Adarsh Patel

Assistant Professor
School of Computing and Electrical Engineering
Indian Institute of Technology Mandi, Himachal Pradesh

Contact Info

A10-410, Cabin-107
North Campus, SCEE
Indina Institute of Technology Mandi
Mandi, Himachal Pradesh - 175005
E-mail: adarsh[AT]iitmandi[DOT]ac[DOT]in

Research Interests

My broad research area is Wireless Communications and Networks with the applications of signal processing, Game Theory, Machine Learning, Tensors, and Optimization based techniques.
In particular, my current research interests are:

  • 5G Wireless Networks (cellular, sensor, ad hoc, relay)
  • Massive MIMO Technology
  • Cooperative Communication
  • Cognitive Radio and License Assisted Access (LAA)
  • Molecuar Communications
  • Optimization and Resource Allocation for 5G
  • Information and coding theory

Publications

Peer Reviwed Journals

  • A. Chawla, A. Patel, A. K. Jagannatham, and P. K. Varshney, “Distributed detection in Massive MIMO Wireless Sensor Networks under Perfect and Imperfect CSI,” in IEEE Transactions on Signal Processing, vol. 67, no. 15, pp. 4055-4068, 1 Aug.1, 2019.

    This paper considers the problem of distributed detection for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). Neyman-Pearson criterion based fusion rules are developed at the fusion center (FC) that also incorporate the local probabilities of detection and false alarm of the constituent sensor nodes. Closed-form expressions are obtained for the probabilities of detection and false alarm at the FC for various signaling schemes employed by the sensors. The fusion rules and analysis are extended to the scenario with imperfect channel state information (CSI). Furthermore, signaling matrices are determined for the massive MIMO WSN to enhance detection performance. The asymptotic detection performance of the WSN is analyzed for the large antenna regime, which yields pertinent power scaling laws with respect to the number of antennas at the FC. Simulation results demonstrate the improved performance of the proposed schemes and also validate the theoretical findings.

  • N. Varshney, A. Patel, W. Haselmayr, A. K. Jagannatham, P. K. Varshney, and A. Nallanathan, “Impact of Intermediate Nanomachines in Multiple Cooperative Nanomachine-Assisted Diffusion Advection Mobile Molecular Communication,” in IEEE Transactions on Communications , vol. 67, no. 7, pp. 4856-4871, July 2019.

    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.

  • N. Varshney, A. Patel, Y.Deng,W.Haselmayr, P. K. Varshney, and A.Nallanathan “AbnormalityDetection inside Blood Vessels with Mobile Nanomachines,” in IEEE Transactions on Molecular, Biological, and Multiscale Communications, vol. 4, no. 3, pp. 189-194, Sept. 2018.

    Motivated by the numerous healthcare applications of molecular communication within Internet of Bio-Nano Things (IoBNT) paradigm, this paper addresses the problem of abnormality detection in a blood vessel using multiple biological embedded computing devices called cooperative biological nanomachines (CNs), and a common receiver called the fusion center (FC). Due to blood flow inside a vessel, each CN and the FC are assumed to be mobile. In this paper, each CN performs abnormality detection with certain probabilities of detection and false alarm. The CNs report their local decisions to an FC over a diffusion-advection blood flow channel using different types of molecules in the presence of inter-symbol interference, multi-source interference, and counting errors. The FC employs the OR and AND logic-based fusion rules to make the final decision after decoding the local decisions using the sub-optimal detectors based on the approximation of the log-likelihood ratio. For the aforementioned system, probabilities of detection, and false alarm at the FC are derived. Finally, simulation results are presented to validate the derived analytical results, which provide important insights.

  • A. Goel, A. Patel, K. G. Nagananda and P. K. Varshney, “Robustness of the Counting Rule for Distributed Detection in Wireless Sensor Networks,” in IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1191-1195, Aug. 2018.

    We consider the problem of energy-efficient distributed detection to infer the presence of a target in a wireless sensor network and analyze its robustness to modeling uncertainties. The sensors make noisy observations of the target's signal power, which follows the isotropic power-attenuation model. Binary local decisions of the sensors are transmitted to a fusion center, where a global inference regarding the target's presence is made, based on the counting rule. We consider uncertain knowledge of: 1) the signal decay exponent of the wireless medium; 2) the power attenuation constant; and 3) the distance between the target and the sensors. For a given degree of uncertainty, we show that there exists a limit on the target's signal power below which the distributed detector fails to achieve the desired performance regardless of the number of sensors deployed. Simulation results are presented to determine the level of sensitivity of the detector to uncertainty in these parameters. The results throw light on the limits of robustness for distributed detection, akin to “SNR walls” for classical detection.

