arXiv subjects

Dheeraj Naidu Amudala

Publications and source records attributed to Dheeraj Naidu Amudala.

6 recordsLinked to original sources

arXiv2026

Is Downlink Training Necessary for User-Centric Cell-Free RSMA Systems With Mobile Users?

We study the spectral efficiency (SE) of a ratesplitting multiple access (RSMA) enabled multi-clustered cell-free (CF) massive multiple-input multiple-output (mMIMO) system. The access points (APs) in each cluster serve mobile user equipments (UEs) by employing RSMA. The UEs employ successive interference cancellation to decode their data. This work emphasizes the role of downlink (DL) pilots in realizing RSMA benefits in practical CF systems with spatially-correlated Rician channels which observe random phase shifts, pilot contamination, and channel aging due to UE mobility. We numerically show that DL pilots are required for RSMA in user-centric CF mMIMO systems with channel aging to outperform spatial division multiple access. We show that the degraded channel quality due to higher UE velocity and longer resource block lengths significantly reduces the RSMA SE. Increasing the number of clusters can compensate for the SE loss.

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arXiv2024

Multi-cell mMIMO IRS Systems With Impairments and Aging: Phase Optimization and Receiver Design

We consider the uplink of a hardware-impaired intelligent reflective surfaces (IRS) aided multi-cell massive multiple-input multiple-output (mMIMO) system with mobile user equipments, whose channel age with time. For this system, we analyze the distortion due to hardware impairments, and the effect of channel aging, and propose a novel distortion-and-aging-aware MMSE (DAA-MMSE) receiver that not only provides a higher spectral efficiency (SE) than conventional maximal ratio and distortion-unaware MMSE (DU-MMSE) receivers, but also reduces the pilot overhead. By considering non-ideal hardware and spatially-correlated Rician fading channels with phase shifts, we also derive the SE lower bound for this system. We show that the SE gain of the DAA-MMSE receiver over DU-MMSE receiver increases with hardware impairments, and channel aging. Along with DAA-MMSE receiver, the IRS is also shown to reduce the pilot overhead in a mMIMO system with channel aging.

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arXiv2023

LSFD for Rician-Faded Cell-Free mMIMO Systems With Channel Aging and Hardware Impairments

We study the impact of channel aging on the uplink of a cell-free massive multiple-input multiple-output system with hardware impairments. We consider a dynamic analog-to-digital converter architecture at the access points (APs), and low-resolution digital-to-analog converters at the user equipments (UEs). We derive a closed-form spectral efficiency expression by considering i) practical spatially-correlated Rician channels; ii) hardware impairments at the APs and the UEs; iii) channel aging; and iv) large-scale fading decoding (LSFD). We show that LSFD can effectively mitigate the detrimental effects of i) channel aging for both low and high UE velocities; and ii) inter-user interference for low-velocity UEs but not for high-velocity UEs.

cs.IT↗

arXiv2022

Hardware-Aware Pilot Decontamination Precoding for Multi-cell mMIMO Systems With Rician Fading

We consider a hardware-impaired multi-cell Rician faded massive multi-input multi-output (mMIMO) system with two-layer pilot decontamination precoding, also known as large-scale fading precoding (LSFP). Each BS is equipped with a flexible dynamic analog-to-digital converter (ADC)/digital-to-analog converter (DAC) architecture and the user equipments (UEs) have low-resolution ADCs. Further, both BS and UEs have hardwareimpaired radio frequency chains. The dynamic ADC/DAC architecture allows us to vary the resolution of ADC/DAC connected to each BS antenna, and suitably choose them to maximize the SE. We propose a distortion-aware minimum mean squared error (DA-MMSE) precoder and investigate its usage with two-layer LSFP and conventional single-layer precoding (SLP) for hardware-impaired mMIMO systems. We discuss the use cases of LSFP and SLP with DA-MMSE and distortion-unaware MMSE (DU-MMSE) precoders, which will provide critical insights to the system designer regarding their usage in practical systems.

cs.IT↗

arXiv2021

Full-Duplex Cell-Free mMIMO Systems: Analysis and Decentralized Optimization

Cell-free (CF) massive multiple-input-multiple-output (mMIMO) deployments are usually investigated with half-duplex nodes and high-capacity fronthaul links. To leverage the possible gains in throughput and energy efficiency (EE) of full-duplex (FD) communications, we consider a FD CF mMIMO system with practical limited-capacity fronthaul links. We derive closed-form spectral efficiency (SE) lower bounds for this system with maximum-ratio combining/maximum-ratio transmission processing and optimal uniform quantization. We then optimize the weighted sum EE (WSEE) via downlink and uplink power control by using a two-layered approach: the first layer formulates the optimization as a generalized convex program, while the {second layer} solves the optimization decentrally using the alternating direction method of multipliers. We analytically show that the proposed two-layered formulation yields a Karush-Kuhn-Tucker point of the original WSEE optimization. We numerically show the influence of weights on the individual EE of the users, which demonstrates the utility of the WSEE metric to incorporate heterogeneous EE requirements of users. We show that low fronthaul capacity reduces the number of users each AP can support, and the cell-free system, consequently, becomes user-centric.

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arXiv2021

FD Cell-Free mMIMO: Analysis and Optimization

Cell-free (CF) massive multiple-input-multiple-output (mMIMO) deployments are usually investigated with half-duplex nodes and high-capacity fronthaul links. To leverage the possible gains in throughput and energy efficiency (EE) of full-duplex (FD) communications, we consider a FD CF mMIMO system with practical limited-capacity fronthaul links. We derive closed-form spectral efficiency (SE) lower bounds for this system with maximum-ratio combining/maximum-ratio transmission processing and optimal uniform quantization. We then optimize the weighted sum EE (WSEE) via downlink and uplink power control by using a {two-layered} approach: the first layer formulates the optimization as a generalized convex program, while the second layer solves the optimization decentrally using alternating direction method of multipliers. We analytically show that the proposed two-layered formulation yields a Karush-Kuhn-Tucker point of the original WSEE optimization. We numerically show the influence of weights on the individual EE of the users, which demonstrates the utility of WSEE metric to incorporate heterogeneous EE requirements of users. We show that the low fronthaul capacity reduces the number of users each AP can support, and the cell-free system, consequently, becomes user-centric.

eess.SP↗