Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

This study focuses on the user clustering performance of millimeter-wave (mmWave) massive multipleinput multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks. Two novel spectral and energy-efficient user clustering algorithms are proposed that exploit the similarity between user channels and condition number (CN) as the correlation index, toward dynamic selection of the number of clusters and number of users in each cluster, thus ensuring that the users in different clusters are spatially uncorrelated. Our simulation results demonstrate that the proposed user clustering techniques attain a $100 \%$ performance gain over a naive random clustering technique.

Nisarg Patel, Mohith Kulkarni, Mihir Parmar, Aashna Budhiraja, Mutsumi Nakamura, Neeraj Varshney, Chitta Baral. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Breast cancer characterization remains a significant and challenging issue in contemporary medicine. Accurately distinguishing between malignant and benign breast lesions is crucial for effective diagnosis and treatment. The anatomical structure of malignant breast ultrasound images is more chaotic than that of benign images due to disease pathologies. However, texture-based analysis alone often fails to identify the extent of chaoticness in malignant breast ultrasound images due to their vague appearance with normal echo patterns, leading to missed diagnoses and increased mortality rates. To address this issue, we proposed an angular feature-based multilevel breast cancer classification framework mBCCf that aims to improve the accuracy and efficiency of classification. The proposed framework mimics the radiologist interpretation procedure by identifying the chaoticness on the periphery of the breast lesion in a breast ultrasound image (level-1). If the lesion contains an acute angle in any part of the periphery, it can be characterized as malignant or otherwise benign. However, solely relying on level-1 analysis may result in misclassification, especially when benign lesions exhibit echo patterns that resemble malignant ones. To overcome this limitation and to make the proposed system highly sensitive, advanced texture-based analysis (using combined shape, texture, and angular features) is performed (level-2). Finally, the performance of the proposed system is evaluated using a cross-dataset (consisting of 1293 breast ultrasound images) and compared with the different individual feature extraction techniques. Encouragingly, our system demonstrated an accuracy of 96.99% for classifying malignant and benign tumors, which is also validated using statistical analysis. The implications of our research lie in its potential to significantly improve breast cancer diagnosis by providing a reliable, efficient, and sensitive tool for radiologists.

This article presents a star-of-star topology for Internet of Things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probability (OP) analysis is performed, assuming imperfect channel state information at the GS. This article employs stochastic geometry to model the random locations of satellites, making the analysis general and independent of any specific constellation. Furthermore, this article examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. This article also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a target OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks.

Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions.Such predictions hamper the reliability of systems and limit their widespread adoption in realworld applications.Selective prediction partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect.While selective prediction is advantageous, it leaves us with a pertinent question 'what to do after abstention'.To this end, we present an explorative study on 'Post-Abstention', a task that allows re-attempting the abstained instances with the aim of increasing coverage of the system without significantly sacrificing its accuracy.We first provide mathematical formulation of this task and then explore several methods to solve it.Comprehensive experiments on 11 QA datasets show that these methods lead to considerable risk improvements -performance metric of the Post-Abstention task-both in the in-domain and the out-of-domain settings.We also conduct a thorough analysis of these results which further leads to several interesting findings.Finally, we believe that our work will encourage and facilitate further research in this important area of addressing the reliability of NLP systems.

Himanshu Gupta, Neeraj Varshney, Swaroop Mishra, Kuntal Kumar Pal, Saurabh Arjun Sawant, Kevin Scaria, Siddharth Goyal, Chitta Baral. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Efficient design of integrated sensing and communication systems can minimize signaling overhead by reducing the size and/or rate of feedback in reporting channel state information (CSI). To minimize the signaling overhead when performing sensing operations at the transmitter, this paper proposes a procedure to reduce the feedback rate. We consider a threshold-based sensing measurement and reporting procedure, such that the CSI is transmitted only if the channel variation exceeds a threshold. However, quantifying the channel variation, determining the threshold, and recovering sensing information with a lower feedback rate are still open problems. In this paper, we first quantify the channel variation by considering several metrics including the Euclidean distance, time-reversal resonating strength, and frequency-reversal resonating strength. We then design an algorithm to adaptively select a threshold, minimizing the feedback rate, while guaranteeing sufficient sensing accuracy by reconstructing high-quality signatures of human movement. To improve sensing accuracy with irregular channel measurements, we further propose two reconstruction schemes, which can be easily employed at the transmitter in case there is no feedback available from the receiver. Finally, the sensing performance of our scheme is extensively evaluated through real and synthetic channel measurements, considering channel estimation and synchronization errors. Our results show that the amount of feedback can be reduced by 50% while maintaining good sensing performance in terms of range and velocity estimations. Moreover, in contrast to other schemes, we show that the Euclidean distance metric is better able to capture various human movements with high channel variation values.

