Network Slicing (NS) is crucial for efficiently enabling divergent network applications in nextgeneration networks. Nonetheless, the complex Quality of Service (QoS) requirements and diverse heterogeneity in network services entail high complexity for Network Slice Provisioning (NSP) optimization. The legacy optimization methods are challenging to meet various low latency and highreliability requirements from network applications. Specifically, we formulate the ONSP problem as an Multi-Objective Integer Programming Optimization (MOIPO) problem. Our simulation results show the effectiveness of the proposed method compared to the state-of-the-art methods with a lower SLA violation rate and network operation cost. Network Slicing (NS) is essential in the next-generation mobile wireless networks [1]. It enables efficient connectivity to various services with diverse requirements by instantiating multiple logical networks on top of the substrate, i.e., the physical network infrastructure. Note that some emerging 5G services, such as those related to the Ultra-Reliable Low Latency Communication (URLLC), require dedicated network resources to achieve the stringent quality of service (QoS) requirements.

Reactive or proactive? Which is best when trying to prevent people from using their phone while driving, and causing a danger to pedestrians and …

Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding. To this end, we propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise, whose amplitudes are trainable variables. We verify that the surrogate model closely matches the behavior of a fixed-point implementation and propose a hand-crafted loss function to realize a trade-off between complexity and error-rate performance. A deep learning-based method is then applied to optimize the message bitwidths. Moreover, we show that parameter sharing can both ensure implementation-friendly solutions and results in faster training convergence than independent parameters. We provide simulation results for 5G low-density parity-check (LDPC) codes and report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits. In addition, we show that the learned bitwidths also generalize to other code rates and channels.

This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed $\textit{AIn}$ approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learning.

In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs). Moreover, as per the Peukert effect, we consider pragmatic non-linear battery discharge for the battery of the UAV. Thus, we formulate the problem in a novel manner that represents the maximization of a fairness-based energy efficiency metric and is named fair energy efficiency (FEE). The FEE metric defines a system that lays importance on both the per-user service fairness and the energy efficiency of the PAP. The formulated problem takes the form of a non-convex problem with non-tractable constraints. To obtain a solution, we represent the problem as a Markov Decision Process (MDP) with continuous state and action spaces. Considering the complexity of the solution space, we use the twin delayed deep deterministic policy gradient (TD3) actor-critic deep reinforcement learning (DRL) framework to learn a policy that maximizes the FEE of the system. We perform two types of RL training to exhibit the effectiveness of our approach: the first (offline) approach keeps the positions of the GNs the same throughout the training phase; the second approach generalizes the learned policy to any arrangement of GNs by changing the positions of GNs after each training episode. Numerical evaluations show that neglecting the Peukert effect overestimates the air-time of the PAP and can be addressed by optimally selecting the PAP's flying speed. Moreover, the user fairness, energy efficiency, and hence the FEE value of the system can be improved by efficiently moving the PAP above the GNs. As such, we notice massive FEE improvements over baseline scenarios of up to 88.31%, 272.34%, and 318.13% for suburban, urban, and dense urban environments, respectively.

In Australia, a new camera system could be the solution to the problem of people using their mobile phone while driving. Using the mobile phone while driving is surely one of the most dangerous driving offences. In the era of individualism and the'attention economy', it only makes more sense that a problem such as this one persists. People are addicted to their phones. The writer of this article is addicted to his phone.

In sliced networks, the shared tenancy of slices requires adaptive admission control of data flows, based on measurements of network resources. In this paper, we investigate the design of measurement-based admission control schemes, deciding whether a new data flow can be admitted and in this case, on which slice. The objective is to devise a joint measurement and decision strategy that returns a correct decision (e.g., the least loaded slice) with a certain level of confidence while minimizing the measurement cost (the number of measurements made before committing to the decision). We study the design of such strategies for several natural admission criteria specifying what a correct decision is. For each of these criteria, using tools from best arm identification in bandits, we first derive an explicit information-theoretical lower bound on the cost of any algorithm returning the correct decision with fixed confidence. We then devise a joint measurement and decision strategy achieving this theoretical limit. We compare empirically the measurement costs of these strategies, and compare them both to the lower bounds as well as a naive measurement scheme. We find that our algorithm significantly outperforms the naive scheme (by a factor $2-8$).

The advent of Fifth Generation (5G) and beyond 5G networks (5G+) has revolutionized the way network operators consider the management and orchestration of their networks. With an increased focus on intelligence and automation through core network functions such as the NWDAF, service providers are tasked with integrating machine learning models and artificial intelligence systems into their existing network operation practices. Due to the dynamic nature of next-generation networks and their supported use cases and applications, model drift is a serious concern, which can deteriorate the performance of intelligent models deployed throughout the network. The work presented in this paper introduces a model drift detection and adaptation module for 5G core networks. Using a functional prototype of a 5G core network, a drift in user behaviour is emulated, and the proposed framework is deployed and tested. The results of this work demonstrate the ability of the drift detection module to accurately characterize a drifted concept as well as the ability of the drift adaptation module to begin the necessary remediation efforts to restore system performance.

This paper considers the impact of general hardware impairments in a multiple-antenna base station and user equipments on the uplink performance. First, the effective channels are analytically derived for distortion-aware receivers when using finite-sized signal constellations. Next, a deep feedforward neural network is designed and trained to estimate the effective channels. Its performance is compared with state-of-the-art distortion-aware and unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise.

Vodafone's motivation to become the first service provider in Germany, and in Europe, to commercially offer 5G Standalone based services was highly driven by their desire of using this new technology to address the industry challenges and needs, such as: The core network in 5G non-standalone solutions is based on existing LTE infrastructure, meaning customers are not able to fully exploit advanced 5G capabilities such as network slicing and ultra-low latency. Vodafone wanted their customers to, for example, be able to experience applications such as lag-free online gaming experiences and virtual reality, and for enterprises, industrial robot applications, or remotely operated drone fleets, which is only possible with 5G Standalone. Better support and accelerate Industry 4.0 realization across Germany Vodafone is considered a key contributor to the development of Germany's digital infrastructure whose customers rely on them to deliver the best in connectivity. The German market has a very high percentage of small and medium enterprises that are world market leaders, and for those industries, it is super important to be competitive now in the journey towards digitalization and industry 4.0. Vodafone wanted to be ready to support them.