References of "Mysore Rama Rao, Bhavani Shankar 50002712"
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See detailDiscrete-Phase Sequence Design with Stopband and PSL Constraints for Cognitive Radar
Alaeekerahroodi, Mohammad UL; Kumar, Sumit UL; Mysore Rama Rao, Bhavani Shankar UL et al

in Proceedings of EuRAD 2020 (in press)

We present the design of discrete-phase sequences considering simultaneously the peak sidelobe level (PSL) and avoiding reserved frequency bands which are occupied by narrowband interferers or ... [more ▼]

We present the design of discrete-phase sequences considering simultaneously the peak sidelobe level (PSL) and avoiding reserved frequency bands which are occupied by narrowband interferers or communications. We use the coordinate descent framework and propose an algorithm to design discrete-phase sequences with spectral power suppressed in arbitrary bands and with low auto-correlation sidelobes in terms of PSL. Our proposed algorithm exploits fast Fourier transform and is, therefore, computationally efficient. The over-the-air experiments using implementation on software-defined radio show reasonable agreement with numerical simulations and feasibility for field-deployment [less ▲]

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See detailTerminal-Aware Multi-Connectivity Scheduler for Uplink Multi-Layer Non-Terrestrial Networks
Dazhi, Michael UL; Al-Hraishawi, Hayder UL; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Global Communications Conference (Globecom) (2023, January 12)

This paper introduces the concept of multi-connectivity (MC) to the multi-orbit non-terrestrial networks (NTNs), where user terminals can be served by more than one satellite to achieve higher peak ... [more ▼]

This paper introduces the concept of multi-connectivity (MC) to the multi-orbit non-terrestrial networks (NTNs), where user terminals can be served by more than one satellite to achieve higher peak throughput. MC is a technique initially introduced by the 3rd Generation Partnership Project (3GPP) for terrestrial communications in 4G and 5G, it has shown much gain in the terrestrial domain and this paper explores areas where this concept can benefit the satellite domain. MC can increase throughput, but this entails increased power consumption at user terminal for uplink transmissions. The energy efficiency of uplink communications can be improved by designing efficient scheduling schemes, and to this end, we developed a terminal aware multi-connectivity scheduling algorithm. This proposed algorithm uses the available radio resources and propagation information to intelligently define a dynamic resource allocation pattern, that optimally routes traffic so as to maximize uplink data rate while minimizing the energy consumption at the UT. The algorithm operates with the terminal differentiating multi-layer NTN resource scheduling architecture, which has a softwarized dispatcher at the network layer that classifies and differentiates the packets based on terminal type. The performance of the proposed algorithm was compared with round robin and joint carrier schedulers in terms of uplink data rate and energy efficiency. We also provide architectural design of implementable schedulers for multi-orbital satellite networks that can operate with different classes of terminals. [less ▲]

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See detailUplink Capacity Optimization for High Throughput Satellites using SDN and Multi-Orbital Dual Connectivity
Dazhi, Michael UL; Al-Hraishawi, Hayder UL; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE International Conference on Communications (ICC) (2022, July 11)

Dual Connectivity is a key approach to achieving optimization of throughput and latency in heterogeneous networks. Originally a technique introduced by the 3rd Generation Partnership Project (3GPP) for ... [more ▼]

Dual Connectivity is a key approach to achieving optimization of throughput and latency in heterogeneous networks. Originally a technique introduced by the 3rd Generation Partnership Project (3GPP) for terrestrial communications, it is not been widely explored in satellite systems. In this paper, Dual Connectivity is implemented in a multi-orbital satellite network, where a network model is developed by employing the diversity gains from Dual Connectivity and Carrier Aggregation for the enhancement of satellite uplink capacity. An introduction of software defined network controller is performed at the network layer coupled with a carefully designed hybrid resource allocation algorithm which is implemented strategically. The algorithm performs optimum dynamic flow control and traffic steering by considering the availability of resources and the channel propagation information of the orbital links to arrive at a resource allocation pattern suitable in enhancing uplink system performance. Simulation results are shown to evaluate the achievable gains in throughput and latency; in addition we provide useful insight in the design of multi-orbital satellite networks with implementable scheduler design. [less ▲]