Molecular communication (MC) can enable the transfer of information between nanomachines using molecules as the information carrier. In MC systems, multiple receiver nanomachines often co-exist in the same communication channel to serve common or different purposes. However, the analytical channel model for a system with multiple fully absorbing receivers (FARs), which is significantly different from the single FAR system due to the mutual influence of FARs, does not exist in the literature. The analytical channel model is essential in analyzing systems with multiple FARs, including MIMO, SIMO, and cognitive molecular communication systems. In this work, we derive an analytical expression for the hitting probability of a molecule emitted from a point source on each FAR in a diffusion-based MC system with$N$FARs. Using these expressions, we derive the channel model for a SIMO system with a single transmitter and multiple FARs arranged in a uniform circular array (UCA). We then analyze the communication performance of this SIMO system under different cooperative detection schemes and develop several interesting insights.

Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-of-sight (NLoS) rays associated with its diffused components. Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. This is followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.

Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Knowledge of difficulty level of questions helps a teacher in several ways, such as estimating students' potential quickly by asking carefully selected questions and improving quality of examination by modifying trivial and hard questions.Can we extract such benefits of instance difficulty in Natural Language Processing?To this end, we conduct Instance-Level Difficulty Analysis of Evaluation data (ILDAE) in a largescale setup of 23 datasets and demonstrate its five novel applications: 1) conducting efficientyet-accurate evaluations with fewer instances saving computational cost and time, 2) improving quality of existing evaluation datasets by repairing erroneous and trivial instances, 3) selecting the best model based on application requirements, 4) analyzing dataset characteristics for guiding future data creation, 5) estimating Out-of-Domain performance reliably.Comprehensive experiments for these applications lead to several interesting results, such as evaluation using just 5% instances (selected via ILDAE) achieves as high as 0.93 Kendall correlation with evaluation using complete dataset and computing weighted accuracy using difficulty scores leads to 5.2% higher correlation with Out-of-Domain performance.We release the difficulty scores 1 and hope our work will encourage research in this important yet understudied field of leveraging instance difficulty in evaluations.

Do all instances need inference through the big models for a correct prediction?Perhaps not; some instances are easy and can be answered correctly by even small capacity models.This provides opportunities for improving the computational efficiency of systems.In this work, we present an explorative study on 'model cascading', a simple technique that utilizes a collection of models of varying capacities to accurately yet efficiently output predictions.Through comprehensive experiments in multiple task settings that differ in the number of models available for cascading (K value), we show that cascading improves both the computational efficiency and the prediction accuracy.For instance, in K=3 setting, cascading saves up to 88.93% computation cost and consistently achieves superior prediction accuracy with an improvement of up to 2.18%.We also study the impact of introducing additional models in the cascade and show that it further increases the efficiency improvements.Finally, we hope that our work will facilitate development of efficient NLP systems making their widespread adoption in real-world applications possible.

Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Virtual reality (VR) coupled with 360° video has been used in a variety of areas, including gaming, remote learning, and healthcare, among others. The 360° video on which VR applications are based today is mostly low resolution and, in order to improve the user experience, bandwidth requirements must increase significantly. Spatial multiplexing (SM) at millimeter wave (mmWave) is an enabling technology introduced in IEEE 802.11ay to support high throughput applications. However, since IEEE 802.11ay commercial off-the-shelf devices are not yet available and the cost for implementation of mmWave testbeds is prohibitive, the expected SM performance in a real application is still unknown. In this paper, we design a mmWave multi-user (MU)-multiple-input multiple-output (MIMO) link-level high fidelity simulation platform, based on IEEE 802.11ay, which is shared as an open source code package. Our simulation platform consists of a measurement-based mmWave channel model and a digital transceiver. To support VR applications, we design the analog-digital hybrid precoders and combiners, enabling SM for MU-MIMO transmissions. We provide an extensive evaluation of the IEEE 802.11ay PHY in terms of throughput and error rates. Our platform reveals that in a living room environment, two users can support up to four streams achieving more than 20Gbit/sec data-rate per user, enabling the transmission of uncompressed 4K videos.

