slow-start state from actively and passively obtained bidirectional packet traces. It relies on an innovative timeless representation of the observed packets series. We implemented our method in our active and passive probes and tested it with CUBIC and BBR under different network conditions. We then picked a few real-life examples to illustrate the value of our representation for easy discrimination between typical faults and for identifying BBR among CCAs variants.

This paper provides a comprehensive review of recent challenges and results in the field of generative AI with application to mobile telecommunications networks. The objective is to classify the literature using an approach that encompasses the type of generative AI technology employed, the functional purpose, and the specific component of the mobile network that each solution targets. Moreover, performance requirements for generative AI applications are considered. Thereafter, state-of-the-art generative AI algorithms and an examination of their use cases across various industry verticals are presented. The discussion extends to the current level of AI integration in telecom standardization bodies, such as the 3rd Generation Partnership Project (3GPP). Finally, the open research challenges that the generative AI technology aims to address are thoroughly investigated.

The wireless revolution requires future wireless networks the capability of intelligently optimizing the spectrum by collaborating and using autonomy to determine not just the best use of the spectrum for its own system, but the best use of spectrum for other systems that share the same spectrum bands. How to develop the wireless paradigm of collaboration, therefore, is a crucial question. In this paper, we discuss how to model collaborative power control in a wireless interference network, where users share the same frequency band. By collaborating with other users, each user exchanges information to maximize not only its own performance but also others’ performances. A game theory framework is developed to determine the optimal power allocation. The proposed framework possesses several advantages over conventional methods, such as low complexity and fast converging algorithmic solutions, distributed implementation, and better user fairness. Simulation results state the proposed approach provides better fairness between users’ data rates, higher performance in the aggregate rate, and lower convergence time.

The integration of sensing and communication has become essential to next-generation vehicular networks. In this paper, we investigate a vehicle-to-infrastructure (V2I) network with multiple roadside units (RSUs) based on the dual-functional radar-communication (DFRC) technique. Since there are multiple RSUs in the system, we first propose a signal-switching model between vehicles and different RSUs. These RSUs estimate and predict vehicles’ motion parameters based on the DFRC signal echoes and the state evolution model. Accordingly, we utilise a neural network to extract angle information from signal echoes instead of traditional methods, thus improving the angle estimation accuracy. To further improve the estimation performance, we formulate an optimisation problem to minimise the Cramer-Rao bound (CRB) on angle estimation by properly allocating power to each RSU. Finally, we propose a novel weighting method to further improve the cooperative localisation accuracy of the multi-RSU system. Simulation results show that the performance of angle estimation can be improved by utilising the proposed neural network method and the novel power allocation scheme. In addition, the novel weighting method can considerably improve the localisation accuracy.

In this paper, we design a low-complexity multiuser millimeter-wave massive-multiple-input-multiple-output system with the help of a hybrid analog/digital precoding architecture. Hybrid precoding is used to reduce the hardware cost and power consumption of millimeter-wave large-scale antenna systems. In this manner, we proposed a novel approach to solve the well-known zero-forcing algorithm by using an iterative optimization method called the conjugate gradient method. The problem is transformed into an optimization problem, and the complex matrix inverse operation required in the zero-forcing algorithm is eliminated. Hence, the complexity of the zero-forcing algorithm is reduced while the spectral efficiency is maintained at the same level as that of the reference zero-forcing detector. The simulation results demonstrate that the proposed conjugate gradient-based algorithm achieves better performance than competing methods in terms of complexity and spectral efficiency.