This paper aims to develop resilient transmission mechanisms to suitably distribute traffic across multiple paths in an arbitrary millimeter-wave (mmWave) network. The main contributions include: (a) the development of proactive transmission mechanisms that build resilience against network disruptions in advance, while achieving a high end-to-end packet rate; (b) the design of a heuristic path selection algorithm that efficiently selects (in polynomial time in the network size) multiple proactively resilient paths with high packet rates; and (c) the development of a hybrid scheduling algorithm that combines the proposed path selection algorithm with a deep reinforcement learning (DRL) based online approach for decentralized adaptation to blocked links and failed paths. To achieve resilience to link failures, a stateof-the-art Soft Actor-Critic DRL algorithm, which adapts the information flow through the network, is investigated. The proposed scheduling algorithm robustly adapts to link failures over different topologies, channel and blockage realizations while offering a superior performance to alternative algorithms. M. G. Dogan and C. Fragouli are with the Electrical and Computer Engineering Department at the University of California, Los Angeles, CA 90095 USA (e-mail: {minedogan96, christina.fragouli}@ucla.edu). The research carried out at UCLA was supported in part by the Army Research Laboratory under Co-Operative Agreement W911NF-17-2-0196 and by the U.S. National Science Foundation (NSF) awards 442521-FC-22071 and 442521-FC-21454. M. Cardone is with the Electrical and Computer Engineering Department of the University of Minnesota, MN 55404 USA (e-mail: cardo089@umn.edu). The work of M. Cardone was supported in part by the NSF under Grants CCF-2045237 and CNS-2146838. Part of this work was presented at the 2021 IEEE Military Communications Conference [1] and at the 2022 IEEE International Symposium on Information Theory [2]. Millimeter Wave (mmWave) (and beyond) is an enabling technology that is playing an increasingly important role in our wireless infrastructure by expanding the available spectrum and enabling multi-gigabit services [3]-[5]. A number of use cases are currently built around multihop mmWave networks, such as Facebook's Terragraph network [6] that uses flexible mmWave backbones to connect clusters of base stations. Other example scenarios include private networks, such as in shopping centers, airports and enterprises; mmWave mesh networks that use mmWave links as backhaul in dense urban scenarios; military applications employing mobile hot spots; and mmWave based vehicle-to-everything (V2X) services, such as cooperative perception [7]-[9].

Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article serves the dual purpose of providing a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations between signal processing, machine learning, and application domains.

Key additions include real-time hardware ray tracing, the ability to sense and optimize different "layers" in your photos, and massive connectivity upgrades whose throughput will likely outpace your home Internet connection. Specifically, AI will be used to power a number of new experiences in Snapdragon-powered phones, which will debut this fall. Smarter cameras will try to interpret what you're shooting and enhance it before you even take the picture, not afterwards, executives said. What Qualcomm now calls an "always sensing" camera will also remain in low-power mode, scanning the world around it; you'll be able to hold up the phone to scan an QR code even if the phone is in standby mode, they said. The phone will also use its AI capabilities to improve cellular connections, as previous phones have done.

Qualcomm has announced its latest flagship mobile chipset, the Snapdragon 8 Gen 2. Along with making it more powerful and efficient than Gen 1 chips, Qualcomm says it has packed more AI smarts into the new platform. The Snapdragon 8 will tap into the latest Qualcomm AI Engine and upgraded Hexagon processor to offer "faster natural language processing with multi-language translation and advanced AI camera features," the company claims. The processor has architectural upgrades that will enable up to 4.35 times the AI performance of Gen 1 chips, according to Qualcomm. There will be support for an AI precision format called Int4, which the company suggests will lead to a 60 percent performance/watt improvement over the previous-gen chipset for sustained AI inferencing. Meanwhile, the Sensing Hub will have dual AI processors, which can support features such as custom wake words.

Network slicing is a well assessed approach enabling virtualization of the mobile core and radio access network (RAN) in the emerging 5th Generation New Radio. Slicing is of paramount importance when dealing with the emerging and diverse vertical applications entailing heterogeneous sets of requirements. 5G is also envisioning Unmanned Aerial Vehicles (UAVs) to be a key element in the cellular network standard, aiming at their use as aerial base stations and exploiting their flexible and quick deployment to enhance the wireless network performance. This work presents a UAV-assisted 5G network, where the aerial base stations (UAV-BS) are empowered with network slicing capabilities aiming at optimizing the Service Level Agreement (SLA) satisfaction ratio of a set of users. The users belong to three heterogeneous categories of 5G service type, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). A first application of multi-agent and multi-decision deep reinforcement learning for UAV-BS in a network slicing context is introduced, aiming at the optimization of the SLA satisfaction ratio of users through the joint allocation of radio resources to slices and refinement of the UAV-BSs 2-dimensional trajectories. The performance of the presented strategy have been tested and compared to benchmark heuristics, highlighting a higher percentage of satisfied users (at least 27% more) in a variety of scenarios.

