Department of Electrical and Electronics Engineering

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Author: search.filters.author.Joshi, SandeepSubject: search.filters.subject.EEE

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    Stochastic diffusivity with time-varying trajectory in mobile molecular communication: performance analysis and channel modeling
    (IEEE, 2025-04) Joshi, Sandeep
    This work considers a three-dimensional mobile molecular communication (MC) with intra-body disease spread applications. The communicating devices in the considered mobile MC system are point transmitters and passive spherical receiver nano-machines (NMs) with emitted information-carrying molecules following the Gaussian Brownian motion. These NMs can be used to detect the presence of disease spread and for targeted drug delivery. We propose stochastic diffusivity models for both communicating devices and information-carrying molecules. Using the stochastic diffusivity model and considering initial distance as a reference, we derive the probability density function of the relative distance between the communicating devices. We allocate the time-varying trajectory to the information-carrying molecules moving towards receiver NM and obtain its diffusivity distribution. Through the proposed stochastic diffusivity model, we characterize the mobile MC channel by channel impulse response and derive its statistical mean. We consider the discrete-time statistical channel model at a high inter-symbol interference regime and analyze the channel performance in terms of error analysis and receiver operating characteristics. We also derive the channel for the considered system model. We show the degree of accuracy through root mean square error for the Poisson and Gaussian distribution models. Furthermore, the numerical results are verified through particle-based simulations.
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    Nonlinear anisotropic diffusion-based channel estimation in 5G wireless networks
    (IEEE, 2025-03) Joshi, Sandeep
    In the context of the fifth-generation new radio downlink scenario, we introduce an innovative approach for channel estimation in this paper that circumvents the requirement for the prior dataset. We incorporate anisotropic diffusion and bit-plane decomposition to remove the noise in channel estimates. We first pre-process wireless channel coefficients with bit-plane decomposition to partially reduce noise interference and maintain the granularity of the information. In the second stage, anisotropic diffusion is performed based on neighboring coefficients, and the gradient-based denoising takes place without prior training. We assess the mean square error (MSE) across varying noise levels compared to the state-of-the-art method and further explore the impact of key parameters governing anisotropic diffusion. The simulation results indicate that the proposed channel estimation technique achieves a 44.77% reduction in average MSE and a significant reduction in computational complexity compared to the baseline reference technique.
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    Redefining channel estimation in underwater acoustic OFDM systems with deep neural network
    (IEEE, 2025-08) Joshi, Sandeep
    This paper introduces a novel method for channel estimation in underwater acoustic communication in an autonomous underwater vehicular network. The proposed method employs a denoising technique to refine least squares (LS) channel estimates using deep image prior (DIP). By establishing an equivalence between underwater acoustic (UWA) channel estimation and image denoising, we leverage DIP to enhance estimation accuracy. The proposed approach is validated on the Norway continental shelf (NCS1) watermark dataset, demonstrating superior performance with average mean square error reductions of 96.64% and 96.09% compared to LS and the deep denoising convolutional neural network (DnCNN), respectively. Furthermore, the proposed analysis of pilot symbol utilization in the DIP-based estimator shows a 46.47 % error reduction, even when using only 25 % of the pilot symbols. By efficiently utilizing available resources, the proposed method enhances spectral efficiency and enables accurate estimation, even with limited pilot signals.
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    IRS-aided UOWC network for IoUT applications: performance analysis
    (IEEE, 2025-08) Chaubey, V.K.; Joshi, Sandeep
    This paper investigates the inclusion of intelligent reflecting surfaces (IRSs) in data acquisition in underwater optical wireless communication (UOWC) systems for the internet of underwater things (IoUT). The paper proposes a medium access control layered protocol that employs slotted ALOHA (S-ALOHA) and code-combining (C-C) to support reliable communication between multiple underwater sensors and a receiver. A buoy-mounted IRS is considered to improve signal reliability and mitigate underwater attenuation, turbulence, and beam misalignment. The underwater communication channel is modeled using the exponentiated Weibull distribution to propose a framework for the outage analysis of both uniformly and randomly distributed sensors with C-C. The throughput performance of the proposed IoUT network for S-ALOHA and C-C demonstrates that system performance is well controlled by the reflecting elements, transmission distance, and fading parameters. Simulation results claim that the IRS-assisted UOWC significantly improves throughput and reduces outage probability under challenging underwater conditions.
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    A multi-modal smart switching based image transmission using semantic communication
    (IEEE, 2025-02) Tripathi, Sharda; Joshi, Sandeep
    The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon’s classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction.