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See detailResource Allocation in Heterogeneously-Distributed Joint Radar-Communications Under Asynchronous Bayesian Tracking Framework
Wu, Linlong; Mishra, Kumar Vijay; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Journal on Selected Areas in Communications (2022), 40(7), 2026-2042

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources ... [more ▼]

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources in colocated joint radar-communications or among identical systems, we investigate this problem for a distributed system comprising heterogeneous radars and multi-tier communications. In particular, we focus on resource allocation in the context of multi-target tracking (MTT) while maintaining stable communications connections. By simultaneously allocating the available power, dwell time and shared bandwidth, we improve the MTT performance under a Bayesian tracking framework and guarantee the communications throughput. Our a lter n ating allo c ation of h eterogene o us r esources (ANCHOR) approach solves the resulting non-convex problem based on the alternating optimization method that monotonically improves the Bayesian Cramér-Rao bound. Numerical experiments demonstrate that ANCHOR significantly improves the tracking error over two baseline allocations and stability under different target scenarios and radar-communications network distributions. [less ▲]

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See detailA 3D Indoor Localization Approach Based on Spherical Wave-front and Channel Spatial Geometry
Liu, Yuan UL; Wu, Linlong UL; Alaeekerahroodi, Mohammad UL et al

in Liu, Yuan; Wu, Linlong; Alaeekerahroodi, Mohammad (Eds.) et al 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) (2022, June)

Because of the near-field nature of radio propagation, spherical wave-front and multipath effect are prominent in indoor scenarios, making localization even more difficult. In this paper, we propose a ... [more ▼]

Because of the near-field nature of radio propagation, spherical wave-front and multipath effect are prominent in indoor scenarios, making localization even more difficult. In this paper, we propose a three-dimensional (3D) indoor localization algorithm that takes these issues into account. Specifically, we first adopted a high-resolution channel parameter estimation method for path delays based on the Space-Alternating Generalized Expectation-maximization (SAGE), and then these path delays are adopted in the 3D localization principles based on the target-antenna geometry. The proposed algorithm is validated by numerical simulations, where the channel data is generated by the propagation graph (PG) to model the true wireless propagation closely in the testing scenarios. The results demonstrate that the proposed approach can deal with both point and non-point targets with 3D localization errors of less than 30 cm for 97% of the testing trails in a 10×20×3 m3 indoor space. [less ▲]

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See detailSpatio-Temporal Rainfall Estimation from Communication Satellite Data using Graph Neural Networks
Krebs, Julian UL; Mishra, Kumar Vijay; Gharanjik, Ahmad et al

Scientific Conference (2022, May)

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See detailOptimal Sensor Placement for Source Localization: A Unified ADMM Approach
Sahu, Nitesh; Wu, Linlong; Babu, Prabhu et al

in IEEE Transactions on Vehicular Technology (2022), 71(4), 4359-4372

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time ... [more ▼]

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA) and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA, AOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a u nified op t imization fra m ework for o ptimal s ensor placemen t (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework. [less ▲]

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See detailWidely Distributed Radar Imaging: Unmediated ADMM Based Approach
Murtada, Ahmed Abdelnaser Elsayed UL; Hu, Ruizhi UL; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Journal of Selected Topics in Signal Processing (2022)

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a ... [more ▼]

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated by exploiting the Civilian Vehicles Dome dataset to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements. [less ▲]

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See detailImproving Pulse-Compression Weather Radar via the Joint Design of Subpulses and Extended Mismatch Filter
Wu, Linlong UL; Alaeekerahroodi, Mohammad UL; Mysore Rama Rao, Bhavani Shankar UL

in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (2022)

Pulse compression can enhance both the performance in range resolution and sensitivity for weather radar. However, it will introduce the issue of high sidelobes if not delicately implemented. Motivated by ... [more ▼]