The design of integrated sensing and communication (ISAC) systems has drawn recent attention for its capacity to solve a number of challenges. Indeed, ISAC can enable numerous benefits, such as the sharing of spectrum resources, hardware, and software, and improving the interoperability of sensing and communication. In this article, we seek to provide a thorough investigation of ISAC. We begin by reviewing the paradigms of sensing-centric design, communication-centric design, and co-design of sensing and communication. We then explore the enabling techniques that are viable for ISAC (i.e., transmit waveform design, environment modeling, sensing source, signal processing, and data processing). We also present some emergent smart-world applications that could benefit from ISAC. Furthermore, we describe some prominent tools used to collect sensing data and publicly available sensing data sets for research and development, as well as some standardization efforts. Finally, we highlight some challenges and new areas of research in ISAC, providing a helpful reference for ISAC researchers and practitioners, as well as the broader research and industry communities.

In this work, a neural network (NN)-based detection for mobile molecular communication via diffusion (MCvD) is proposed. The proposed detector employs a scaled conjugate gradient (SCG) algorithm for updating the weights of the NN. Moreover, three different techniques are used in training and detection by the NN. These techniques correspond to i) filtered signal, ii) slope values of the filtered signal, and iii) concentration difference of the filtered signal in a bit interval. More specifically, a sequence of transmitted bit pattern and each of the above three techniques are used separately to train the NN. After training, the NN-based detector performs detection under a time-varying channel. The bit error rate (BER) performance of the proposed SCG algorithm for the NN-based detector is also compared with a first-order algorithm Gradient Descent (GD) and a second-order algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) for different coherence times of the channel. Simulation results demonstrate that the NN detector using SCG outperforms the BFGS if slope values are used for training the NN. Further, the SCG algorithm has a significant performance gain compared to the GD algorithm.

In this paper, we propose beamformed energy detection (BFED) spectrum sensing schemes for a single secondary user (SU) or a cognitive radio to detect a primary user (PU) transmission in the presence of an interferer. In the millimeter wave (mmWave) band, due to high attenuation, there are fewer multipaths, and the channel is sparse, giving rise to fewer directions of arrivals (DoAs). Sensing in only these paths instead of blind energy detection can reap significant benefits. An analog beamforming weight vector is designed such that the beamforming gain in the true DoAs of the PU signal is maximized while minimizing interference from the interferer. To demonstrate the bound on the system performance, the proposed sensing scheme is designed under the knowledge of full channel state information (CSI) at the SU for the PU-SU and Interferer-SU channels. However, as the CSI may not be available at the SU, another BFED sensing scheme is proposed, which only utilizes the estimate the DoAs. To model the estimates of DoAs, perturbations are added to the true DoAs. The distribution of the test statistic for BFED with full CSI schemes is derived under the null hypothesis so that the threshold of the Neyman-Pearson detector can be found analytically. The performance of both schemes is also compared with the traditional energy detector for multi-antenna systems.

Molecular communication often involves coexisting links where certain links may have priority over others. In this work, we consider a system in three-dimensional (3-D) space with two coexisting communication links, each between a point transmitter and a spherical fully-absorbing receiver (FAR), where one link (termed primary) has priority over the second link (termed secondary). The system implements the underlay cognitive-communication strategy for the co-existence of both links, which use the same type of molecules for information transfer. The mutual influence of FARs existing in the same communication medium results in competition for capturing the information-carrying molecules. In this work, first, we derive an approximate hitting probability equation for a diffusion-limited molecular communication system with two spherical FARs of different sizes, considering the effect of molecular degradation. The derived equation is then used for the performance analysis of primary and secondary links in a cognitive molecular communication scenario. We show that the simple transmit control strategy at the secondary transmitter can improve the overall system’s performance. We study the influence of molecular degradation and decision threshold on the system performance. We also show that the parameters of the system need to be carefully set to improve the performance.

