Srivastava, Suraj

2024, Priyanka Maity, Srivastava, Suraj, Aditya K. Jagannatham

This work conceives a novel sparse Bayesian learning (SBL)-based extended target parameter estimation scheme for an orthogonal frequency division multiplexing (OFDM) wave-form based-mmWave MIMO joint radar and communication (JRC) system. The proposed framework also incorporates the intercarrier interference (ICI) effect arising due to the Doppler shift together with radar clutter. The proposed algorithms are based on the hybrid mmWave MIMO architecture that requires a significantly fewer number of radio frequency (RF) chains in comparison to the number of antennas. A range, Doppler and angular (RDA)-domain representation of the target-plus-clutter echo is conceived toward target parameter estimation. The SBL framework is developed that exploits the 3-dimensional (3D)-sparsity arising in the RDA domain, given the limited number of targets and clutter, to jointly estimate the angles, range, velocity and radar cross-section (RCS) coefficients of an extended target. Simulation results demonstrate the imaging and accuracy of estimation of the target parameters in comparison to other existing techniques.

2024, Jitendra Singh, Srivastava, Suraj, Aditya K. Jagannatham

This paper conceives a hybrid beamforming (HBF) design that maximizes the energy efficiency (EE) of an integrated sensing and communication (ISAC)-enabled millimeter wave (mmWave) multiple-input multiple-output (MIMO) system. In the system under consideration, an ISAC base station (BS) with the hybrid MIMO architecture communicates with multiple users and simultaneously detects multiple targets. The proposed scheme seeks to maximize the EE of the system, considering the signal-to-interference and noise ratio (SINR) as the user's quality of service (QoS) and the sensing beampattern gain of the targets as constraints. To solve this non-convex problem, we initially adopt Dinkelbach's method to convert the fractional objective function to subtractive form and subsequently obtain the sub-optimal fully-digital transmit beamformer by leveraging the principle of semi-definite relaxation. Subsequently, we propose a penalty-based manifold optimization scheme in conjunction with an alternating minimization method to determine the baseband (BB) and analog beamformers based on the designed fully-digital transmit beamformer. Finally, simulation results are given to demonstrate the efficacy of our proposed algorithm with respect to the benchmarks.

2024-01-01, Singh, Jitendra, Srivastava, Suraj, Yadav, Surya P., Jagannatham, Aditya K., Hanzo, Lajos

In this work, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) cognitive radio (CR) downlink operating in the underlay mode is investigated. The cognitive base station (CBS) communicates with multiple secondary users (SUs), each having multiple RF chains in the presence of a primary user (PU). We conceive a joint hybrid transmit precoder (TPC), receiver combiner (RC), and RIS reflection matrix (RM) design, which maximizes the sum spectral efficiency (SE) of the secondary system while maintaining the interference induced at the PU below a specified threshold. To this end, we formulate the sum-SE maximization problem considering the total transmit power (TP), the interference power (IP), and the non-convex unity modulus constraints of the RF TPC, RF RC, and RM. To solve this highly non-convex problem, we propose a two-stage hybrid transceiver design in conjunction with a novel block coordinate descent (BCD)-successive Riemannian conjugate gradient (SRCG) algorithm. We initially decompose the RF TPC, RC, and RM optimization problem into a series of sub-problems and subsequently design pairs of RF TPC and RC vectors, followed by successively optimizing the elements of the RM using the iterative BCD-SRCG algorithm. Furthermore, based on the effective baseband (BB) channel, the BB TPC and BB RC are designed using the proposed direct singular value decomposition (D-SVD) and projection based SVD (P-SVD) methods. Subsequently, the proportional water-filling solution is proposed for optimizing the power, which maximizes the weighted sum-SE of the system. Finally, simulation results are provided to compare our proposed schemes to several benchmarks and quantify the impact of other parameters on the sum-SE of the system.

