In this paper, we study a novel Stochastic Network Utility Maximization (NUM) problem where the utilities of agents are unknown. The utility of each agent depends on the amount of resource it receives from a network operator/controller. The operator desires to do a resource allocation that maximizes the expected total utility of the network. We consider threshold type utility functions where each agent gets non-zero utility if the amount of resource it receives is higher than a certain threshold. Otherwise, its utility is zero (hard real-time). We pose this NUM setup with unknown utilities as a regret minimization problem. Our goal is to identify a policy that performs as `good' as an oracle policy that knows the utilities of agents. We model this problem setting as a bandit setting where feedback obtained in each round depends on the resource allocated to the agents. We propose algorithms for this novel setting using ideas from Multiple-Play Multi-Armed Bandits and Combinatorial Semi-Bandits. We show that the proposed algorithm is optimal when all agents have the same utility. We validate the performance guarantees of our proposed algorithms through numerical experiments.

We present a framework to address a class of sequential decision making problems. Our framework features learning the optimal control policy with robustness to noisy data, determining the unknown state and action parameters, and performing sensitivity analysis with respect to problem parameters. We consider two broad categories of sequential decision making problems modelled as infinite horizon Markov Decision Processes (MDPs) with (and without) an absorbing state. The central idea underlying our framework is to quantify exploration in terms of the Shannon Entropy of the trajectories under the MDP and determine the stochastic policy that maximizes it while guaranteeing a low value of the expected cost along a trajectory. This resulting policy enhances the quality of exploration early on in the learning process, and consequently allows faster convergence rates and robust solutions even in the presence of noisy data as demonstrated in our comparisons to popular algorithms such as Q-learning, Double Q-learning and entropy regularized Soft Q-learning. The framework extends to the class of parameterized MDP and RL problems, where states and actions are parameter dependent, and the objective is to determine the optimal parameters along with the corresponding optimal policy. Here, the associated cost function can possibly be non-convex with multiple poor local minima. Simulation results applied to a 5G small cell network problem demonstrate successful determination of communication routes and the small cell locations. We also obtain sensitivity measures to problem parameters and robustness to noisy environment data.

The US is bending its hardline stance on Huawei... if only very slightly. The Commerce Department has instituted a rule allowing American companies to participate in developing standards where Huawei is involved, such as 5G wireless, AI and self-driving cars. Not surprisingly, the move has its roots in pride and pragmatism. The US doesn't want to "cede leadership in global innovation" by sitting out important decisions on future technology, according to Commerce Secretary Wilbur Ross. Reuters noted that the change had been drafted in May, but was waiting for approval.

Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this context, designing low-complexity policies with local observations, yet able to adapt the user association with respect to the global network state and to the network dynamics is a challenge. In fact, the frameworks proposed in literature require continuous access to global network information and to recompute the association when the radio environment changes. With the complexity associated to such an approach, these solutions are not well suited to dense 5G networks. In this paper, we address this issue by designing a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. In this approach, users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Since there is no direct information exchange among the agents, we also limit the signaling overhead. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.

Artificial Intelligence And Machine Learning Are More Important Than You Might Think For Mobile ... [ ] Devices AI is a common buzz word these days, but most consumers probably aren't aware how it's interwoven in their everyday lives. Some of us in the analyst and tech press communities may also scoff at how often the term is used for some technologies that hardly resemble true artificial intelligence. That said, there are a few platforms, beyond just powerful data centers, that are a natural for AI processing and the NNs (Neural Networks) that drive them. One of those is AI inferencing (using the AI to infer information, versus training an NN) at the edge, and in your pocket, with a smartphone. As you might imagine, smartphone platforms from Android to Apple vary greatly, but there are common workloads like speech-to-text translation, and recommender engines (like Google Assistant and Siri), that make heavy use of common AI NN models, and they do so on-device for speed and latency advantages.

