Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Author: search.filters.author.Krishnan, P.Subject: search.filters.subject.Mobile telecommunication systems

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    Asymptotic bit error rate analysis of convergent underwater wireless optical communication-free-space optical system over combined channel model for different turbulence and weather conditions with pointing errors
    (SPIE, 2020) Bhargava Kumar, B.K.; Krishnan, P.
    The differential phase-shift keying-based dual-hop underwater wireless optical communication-free-space optics (UWOC-FSO) convergent system is proposed for UOWSNs and Internet of Underwater Things (IoUT) applications. In the proposed system, the collected sensor data are transmitted to a decode-and-forward relay using underwater optical wireless communication links modeled as gamma-gamma distribution. The relay transmits the signal to the terrestrial destination using free-space optical link modeled as Malaga distribution. The end-to-end performance of the system (novel expression for asymptotic bit error rate) is derived and analyzed over combined channel model (including the effects of attenuation, turbulence, and pointing errors for both FSO and UWOC channels). The in-depth study is carried out for different weather conditions of FSO (attenuation - very clear, haze, rain, and fog; turbulence - weak and strong; and pointing error - weak and strong based on the g values 1, 2, and 6) and UWOC (attenuation - clear, coastal ocean, and turbid harbor; turbulence - weak, moderate, and strong; and pointing error - weak and strong based on the g values 1, 2, and 6), respectively. The proposed system is highly useful in coastal environments, where the climate is changing adequately as clear, rain, haze, and fog. © 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).
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    Bit error rate analysis of polarization shift keying based free space optical link over different weather conditions for inter unmanned aerial vehicles communications
    (Springer, 2021) Nallagonda, V.; Krishnan, P.
    The increasing availability of unmanned aerial vehicles (UAVs) is an exciting part of future emerging technology with advanced scientific and industrial interests. Free space optical (FSO) communications’ ability to offer very high data rates and the mobility of unmanned aerial vehicle (UAV) flying platforms make the delivery of Fifth-Generation (5G) wireless networking services appealing to FSO-UAV-based solutions. UAVs play a greater role in end-to-end delivery in next- generation wireless networking systems, serving as a base station, capacity enhancement, high data access, and other disaster management systems. To establish a link between unmanned aerial vehicles and ground stations, FSO can be applied. But, the different weather conditions liken rain, fog effects on the performance of the FSO link, contributing to the loss of the signal. In this paper, we proposed polarization shift keying (POLSK) modulated FSO link based UAV–UAV communication system for 6G beyond applications. We examine the effect of different weather conditions such as rain, fog on the bit error rate (BER) performance of the proposed system. Novel closed-form expressions for UAV–UAV based FSO propagation channel are derived, and BER performance is investigated under different weather conditions. Fog and rain are the main limiting factors mitigated in this paper by suitable mitigation techniques by increasing receiver field of view. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    CAR-BRAINet: Sub-6 GHz aided spatial adaptive beam prediction with multi head attention for heterogeneous vehicular networks
    (Institute of Physics, 2025) Menon, A.G.; Krishnan, P.; Lal, S.
    Heterogeneous Vehicular Networks (HetVNets) play a crucial role by integrating different communication technologies, such as sub-6 GHz, mm-wave, and DSRC, to meet the diverse connectivity requirements of 5G/B5G vehicular networks. HetVNet helps address humongous user demands, but maintaining a steady connection in highly mobile, real-world conditions remains challenging. Though ample studies have been conducted on beam prediction models, a dedicated solution for HetVNets has been sparsely explored. Hence, developing a reliable beam prediction model, specifically for HetVNets, is necessary. This paper introduces a lightweight deep learning-based model termed ‘CAR-BRAINet’, which consists of convolutional neural networks with a powerful multi-head attention (MHA) mechanism. Existing literature on beam prediction is primarily studied under a limited, idealised vehicular scenario, often overlooking the real-time complexities and intricacies of vehicular networks. Therefore, this study aims to mimic the complexities of a real-time driving scenario by incorporating key factors, such as prominent MAC protocols (3GPP-C-V2X and IEEE 802.11BD), the effect of Doppler shifts under high velocity and varying distance, and SNR levels, into three high-quality dynamic data sets for urban, rural, and highway vehicular networks. CAR-BRAINet achieves a steady improvement of 11.6467% in spectral efficiency, with a 93.1638% lighter architecture compared to existing methods, resulting in a 94.7103% reduction in prediction time. Therefore, demonstrating a precise beam prediction across all vehicular scenarios, with minimal beam overhead. Thus, this study justifies the effectiveness of CAR-BRAINet in complex HetVNets, offering promising performance without relying on mobile users’ location, angle, and antenna dimensions, thereby reducing redundant sensor latency. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.