arXiv subjects

Ravi Sharan Bhagavathula

Publications and source records attributed to Ravi Sharan Bhagavathula.

4 recordsLinked to original sources

arXiv2026

MU-MIMO Uplink Timely Throughput Maximization for Extended Reality Applications

In this work, we study the cross-layer timely throughput maximization for extended reality (XR) applications through uplink multi-user MIMO (MU-MIMO) scheduling. Timely scheduling opportunities are characterized by the peak age of information (PAoI)-metric and are incorporated into a network-side optimization problem as constraints modeling user satisfaction. The problem being NP-hard, we resort to a signaling-free, weighted proportional fair-based iterative heuristic algorithm, where the weights are derived with respect to the PAoI metric. Extensive numerical simulation results demonstrate that the proposed algorithm consistently outperforms existing baselines in terms of XR capacity without sacrificing the overall system throughput.

cs.IT↗

arXiv2026

Energy Efficient Downlink mMIMO Using Dynamic Antenna and Power Adaptation

Massive multiple-input multiple-output (mMIMO) technology and its future evolutions are expected to address the high data rate demands of sixth generation (6G) communication systems. At the same time, network energy savings (NES) is essential in reducing the operational costs and meeting the sustainability goals of network operators. In this regard, we propose a dynamic scheme for joint antenna and power adaptation to improve NES from a user scheduling and resource allocation perspective. Antenna adaptation is performed using the multiple channel state information resource signal (CSI-RS) framework. Furthermore, the recently introduced transmit power-aware link adaptation scheme, referred to as POLITE for short, is used as the power adaptation technique. The proposed scheme adapts to variations in users' instantaneous traffic and channel conditions to opportunistically maximize NES while also inherently accounting for the user throughput. Numerical simulation results show that the proposed scheme consistently achieves a balance between NES and user perceived throughput (UPT) for different network load conditions. Especially in low and light load conditions, the proposed scheme significantly improves the intra-cell interference and boosts the overall NES, while ensuring that UPT is unaffected.

cs.IT↗

arXiv2025

CQI-Based Interference Prediction for Link Adaptation in Industrial Sub-networks

We propose a novel interference prediction scheme to improve link adaptation (LA) in densely deployed industrial sub-networks (SNs) with high-reliability and low-latency communication (HRLLC) requirements. The proposed method aims to improve the LA framework by predicting and leveraging the heavy-tailed interference probability density function (pdf). Interference is modeled as a latent vector of available channel quality indicator (CQI), using a vector discrete-time state-space model (vDSSM) at the SN controller, where the CQI is subjected to compression, quantization, and delay-induced errors. To robustly estimate interference power values under these impairments, we employ a low-complexity, outlier-robust, sparse Student-t process regression (SPTPR) method. This is integrated into a modified unscented Kalman filter, which recursively refines predicted interference using CQI, enabling accurate estimation and compensating protocol feedback delays, crucial for accurate LA. Numerical results show that the proposed method achieves over 10x lower complexity compared to a similar non-parametric baseline. It also maintains a BLER below the 90th percentile target of 1e-6 while delivering performance comparable to a state-of-the-art supervised technique using only CQI reports.

eess.SP↗

arXiv2024

Dynamic Interference Prediction for In-X 6G Sub-networks

The sixth generation (6G) industrial Sub-networks (SNs) face several challenges in meeting extreme latency and reliability requirements in the order of 0.1-1 ms and 99.999 -to-99.99999 percentile, respectively. Interference management (IM) plays an integral role in addressing these requirements, especially in ultra-dense SN environments with rapidly varying interference induced by channel characteristics, mobility, and resource limitations. In general, IM can be achieved using resource allocation and \textit{accurate} Link adaptation (LA). In this work, we focus on the latter, where we first model interference at SN devices using the spatially consistent 3GPP channel model. Following this, we present a discrete-time dynamic state space model (DSSM) at a SN access point (AP), where interference power values (IPVs) are modeled as latent variables incorporating underlying modeling errors as well as transmission/protocol delays. Necessary approximations are then presented to simplify the DSSM and to efficiently employ the extended Kalman filter (EKF) for interference prediction. Unlike baseline methods, our proposed approach predicts IPVs solely based on the channel quality indicator (CQI) reports available at the SN AP at every transmission time interval (TTI). Numerical results demonstrate that our proposed approach clearly outperforms the conventional baseline. Furthermore, we also show that despite predicting with limited information, our proposed approach consistently achieves a comparable performance w.r.t the off-the-shelf supervised learning based baseline.

cs.IT↗