Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).

Both single-agent and multi-agent actor-critic algorithms are an important class of Reinforcement Learning algorithms. In this work, we propose three fully decentralized multi-agent natural actor-critic (MAN) algorithms. The agents' objective is to collectively learn a joint policy that maximizes the sum of averaged long-term returns of these agents. In the absence of a central controller, agents communicate the information to their neighbors via a time-varying communication network while preserving privacy. We prove the convergence of all the 3 MAN algorithms to a globally asymptotically stable point of the ODE corresponding to the actor update; these use linear function approximations. We use the Fisher information matrix to obtain the natural gradients. The Fisher information matrix captures the curvature of the Kullback-Leibler (KL) divergence between polices at successive iterates. We also show that the gradient of this KL divergence between policies of successive iterates is proportional to the objective function's gradient. Our MAN algorithms indeed use this \emph{representation} of the objective function's gradient. Under certain conditions on the Fisher information matrix, we prove that at each iterate, the optimal value via MAN algorithms can be better than that of the multi-agent actor-critic (MAAC) algorithm using the standard gradients. To validate the usefulness of our proposed algorithms, we implement all the 3 MAN algorithms on a bi-lane traffic network to reduce the average network congestion. We observe an almost 25% reduction in the average congestion in 2 MAN algorithms; the average congestion in another MAN algorithm is on par with the MAAC algorithm. We also consider a generic 15 agent MARL; the performance of the MAN algorithms is again as good as the MAAC algorithm. We attribute the better performance of the MAN algorithms to their use of the above representation.

Take a look at how 5G can push AI to release its full potential and few application scenarios. Field trials are underway, components are coming, and testing covers the spectrum in more ways than one. What are the challenges and how is the ecosystem shaping up? Find out more in this month's In Focus series. The power of AI applications will always depend on the strength of their networks.

VOXOX, a 5G-enabled AI cloud communications company, today announced the expansion of its UI/UX and data analytics teams for strategic growth. This step forward makes room for additional research and development of solutions to help automation become an essential and integral option for small businesses. VOXOX's engineering and data science teams have spent years analyzing billions of data points within the voice and text networks to help them develop artificial intelligence that can analyze content, respond like a real person, handle daily tasks like customer service inquiries & support jobs, schedule appointments, and write effective text campaigns. "The future of 5G AI telecommunications is at hand and we are eager to be growing rapidly in this field," said CEO of VOXOX, Cleve Adams. "VOXOX is leaping forward through the growth of our teams by bringing in skilled experts who have a great focus on providing remarkable automated solutions. Our intelligent assistant, virtual receptionists, and SMS drip campaigns are only the beginning of the AI-powered features that VOXOX has to offer."

VOXOX, a 5G-enabled AI cloud communications company, today announced the expansion of its UI/UX and data analytics teams for strategic growth. This step forward makes room for additional research and development of solutions to help automation become an essential and integral option for small businesses. VOXOX's engineering and data science teams have spent years analyzing billions of data points within the voice and text networks to help them develop artificial intelligence that can analyze content, respond like a real person, handle daily tasks like customer service inquiries & support jobs, schedule appointments, and write effective text campaigns. "The future of 5G AI telecommunications is at hand and we are eager to be growing rapidly in this field," said CEO of VOXOX, Cleve Adams. "VOXOX is leaping forward through the growth of our teams by bringing in skilled experts who have a great focus on providing remarkable automated solutions. Our intelligent assistant, virtual receptionists, and SMS drip campaigns are only the beginning of the AI-powered features that VOXOX has to offer."

The powerful learning ability of deep neural networks enables reinforcement learning (RL) agents to learn competent control policies directly from high-dimensional and continuous environments. In theory, to achieve stable performance, neural networks assume i.i.d. inputs, which unfortunately does no hold in the general RL paradigm where the training data is temporally correlated and non-stationary. This issue may lead to the phenomenon of "catastrophic interference" and the collapse in performance as later training is likely to overwrite and interfer with previously learned policies. In this paper, we introduce the concept of "context" into single-task RL and develop a novel scheme, termed as Context Division and Knowledge Distillation (CDaKD) driven RL, to divide all states experienced during training into a series of contexts. Its motivation is to mitigate the challenge of aforementioned catastrophic interference in deep RL, thereby improving the stability and plasticity of RL models. At the heart of CDaKD is a value function, parameterized by a neural network feature extractor shared across all contexts, and a set of output heads, each specializing on an individual context. In CDaKD, we exploit online clustering to achieve context division, and interference is further alleviated by a knowledge distillation regularization term on the output layers for learned contexts. In addition, to effectively obtain the context division in high-dimensional state spaces (e.g., image inputs), we perform clustering in the lower-dimensional representation space of a randomly initialized convolutional encoder, which is fixed throughout training. Our results show that, with various replay memory capacities, CDaKD can consistently improve the performance of existing RL algorithms on classic OpenAI Gym tasks and the more complex high-dimensional Atari tasks, incurring only moderate computational overhead.

