MWN Lab IITK

Indian Institute of Technology Kanpur | IIT Kanpur · Department of Electrical Engineering

This paper develop novel approaches for designing robust transceivers and energy covariance in an IoT network powered by energy harvesting. Our goal is to minimize the mean square error (MSE) at the fusion center (FC) while considering the uncertainty of channel state information (CSI). The proposed designs incorporate both Gaussian and bounded CSI...

Privacy-preserving distributed beamforming designs are conceived for temporally correlated vector parameter estimation in an orthogonal frequency division multiplexing (OFDM)-based wireless sensor network (WSN). The temporal correlation inherent in the parameter vector is exploited by the rate distortion theory-based bit allocation framework used f...

An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are...

This work conceives a sparse channel estimation (CE) scheme for multiuser (MU) intelligent reflecting surface (IRS)-aided Terahertz (THz) systems. The proposed framework also incorporates hardware impairments that arise due to manufacturing errors in practical THz systems, such as mutual coupling, irregular antenna spacing, and antenna gain/phase e...

This paper considers a joint radar and communication (JRC) system towards radar cross-section (RCS) parameter and channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithms are based on the hybrid mmWave MIMO architecture. First, the orthogo...

Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre-and post-processing of the sensor observations and received signal are proposed for the minimum mean square error (MMSE) estimatio...

An orthogonal affine-precoded superimposed pilot-based architecture is developed for the cyclic prefix (CP)-aided SISO and MIMO orthogonal time frequency space systems relying on arbitrary transmitter-receiver pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler-domain followed by the developme...

Hybrid transceivers are designed for linear decentralized estimation (LDE) in a mmWave multiple-input multiple-output (MIMO) IoT network (IoTNe). For a noiseless fusion center (FC), it is demonstrated that the MSE performance is determined by the number of RF chains used at each IoT node (IoTNo). Next, the minimum-MSE RF transmit precoders (TPCs) a...

Hybrid precoders and combiners are designed for cooperative cell-free multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular networks for low complexity interference mitigation. Initially, we derive an optimal hybrid transmit beamformer (HTBF) for a broadcast scenario considering both total and per access point (AP) powe...

Robust linear decentralized tracking of a time varying sparse parameter is studied in a multiple-input multiple-output (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. Initially, assuming perfect CSI availability, a novel sparse Bayesian learning-based Kalman filtering (SBL-KF) framework is developed in order...

A hybrid transceiver architecture is conceived for a cognitive radio (CR) aided millimeter wave (mmWave) multiuser (MU) multiple-input multiple-output (MIMO) downlink system relying on multiple radio frequency (RF) chains both at the CR base station (CBS) and the secondary users (SUs). To begin with, a hybrid transceiver design algorithm is propose...

Hybrid transceivers are designed for linear decentralized estimation (LDE) in a mmWave multiple-input multiple-output (MIMO) IoT network (IoTNe). For a noiseless fusion center (FC), it is demonstrated that the MSE performance is determined by the number of RF chains used at each IoT node (IoTNo). Next, the minimum-MSE RF transmit precoders (TPCs) a...

Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre-and post-processing of the sensor observations and received signal are proposed for the minimum mean square error (MMSE) estimatio...

The downlink of a reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) multiple-input multiple-output (MIMO) system relying on a non-diagonal RIS (NDRIS) phase shift matrix is considered. A max-min fairness (MMF) problem is formulated under the total transmit power constraint while employing joint active hybrid be...

Linear hybrid beamformer designs are conceived for the decentralized estimation of a vector parameter in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) Internet of Things network (IoTNe). The proposed designs incorporate both total IoTNe and individual IoTNo power constraints, while also eliminating the need for a baseband receive...

Linear hybrid beamformer designs are conceived for the decentralized estimation of a vector parameter in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) Internet of Things network (IoTNe). The proposed designs incorporate both total IoTNe and individual IoTNo power constraints, while also eliminating the need for a baseband receive...

Novel hybrid beamformer designs are conceived for a multi-user multi-cell (MUMC) mmWave system relying on base station (BS) coordination and total transmit power minimization subject to realistic signal-to-interference-plus-noise ratio (SINR) constraints at each mobile station (MS). Initially, a semidefinite relaxation (SDR)-based approach is devel...

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-sig...

A convenient delay, Doppler and angular-(DDA) domain representation of the multiple-input multiple-output (MIMO) wireless channel is conceived for deriving the end to end relationship in the delay-Doppler (DD)-domain for orthogonal time frequency space (OTFS)-based communications. Subsequently, a time-domain pilot based model is developed for estim...

A simultaneous wireless information and power transfer (SWIPT)-assisted cognitive overlay-based multiple input multiple output (MIMO) downlink (DL) cooperative relaying system has been considered wherein a secondary network comprising of an energy harvesting (EH) base station (BS) and possibly multiple fully wireless powered secondary users (SUs) s...