2024, Meesam Jafri, Srivastava, Suraj, Aditya K. Jagannatham, Sunil Kumar

In this work, we conceive novel robust hybrid beamformer design schemes for millimeter-wave (mmWave) multi-cell multi-user (MCMU) systems in the presence of channel state information (CSI) uncertainty, that relies on base station (BS) coordination and minimization of total transmit power while ensuring compliance to practical signal-to-interference-noise ratio (SINR) constraints for each user. We consider a scenario where some of the users are located close to the cell boundary and thus desire to receive the signal of interest transmitted by multiple BSs while ensuring the quality-of-service (QoS) constraint. Initially, a Bayesian learning (BL) framework is developed for estimating the sparse mmWave channel of each user in the system. Next, a semidefinite relaxation (SDR) based technique has been proposed for a centralized MCMU system toward designing the fully digital beamformer (FDBF) in the presence of stochastic uncertainty in the estimated channel. Subsequently, a BL technique is employed to split the FDBF into its analog and digital constituents toward obtaining a hybrid transmit precoder (TPC). However, the centralized TPC design requires global CSI, resulting in a high signaling overhead. Next, a distributed coordinated hybrid TPC utilizing the alternating direction method of multipliers (ADMM) algorithm is developed for the mmWave MCMU system in the presence of cell-edge (CE) users. The distributed TPC design solely relies on CSI and only requires a limited exchange of information between the BSs, consequently eliminating the need for the high signaling overheads that come along with the centralized method. Our simulation results illustrate the superior performance of the proposed centralized and distributed robust TPC design methods in comparison to non-coordinated systems.

2024, Anand Mehrotra, Srivastava, Suraj, Aditya K. Jagannatham

In this paper, adaptive channel state information (CSI) estimation techniques are conceived for intelligent reflective surface (IRS)-assisted single input and single output (SISO) and multiple input multiple output (MIMO) systems. Initially, the input-output system model is derived for an IRS-assisted SISO system, and the block least mean square (BLMS) and block recursive least square (BRLS) techniques are proposed for adaptive CSI estimation. Subsequently, the system model is also determined for IRS-assisted MIMO systems, and the adaptive CSI estimation schemes described above are also extended to this scenario. Convergence analysis is presented and the asymptotic mean square error (MSE) of estimation expressions are determined for the BLMS and BRLS algorithms. Finally, the simulation results are presented to demonstrate the performance and also validate the analytical results derived for the above adaptive CSI estimation schemes for IRS-assisted SISO and MIMO systems.

2024, Abhisha Garg, Akash Kumar, Srivastava, Suraj, Aditya K. Jagannatham

This paper conceives a new generalized simultane-ous orthogonal matching pursuit (GSOMP)-based algorithm to effectively estimate the sparse channel state information (CSI) in a multiuser (MU) THz hybrid MIMO system. The proposed framework also incorporates low-resolution analog-to-digital converters (ADCs) together with a sampled version of the transmit pulse shaping filter. The proposed techniques are based on a practical dual-wideband THz channel model that is developed initially, which considers both the spatial and frequency wide-band effects. The model also embraces the reflection, absorption and free-spaces losses that are a characteristic feature of the THz band. Subsequently, a novel MU hybrid transceiver design framework is advanced, based on the generalized alternating direction method of multipliers (MU-GADMM), which generates a new set of basis vectors toward robust approximation of the optimal precoder, in order to account for the beamsquint effect. Extensive simulations are conducted to evaluate the performance of the proposed CSI learning and beamforming techniques in a practical THz channel generated using the high-resolution transmission (HITRAN) database.

