The increasing need for high data rates and reliable connectivity has driven a growing interest in reconfigurable intelligent surfaces (RIS) and artificial intelligence (AI)-enabled wireless technologies. RIS is composed of reflective antenna elements that reconfigure the wireless environment by tuning the phase and amplitude of the reflected signals. To improve RIS utilization, it is essential to allocate it to multiple user equipment (UE). This poster formulates a joint optimization problem for RIS phase design and element allocation within an α-fair scheduling framework, highlighting the computational complexity of conventional iterative optimization as the number of RIS elements increases. To address this challenge, we propose an unsupervised neural network-based solution that efficiently learns the allocation and phase of the RIS elements. The simulation results indicate that the proposed approach improves α-mean throughput by approximately 3.8% compared to existing RIS subarray allocation methods. In addition, it significantly reduces computational complexity, making it a more practical and scalable solution for next-generation RIS-aided wireless systems.

Numerology ($\mu$) is the key design parameter of the physical layer in Fifth Generation New Radio (5G NR) networks and beyond, including Vehicle-to-everything networks (V2X). $\mu$ shapes communication link specifications, such as Spectral Efficiency (SE) and link rate. The subcarrier spacing $\Delta f$ and the guard interval $T_{g}$ mitigate the effects of mobility and multipath, respectively. Relying on the functionality of $\Delta f$ and $T_{g}$, this paper proposes a novel adaptive numerology technique for maximizing the SE in 5 G NR networks and beyond, including V2X networks (ANV2X). ANV2X is a channel-aware adaptive numerology technique that links channel parameters, including mean, maximum, root mean square (RMS) delay, and Doppler spread for numerology selection (allocation), represented by $\Delta f$ and $T_{g}$. ANV2X introduces exact bounds on $\Delta f$ and $T_{g}$, revealing a crucial result that $\Delta f$ and $T_{g}$ are highly coupled with the channel parameters. Extensive simulation results demonstrate that ANV2X outperforms state-of-the-art adaptive numerology systems and Fixed Numerology Systems (FNS) for SE optimization across various frequency ranges. For example, in Case 1, Sub-6GHz system, ANV2X enhances the SE by 7.005×, 6.5624×, 3.2986×, and 0.3603× compared to ANS, ASB, FNS systems with $\mu _{0}$, $\mu _{1}$, and $\mu _{2}$, respectively. ANV2X has the potential to be a new technique for 5 G NR networks and beyond, as it characterizes the commonality of links, including adaptive numerology and a flexible physical layer design.

Future wireless communication systems are envisioned to simultaneously operate in sub-6 GHz and millimeter-wave (mmWave) frequency bands, achieving high data rates, enhanced reliability, and wide-area coverage. However, sub-6 GHz bands are becoming increasingly congested, whereas mmWave links experience severe path loss and are highly susceptible to blockage in non-line-of-sight (NLoS) conditions. Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising passive solution to reconfigure wireless propagation; yet, their frequency-dependent behavior remains insufficiently understood. This work presents an experimental study of commercially available RIS panels operating at 5.2 GHz and 28 GHz, conducted as an indoor evaluation at NTT Labs, Japan. An NLoS scenario is enforced by using an electromagnetic wave absorber frame that blocks the direct LoS path, ensuring that only RIS-reflected components reach the receiver (Rx) grid. The received power is measured across a uniform grid for both RIS-inactive and RIS-active cases. The results demonstrate average RIS-induced power gains of 8–10 dBm at sub-6 GHz and 15–20 dBm at mmWave frequencies, confirming the strong potential of RIS to mitigate NLoS losses and enhance link robustness across heterogeneous frequency bands.

High-impedance arc faults (HIAFs) pose significant detection challenges in microgrids, particularly as the integration of distributed energy resources (DERs) increases, where conventional overcurrent-based protection schemes often fail. This study presents a novel, cost-effective approach for enhanced HIAF detection based solely on residual voltage measurements. The proposed method applies Feature Mode Decomposition (FMD) to extract multiple signal modes from residual-voltage measurement data, then selects a mode to compute differential energy and formulate a fault detector index (FDIN). The fault detection framework is rigorously tested under diverse operating conditions, including unbalanced loading, noise, variable wind speed, low-impedance faults, and common switching events (e.g., capacitor banks, loads, generators, and transformers), across various grounding configurations (solid, impedance, and resonant grounding). Following fault detection, a residual-feature-driven bagged tree ensemble classifier (RFBTEC) is employed to distinguish HIAFs from normal system conditions and accurately classify the faulty feeder. The effectiveness and practicality of the proposed scheme are validated through real-time simulations on an OPAL-RT (OP4510) platform and further corroborated using field data from Delhi Transco Limited. The results confirm that the proposed voltage-only method achieves fast, accurate, robust, and secure HIAF detection, thereby supporting the reliable and resilient operation of the microgrid system.