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB). Open Radio Access Network (O-RAN), proposes an open and disaggregated concept of RAN by modulizing the functionalities into independent components. Network slicing for O-RAN can significantly improve performance. Therefore, an advanced resource allocation solution for network slicing in O-RAN is proposed in this study by applying Reinforcement Learning (RL). This research demonstrates an RL compatible simplified edge network simulator with three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This simulator is later used to discover how to improve throughput for targeted network slice(s) by dynamically allocating unused bandwidth from other slices. Increasing the throughput for certain network slicing can also benefit the end users with a higher average data rate, peak rate, or shorter transmission time. The results show that the RL model can provide eMBB traffic with a high peak rate and shorter transmission time for URLLC compared to balanced and eMBB focus baselines.

An example of counterfactual statement is "I got no effect since I made no action but something would have happened had I acted". Counterfactuals are used in many fields, ranging from algorithmic recourse [Karimi et al., 2021] to online advertisement and customer relationship management [Li and Pearl, 2019]. Counterfactuals have been formally defined in terms of structural causal models by Pearl [2009]. Nevertheless, since a counterfactual statement cannot be directly observed, the research focuses on estimating or bounding their probability (e.g. the probability that we have an effect given a treatment and no effect else). The probability of some specific counterfactual expressions have been studied in the literature [Tian and Pearl, 2000] because of their relevance in causal decision-making. The probability of necessity (PN) is the probability that an event y would not have occurred in the absence of an action or treatment t, given that y and t in fact occurred. Conversely, the probability of sufficiency (PS) is the probability that event y would have occurred in the presence of an action t, given that both y and t in fact did not occur. Lastly, the probability of necessity and sufficiency (PNS) is the probability that the event y occurs if and only if the event t occurs. In the case of incomplete knowledge about the causal model, identification procedures indicate when and how the probability of counterfactuals can be computed from a combination of observational data, experimental data (i.e.

We explore the feasibility of AI assisted hand-gesture recognition using 802.11ad 60GHz (mmWave) technology in smartphones. Range-Doppler information (RDI) is obtained by using pulse Doppler radar for gesture recognition. We built a prototype system, where radar sensing and WLAN communication waveform can coexist by time-division duplex (TDD), to demonstrate the real-time hand-gesture inference. It can gather sensing data and predict gestures within 100 milliseconds. First, we build the pipeline for the real-time feature processing, which is robust to occasional frame drops in the data stream. RDI sequence restoration is implemented to handle the frame dropping in the continuous data stream, and also applied to data augmentation. Second, different gestures RDI are analyzed, where finger and hand motions can clearly show distinctive features. Third, five typical gestures (swipe, palm-holding, pull-push, finger-sliding and noise) are experimented with, and a classification framework is explored to segment the different gestures in the continuous gesture sequence with arbitrary inputs. We evaluate our architecture on a large multi-person dataset and report > 95% accuracy with one CNN + LSTM model. Further, a pure CNN model is developed to fit to on-device implementation, which minimizes the inference latency, power consumption and computation cost. And the accuracy of this CNN model is more than 93% with only 2.29K parameters.

Mobile cloud offloading is indispensable for inference tasks based on large-scale deep models. However, transmitting privacy-rich inference data to the cloud incurs concerns. This paper presents the design of a system called PriMask, in which the mobile device uses a secret small-scale neural network called MaskNet to mask the data before transmission. PriMask significantly weakens the cloud's capability to recover the data or extract certain private attributes. The MaskNet is em cascadable in that the mobile can opt in to or out of its use seamlessly without any modifications to the cloud's inference service. Moreover, the mobiles use different MaskNets, such that the collusion between the cloud and some mobiles does not weaken the protection for other mobiles. We devise a {\em split adversarial learning} method to train a neural network that generates a new MaskNet quickly (within two seconds) at run time. We apply PriMask to three mobile sensing applications with diverse modalities and complexities, i.e., human activity recognition, urban environment crowdsensing, and driver behavior recognition. Results show PriMask's effectiveness in all three applications.

Motivated by the needs of resiliency, scalability, and plug-and-play operation, distributed decision-making is becoming increasingly prevalent. The problem of achieving consensus in a multi-agent system is at the core of distributed decision-making. In this article, we study the problem of achieving average consensus over a directed multi-agent network when the communication links are corrupted with noise. We propose an algorithm where each agent updates its estimates based on the local mixing of information and adds its weighted noise-free initial information to its updates during every iteration. We demonstrate that with appropriately designed weights the agents achieve consensus under additive communication noise. We establish that when the communication links are noiseless the proposed algorithm moves towards consensus at a geometric rate. Under communication noise, we prove that the agent estimates reach a consensus value almost surely. We present numerical experiments to corroborate the efficacy of the proposed algorithm under different noise realizations and various algorithm parameters.