Orthogonal time frequency space (OTFS) waveform based millimeter wave (mmWave) MIMO systems are capable of achieving high data rates in high-mobility scenarios. Hence, transceivers are designed for both analog beamforming (AB) and hybrid beamforming (HB), where we commence by deriving the delay-Doppler (DD)-domain input-output relationship consider...

A hybrid transceiver architecture along with the optimal power allocation is conceived for a downlink millimeter wave (mmWave) multi-input multi-output (MIMO) cognitive radio (CR) system operating in the underlay mode. Towards this, the non-convex objective and constraints of the sum spectral ef-ficiency (SE) maximization problem are simplified by...

Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasistatic single-input single-output (SISO)-FBMC system, rely...

Wireless sensor networks (WSNs) are vulnerable to eavesdropping as the sensor nodes (SNs) communicate over an open radio channel. Intelligent reflecting surface (IRS) technology can be leveraged for physical layer security in WSNs. In this paper, we propose a joint transmit and reflective beamformer (JTRB) design for secure parameter estimation at...

This paper presents low-complexity decision rules as well as the pertinent analysis for data fusion in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). The proposed framework considers both unknown and known parameter scenarios, and the spatial correlation arising due to close proximity of the...

Wireless sensor networks (WSNs) are vulnerable to eavesdropping as the sensor nodes (SNs) communicate over an open radio channel. Intelligent reflecting surface (IRS) technology can be leveraged for physical layer security in WSNs. In this letter, we propose a joint transmit and reflective beamformer (JTRB) design for secure parameter estimation at...

This work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivity, beam squint effect is significant in THz system...

This work conceives novel target detection and parameter estimation schemes in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) radar (mMR) systems for both stationary and mobile targets/radar platform. Initially, the orthogonal matching pursuit (OMP)-based mmR (OmMR) algorithm is proposed for stationary targets to estimate their rada...

A sparse channel state information (CSI) estimation model is proposed for reducing the pilot overhead of orthogonal time frequency space (OTFS) modulation aided multiple-input multiple-output (MIMO) systems. Explicitly, the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI that...

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-sig...

A novel sparse channel state information (CSI) estimation scheme is proposed for orthogonal time frequency space (OTFS) modulated systems, in which the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI. The proposed CSI estimation model leads to a reduction in the pilot overhea...

A novel sparse channel state information (CSI) estimation scheme is proposed for orthogonal time-frequency space (OTFS) modulated systems, in which the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI. The proposed CSI estimation model leads to a reduction in the pilot overhea...

An optimal precoder design is conceived for the decentralized estimation of an unknown spatially as well as temporally correlated parameter vector in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based wireless sensor network (WSN). Furthermore, exploiting the temporal correlation present in the parameter...

Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). Our proposed transceiver designs rely on majorization theory and hyperparameter estimates obtained from the BL module...

Multi-sensor millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs) relying on both distributed (D-MIMO) and centralized (C-MIMO) configurations are conceived. Hybrid combining based low complexity fusion rules are constructed for the fusion center (FC) for both D-MIMO and C-MIMO systems employing a p...

This paper proposes affine-precoded superimposed pilot (SIP) design, followed by channel state information (CSI) estimation techniques for millimeter wave (mmWave) MIMO-OFDM systems. Toward this end, a multiple measurement vector (MMV) sparse Bayesian learning (SBL)-based SIP (MSIP) technique is initially derived to exploit the simultaneous-sparsit...

This work conceives the robust linear transceivers for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) multi-sensor network under imperfect channel state information (CSI) at the fusion center (FC). Both the popular stochastic (S-) and norm ball CSI uncertainty (N...

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-...

This work conceives techniques for the design of linear hybrid precoders toward decentralized parameter estimation in a millimeter wave (mmWave) wireless sensor network (WSN). To achieve this objective, a novel system model is proposed for mmWave WSNs, wherein the sensors pre-process their observations using hybrid baseband and radio frequency (RF)...

This work conceives techniques for the design of hybrid precoders/combiners for optimal bit allocation in frequency selective millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, toward transmission rate maximization. Initially, the optimal fully digital ideal precoder/ combiner d...

Adaptive block-based least-mean squares (BLMS)-based techniques are conceived for channel estimation in wideband millimeter wave (mmWave) hybrid MIMO systems. In this context, a frequency-domain wideband channel estimation model is developed followed by a novel fast BLMS (FBLMS) technique, which has a significantly lower computational complexity th...