  • A. Patel, H. Ram, A. K. Jagannatham and P. K. Varshney, “Robust Cooperative Spectrum Sensing for MIMO Cognitive Radio Networks under CSI Uncertainty,” in IEEE Transactions on Signal Processing, vol. 66, no. 1, pp. 18-33, Jan, 2018.

    This paper considers the problem of cooperative spectrum sensing in multiuser multiple-input multiple-output cognitive radio networks considering the presence of uncertainty in the channel state information (CS!) of the secondary user channels available at the fusion center. Several schemes are proposed that employ cooperative decision rules based on local sensor decisions transmitted to the fusion center by thecooperating nodes over an orthogonal multiple access channel. First, fusion rules are derived under perfect CS! at the fusion center for both antipodal and nonantipodal signaling. Then, a robust detector, termed the uncertainty statisticsbased likelihood ratio test, which optimally combines the decisions of different secondary users, is obtained for scenarios with CS! uncertainty. A generalized likelihood ratio test based robust detector is also derived for this scenario. Closed-form expressions are obtained to characterize the probabilities of false alarm (PFA) and detection (PD) at the fusion center. Simulation results are presented to compare the performance of the proposed schemes with that of the conventional uncertainty agnostic detectors and also to corroborate the analytical expressions developed.

  • A. Patel, S. Biswas and A. K. Jagannatham, “Optimal GLRT-Based Robust Spectrum Sensing for MIMO Cognitive Radio Networks with CSI Uncertainty,” in IEEE Transactions on Signal Processing, vol. 64, no. 6, pp. 1621-1633, March, 2016.

    In this paper, we develop generalized likelihood ratio test (GLRT)-based detectors for robust spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio networks considering uncertainty in the available channel state information (CSI). Initially, for a scenario with known CSI uncertainty statistics, we derive the novel robust estimator-correlator detector (RECD) and the robust generalized likelihood detector (RGLD), which are robust against the uncertainty in the available estimates of the channel coefficients. Subsequently, for a scenario with unknown CSI uncertainty statistics, we develop a generalized likelihood ratio test (GLRT) based composite hypothesis robust detector (CHRD) for spectrum sensing. Closed form expressions are presented for the probability of detection (PD) and the probability of false alarm (PFA) to characterize the detection performance of the proposed robust spectrum sensing schemes. Further, a deflection coefficient based optimization framework is also developed and solved to derive closed form expressions for the optimal beacon sequences. Simulation results are presented to demonstrate the performance improvement achieved by the proposed robust spectrum sensing schemes and to verify the analytical results derived.

  • A. Patel, B. Tripathi and A. K. Jagannatham, “Robust Estimator-Correlator for Spectrum Sensing in MIMO CR Networks with CSI Uncertainty,” in IEEE Wireless Communications Letters, vol. 3, no. 3, pp. 253-256, June 2014.

    This work introduces novel detection schemes which are robust with respect to the uncertainty in the estimate of the signal covariance matrix for non-coherent spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio networks. We employ an eigenvalue perturbation theory based approach to model the uncertainty in the estimated signal covariance matrix. Subsequently, we derive an optimization framework for the generalized likelihood ratio test (GLRT) based robust test statistic detector (RTSD) and robust estimator-correlator detector (RECD) towards primary user detection, which incorporate the channel state information (CSI) uncertainty inherent in such scenarios. Further, employing the Karush-Kuhn-Tucker (KKT) conditions, we derive closed form expressions for the proposed robust spectrum sensing schemes. Simulation results demonstrate the superior performance of the proposed robust detectors in comparison to the uncertainty agnostic estimator-correlator (EC) detector for spectrum sensing in MIMO cognitive radio networks with CSI uncertainty.

  • A. Patel and A. K. Jagannatham, “Non-Antipodal Signaling Based Robust Detection for Cooperative Spectrum Sensing in MIMO Cognitive Radio Networks,” in IEEE Signal Processing Letters, vol. 20, no. 7, pp. 661-664, July 2013.

