In this work, we investigate the performance of a joint sensing and communication (JSC) network consisting of multiple base stations (BSs) that cooperate through a fusion center (FC) to exchange information about the sensed environment while concurrently establishing communication links with a set of user equipments (UEs). Each BS within the network operates as a monostatic radar system, enabling comprehensive scanning of the monitored area and generating range-angle maps that provide information regarding the position of a group of heterogeneous objects. The acquired maps are subsequently fused in the FC. Then, a convolutional neural network (CNN) is employed to infer the category of the targets, e.g., pedestrians or vehicles, and such information is exploited by an adaptive clustering algorithm to group the detections originating from the same target more effectively. Finally, two multi-target tracking algorithms, the probability hypothesis density (PHD) filter and multi-Bernoulli mixture (MBM) filter, are applied to estimate the state of the targets. Numerical results demonstrated that our framework could provide remarkable sensing performance, achieving an optimal sub-pattern assignment (OSPA) less than 60 cm, while keeping communication services to UEs with a reduction of the communication capacity in the order of 10% to 20%. The impact of the number of BSs engaged in sensing is also examined, and we show that in the specific case study, 3 BSs ensure a localization error below 1 m.

Believe it: Qualcomm's Snapdragon X Elite looks ready to live up to its incredibly lofty promises based on a suite of early benchmarks we saw Qualcomm run late last week. But are AMD and Intel out of the race? Not at all, as the numbers also reveal how both competitors will likely spin their way back into the conversation. Qualcomm closed out its Snapdragon Technology Summit in Maui by allowing reporters to view, if not actually run, benchmarks across two demo systems with undisclosed Snapdragon X Elite chips inside them. In general, they met claims that Qualcomm executives had made earlier in the show -- that Snapdragon X Elite would meet if not significantly exceed its X86 rivals, specifically the Intel 13th-gen Core chips.

Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental and regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns.

Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncertainty estimates. Given an entity in the graph, CF-GNN produces a prediction set/interval that provably contains the true label with pre-defined coverage probability (e.g. 90%). We establish a permutation invariance condition that enables the validity of CP on graph data and provide an exact characterization of the test-time coverage. Moreover, besides valid coverage, it is crucial to reduce the prediction set size/interval length for practical use. We observe a key connection between non-conformity scores and network structures, which motivates us to develop a topology-aware output correction model that learns to update the prediction and produces more efficient prediction sets/intervals. Extensive experiments show that CF-GNN achieves any pre-defined target marginal coverage while significantly reducing the prediction set/interval size by up to 74% over the baselines. It also empirically achieves satisfactory conditional coverage over various raw and network features.

Robocalls are rampant, using AI and other tools to disrupt day-to-day life and scam Americans out of their money through impersonations of family members, phone providers and more. On October 24, the Senate Commerce Committee's Subcommittee on Communications, Media, and Broadband heard the latest issue and solution floating around: AI. Currently, bad actors are using AI to steal people's voices and repurpose them in calls to loved ones -- often presenting a state of distress. This advancement goes beyond seemingly real calls from banks and credit card companies, providing a disturbing and jarring experience: not knowing if you're speaking to someone you know. The financial repercussions (not to mention potential mental distress) are tremendous. Senator Ben Ray Luján, chair of the subcommittee, estimates that individuals nationwide receive 1.5 billion to 3 billion scam calls monthly, defrauding Americans out of $39 billion in 2022.

Qualcomm is no stranger in running artificial intelligence and machine learning systems on-device and without an internet connection. They've been doing it with their camera chipsets for years. But on Tuesday at Snapdragon Summit 2023, the company announced that on-device AI is finally coming to mobile devices and Windows 11 PCs as part of the new Snapdragon 8 Gen 3 and X Elite chips. Both chipsets were built from the ground up with generative AI capabilities in mind and are able to support a variety of large language models (LLM), language vision models (LVM), and transformer network-based automatic speech recognition (ASR) models, up to 10 billion parameters for the SD8 gen 3 and 13 billion parameters for the X Elite, entirely on-device. That means you'll be able to run anything from Baidu's ERNIE 3.5 to OpenAI's Whisper, Meta's Llama 2 or Google's Gecko on your phone or laptop, without an internet connection.

At its annual Snapdragon Summit on Tuesday, Qualcomm revealed its latest mobile chipset. Perhaps the biggest change in the Snapdragon 8 Gen 3 is the introduction of on-device generative AI (akin to Google's Tensor G3). The chipset's AI Engine supports multi-modal generative AI models and what Qualcomm claims is the world's fastest Stable Diffusion system with the ability to generate an image in under a second. So, you should be able to whip up backgrounds and images for social media posts in a flash. Because GAI requests are handled on-device, Qualcomm says they remain private.

On Tuesday, at its annual Snapdragon Summit in Hawaii, Qualcomm announced a major addition to its line of mobile chips with the Snapdragon X Elite, which the company is calling its most powerful processor to date. The Arm-based Snapdragon X Elite is the successor to last year's Snapdragon 8cx Gen 3 line of laptop chips, which recently got a name change to reflect the huge leap in performance for this upcoming generation. Powered by 12 Oryon cores, Qualcomm claims the X Elite provides up to two times faster CPU performance compared to Intel's 13th-gen Core i7-1360P and i7-1355U processors while also drawing up to 68 percent less power. The chip is based on a 4nm design fabricated by TSMC with standard clock speeds of 3.8GHz with a dual-core boost of up to 4.3GHz. Qualcomm also includes 42MB of total cache with an LPDDR5x memory bandwidth of 136 GB/s.

Qualcomm and its Snapdragon chips are officially back inside the PC. Today, Qualcomm formally launched the Snapdragon X Elite, the flagship platform of its Snapdragon X family that leverages its Oryon CPU core, and promises to double -- yes, double -- the performance of some of the most popular 13th-gen Core chips from AMD and Intel. Qualcomm promised the same with its earlier Snapdragon 8-series chips, and really didn't deliver. But after buying chip designer Nuvia in 2021, Qualcomm is trying again, hoping that its superpowered Arm chips can once again make Windows on Arm PCs a competitor to conventional X86 PCs when they launch in mid-2024. And they're talking some big numbers to prove it.

While companies like Microsoft and Nvidia are all-in on the power of next-generation machine learning algorithms, some regulators are dreading what it might mean for our already-stressed communication networks. The chairwoman of the US Federal Communications Commission, for one, who's just proposed an investigation into what "AI" could mean for even more spam calls and texts. The FCC will vote to adopt a multi-tiered action in November. Chairwoman Rosencworcel, who's served on the Commission since 2012 and as its executive since being confirmed late in 2021, is particularly concerned with how newly empowered AI tools could affect senior citizens. The FCC's initial press release (PDF link) lists four main goals: determining whether AI technologies fall under the Comission's jurisdiction via the Telephone Consumer Protection Act of 1991, if and when future AI tech might do the same, how AI impacts existing regulatory frameworks, and if the FCC should consider ways to verify the authenticity of auto-generated AI voice and text from "trusted sources."