2024, Surbhi Gehlot, Narendra Kumar, Srivastava, Suraj, Yadav, Sandeep Kumar

Automatic modulation classification (AMC) is essential in non-orthogonal multiple access (NOMA) systems, since it enables dynamic adaptation of modulation schemes to optimize spectral efficiency and reduce inter-user interference. To address the AMC of the interference users in NOMA downlink systems, a lightweight Dual-Pool convolutional neural network (Dual-Pool CNN) algorithm is proposed, which leverages the power of the pooling layers in CNNs for better classification accuracy. It synergistically combines max-pooling and average-pooling to extract both general as well as sharp features from the distorted, faded, and superposed constellation diagrams. Our extensive experimentation demonstrates that the proposed model can classify the signals of four different modulation schemes with an accuracy score of more than 90% at an SNR of 14 dB and above. Moreover, this higher accuracy is obtained at lower computational complexity using the superposed constellation density grid (SCDG) approach followed by lightweight Dual-Pool CNN architecture.

2024, Shubham Saxena, Srivastava, Suraj, Saurabh Sharma, Aditya K. Jagannatham

This paper presents an innovative scheme for estimating the channel impulse response (CIR) in sparse multipath conditions for asymmetrically clipped direct current-biased op-tical OFDM (ADO-OFDM) visible light communication (VLC) systems, utilizing Bayesian learning (BL) techniques. We derive a multipath CIR model capturing both specular and diffusive reflections within the VLC system. Subsequently, we present a novel scheme for estimating the CIR in sparse multipath scenar-ios using the BL paradigm, which leverages the inherent sparsity of the multipath CIR in the delay domain. This scheme neces-sitates a constrained set of pilot subcarriers, thereby reducing pilot overhead when juxtaposed with traditional state-of-the-art channel estimation (CE) techniques. To assess the performance of the proposed BL-based paradigm for estimation, we compute the Oracle-MMSE (O-MMSE) along with the Bayesian Cramer Rao lower bound (BCRLB). Our extensive simulations reveal that even with a lower pilot overhead, the suggested BL method surpasses other conventional and sparse CE techniques across key metrics such as bit error-rate (BER) and normalized mean-square-error (NMSE).

2024, Meesam Jafri, Srivastava, Suraj, Aditya K. Jagannatham

This paper proposes an orthogonal time-frequency space (OTFS) modulation aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) phased-array radar (OmM-PAR) system for sparse radar target parameter estimation. Initially, we derive the delay-Doppler (DD)-domain end-to-end input-output model for the OmM-PAR system, which employs a single RF chain (RFC) both at the radar transmitter and receiver (R-TRX). Subsequently, a Bayesian learning (BL)-based procedure is developed for improved sparse radar target parameter estimation. Finally, our simulation results illustrate the enhanced performance of the proposed parameter learning framework for OmM-PAR systems. Furthermore, the performance of the proposed scheme is also benchmarked against the Bayesian Cramer-Rao lower bounds (BCRLB).

2024, Palla Siva Kumar, Apoorva Chawla, Srivastava, Suraj, Aditya K. Jagannatham, Lajos Hanzo

Low-complexity fusion rules relying on hybrid combining are proposed for decision fusion in frequency selective millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) sensor networks (SNs). Both centralized (C-MIMO) and distributed (D-MIMO) antenna architectures are considered, where the error-prone local sensor decisions are transmitted over orthogonal subcarriers to a fusion center (FC) employing a large antenna array. Fusion rules are designed for the FC, followed by closed-form expressions of the false alarm and detection probabilities to comprehensively characterize the performance of distributed detection. Furthermore, efficient transmit signaling vectors are designed for optimizing the detection performance. Both the asymptotic performance analysis and the pertinent power reduction laws are presented for the large antenna regime considering both the C-MIMO and D-MIMO topologies, which potentially lead to a significant transmit power reduction. Low-complexity fusion rules and their analyses are also given for the realistic scenario of incorporating channel state information (CSI) uncertainty, where the sparse Bayesian learning (SBL) framework is utilized for the estimation of the sparse frequency selective mmWave massive MIMO channel. Finally, the performance of the proposed low-complexity detectors is characterized through extensive simulation results for different scenarios.