    In this work, we present novel detection schemes for non-antipodal signaling based cooperative spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio (CR) networks, which are robust against the uncertainty in channel estimates. We consider a scenario in which the secondary users (SU) cooperate by reporting the sensed data to the fusion center for soft combining towards primary user (PU) detection. We formulate this problem employing the optimal linear discriminant and model the uncertainties in the channel state information (CSI) as ellipsoidal uncertainty sets. It is then demonstrated that this problem of PU detection with uncertainty in the channel estimates for cooperative spectrum sensing in a CR system can be formulated as second order cone program (SOCP). Further, we extend this paradigm to the associated relaxed robust detector (RRD) and multicriterion robust detector (MRD) that maximally separate the hypothesis ellipsoids in low signal-to-noise power (SNR) and deep fade channel conditions. We present a closed form solution for the proposed robust detector for the above MIMO cooperative spectrum sensing scenario. Simulation results demonstrate a significant improvement in the detection performance of the proposed uncertainty aware robust detection schemes in comparison to the conventional uncertainty agnostic matched filter detector for cooperative MIMO PU detection.

Peer Reviewed Conferences

  • A. Chawla, A. Patel, A. K. Jagannatham and P. K. Varshney, “Robust Distributed Detection in Massive MIMO Wireless Sensor Networks Under CSI Uncertainty,” 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA, 2018, pp. 1-5.

    This paper presents a Neyman-Pearson (NP) criterion based optimal distributed detection framework for a massive multiple-input multiple-output (MIMO) wireless sensor network (WSN). Robust fusion rules are determined for the local decisions transmitted by the sensor nodes, considering the availability of both perfect as well as imperfect channel state information (CSI) at the fusion center. Further, the probability of error of the individual sensor decisions, which arises in practical scenarios, is also incorporated in the decision framework. Closed form expressions are derived to characterize the resulting probabilities of detection and false alarm for the system. Simulation results are presented to demonstrate the improved performance of the proposed detectors in comparison to the existing detectors and to validate the theoretical findings.

  • N. Varshney, A. Patel, W. Haselmayr, A. K. Jagannatham, P. K. Varshney and W. Guo, “Impact of Cooperation in Flow-Induced Diffusive Mobile Molecular Communication,” 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2018, pp. 494-498.

    Motivated by the numerous healthcare applications of molecular communication (MC) inside blood vessels, this work considers relay/cooperative nanomachine (CN)-assisted mobile MC between a source nanomachine (SN) and a destination nanomachine (DN) where each nanomachine is mobile in a flow-induced diffusive channel. Using the first hitting time model, the impact of an intermediate CN on the performance of the CN-assisted diffusive mobile MC system with fully absorbing receivers is analyzed in the presence of inter-symbol interference, multi-source interference, and counting errors. For this purpose, the likelihood ratio test based optimal symbol detection scheme is obtained at the DN considering the non-ideal nature of CN, i.e., CN can be in error with a finite probability. Further, to characterize the system performance, closed-form expressions for the end-to-end probabilities of detection and false alarm at the DN are derived between the SN-DN pair incorporating the detection performance of the intermediate CN. In addition, the channel capacity expression is also derived for the aforementioned scenario. Simulation results are presented to corroborate the theoretical results derived and also, to yield insights into system performance.

  • A. Patel, A. Ahmad and R. Tripathi, “Multiple Beacon Based Robust Cooperative Spectrum Sensing in MIMO Cognitive Radio Networks under CSI Uncertainty,” 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, 2017, pp. 1-5.

    This paper presents multiple beacon vectors based robust detection schemes for cooperative spectrum sensing (CSS) in multiple-input multiple-output (MIMO) cognitive radio (CR) networks under channel state information (CSI) uncertainty. The inaccuracies in the estimate of the CSI are modeled as the standard ellipsoidal uncertainty set. We develop a multiple beacon vector based linear discriminant framework to obtain robust detectors for the problem of primary user detection in MIMO cognitive radio networks under ellipsoidal CSI uncertainty. Next, we employ this framework to develop signaling scheme based application specific detectors, namely the antipodal signaling based robust detector and on-off signaling based robust detector, along with their closed form expressions. Further, for the two signalling schemes, we even present allied detectors that are advantageous under low signal-to-noise ratio (SNR) conditions. Simulation results demonstrate a superior detection performance of the proposed robust detection schemes in comparison to the uncertainty agnostic matched filter detector for CSS in MIMO cognitive radio networks.

  • M. Rajput, S. Dwivedi, Adarsh Patel and A. K. Jagannatham, “Subspace based multi-user spectrum sensing in frequency selective cognitive radio systems,” 2016 IEEE Region 10 Conference (TENCON), Singapore, 2016, pp. 1154-1157.

    This paper proposes an optimal subspace based detector for multiple primary user spectrum sensing in frequency selective Rayleigh fading wireless channels. The proposed sensing scheme employs the optimal zero-forcing algorithm to null the effect of inter block interference (IBI) occurring due to the multipath nature of the wireless channel and also the multi user interference (MUI) arising due to the presence of other interfering primary users. This is followed by deriving the optimal Neyman-Pearson (NP) criterion based subspace detector to efficiently sense the presence/absence of the desired user. To characterize the efficacy of the proposed detection framework, expressions for the probabilities of false alarm and detection are also derived. Finally, performance comparison with existing detectors is presented.

