Cooperative beamforming design has been recognized as an effective approach in modern wireless networks to meet the dramatically increasing demand of various wireless data traffics. It is formulated as an optimization problem in conventional approaches and solved iteratively in an instance-by-instance manner. Recently, learning-based methods have emerged with real-time implementation by approximating the mapping function from the problem instances to the corresponding solutions. Among various neural network architectures, graph neural networks (GNNs) can effectively utilize the graph topology in wireless networks to achieve better generalization ability on unseen problem sizes. However, the current GNNs are only equipped with the node-update mechanism, which restricts it from modeling more complicated problems such as the cooperative beamforming design, where the beamformers are on the graph edges of wireless networks. To fill this gap, we propose an edge-graph-neural-network (Edge-GNN) by incorporating an edge-update mechanism into the GNN, which learns the cooperative beamforming on the graph edges. Simulation results show that the proposed Edge-GNN achieves higher sum rate with much shorter computation time than state-of-the-art approaches, and generalizes well to different numbers of base stations and user equipments.

This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks. As far as the Radio Access Network (RAN) is concerned, centralized pre-configured resource allocation requires scheduling grants to be disseminated to the User Equipments (UEs) before uplink transmissions, which is not efficient for URLLC, especially in case of flexible/unpredictable traffic. To alleviate this burden, we study a distributed, user-centric scheme based on machine learning in which UEs autonomously select their uplink radio resources without the need to wait for scheduling grants or preconfiguration of connections. Using simulation, we demonstrate that a Multi-Armed Bandit (MAB) approach represents a desirable solution to allocate resources with URLLC in mind in an IIoT environment, in case of both periodic and aperiodic traffic, even considering highly populated networks and aggressive traffic.

In the course of time, data science has proved its high value and efficiency. Data scientists find more and more new ways to implement big data solutions in daily life. Nowadays data is a fuel needed for a successful company. Telecommunication companies are not an exception. Due to these circumstances, they cannot afford not to use data science.

Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task. To mitigate the high degree of effort involved in prompt-design, we instead ask whether producing multiple effective, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed prompting method, ASK ME ANYTHING (AMA). We first develop an understanding of the effective prompt formats, finding that question-answering (QA) prompts, which encourage open-ended generation ("Who went to the park?") tend to outperform those that restrict the model outputs ("John went to the park. Output True or False."). Our approach recursively uses the LLM itself to transform task inputs to the effective QA format. We apply the collected prompts to obtain several noisy votes for the input's true label. We find that the prompts can have very different accuracies and complex dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions for the inputs. We evaluate AMA across open-source model families (e.g., EleutherAI, BLOOM, OPT, and T0) and model sizes (125M-175B parameters), demonstrating an average performance lift of 10.2% over the few-shot baseline. This simple strategy enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 popular benchmarks. Averaged across these tasks, the GPT-J-6B model outperforms few-shot GPT3-175B. We release our code here: https://github.com/HazyResearch/ama_prompting

Earlier this year, we announced our vision to empower any developer to become a space developer through Azure. With over 90 million developers on GitHub, we have created a powerful ecosystem and we are focused on empowering the next generation of developers for space. Today, we are announcing a crucial step towards democratizing access to space development, with the preview release of Azure Orbital Space SDK (software development kit)--a secure hosting platform and application toolkit designed to enable developers to create, deploy, and operate applications on-orbit. By bringing modern cloud-based applications to spacecrafts we not only increase the efficiency, value, and speed of insights from space data but also increase the value of that data through the optimization of ground communication. Many of the fundamental technological improvements that have accelerated the growth of Internet of Things (IoT) in the past decade remain untapped by space development missions today.

Qualcomm subtly adjusted its marketing strategy for its PC compute business on Wednesday, naming its next-gen CPU "Oryon" and claiming that the company's goal was to "bring the best of the smartphone to your laptop." Qualcomm didn't announce a new PC CPU chipset, however. Qualcomm wasn't expected to show the fruits of its Nuvia acquisition, revealing earlier this year that the chips are due in late 2023. It might be a small thing, but Qualcomm executives said that the Oryon chips would ship in 2023, sans the "late" qualifier. Qualcomm executives didn't address an unexpected lawsuit by the company's IP provider, Arm Ltd., against Qualcomm earlier this year.

If companies are going to make augmented reality glasses you'd actually want to wear, they'll need chips that are powerful but won't require a large battery on your head. Qualcomm thinks it can help. The company has unveiled a Snapdragon AR2 Gen 1 platform that's built with slim AR glasses in mind. The multi-chip design reportedly delivers 2.5 times the AI performance of the company's XR2-based reference design while using half the power. You could have eyewear that intelligently detects objects in the room while remaining slim and light enough to use for hours at a time.

Qualcomm launched its latest Snapdragon processor on Tuesday for the next crop of top-tier Android smartphones--with artificial intelligence infused throughout the chip to boost photography, sound, connectivity, gaming and security. At its Snapdragon Summit event in Hawaii, Qualcomm made artificial intelligence the centerpiece of its marketing pitch for its new Snapdragon 8 Gen 2 as it strives to build the Snapdragon name into a globally recognized brand. Artificial intelligence generally refers to computer systems that can simulate human intelligence to a certain extent. It is among the hottest fields in technology today. Qualcomm has long worked on artificial intelligence and machine learning, but mostly behind the scenes.

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The Snapdragon Tech Summit is a multi-day event that showcases the latest mobile technology Qualcomm has to offer. This is the second year that Qualcomm has held simultaneous events in China and Hawaii, as well as streaming the keynote addresses. Day 1 of the Snapdragon Tech Summit kicked off with the introduction of the latest smartphone system-on-chip (SoC) for smartphones – the Snapdragon 8 Gen 2. As expected, it delivers improvements in performance and efficiency for camera, connectivity, gaming, sound, and security. But the biggest punch comes from the use of artificial intelligence (AI) in just about every area. The company went so far as to call it "purpose built for AI." Qualcomm uses all of the Snapdragon SoC's processing elements for AI processing and calls the combination of these processing elements the "AI engine."