In this article, we provide an overview of machine learning (ML) methods, both classical and deep variants, that are used to implement self-healing for cell outages in cellular networks. Self-healing is a promising approach to network management, which aims to detect and compensate for cell outages in an autonomous way. This technology aims to decrease the expenses associated with the installation and maintenance of existing 4G and 5G, i.e. emerging 6G networks by simplifying operational tasks through its ability to heal itself. We provide an overview of the basic concepts and taxonomy for SON, self-healing, and ML techniques, in network management. Moreover, we review the state-of-the-art in literature for cell outages, with a particular emphasis on ML-based approaches.

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors.

Anyone who attends a media event in the technology space does their own reporting, draws their own conclusions. And we all shared the same experiences in a small bubble on Maui. But yes, a few years of struggling to convince the world that Windows on Arm (Qualcomm) could work, Qualcomm seems to be back on track in laptops, thanks to the Oryon CPU and the Snapdragon X Elite chip. Reporters (I, among them) headed to Maui with concerns that it would happen yet again. Qualcomm projected a sense of confidence in eye-popping numbers that could double Intel's performance in various categories.

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.