  • A. Patel, S. Biswas and A. K. Jagannatham, “Multiple Beacon Based Robust Cooperative Spectrum Sensing in MIMO Cognitive Radio Networks,” Vehicular Technology Conference (VTC Fall), 2013 IEEE 78th, Las Vegas, NV, 2013, pp. 1-5.

    This paper introduces novel detection schemes for multiple beacon signaling based cooperative spectrum sensing in multiple-input multiple-output (MIMO) wireless cognitive radio (CR) networks with channel state information (CSI) uncertainty. We consider a scenario in which the fusion center employs soft combining of the samples sensed by the cooperating secondary users corresponding to the primary user base-station beacon signals. We formulate the multiple beacon signaling based robust detectors, namely the robust estimator-correlator detector (RECD) and the robust generalized likelihood detector (RGLD), that can be employed at the fusion center towards primary user detection incorporating CSI uncertainty for cooperative spectrum sensing in MIMO CR networks. Further, we formulate a deflection coefficient based optimization framework and derive the optimal beacon sequence to maximize the probability of primary user detection at the fusion center. Simulation results show that the proposed robust detectors yield a significant improvement in the detection performance compared to the conventional CSI uncertainty agnostic matched filter (MF) detector for cooperative spectrum sensing in MIMO CR networks. Moreover, the optimal beacon signaling based robust detectors result in additional enhancement in the accuracy of primary user detection over suboptimal beacon structure based detectors.

  • A. Patel and A. K. Jagannatham, “SOCP based robust detector for cooperative spectrum sensing in MIMO cognitive radio,” Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012 IEEE 7th, Hoboken, NJ, 2012, pp. 133-136.

    In this paper we present a robust detection scheme for cooperative spectrum sensing in cognitive radio (CR) networks with channel uncertainties. We consider a soft-decision scenario at the fusion center for primary user detection, based on the sensed test statistics submitted by the cooperating secondary users. The scheme presented models the channel state information (CSI) uncertainty employing an ellipsoidal uncertainty set. It is then demonstrated that the optimal linear discriminator for cooperative spectrum sensing towards primary user detection in a CR system can be formulated as a second order cone program (SOCP). Further, we also formulate a relaxed robust detector (RRD) and a multicriterion robust detector (MRD) that maximally separate the hypothesis ellipsoids at low signal-to-noise ratio (SNR) and deep fade conditions. Simulation results demonstrate that the detection error performance of the proposed CSI uncertainty aware robust spectrum sensing schemes are significantly lower compared to other uncertainty agnostic schemes.

  • A. Patel and A. Trivedi, “Particle-Swarm-Optimization-Based Multiuser Detector for Multicarrier CDMA Communications,” International Conference on Computational Intelligence and Communication Networks (CICN), 2010 IEEE, Bhopal, 2010, pp. 534-538.

    Multi-carrier code division multiple access (MCCDMA) mobile communication systems targets to overcome the multi-path fading influences and to increase system capacity. In this paper, we present a modified version of evolutionary algorithm, called as Particle swarm optimization (PSO) which quickly converges to global optimal solution. This algorithm is used to develop a suboptimal multi-user detection (MUD) strategy for MC-CDMA systems. More specifically, after getting the initial population from the front-end frequency domain maximal ratio combining (MRC) or minimum mean-square error (MMSE) equalizer, simplified PSO optimization is applied. Simulation results show that the proposed PSO algorithm remarkably improves the performance of both, MRC and MMSE detector. Results of proposed MMSE-PSO scheme are better than the MMSE detector and it also outperforms MRC-PSO scheme.

Technical Reports

  • A. Chawla, A. Patel, A. K. Jagannatham, and P. K. Varshney, “Technical Report: Distributed detection in Massive MIMO Wireless Sensor Networks under Perfect and Imperfect CSI,” 2019.
  • N. Varshney, A. Patel, Y. Deng, W. Haselmayr, P. K. Varshney, and A. Nallanathan “Technical Report: Abnormality Detection inside Blood Vessels with Mobile Nanomachines,” 2018.
  • A. Patel, H. Ram, A. K. Jagannatham, P. K. Varshney, “Technical report: Robust cooperative spectrum sensing for MIMO cognitive radio networks under CSI uncertainty”, 2017.

