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Vijay K. Shah

Publications and source records attributed to Vijay K. Shah.

39 records · Page 3Linked to original sources

Joint Age of Information and Self Risk Assessment for Safer 802.11p based V2V Networks

Emerging 802.11p vehicle-to-vehicle (V2V) networks rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive safety-critical information, such as vehicle position, speed, and heading -- that enables several safety applications and has the potential to improve on-road safety. Due to mobility, lack of global-knowledge and limited communication resources, designing an optimal BSM broadcast rate-control protocol is challenging. Recently, minimizing Age of Information (AoI) has gained momentum in designing BSM broadcast rate-control protocols. In this paper, we show that minimizing AoI solely does not always improve the safety of V2V networks. Specifically, we propose a novel metric, termed Trackability-aware Age of Information TAoI, that in addition to AoI, takes into account the self risk assessment of vehicles, quantified in terms of self tracking error (self-TE) -- which provides an indication of collision risk posed by the vehicle. Self-TE is defined as the difference between the actual location of a certain vehicle and its self-estimated location. Our extensive experiments, based on realistic SUMO traffic traces on top of ns-3 simulator, demonstrate that TAoI based rate-protocol significantly outperforms baseline AoI based rate protocol and default $10$ Hz broadcast rate in terms of safety performance, i.e., collision risk, in all considered V2V settings.

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Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks

As several new spectrum bands are opening up for shared use, a new paradigm of \textit{Diverse Band-aware Dynamic Spectrum Access} (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in \textit{multiple bands}, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.

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Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks

This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA. By utilizing a subset of beams in the IA process, DeepIA removes the need for an exhaustive beam search thereby reducing the beam sweep time in IA. A deep neural network (DNN) is trained to learn the complex mapping from the received signal strengths (RSSs) collected with a reduced number of beams to the optimal spatial beam of the receiver (among a larger set of beams). In test time, DeepIA measures RSSs only from a small number of beams and runs the DNN to predict the best beam for IA. We show that DeepIA reduces the IA time by sweeping fewer beams and significantly outperforms the conventional IA's beam prediction accuracy in both line of sight (LoS) and non-line of sight (NLoS) mmWave channel conditions.

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