This paper analyzes the performance of low earth orbit (LEO) satellites based internet-of-things (IoT) network, where each IoT node makes use of multiple satellites to communicate with the ground station (GS). In this work, we consider fixed and variable gain amplify-and-forward (AF) relaying protocol at each satellite, where the received signal from each IoT node is amplified before transmitting to the GS for data processing. The performance of this novel LEO satellites based direct access architecture is analyzed by deriving closed-form expressions of outage probability (OP) for two combining schemes at the GS: (i) selection combining (ii) maximal ratio combining. Both these schemes are also compared with single satellite (SS) scenario. Further, to gain more insights for diversity order and coding gain, asymptotic OP analysis at high SNR for both schemes is also performed. Finally, simulation results are presented to validate the analytical results derived and also to develop several interesting insights into the system performance.

In this paper, we analyze the performance of link-level abstraction for orthogonal frequency-division multiplexing (OFDM) and single-carrier (SC) modes in IEEE 802.11ay wireless systems over the 60GHz millimeter-wave band. In particular, we evaluate the effectiveness of the three existing effective signal-to-noise ratio (SNR) metric (ESM) schemes (i.e., exponential ESM (EESM), mean mutual information per coded bit (MMIB) and post-processing ESM (PPESM)). Furthermore, to deal with the issue that EESM calibration is dominated by channel realizations with poor error performance, we introduce a classification based EESM (CEESM) scheme with a new metric named coefficient of variation, which is used to measure the severity of frequency-selective fading. Finally, we present several important insights developed through extensive experimentation. Based on our validation results, the MMIB and PPESM can be employed with minimum computational complexity for OFDM and SC modes, respectively. In contrast, EESM and CEESM can be considered for both modes with better accuracy, but at a cost of high implementation complexity.

Data transmission rate in molecular communication systems can be improved by using multiple transmitters and receivers. In molecular multiple-input multiple-output (MIMO) systems which use only single type of molecules, the performance at the destination is limited by inter-symbol interference (ISI), inter-link interference (ILI) and multi-user interference (MUI). This work proposes a new hybrid modulation for a system with multiple transmitters and receivers which uses different types of molecules to eliminate ILI. Further, to enhance the data rate of the proposed system under ISI and MUI, Mary CSK modulation scheme is used between each transmitter-receiver pair. In this paper, the random locations of transmitters present in the three dimensional (3-D) space are modeled as homogeneous Poisson point process (HPPP). Using stochastic geometry tools, analytical expression is derived for the probability of symbol error for the aforementioned scenario. Finally, the performance of the proposed system is compared using the different existing modulation schemes such as on-off keying (OOK), binary concentration shift keying (BCSK) and quadruple concentration shift keying (QCSK) to develop several important insights.

In this paper, we consider diffusion-based molecular communication with and without drift between two static nano-machines. We employ type-based information encoding, releasing a single molecule per information bit. At the receiver, we consider an asynchronous detection algorithm which exploits the arrival order of the molecules. In such systems, transposition errors fundamentally undermine reliability and capacity. Thus, in this paper, we study the impact of transpositions on the system performance. Toward this, we present an analytical expression for the exact bit error probability (BEP) caused by transpositions and derive computationally tractable approximations of the BEP for diffusion-based channels with and without drift. Based on these results, we analyze the BEP when background is not negligible and derive the optimal bit interval that minimizes the BEP. Simulation results confirm the theoretical results and show the error and goodput performance for different parameters such as block size or noise generation rate.

Motivated by the numerous healthcare applications of molecular communication inside blood vessels of the human body, this paper considers multiple relay/cooperative nanomachine (CN)-assisted molecular communication between a source nanomachine (SN) and a destination nanomachine (DN) where each nanomachine is mobile in a diffusion-advection flow channel. Using the first hitting time model, the impact of the intermediate CNs on the performance of the aforementioned system with fully absorbing receivers is comprehensively analyzed taking into account the presence of various degrading factors, such as inter-symbol interference, multi-source interference, and counting errors. For this purpose, the optimal decision rules are derived for symbol detection at each of the CNs and the DN. Furthermore, closed-form expressions are derived for the probabilities of detection and false alarm at each CN and DN, along with the overall end-to-end probability of error and channel achievable rate for communication between the SN and DN. Simulation results are presented to corroborate the theoretical results derived and also to yield insights into the system performance under various mobility conditions.