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    When to Reach for the Skies? A DRL-Based Routing Framework for Non-Terrestrial Networks
    (IEEE, 2025-01) Tripathi, Sharda; Joshi, Sandeep
    Non-terrestrial networks are envisioned to be an integral component of the beyond-fifth-generation wireless communication networks, catering to both conventional and emerging communication applications. In particular, a plethora of use cases are emerging for ultra-reliable low-latency communication, which require dynamic and quality of service compliant frameworks. In this letter, we formulate a binary integer non-linear programming problem to route time-critical traffic through non-terrestrial nodes. As the problem is NP-hard, we propose the solution using a deep reinforcement learning framework, taking into account the interactions between the terrestrial and various non-terrestrial nodes with an end-to-end latency target while maximizing the coverage probability. We perform simulations for multiple latency deadlines and outage thresholds and the results corroborate the efficiency of the proposed framework. Furthermore, we benchmark the proposed framework and show an improvement of 96.31% in coverage while incurring only 3.2% latency violations compared to the state-of-the-art.
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    Employee Face Recognition Scheme Using A Common Space Mapping Approach
    (IEEE, 2022-07) Joshi, Sandeep
    In this work, we present a FaceNet based ‘two branch’ model for employee face recognition in low resolution images captured using substandard camera sensors. Our model involves a common space mapping approach using two deep convolutional neural networks (DCNNs) that map the low resolution and high resolution face images to a common space. The model is trained such that the distance between the two mapped images in the common space is minimized. Then, a logistic regression classifier is used to classify the mapped image by the identity of the employee. We show through simulations that the presented model achieves a recognition accuracy of 99.84%, 98.88%, and 95.53% on 36×36, 24×24, and 16×16 resolution images, respectively, for 209 subjects. Furthermore, the proposed model has less space (90 Megabytes) and computation requirements making it suitable for systems having low computing power and memory.
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    Error Analysis of an Optimal Rotated M-PSK Constellation in a SOMA-Based Wireless Communication System
    (IEEE, 2022) Joshi, Sandeep
    In this work, we consider a two-user receive diversity wireless communication system with a rotated M-ary phase-shift keying (M-PSK) modulation, also known as semi-orthogonal multiple access (SOMA), for data transmission at the transmitter. At the receiver of the considered wireless communication system, each user utilizes a maximum ratio combining (MRC) technique and an optimal maximum-likelihood (ML) rule for transmitted data detection. We provide an analytical framework for the error analysis of the receive diversity wireless communication system and derive the closed-form expressions of the symbol error probability (SEP) for the users. Further, we also obtain the asymptotic expressions of the SEP at a high signal-to-noise ratio (SNR). We provide a numerical solution for the problem of finding the optimal rotation angle for the M-PSK constellation minimizing the SEP. The effects of the system parameters, namely, the number of diversity branches, SNR, and M, on the optimal rotation angle are demonstrated via numerical results. Furthermore, the simulation results corroborate the presented analytical results.
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    Optimal Rotated QPSK Constellation for a Semi-Orthogonal Multiple Access Visible Light Communication System
    (IEEE, 2023-03) Joshi, Sandeep
    A rotated quadrature phase-shift keying (QPSK) based semi-orthogonal multiple access (SOMA) data transmission is considered for a visible light communication (VLC) system with two users. The rotation of the QPSK constellation at the transmitter followed by a data pre-processing technique at the receiver of each user ensures the elimination of the successive interference cancellation unit. An optimal maximum likelihood receiver is proposed for the system under consideration using which, the closed-form expressions for the symbol error probability (SEP) for both the users in the VLC system are derived. The optimization problem to obtain the optimal angle of rotation of the QPSK constellation which minimizes the SEP of the users is formulated and solved. The dependency of the optimality of the QPSK rotation angle on various VLC system parameters is studied via numerical results which lead to the observation of a value of the signal-to-noise ratio of the system around 8.8 dB about which the dependency of the optimal rotation angle on the other VLC system parameters interchange, thus providing design aspects for a SOMA-VLC system.
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    Performance of SSK-based Receive Diversity RIS-assisted System with Nakagami-m Fading Channels
    (IEEE, 2023-08) Joshi, Sandeep
    Over recent years, reflective intelligent surface (RIS) and index modulation (IM) technologies have proven to be potential technologies for improving the performance of the fifth generation (5G) and beyond 5G wireless communication systems. In this paper, a space-shift keying (SSK) (an IM scheme)-based receive diversity and Nakagami-m faded wireless system is considered, which utilizes a RIS system in between the transmitter and the receiver. A greedy detector is proposed for the system, which chooses a target receiver antenna based on the maximum received energy at the diversity branches. An analytical framework is proposed based on a characteristic function approach to obtain a closed-form expression of the probability of erroneous detection of the target antenna. Asymptotic expressions at high and low average signal-to-noise ratios lead to the observation of a performance saturation, attributed to the structure of the greedy detector. Furthermore, numerical results are presented to show the dependency of the system’s performance on the various system parameters.