Pulse compression can enhance both the performance in range resolution and sensitivity for weather radar. However, it will introduce the issue of high sidelobes if not delicately implemented. Motivated by this fact, we focus on the pulse compression design for weather radar in this paper. Specifically, we jointly design both the subpulse codes and extended mismatch filter based on the alternating direction method of multipliers (ADMM). This joint design will yield a pulse compression with low sidelobes, which equivalently implies a high signal-to-interference-plus-noise ratio (SINR) and a low estimation error on meteorological reflectivity. The experiment results demonstrate the efficacy of the proposed pulse compression strategy since its achieved meteorological reflectivity estimations are highly similar to the ground truth. [less ▲]

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See detailDouble-Phase-Shifter Based Hybrid Beamforming for mmWave DFRC in the Presence of Extended Target and Clutters
Cheng, Ziyang; Wu, Linlong; Wang, Bowen et al

in IEEE Transactions on Wireless Communications ( Early Access ) (2022)

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this ... [more ▼]

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this work, in the presence of extended target and clutters, a HBF design based on the subarray connection architecture is proposed for a multiple-input multiple-output (MIMO) DFRC system. In this HBF, the double-phase-shifter (DPS) structure is embedded to further increase the design flexibility. We derive the communication spectral efficiency (SE) and radar signal-to-interference-plus-noise-ratio (SINR) with respect to the transmit HBF and radar receiver, and formulate the HBF design problem as the SE maximization subjecting to the radar SINR and power constraints. To solve the formulated nonconvex problem, the joinT Hybrid bEamforming and Radar rEceiver OptimizatioN (THEREON) is proposed, in which the radar receiver is optimized via the generalized eigenvalue decomposition, and the transmit HBF is updated with low complexity in a parallel manner using the consensus alternating direction method of multipliers (consensus-ADMM). Furthermore, we extend the proposed method to the multi-user multiple-input single-output (MU-MISO) scenario. Numerical simulations demonstrate the efficacy of the proposed algorithm and show that the solution provides a good trade-off between number of phase shifters and performance gain of the DPS HBF. [less ▲]

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See detailContactless radar-based breathing monitoring of premature infants in the neonatal intensive care unit
Tedgue Beltrao, Gabriel UL; Stutz, Regine; Hornberger, Franziska et al

in Scientific Reports (2022), 12(1), 1--15

Vital sign monitoring systems are essential in the care of hospitalized neonates. Due to the immaturity of their organs and immune system, premature infants require continuous monitoring of their vital ... [more ▼]

Vital sign monitoring systems are essential in the care of hospitalized neonates. Due to the immaturity of their organs and immune system, premature infants require continuous monitoring of their vital parameters and sensors need to be directly attached to their fragile skin. Besides mobility restrictions and stress, these sensors often cause skin irritation and may lead to pressure necrosis. In this work, we show that a contactless radar-based approach is viable for breathing monitoring in the Neonatal intensive care unit (NICU). For the first time, different scenarios common to the NICU daily routine are investigated, and the challenges of monitoring in a real clinical setup are addressed through different contributions in the signal processing framework. Rather than just discarding measurements under strong interference, we present a novel random body movement mitigation technique based on the time-frequency decomposition of the recovered signal. In addition, we propose a simple and accurate frequency estimator which explores the harmonic structure of the breathing signal. As a result, the proposed radar-based solution is able to provide reliable breathing frequency estimation, which is close to the reference cabled device values most of the time. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a completely contactless solution for vital signs monitoring. [less ▲]

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See detailNonlinear Least Squares Estimation for Breathing Monitoring Using FMCW Radars
Tedgue Beltrao, Gabriel UL; Alaeekerahroodi, Mohammad UL; Schroeder, Udo et al

in 2021 18th Eur. Radar Conf. (2022)

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See detailStatistical Performance Analysis of Radar-Based Vital-Sign Processing Techniques
Tedgue Beltrao, Gabriel UL; Alaeekerahroodi, Mohammad UL; Schroeder, Udo et al

Scientific Conference (2022)

Radar-based vital-sign monitoring provides several advantages over standard methodologies. Despite the huge amount of recent work, the preference for particular technique(s) is in debt, due to lack of a ... [more ▼]

