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Anay Ajit Deshpande

Publications and source records attributed to Anay Ajit Deshpande.

8 recordsLinked to original sources

WiFi-Based People Counting Using Beam-Steerable Antennas: A Test-bed Study

Ubiquitous perception through RF signals is a pivotal opportunity for future technology: it enables personalized services such as smart living, remote healthcare, automated logistics or interaction through free-space gestures. The ubiquity of Wi-Fi and cellular networks presents a promising platform for the development of innovative sensing tools. Future standards will also introduce dedicated sensing features which, for example, will allow routers to work as frequency modulated continuous wave radios targeting radar applications. Most of the current chip designs support ad-hoc firmware for CSI extraction with MIMO arrangements of the transmitter (TX) and receiver (RX) antennas and OFDM subcarriers. The CSI describes the phase shift and amplitude attenuation of multiple propagation paths on each subcarrier. The latest IEEE 802.11be standard (Wi-Fi 7) offers a wider subcarrier bandwidth of 160MHz (up to 320MHz), providing at least 120 usable pilot subcarriers for CSI or CIR estimation. Additionally, Wi-Fi signals have been recently exploited to track daily human movements and behaviors, while Wi-Fi signal variations have been shown to differ between different people and can consequently be used for their re-identification.

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Sensing-Based Beamformed Resource Allocation in Standalone Millimeter-Wave Vehicular Networks

In 3GPP New Radio (NR) Vehicle-to-Everything (V2X), the new standard for next-generation vehicular networks, vehicles can autonomously select sidelink resources for data transmission, which permits network operations without cellular coverage. However, standalone resource allocation is uncoordinated, and is complicated by the high mobility of the nodes that may introduce unforeseen channel collisions (e.g., when a transmitting vehicle changes path) or free up resources (e.g., when a vehicle moves outside of the communication area). Moreover, unscheduled resource allocation is prone to the hidden node and exposed node problems, which are particularly critical considering directional transmissions. In this paper, we implement and demonstrate a new channel access scheme for NR V2X in Frequency Range 2 (FR2), i.e., at millimeter wave (mmWave) frequencies, based on directional and beamformed transmissions along with Sidelink Control Information (SCI) to select resources for transmission. We prove via simulation that this approach can reduce the probability of collision for resource allocation, compared to a baseline solution that does not configure SCI transmissions.

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Energy-Efficient Internet of Things Monitoring with Content-Based Wake-Up Radio

The use of Wake-Up Radio (WUR) in Internet of Things (IoT) networks can significantly improve their energy efficiency: battery-powered sensors can remain in a low-power (sleep) mode while listening for wake-up messages using their WUR and reactivate only when polled. However, polling-based WUR may still lead to wasted energy if values sensed by the polled sensors provide no new information to the receiver, or in general have a low Value of Information (VoI). In this paper, we design a content-based WUR that tracks the process observed by the sensors and only wakes up the sensor if its estimated update's VoI is higher than a threshold communicated through the poll. If the sensor does not reply to the polling request, the Gateway (GW) can make a Bayesian update, knowing that either the sensor value substantially confirms its current estimate or the transmission failed due to the wireless channel. We analyze the trade-off between the tracking error and the battery lifetime of the sensors, showing that content-based WUR can provide fine-grained control of this trade-off and significantly increase the battery lifetime of the node with a minimal Mean Squared Error (MSE) increase.

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Low-Latency Massive Access with Multicast Wake Up Radio

The use of Wake-Up Radio (WUR) in Internet of Things (IoT) networks can significantly improve their energy efficiency: battery-powered sensors can remain in a low-power (sleep) mode while listening for wake-up messages using their WUR and reactivate only when polled, saving energy. However, polling-based Time Division Multiple Access (TDMA) may significantly increase data transmission delay if packets are generated sporadically, as nodes with no information still need to be polled. In this paper, we examine the effect of multicast polling for WUR-enabled wireless nodes. The idea is to assign nodes to multicast groups so that all nodes in the same group can be solicited by a multicast polling message. This may cause collisions, which can be solved by requesting retransmissions from the involved nodes. We analyze the performance of different multicast polling and retransmission strategies, showing that the optimal approach can significantly reduce the delay over TDMA and ALOHA in low-traffic scenarios while keeping good energy efficiency.

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Beam Aware Stochastic Multihop Routing for Flying Ad-hoc Networks

Routing is a crucial component in the design of Flying Ad-Hoc Networks (FANETs). State of the art routing solutions exploit the position of Unmanned Aerial Vehicles (UAVs) and their mobility information to determine the existence of links between them, but this information is often unreliable, as the topology of FANETs can change quickly and unpredictably. In order to improve the tracking performance, the uncertainty introduced by imperfect measurements and tracking algorithms needs to be accounted for in the routing. Another important element to consider is beamforming, which can reduce interference, but requires accurate channel and position information to work. In this work, we present the Beam Aware Stochastic Multihop Routing for FANETs (BA-SMURF), a Software-Defined Networking (SDN) routing scheme that takes into account the positioning uncertainty and beamforming design to find the most reliable routes in a FANET. Our simulation results show that joint consideration of the beamforming and routing can provide a 5% throughput improvement with respect to the state of the art.

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User Clustering for Rate Splitting using Machine Learning

Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide high throughput for all users while minimising interference among them. Hence, one of the major challenges for such HRS schemes is the necessity to know the optimal clustering of these users based only on their Channel State Information (CSI). This clustering problem is known to be NP hard and, to deal with the unmanageable complexity of finding an optimal solution, in this work a scalable and much lighter clustering mechanism based on Neural Network (NN) is proposed. The accuracy and performance metrics show that the NN is able to learn and cluster the users based on the noisy channel response and is able to achieve a rate comparable to other more complex clustering schemes from the literature.

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Energy-Efficient Design for RIS-assisted UAVcommunications in beyond-5G Networks

The usage of Reconfigurable Intelligent Surfaces (RIS) in conjunction with Unmanned Ariel Vehicles (UAVs) is being investigated as a way to provide energy-efficient communication to ground users in dense urban areas. In this paper, we devise an optimization scenario to reduce overall energy consumption in the network while guaranteeing certain Quality of Service (QoS) to the ground users in the area. Due to the complex nature of the optimization problem, we provide a joint UAV trajectory and RIS phase decision to minimize transmission power of the UAV and Base Station (BS) that yields good performance with lower complexity. So, the proposed method uses a Successive Convex Approximation (SCA) to iteratively determine a joint optimal solution for UAV Trajectory, RIS phase and BS and UAV Transmission Power. The approach has, therefore, been analytically evaluated under different sets of criterion.

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QUIC-EST: A Transmission Scheme to Maximize VoI of Multi-Stream Correlated Data Flows

New advanced applications, such as autonomous driving and haptic communication, require to transmit multi-sensory data and require low latency and high reliability. These applications include. Existing implementations for such services have mostly relied on ad hoc scheduling and send rate adaptation mechanisms, implemented directly by the application and running over UDP. In this work, we propose a transmission scheme that relies on the features of the recently developed QUIC transport protocol, providing reliability where needed, and standardized congestion control, without compromising latency. Furthermore, we propose a scheduler for sensor data transmissions on the transport layer that can exploit the correlations over time and across sensors. This mechanism allows applications to maximize the Value of Information (VoI) of the transmitted data, as we demonstrate through simulations in two realistic application scenarios.

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