This paper analyzes the memory and latency requirements at a 5 G -NR base station that supports dual-SIM dual-radio cell phones that periodically tune away to monitor voice-call pages on a 3G network. Data transfer to the phone is modeled using a stop-and-wait protocol. We derive expressions for the mean of the number of attempts and the mean of the square of attempts taken to transmit the data. Data arrival at the base-station is modeled as a Poisson process, and the Average Number in Queue and the Average Wait Time are derived using the Pollaczek-Khintchine formula for an M/G/1 queue. It is shown that both are higher for a given arrival rate when there is a complete tune-away versus the tune-away of a single antenna. Consequently, we propose a protocol that requires a dual-SIM phone to request a lower rank to tune away only the diversity antenna. Further, to operate within the memory and latency corresponding to the original single-SIM budgets, we propose a protocol between the base station and the data source to reduce the data-rate. An algorithm is proposed to determine the data-rate based on the tune-away duration and the original budgets.

The increasing demand for high data rates and seamless connectivity in wireless systems has sparked significant interest in reconfigurable intelligent surfaces (RIS) and artificial intelligence-based wireless applications. RIS typically comprises passive reflective antenna elements that control the wireless propagation environment by adequately tuning the phase of the reflective elements. The allocation of RIS elements to multipleuser equipment (UEs) is crucial for efficiently utilizing RIS. In this work, we formulate a joint optimization problem that optimizes the RIS phase configuration and resource allocation under an α-fair scheduling framework and propose an efficient way of allocating RIS elements. Conventional iterative optimization methods, however, suffer from exponentially increasing computational complexity as the number of RIS elements increases and also complicate the generation of training labels for supervised learning. To overcome these challenges, we propose a five-layer fully connected neural network (FNN) combined with a preprocessing technique to significantly reduce input dimensionality, lower computational complexity, and enhance scalability. The simulation results show that our proposed NN-based solution reduces computational overhead while significantly improving system throughput by 6.8

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted downlink nonorthogonal multiple access (NOMA) system while accounting for practical limitations, such as imperfect successive interference cancellation (SIC) and phase compensation errors. We derive achievable data rate expressions for NOMA users as functions of these imperfections and establish bounds on power allocation and the SIC imperfection factor to ensure that NOMA outperforms orthogonal multiple access (OMA) systems. Based on these bounds, we propose a hybrid NOMA/OMA scheduling algorithm that performs NOMA pairing and power allocation to maximize the sum rate. Through extensive numerical simulations, we demonstrate that the proposed hybrid scheduling algorithm achieves superior performance compared to existing benchmark algorithms.

The existing 5G new radio (NR) numerology supports certain values of parameters like subcarrier spacing, symbol duration, and guard interval for vehicle-to-everything (V2X) communications. However, with ever evolving use cases for enhanced-V2X in beyond 5G networks, these recommended values are not sufficient to provide the required quality of service (QoS). Motivated by this, we propose an adaptive numerology scheme (ANS) that dynamically selects these parameters to mitigate the effect of Doppler and multi-path. We present exhaustive simulation results demonstrating the efficiency of the proposed scheme for the dynamic nature of V2X communication networks.

Orthogonal Time-Frequency Space (OTFS) modulation is a promising technique for wireless communication in high-mobility environments, especially for future 6G wireless systems. Unlike Orthogonal Frequency Division Multiplexing (OFDM), which sends data in the time-frequency domain, OTFS works in the delay-Doppler domain, where the channel appears more stable and easier to handle. For OTFS to work well, pulse shaping in the time domain is crucial as it affects the communication system’s capability to manage interference and effectively utilize the available bandwidth. Conventional pulse shapes like rectangular, raised cosine, and Gaussian are easy to implement but often lead to Inter-carrier Interference (ICI) and Out of Band (OoB) power leakage, which lowers the communication system’s performance. In this paper, we propose new time-limited Nyquist pulse shapes generated by convolving existing time-domain Nyquist pulses, such as the Double Jump (DJ1) pulse with Better Than Raised Cosine (BTRC) and Gaussian pulse. These new pulses aim to reduce ICI and improve the system performance. Through exhaustive numerical results, we show that for an OTFS communication systems, the proposed pulses outperform the traditional ones in terms of Bit Error Rate (BER) and Peak-to -Average Ratio (PAPR).

