When's the last time you chirped, "Hey Google" (or Siri for that matter), and asked your phone for a recommendation for good sushi in the area, or perhaps asked what time sunset would be? Most folks these days perform these tasks on a regular basis on their phones, but you may not have realized there were multiple AI (Artificial Intelligence) engines involved in quickly delivering the results for your request. In these examples, AI neural network models were used to process natural language recognition, and then also inferred what you were looking for, to deliver relevant search results from internet databases around the globe, but also targeting the most appropriate results based on your location and a number of other factors as well. These are just a couple of examples but, in short, AI or machine learning processing is a big requirement of smartphone experiences these days, from recommendation engines to translation, computational photography and more. As such, benchmarking tools are now becoming more prevalent, in an effort to measure mobile platform performance. MLPerf is one such tool that nicely covers the gamut of AI workloads, and today Qualcomm is highlighting some fairly impressive results in a recent major update to the MLCommons database.

In the next few years, mobile network operators might be using artificial intelligence to put the right infrastructure in the right place, according to research conducted by Bain & Company. Wireless infrastructure has an important place within the universe of mobile network infrastructure, which also includes a core switched network for voice calls and text, a packet switched network for mobile data and the public switched telephone network to connect subscribers to the wider telephony network. Wireless infrastructure includes the radio base stations, antennas and their support structures, and cables and optical fiber that connect antennas, base stations and network cores. According to IBM Cloud Education, at its simplest form, artificial intelligence combines computer science and robust datasets to enable problem solving. It also encompasses machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence, IBM's description reads.

Colosseum is an open-access and publicly-available large-scale wireless testbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, "white-box" platform. Through 256 state-of-the-art Software-defined Radios and a Massive Channel Emulator core, Colosseum can model virtually any scenario, enabling the design, development and testing of solutions at scale in a variety of deployments and channel conditions. These Colosseum radio-frequency scenarios are reproduced through high-fidelity FPGA-based emulation with finite-impulse response filters. Filters model the taps of desired wireless channels and apply them to the signals generated by the radio nodes, faithfully mimicking the conditions of real-world wireless environments. In this paper we describe the architecture of Colosseum and its experimentation and emulation capabilities. We then demonstrate the effectiveness of Colosseum for experimental research at scale through exemplary use cases including prevailing wireless technologies (e.g., cellular and Wi-Fi) in spectrum sharing and unmanned aerial vehicle scenarios. A roadmap for Colosseum future updates concludes the paper.

"When it comes to the economy, data is the new oil," said entrepreneur Clive Humby in 2006. History has proven him right beyond all expectations. Empires have been built on bigger and bigger mountains of data. Over the course of a decade, GAFA (Google, Amazon, Facebook, Apple) increased their sales from $78 billion in 2008 to $773 billion in 2019. From the outset, GAFA put everything into this new oil, developing data-driven services such as a search engine (Google), online sales (Amazon) and social networking (Facebook).

We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no central coordination or cooperation among them. Our goal is to design efficient distributed beam scheduling and power allocation algorithms such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference situations by learning from experience and can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.

Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments.To address them, we first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL's effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.

Communications standards are designed via committees of humans holding repeated meetings over months or even years until consensus is achieved. This includes decisions regarding the modulation and coding schemes to be supported over an air interface. We propose a way to "automate" the selection of the set of modulation and coding schemes to be supported over a given air interface and thereby streamline both the standards design process and the ease of extending the standard to support new modulation schemes applicable to new higher-level applications and services. Our scheme involves machine learning, whereby a constructor entity submits proposals to an evaluator entity, which returns a score for the proposal. The constructor employs reinforcement learning to iterate on its submitted proposals until a score is achieved that was previously agreed upon by both constructor and evaluator to be indicative of satisfying the required design criteria (including performance metrics for transmissions over the interface).

According to a recent study by Fact.MR, the Video Switchers market is set to see an impressive CAGR of more than 7% during the forecast period 2021-2031. Demand from end-users may provide impressive growth in terms of volume and value at the same time. In addition, demand cuts from the telecommunications industry and source filing are aimed at reducing productivity over the next few years. This is because the semiconductor and electronics industries are slowly recovering. Or, the tendency to deal with digital play may create new opportunities in the forecast year.

The management of networks is automated by closed loops. Concurrent closed loops aiming for individual optimization cause conflicts which, left unresolved, leads to significant degradation in performance indicators, resulting in sub-optimal network performance. Centralized optimization avoids conflicts, but impractical in large-scale networks for time-critical applications. Distributed, pervasive intelligence is therefore envisaged in the evolution to B5G networks. In this letter, we propose a Q-Learning-based distributed architecture (QLC), addressing the conflict issue by encouraging cooperation among intelligent agents. We design a realistic B5G network slice auto-scaling model and validate the performance of QLC via simulations, justifying further research in this direction.

Monetizing data has long been a goal of entrepreneurial CSPs (communication service providers). For example, operators like A1 in Austria, and O2 in the UK, have developed mobility insights units, enabling them to form new revenue streams by providing demographic data to governments and businesses. Today, connecting billions of sensors and devices is a reality thanks to 5G. As a result, the volume of data generated across 5G is set to explode, with estimates of up to a mind-blowing 79.4 zettabytes by 2025, according to IDC. That's a lot of data with a lot of potential value, and CSPs are looking to the 3GPP Network Data Analytics Function (NWDAF) and ONAP's Data Collection, Analytics and Events (DCAE) framework to unlock some of it. With this standardized approach to collecting and analyzing network data, NWDAF and DCAE allow CSPs to manage, automate, and optimize their 5G network operations much more efficiently.