Qualcomm has launched Flight RB5 5G, the "world's first" autonomous drone platform with 5G and AI capabilities. The company says the platform will support and accelerate the development and deployment of autonomous drones for commercial, enterprise, and industrial purposes. An onboard Qualcomm Secure Processing Unit achieves the modern cybersecurity requirements for drones. "We have continued to engage many leading drone companies, enabling 200 global robotics and drone ecosystem members in addition to consistently driving and promoting worldwide drone standardization and transformative 5G capabilities in organisations such as 3GPP, GSMA, the Global UTM Alliance, the Aerial Connectivity Joint Initiative (ACJA) and ASTM. We are proud to continue our momentum of enabling the digital transformation of global industries by unveiling the Qualcomm Flight RB5 5G Platform, a solution that is purpose-built for drone development with enhanced autonomy and intelligence features, bringing premium connected flight capabilities to industrial, enterprise and commercial segments."

Deep Neural Networks (DNNs) have emerged as the core enabler of many major applications on mobile devices. To achieve high accuracy, DNN models have become increasingly deep with hundreds or even thousands of operator layers, leading to high memory and computational requirements for inference. Operator fusion (or kernel/layer fusion) is key optimization in many state-of-the-art DNN execution frameworks, such as TensorFlow, TVM, and MNN. However, these frameworks usually adopt fusion approaches based on certain patterns that are too restrictive to cover the diversity of operators and layer connections. Polyhedral-based loop fusion techniques, on the other hand, work on a low-level view of the computation without operator-level information, and can also miss potential fusion opportunities. To address this challenge, this paper proposes a novel and extensive loop fusion framework called DNNFusion. The basic idea of this work is to work at an operator view of DNNs, but expand fusion opportunities by developing a classification of both individual operators and their combinations. In addition, DNNFusion includes 1) a novel mathematical-property-based graph rewriting framework to reduce evaluation costs and facilitate subsequent operator fusion, 2) an integrated fusion plan generation that leverages the high-level analysis and accurate light-weight profiling, and 3) additional optimizations during fusion code generation. DNNFusion is extensively evaluated on 15 DNN models with varied types of tasks, model sizes, and layer counts. The evaluation results demonstrate that DNNFusion finds up to 8.8x higher fusion opportunities, outperforms four state-of-the-art DNN execution frameworks with 9.3x speedup. The memory requirement reduction and speedups can enable the execution of many of the target models on mobile devices and even make them part of a real-time application.

Network troubleshooting is still a heavily human-intensive process. To reduce the time spent by human operators in the diagnosis process, we present a system based on (i) unsupervised learning methods for detecting anomalies in the time domain, (ii) an attention mechanism to rank features in the feature space and finally (iii) an expert knowledge module able to seamlessly incorporate previously collected domain-knowledge. In this paper, we thoroughly evaluate the performance of the full system and of its individual building blocks: particularly, we consider (i) 10 anomaly detection algorithms as well as (ii) 10 attention mechanisms, that comprehensively represent the current state of the art in the respective fields. Leveraging a unique collection of expert-labeled datasets worth several months of real router telemetry data, we perform a thorough performance evaluation contrasting practical results in constrained stream-mode settings, with the results achievable by an ideal oracle in academic settings. Our experimental evaluation shows that (i) the proposed system is effective in achieving high levels of agreement with the expert, and (ii) that even a simple statistical approach is able to extract useful information from expert knowledge gained in past cases, significantly improving troubleshooting performance.

From 2021 to 2028, the worldwide telecom services industry will increase at a compound growth rate of 5.4%. By 2025, the market for Telecom Equipment is expected to develop at a rate of 11.23%. One of the main aspects fuelling this market is an increased investment in 5G infrastructure deployment due to a shift in customer preference for next-generation technologies and smartphone devices. Increased need for value-added managed services, a growing number of mobile users, and surging demand for high-speed data connectivity are all major market drivers. Over the last few decades, the global communication network has clearly been one of the most important areas for continuing technical advancement.