A cooperative cognitive radio network (CRN) with improved energy detection-based spectrum sensing is analyzed in various fading scenarios. Generalized multipath (Nakagami-m) fading and enriched multipath (Nakagami-q) fading models are considered along with two other general fading distributions, i.e., κ-μ and η-μ distributions. For spectrum sensing, instead of conventional power 2-based detection method, the arbitrary power p-based improved energy detector is used. The impact of the cognitive user mobility on the performance of the improved energy detector is investigated. Specifically, the statistics for the received signal are derived over various fading environments. The performance of the considered CRN system is evaluated in terms of probability of false alarm, probability of missed detection, and receiver operating characteristics (ROC) curves. Moreover, the area under the ROC curve is also obtained. Simulation results are provided to corroborate the theoretical results derived in this paper.

This paper considers path selection based decode- and-forward (DF) cooperation for wireless communication in which the source chooses either of the direct source-destination or source-relay- destination links for signal transmission. A novel aspect of the work under consideration is that it proposes a comprehensive unified framework, which is applicable for several PHY layer schemes, unlike comparative works in existing literature which are restricted to specific PHY layer schemes and thus cannot be generalized. An asymptotic generalized bound is derived for the end-to-end symbol error rate performance for cooperation over arbitrarily distributed source-destination, source-relay, relay-destination fading links. Also, the ensuing diversity order as well as optimal source-relay power allocation is derived for cooperative communication. The flexibility arising from the unified nature of the proposed framework is demonstrated by illustrating its applicability across a wide range of disparate systems such as MIMO-OSTBC, Joint Transmit- Receive Antenna Path Selection (JTRAPS), and Hybrid Satellite-Terrestrial Communication (HSTC). Simulation results are presented in the end to validate the performance of the system.

This paper analyzes the performance of a selective Decode-and-Forward (DF) protocol based cooperative space-time block coded spatial modulation (STBC-SM) MIMO system. Further, the analysis is presented for a Kronecker MIMO channel model considering spatial correlated channel conditions. The results are also derived for the diversity order of the system based on a high signal to noise ratio (SNR) characterization of the performance followed by the optimal power allocation between the source and relay. Simulation results demonstrate that the proposed scheme achieves a lower pair-wise error probability (PEP) in comparison to other schemes such as the conventional cooperative Alamouti-STBC and non-cooperative transmission.

This paper presents an analysis for the ergodic capacity of a multiple-input multiple-output (MIMO) beamforming based cooperative wireless communication system considering the effect of node mobility and channel estimation errors. Closed form expressions are derived for the ergodic capacity of a path selection based decode-and-forward (DF) relaying protocol considering the availability of both full SNR and only one-bit feedback for path selection. Simulation results are presented to illustrate the impact of node mobility and imperfect channel estimates on the ergodic capacity of the MIMO beamforming based DF cooperative path selection scheme and verify the analytical results derived.

In this paper, we consider the performance of a selective decode-and-forward (DF) relaying based multiple-input multiple-output (MIMO) space-time block coded (STBC) cooperative communication system with single and multiple relays. We begin with a single relay based MIMO STBC system and derive the closed form expression for the end-to-end PEP of coded block detection at the destination node. It is also demonstrated that the MIMO STBC cooperative communication system achieves the full diversity order of the system. We also derive the optimal source relay power allocation, which minimizes the end-to-end decoding error of the cooperative system for a given power budget. Subsequently, for the multiple relay scenario, we consider two different relaying protocols based on two-phase and multi-phase communication. For each of these multi-relay protocols, we derive the closed form expressions for the end-to-end error rate, diversity order, and optimal power allocation. Simulation results are presented to validate the performance of the proposed single and multiple relay based cooperative communication schemes and the derived analytical results. Further, these schemes can also be seen to lead to a performance improvement compared to several other relaying schemes in existing literature.