Radar-based vital-sign monitoring provides several advantages over standard methodologies. Despite the huge amount of recent work, the preference for particular technique(s) is in debt, due to lack of a formal comparison between them. In addition, collection of real data is a time-consuming process and therefore most of the proposed solutions are only evaluated under very limited scenarios. In this paper we present a simulation framework and a selection of results which allow easy performance comparison between radar-based vital-sign processing techniques. The proposed simulation tool scans over multiple breathing and heartbeat frequencies, and the combined effects along the entire signal processing chain can be analyzed, for different combinations of scenarios and techniques. The results have shown specific limitations for each method, thus indicating a need for proper selection based on operating conditions. In addition, while breathing estimation performance is only limited by noise, heartbeat estimation is limited by the presence of breathing harmonics and, despite promising results at specific breathing/heartbeat frequencies, the presented methods fail to fully mitigate this type of interference in all scenarios. [less ▲]

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See detailAdaptive Nonlinear Least Squares Framework for Contactless Vital Sign Monitoring
Tedgue Beltrao, Gabriel UL; Alves Martins, Wallace UL; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Transactions on Microwave Theory and Techniques (2022)

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See detailRecurrent Design of Probing Waveform for Sparse Bayesian Learning Based DOA Estimation
Wu, Linlong; Dai, Jisheng; Mysore Rama Rao, Bhavani Shankar UL et al

in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)

Direction-of-arrival (DOA) estimation can be represented as a sparse signal recovery problem and effectively solved by sparse Bayesian learning (SBL). For the DOA estimation in active sensing, the SBL ... [more ▼]

Direction-of-arrival (DOA) estimation can be represented as a sparse signal recovery problem and effectively solved by sparse Bayesian learning (SBL). For the DOA estimation in active sensing, the SBL-based estimation error is related to the transmitted probing waveform. Therefore, it is expected to improve the estimation by waveform optimization. In this paper, we propose a recurrent scheme of waveform design by sequentially leveraging on the previous-round SBL estimates. Within this scheme, we formulate the waveform design problem as a minimization of the SBL estimation variance, which is non-convex and then solved by a majorization-minimization based algorithm. The simulations demonstrate the efficacy of the proposed design scheme in terms of avoiding incorrect detection and accelerating the DOA estimation convergence. Further, the results indicate that the waveform design is essentially a beampattern shaping methodology. [less ▲]

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See detailJoint Multislot Scheduling and Precoding for Unicast and Multicast Scenarios in Multiuser MISO Systems
Bandi, Ashok UL; Mysore Rama Rao, Bhavani Shankar UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2022), 21(7), 5004-5018

This paper studies the joint multislot design of user scheduling and precoding to minimize the time needed to serve all the users for unicast and multicast transmission in single-cell multiuser MISO ... [more ▼]

This paper studies the joint multislot design of user scheduling and precoding to minimize the time needed to serve all the users for unicast and multicast transmission in single-cell multiuser MISO downlink systems. In the literature, the joint design of scheduling and precoding is typically undertaken based on feedback from previous slots. In a system with time-varying channels and QoS requirements, joint multislot designs can achieve better performance since they have the flexibility to schedule users over multiple slots and also can split users across slots efficiently. Further, a joint multislot design can provide a feasible solution even when the sequential design fails. In this paper, scheduling is represented by a binary matrix where the rows represent users, columns represent slots and entries represent scheduling of users in the slots. Noticing that the users may not be permuted across slots for time-varying channels, service time needed for scheduling is rendered as the highest column index corresponding to non-zero columns. With the help of binary scheduling matrix, service time minimization is formulated as a structured mixed-Boolean fractional programming. Further, by exploiting the hidden convex-concave structure in the problem, a convex-concave procedure-based iterative algorithm is proposed. Finally, we vindicate the necessity and illustrate the superiority in performance of joint multislot design over the sequential solution through Monte-Carlo simulations. [less ▲]