We formulate a scheduling problem to derive the optimal time fraction jointly for a hybrid orthogonal multiple access (OMA)-non-orthogonal multiple access (NOMA) system using the alpha-fair utility function, while accounting for imperfections in successive interference cancellation (SIC). Our study demonstrates that the optimal solution derived through a solver matches the theoretical results. Furthermore, we assess the performance of these derived time fractions in comparison to an existing adaptive user pairing (AUP) algorithm, designed to mitigate imperfections in SIC. Through extensive simulations, we illustrate that the derived time fraction consistently yields improved mean user link rates when employed with the AUP algorithm, across varying levels of imperfections in SIC, under the log-rate model.

Narrowband-Internet of Things (NB-IoT) is a cellular-service-based low-power wide area network technology introduced by 3rd Generation Partnership Project (3GPP). NB-IoT has a design skeleton similar to that of Long-Term Evolution (LTE)/New Radio (NR) and operates on a bandwidth of 180 kHz. The technology co-exists with LTE/NR and can communicate with a large number of IoT devices. Typically, the mobile users operating in the existing LTE deployments require higher data rates and lower latencies, whereas the IoT devices in NB-IoT technology are low-cost, delay tolerant, and require smaller data rates. Due to these new characteristics of the NB-IoT, the existing legacy LTE resource management techniques are not directly applicable in the context of the NB-IoT. Motivated by this, we consider various resource management techniques for the NB-IoT systems that optimally utilize the limited bandwidth, ensure device fairness and Quality of Service in resource allocation, and minimize the power consumption of the low-cost IoT devices. Through numerical results, we show that the considered techniques significantly improve the network performance. Further, we identify that Cloud Radio Access Network (CRAN) is a suitable candidate for NB-IoT systems and discuss the potential benefits of implementing NB-IoT with CRAN. We also provide up-to-date information about the current NB-IoT standardization activities that may further encourage the readers to explore this domain.

This paper presents the design and analysis of a modified volleyball premier league-optimized 3-DOF (FOPI)-FOPD controller for a proactive LFC scheme. The proposed controller incorporates forecasted load demand as one of its inputs. This unique configuration empowers the controller to proactively eliminate the disturbances. To validate the controller performance, a truncated model of the DPS has been developed which is subjected to different load profiles and are assumed to be known. It was observed that the predicted disturbance to the system (change in load demand) is of utmost importance for this proposed configuration. Thus, improved LSTM, incorporating a multi-approach feature selection and DWT has been developed, which is used to forecast day-ahead load demand. This forecasted demand is given as an input to the controller. The dynamic performance of proposed controller is evaluated by subjecting the modelled DPS with proposed controller to standard test signals. The obtained simulation results are also validated through OPAL-RT real-time simulator. The results demonstrate the superior performance and robustness of the proposed LFC approach in maintaining the frequency of the power system within acceptable limits.

In fifth generation (5G) and beyond networks, the millimeter-wave frequency band is expected to cater to the ever increasing user demand for enhanced data rates. However, the denser deployments in mmwave with varying user velocities, will lead to frequent handovers (HOs), resulting in degradation of quality of service (QoS) for the end user in terms of both the number of HOs and lower throughput. Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for throughput enhancement in 5G and beyond networks. Motivated by this, we analyze the performance of a hybrid OMA-NOMA system considering the imperfections in successive interference cancellation (SIC) for various HO algorithms. We propose two HO algorithms: Algorithm 1 - the HO decision is based on the orthogonal multiple access (OMA) signal to interference-plus noise ratio (SINR) in the control plane, whereas, NOMA is only used for rate enhancements in the data plane; and Algorithm 2 - NOMA-based rates are taken into consideration for the HO decision. We compare the proposed algorithms with benchmark OMA based algorithms. Through extensive simulation we show that the proposed algorithms result in significant enhancement in throughput and reduction in the average number of HOs in the system.

The industrial sector has experienced a tremendous advancement in deep supervised learning due to its representation ability, but it comes with high computing and labeled data demands. Recently, the demand for intelligent IoT devices on assembly and disassembly lines has surged. This necessitates algorithms that can use data to make intelligent decisions and a framework that can enable multiple IoT devices to learn collaboratively. Further, huge image generation using IoTs also needs an efficient data annotation scheme for classification problems. WeCollab is one such framework in federated learning that significantly reduces human efforts in data annotation with breakthroughs in self-supervised learning. The proposed framework is generic and can be adapted to any specific image data generated by industrial robots involved in assembly and disassembly lines. Our method outperforms supervised learning by 25% and 20% on the CIFAR-10 and CINIC-10 datasets, respectively, for the labeling task. We generate pseudo-labels for the unlabeled part of the data and train a model to achieve 30% better test accuracy on CIFAR-10 and 20% better test accuracy on the CINIC-10 dataset as compared to supervised learning. Extensive experiments unveil the effectiveness of the method and proposed combination of loss functions used by WeCollab.