Adaptive block-based least-mean squares (BLMS)-based techniques are conceived for channel estimation in single carrier (SC) wideband millimeter wave (mmWave) hybrid MIMO systems. In this context, a frequency-domain channel estimation model is developed for SC wideband systems, followed by a novel fast BLMS (FBLMS) technique, which has a significant...

This paper designs a novel multiple measurement vector (MMV)-based sparse Bayesian learning (MSBL) technique for wideband channel estimation in single-carrier (SC) mmWave hybrid multiple input multiple output (MIMO) systems. The notable features of the proposed technique are that it leverages the simultaneous sparsity innate in the beamspace channe...

We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF)...

Distributed parameter detection is conceived for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs), where multiple sensors collaborate to detect the presence/ absence of a spatially correlated parameter. Neyman-Pearson (NP) and generalized likelihood ratio test (GLRT)-based detectors are developed at the fusion center (F...

Semi-blind (SB) channel estimation is conceived for millimeter wave (mmWave) analog-beamforming (AB) and hybrid-beamforming (HB)-based multiple-input multiple-output (MIMO) systems, which also exploits the data symbols for improving the estimation accuracy. A novel aspect of the proposed framework is that it directly estimates the analog beamformer...

This paper considers a distributed detection framework for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). A hybrid combining based low complexity fusion rule is derived at the fusion center (FC) that also incorporates the local probabilities of detection and false alarm of the individual sens...

This work proposes novel techniques toward the design of optimal pilot sequences to perform channel estimation in block transmission systems over wideband frequency selective wireless fading channels. The framework developed is based on minimization of the Bayesian Cramér-Rao bound (BCRB) for the mean squared error (MSE) of the channel state inform...

This paper presents limited feedback-based precoder quantization schemes for Interference Alignment (IA) with bounded channel state information (CSI) uncertainty. First, this work generalizes the min-max mean squared error (MSE) framework, followed by the development of robust precoder and decoder designs based on worst case MSE minimization. The p...

Hierarchical Bayesian Kalman filter (HBKF) based schemes are conceived for doubly-selective sparse channel estimation in orthogonal space-time block coded (OSTBC) multipleinput multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless systems. Initially, a pilot based multiple measurement vector (MMV) model is formulated for...

This paper proposes a robust reconstruction scheme for image/ video transmission in space-time trellis coding (STTC) based MIMO wireless multimedia sensor networks (WMSN). The information bits of the image/ video stream are modulated using STTC prior to transmission over the MIMO wireless channel between the multimedia sensors and the cluster head....

A finite blocklength (FBL) twin-user non-orthogonal cooperative downlink system is considered, wherein a base station simultaneously communicates with the users whilst relying on simultaneous wireless information and power transfer (SWIPT) enabled energy harvesting relay. Closed-form analytical expressions are obtained for the end-to-end average bl...

This work considers multiple user power-domain non-orthogonal multiple access (NOMA)-based downlink (DL) and uplink (UL) communication systems with potentially dissimilar fading links for all the users that can follow one of several possible distributions such as Rayleigh, Rician, Nakagami- $m$ , Nakagami- $q$ , $\kappa -\mu $ , $\eta -\mu $...

Sparse Bayesian learning (SBL)-based approximately sparse channel estimation schemes are conceived for space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems relying on trellis-based encoding and decoding over the data subcarriers. First, a pilot-aided channel estimation schem...

This paper analyses the performance of filter bank multicarrier (FBMC) signaling in conjunction with offset quadrature amplitude modulation (OQAM) in multi-user (MU) massive multiple-input multiple-output (MIMO) systems. Initially, closed form expressions are derived for tight lower bounds corresponding to the achievable uplink sum-rates for FBMC-b...

Motivated by the numerous healthcare applications of molecular communication inside blood vessels of the human body, this work considers multiple relay/ cooperative nanoma-chine (CN)-assisted molecular communication between a source nanomachine (SN) and a destination nanomachine (DN) where each nanomachine is mobile in a diffusion-advection flow ch...

This paper considers the problem of distributed detection for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). Neyman-Pearson (NP) 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...

This paper investigates the uplink asymptotic performance of single-cell multiuser multiple-input multiple-output (MU-MIMO) system with a very large antenna array for time-selective fading channels, resulting from user mobility. To exploit the temporal correlation of the channel, the Kalman filter (KF) is developed for channel estimation, followed...

This paper develops schemes for block-sparse channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems that exploit the spatial sparsity inherent in such channels. Initially, a novel sparse Bayesian learning (SBL) based block-sparse channel estimation technique is developed for a mmWave hybrid MIMO system with mul...

A novel total variation (TV) framework is conceived for joint detection and dynamic state estimation (JDSE) for wireless transmission from the measurement devices to the control center in a smart grid. The proposed scheme employs a TV regularization based decoder in conjunction with a Kalman filter based dynamic power system state estimator to mini...