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See detailUnlimited Sampling for FMCW Radars: A Proof of Concept
Feuillen, Thomas; Alaeekerahroodi, Mohammad UL; Bhandari, Ayush et al

in 2022 IEEE Radar Conference (RadarConf22) (2022)

High-resolution FMCW radar systems are becoming an integral aspect of applications ranging from automotive safety and autonomous driving to health monitoring of infants and the elderly. This integration ... [more ▼]

High-resolution FMCW radar systems are becoming an integral aspect of applications ranging from automotive safety and autonomous driving to health monitoring of infants and the elderly. This integration provides challenging scenarios that require radars with extremely high dynamic range (HDR) ADCs; these ADCs need to avoid saturation while offering high-performance and high-fidelity data-acquisition. The recent concept of Unlimited Sensing allows one to achieve high dynamic range (HDR) acquisition by recording low dynamic range, modulo samples. Interestingly, oversampling of these folded measurements, with a sampling rate independent of the modulo threshold, is sufficient to guarantee their perfect reconstruction for band-limited signals. This contrasts with the traditional methodology of increasing the dynamic range by adding a programmable-gain amplifier or operating multiple ADCs in parallel. This paper demonstrates an FMCW radar prototype that utilises the unlimited sampling strategy. Our hardware experiments show that even with the use of a modulo measurements of lower precision, the US reconstruction is able to match the performances of the conventional acquisition. Furthermore, our real-time processing capability demonstrates that our “proof-of-concept” approach is a viable solution for HDR FMCW radar signal processing, thus opening a pathway for future hardware-software optimization and integration of this technology with other mainstream systems. [less ▲]

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See detailAccelerated Consensus ADMM for Widely Distributed Radar Imaging
Murtada, Ahmed Abdelnaser Elsayed UL; Mysore Rama Rao, Bhavani Shankar UL; Hu, Ruizhi UL et al

in 2022 IEEE Radar Conference (RadarConf22) Proceedings (2022)

Widely distributed radar systems are expected to enhance radar imaging performance due to their ability to capture diverse spatial scattering proprieties. Optimization-based sub-aperture imaging methods ... [more ▼]

Widely distributed radar systems are expected to enhance radar imaging performance due to their ability to capture diverse spatial scattering proprieties. Optimization-based sub-aperture imaging methods are used to adopt the isotropic scattering assumption within a narrow angular extent and reconstruct the scene image by fusing sub-aperture images. A previously proposed method based on consensus alternating direction method of multipliers (CADMM) provides a joint reconstruction of sub-aperture images along with a global image that represents the anisotropic scene. In this paper, we propose a modified version of CADMM which features lower complexity and faster convergence. By gradually learning the scene support during the iterative reconstruction, our proposed algorithm focuses on the image portion that contains the scattering targets and updates the sub-images accordingly. It also reduces the communication cost between the distributed sensors which need to exchange local image updates during CADMM iterations. [less ▲]

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See detailPhysics-Based Cognitive Radar Modeling and Parameter Estimation
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Mishra, Kumar Vijay et al

in IEEE Radar Conference (2022)

We consider the problem of channel response estimation in cognitive fully adaptive radar (CoFAR). We show that this problem can be expressed as a constrained channel estimation problem exploiting the ... [more ▼]

We consider the problem of channel response estimation in cognitive fully adaptive radar (CoFAR). We show that this problem can be expressed as a constrained channel estimation problem exploiting the similarity between the channel impulse responses (CIRs) of the adjacent channels. We develop a constrained CIR estimation (CCIRE) algorithm enhancing estimation performance compared to the unconstrained CIR estimation where the similarity between the CIRs of the adjacent channels is not employed. Further, we we derive the Cram\'{e}r-Rao bound (CRB) for the CCIRE and show the optimality of the proposed CCIRE through comparing its performance with the derived CRB. [less ▲]

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See detailA family of deep learning architectures for channel estimation and hybrid beamforming in multi-carrier mm-wave massive MIMO.
Elbir, Ahmet M.; Mishra, Kumar Vijay; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Transactions on Cognitive Communications and Networking (2021)

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive ... [more ▼]

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment. [less ▲]

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