In a rapidly evolving world, every child should have the opportunity to dream and, more importantly, the tools to achieve those dreams. India, with its diverse educational landscape, faces a substantial gap in providing career guidance and mentorship to rural students. In the current scenario, while the rest of India is flourishing in the education sector, its hinterland is critically facing a gap in effective career guidance and mentorship. Numerous factors contribute to this, like financial constraints and even limited exposure to global market needs. To fill this unequal gap, we propose “Kimochi” an innovative edtech platform from a unique collaboration among Suzuki Motor Corporation, Suzuki Innovation Centre, and Indian Institute of Technology (IIT) Hyderabad. The methodology followed combines Agile and Design thinking with rapid prototyping and co-design. Our platform serves students in grades 8 to 12 in rural India who aspire to excel in their careers through practical guidance from mentors from premier education institutes like IIT.

Reconfigurable Intelligent Surfaces (RISs) are acknowledged as key technologies for the upcoming sixth generation (6G) of mobile communication networks. The performance of the RIS systems heavily relies on the accuracy of phase compensation, which is often imperfect in practical scenarios. In this poster, we analyze the performance of RIS systems by allocating the fraction of total elements within the RIS used for a particular user, taking into account this imperfect phase compensation (IPC). We then derive a lower bound on the element allocation factor required for a user to achieve higher data rates when compared to benchmark scenario (without RIS). Additionally, we establish an upper bound on the IPC to assess in advance whether this system can provide higher spectral efficiency in comparison to the benchmark system. Furthermore, we demonstrate a trade-off between the IPC and the fraction of elements of RIS utilized by a user to obtain a specific data rate.

This article introduces an innovative approach for online detection and localization of forced oscillation (FO) in power systems with significant integration of wind turbine generation (WTG). The synergistic combination of Multichannel Prony analysis with Periodogram and dissipating energy flow (DEF) approach is proposed in this paper to overcome the challenges inherent in detecting FO amidst the complexities introduced by WTG integration. An adaptive version of multichannel Prony analysis is proposed in this paper that dynamically estimates Prony’s system order via the recursive process. It estimates the frequency and damping ratio of all oscillatory modes, which is used to identify low damping modes that are potentially attributed to FO. Leveraging this information, the proposed method enhances both FO detection and source localization capabilities. The proposed method is verified on measurements taken from IEEE benchmark 4-machine, 11-bus system and 10-machine, 39-bus system with high wind energy penetration. Comprehensive testing demonstrates the proposed approach’s effectiveness across various FO scenarios, including governor valve malfunction, exciter malfunction, and cyclic loads. Furthermore, the methodology is evaluated under resonant conditions and scenarios involving the simultaneous identification of multiple disturbances.

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system is a key tech-nology for next-generation wireless communication systems to enhance coverage, optimize spectrum utilization, and increase throughput. In this poster, we examine Infrastructure-to- Vehicle (I2V) communication through RIS, employing a hybrid orthogo-nal multiple access (OMA)-NOMA system with imperfect phase compensation. We consider lower and upper bounds on the power allocation factor and formulate an optimization problem to achieve a-fairness among the vehicles. Additionally, we propose a vehicle pairing and power allocation strategy that ensures the achievable data rate is greater than its OMA rate. Through extensive simulations, we compare and show that the proposed algorithm outperforms state-of-the-art algorithms.

Non-orthogonal multiple access (NOMA) is recognized as a promising radio access technique for the next generation wireless systems. We consider a practical downlink NOMA system with imperfect successive interference cancellation and derive bounds on the power allocation factors for a given number of users in each cluster. We propose a minimum signal-to-interference-plus-noise ratio difference criterion between two successive NOMA users in a cluster of users to achieve higher rates than an equivalent orthogonal multiple access (OMA) system. We then propose adaptive multi-user clustering and power allocation algorithms for downlink NOMA systems. Through extensive simulations, we show that the proposed algorithms achieve higher rates than the state-of-the-art algorithms.

Perceptual quality metrics derived from deep features have led to a boost in modelling the Human Visual System (HVS) to perceive the quality of visual content. In this work, we study the effectiveness of fine-tuning three standard convolutional neural networks (CNNs) viz. ResNet50, VGG16 and MobileNetV2 to predict the quality of stereoscopic images in the no-reference setting. This work also aims to understand the impact of using disparity maps for quality prediction. Interestingly, our experiments demonstrate that disparity maps do not significantly contribute to improving perceptual quality estimation in the deep learning framework. To the best of our knowledge, this is the first study that explores the impact of disparity along with the chosen models for Stereoscopic Image Quality Assessment. We present a detailed study of our experiments with various architectural configurations on the LIVE Phase I and II datasets. Further, our results demonstrate the innate capability of deep features for quality prediction. Finally, the simple fine-tuning of the models results in solutions that compete with state-of-the-art patch-based stereoscopic image quality assessment methods.