Teaching

Classes

EE304 Communication Theory (Fall'20)
EE503 Advance Communication Theory (Spring'19)

Laboratory

EE304P Communication Theory Lab (Fall'20)
IC101P Reverse Engineering Lab (Spring'19)

Specialized Lecture/ Tutorial

  • Lecture, “Tensors: More than d-way arrays of numbers”, SYRACUSE UNIVERSITY, Dec. 2018.
  • Tutorials and Mini-Project, short course on Spectrum Sensing for MIMO-OFDM Cognitive Radio Systems, held at IIT KANPUR, Apr. 2017.
  • Tutorials, short course on Signal Processing for 5G Massive MIMO Wireless Systems, held at IIT KANPUR, Jan. 2017.
  • Talk, “Robust Detection schemes for Multiple-Input Multiple-Output (MIMO) Cognitive Radio Networks”, Tata Consultancy Services Limited, TCS Bangalore, Jan. 2017.
  • Tutorials, short course on Cognitive Radio and Wireless Communications-Theory, Practice and Security under Global Initiative for Academic Networks (GIAN), held at IIT KANPUR, Sep. 2016.
  • Lecture, “Robust Cooperative Spectrum Sensing in MIMO Cognitive Radio (CR) Networks,” at short course on Convex Optimization for Wireless Communications, held at IIT KANPUR, Oct. 2014.
  • Lecture, “Convex Optimization and Game Theory,” at short course on Applied Game Theory for Engineers and Managers, held at IIT KANPUR, June 2014.
  • Lecture, “Optimal Frame Rate Allocation and Quantizer Selection for Unicast and Multicast Wireless Scalable Video Communication,” held at IIT BOMBAY, Apr. 2014.
  • Lecture, “Convex Optimization,” at short course on Cognitive Radio: The Next Frontier in Wireless Communications, held at IIT KANPUR, Oct. 2011.

NPTEL-MOOC (Massive Open Online Course), Teaching Assistant

  • Strategy: An Introduction to Game Theory taught by Prof. Adityia K. Jagannatham (Fall'15)
  • Principles of Modern CDMA/MIMO/ OFDM Wireless Communications taught by Prof. Adityia K. Jagannatham (Spring'15)

Service

Professional Service

  • Journal Reviewer
    • IEEE Access
    • IEEE Transactions on Aerospace and Electronic Systems
    • IEEE Transactions on Cognitive Communications and Networking
    • IEEE Transactions on Emerging Topics in Computing
    • IEEE Transactions on Signal and Information Processing over Networks
    • IEEE Transactions on Signal Processing
    • IEEE Transactions on Vehicular Technology
    • IEEE Communication Letters
    • IEEE Signal Processing Letters
    • IEEE Wireless Communication Letters
  • Conference Reviewer
    • WCNC-2019
    • FUSION-2018
    • GLOBECOM-2018, 2016, 2012
    • ISWTA-2012, 2013
    • SPCOM (2012 to 2016)
    • NCC (2013 to 2017)
    • CONNECT-2014
    • MILCOM-2013
    • ICC-2015
    • APACE-2012
    • CICN-2013
    • ANTS-2014

Affliation

Resources

Learning LaTeX

Bio

Adarsh Patel was born in Lucknow, UP, India. He received the B.Tech. degree in electronics and communication engineering from Integral University, Lucknow, UP, India in 2008, and the M.Tech. degree in computer science and engineering from ABV – Indian Institute of Information Technology and Management Gwalior, MP, India in 2010, and the Ph.D. degree in electrical engineering from Indian Institute of Technology Kanpur, UP, India in 2017. From Sep '17 to Apr '19 he was employed as a postdoctoral research staff in the Sensor Fusion Lab, headed by Prof. P. K. Varshney, in the department of electrical engineering and computer science at Syracuse University, New York, USA.

He is currently an assistant professor in the school of computing and electrical engineering at IIT Mandi, Himachal Pradesh, India. His Ph.D. thesis, titled “Robust Spectrum Sensing for Multiple-Input Multiple-output (MIMO) Cognitive Radio Networks”, has been awarded the Innovative Student Project Award 2017 by the Indian National Academy of Engineering. In 2012 and 2017, he was awarded with TCS Research fellowship and National-Postdoctoral fellowship (N-PDF) to conduct research at IIT Kanpur for five years and IISc Bangalore for two years, respectively. His research interests are in the area of next-generation 5G Wireless Networks, with special emphasis on various technologies such as massive MIMO, Cognitive Radio and License Assisted Access, Cooperative Communication, Molecular Communications, Sensor Networks, and others.