Spiking neural networks are nature's solution for parallel information processing with high temporal precision at a low metabolic energy cost. To that end, biological neurons integrate inputs as an analog sum and communicate their outputs digitally as spikes, i.e., sparse binary events in time. These architectural principles can be mirrored effectively in analog neuromorphic hardware. Nevertheless, training spiking neural networks with sparse activity on hardware devices remains a major challenge. Primarily this is due to the lack of suitable training methods that take into account device-specific imperfections and operate at the level of individual spikes instead of firing rates. To tackle this issue, we developed a hardware-in-the-loop strategy to train multi-layer spiking networks using surrogate gradients on the analog BrainScales-2 chip. Specifically, we used the hardware to compute the forward pass of the network, while the backward pass was computed in software. We evaluated our approach on downscaled 16x16 versions of the MNIST and the fashion MNIST datasets in which spike latencies encoded pixel intensities. The analog neuromorphic substrate closely matched the performance of equivalently sized networks implemented in software. It is capable of processing 70 k patterns per second with a power consumption of less than 300 mW. Added activity regularization resulted in sparse network activity with about 20 spikes per input, at little to no reduction in classification performance. Thus, overall, our work demonstrates low-energy spiking network processing on an analog neuromorphic substrate and sets several new benchmarks for hardware systems in terms of classification accuracy, processing speed, and efficiency. Importantly, our work emphasizes the value of hardware-in-the-loop training and paves the way toward energy-efficient information processing on non-von-Neumann architectures.

At 23, Shunji Sugaya had what he calls a "life-changing episode." It was March 2000, and Sugaya had just won an award at a business contest where Masayoshi Son, the founder of what was then called SoftBank Corp., was a judge. He sent Son an email to thank him, the two met up, and before long SoftBank offered to buy Sugaya's idea for $2.8 million or for Sugaya to join the company and receive stock options. "It gave me a big boost in confidence, as I was a student -- I was so happy I could dance," he said. "We were very grateful for the offer but we politely declined and decided to do it ourselves."

Global Navigation Satellite System typically performs poorly in urban environments when there is no line-of-sight between the devices and the satellites, and thus alternative localization methods are often required. We present a simple yet effective method for localization based on pathloss. In our approach, the user to be localized reports the received signal strength from a set of base stations with known locations. For each base station we have a good approximation of the pathloss at each location in the map, provided by RadioUNet, an efficient deep learning-based simulator of pathloss functions in urban environment, akin to ray-tracing. Using the approximations of the pathloss functions of all base stations and the reported signal strengths, we are able to extract a very accurate approximation of the location of the user.

Mobile network operators store an enormous amount of information like log files that describe various events and users' activities. Analysis of these logs might be used in many critical applications such as detecting cyber-attacks, finding behavioral patterns of users, security incident response, network forensics, etc. In a cellular network Call Detail Records (CDR) is one type of such logs containing metadata of calls and usually includes valuable information about contact such as the phone numbers of originating and receiving subscribers, call duration, the area of activity, type of call (SMS or voice call) and a timestamp. With anomaly detection, it is possible to determine abnormal reduction or increment of network traffic in an area or for a particular person. This paper's primary goal is to study subscribers' behavior in a cellular network, mainly predicting the number of calls in a region and detecting anomalies in the network traffic. In this paper, a new hybrid method is proposed based on various anomaly detection methods such as GARCH, K-means, and Neural Network to determine the anomalous data. Moreover, we have discussed the possible causes of such anomalies.

It's the rise of the robots: Japan's second-largest company is now a maker of industrial automation systems, highlighting the rising importance of a less visible sector to a nation long associated with consumer-facing brands. Keyence Corp., a maker of machine vision systems and sensors for factories, has jumped 17 percent this year to become Japan's second-largest company by market value. At a valuation of almost ¥11 trillion ($100 billion), it has overtaken telecommunications giants SoftBank Group Corp., and NTT Docomo Inc., which have jostled for the honor to sit behind Toyota Motor Corp. over most of the past decade. Keyence is famed for its dizzying profitability with an operating profit margin of more than 50 percent, among the country's highest. That's enabled by its "fabless" output model, according to analysts, with production of its array of pressure sensors, barcode readers and laser scanners outsourced to avoid high capital costs.