arXiv subjects

Chandra R. Murthy

Publications and source records attributed to Chandra R. Murthy.

At least 19 recordsLinked to original sources

arXiv2026

Practical RIS Gain without the Pain via Randomization and Opportunistic Scheduling in 5G NR Wireless Systems: Theory and Experiments

In this paper, we theoretically analyze and experimentally demonstrate the performance gains achievable by integrating an in-house built reconfigurable intelligent surface (RIS) with a 5G new radio (NR) system implemented using the OpenAirInterface (OAI) software stack. Unlike conventional RIS-assisted systems that rely on explicit channel state information (CSI) estimation followed by RIS phase configuration optimization, we adopt a low-complexity approach in which the RIS phase states are randomly switched among predefined configurations. The resulting channel fluctuations are opportunistically exploited by the inherent proportional fair (PF) scheduling mechanism of 5G NR. We develop a theoretical framework that characterizes the interaction between RIS switching dynamics and PF scheduling. Based on this framework and the associated analysis, we provide design guidelines for selecting the RIS switching time $T_s$ and the PF throughput averaging window $T_c$ that maximize the system throughput. Experimental evaluations on the 5G NR testbed demonstrate improvements in key performance metrics, including reference signal received power (RSRP), block error rate (BLER), modulation and coding scheme (MCS) index, and throughput. Our key takeaway is that randomly configured RIS operation with appropriately chosen system parameters can achieve performance comparable to optimized RIS designs, with no additional overhead compared to a conventional 5G NR system. More importantly, it requires no coordination between the RIS and the 5G NR system.

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arXiv2026

Practical RIS Gain without the Pain: Randomization and Opportunistic Scheduling in 5G NR

We experimentally demonstrate the performance gains achieved by an in-house built reconfigurable intelligent surface (RIS) integrated with a real-time 5G new radio (NR) system implemented using the OpenAirInterface (OAI) framework. We first quantify the gain in throughput achievable by integrating an RIS with a 5G system. Next, we show that randomly setting the RIS phase configuration and leveraging the inherent proportional fair (PF) scheduling mechanism of 5G NR can yield near-optimal throughput, provided the throughput averaging window of the PF scheduler is chosen judiciously. This occurs because, in each time slot, the PF scheduler naturally prioritizes data transmission to the user equipment (UE) that experiences the best channel conditions, namely, the UE to which the randomly configured RIS is aligned. Subsequently, we experimentally evaluate key performance metrics, including the reference signal received power (RSRP), block error rate (BLER), modulation and coding scheme (MCS) index, and throughput, under random RIS configurations. These results confirm that even a randomly configured RIS with negligible overhead can deliver performance comparable to optimized RIS designs, in real-world 5G NR wireless communication systems.

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arXiv2025

Channel Estimation and Data Detection in DS-Spread Channels: A Unified Framework, Novel Algorithms, and Waveform Comparison

We present a unified receiver processing framework for communication over delay-scale (DS)-spread channels that arise in underwater acoustic (UWA) communications that addresses both channel estimation (CE) and data detection for different modulation waveforms, namely OFDM, OTFS, OCDM, and ODSS, through a common input--output relation. Using this framework, we conduct a fair and comprehensive comparative study of these waveforms under DS-spread UWA channels and similar receiver complexities. We also develop a novel iterative variational Bayesian (VB) off-grid CE algorithm to estimate the delay and scale parameters of the channel paths, via two approaches: a first-order approximation scheme (FVB) and a second-order approximation scheme (SVB). We propose a low-complexity variational soft symbol detection (VSSD) algorithm that outputs soft symbols and log-likelihood ratios for the data bits, and a data-aided iterative CE and data detection (ICED) scheme that utilizes detected data symbols as \emph{virtual} pilots to further improve the CE and data detection accuracy. Our numerical results reveal the efficacy of the proposed algorithms for CE and data detection. In terms of relative performance of different waveforms, in uncoded communications, (a) with a low-complexity subcarrier-by-subcarrier equalizer, ODSS offers the best performance, followed by OCDM and OTFS, while OFDM performs the worst, and (b) with the VSSD algorithm, OTFS, OCDM, and ODSS perform similarly, and they outperform OFDM. With coded communications, interestingly, all waveforms offer nearly the same BER when the VSSD receiver is employed. Hence, we conclude that when the receiver complexity is constrained, waveform choice matters, especially under harsh channel conditions, whereas with more sophisticated receiver algorithms, these differences disappear.

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arXiv2025

Discrete-Time Linear Dynamical System Control Using Sparse Inputs With Time-Varying Support

In networked control systems, communication resource constraints often necessitate the use of \emph{sparse} control input vectors. A prototypical problem is how to ensure controllability of a linear dynamical system when only a limited number of actuators (inputs) can be active at each time step. In this work, we first present an algorithm for determining the \emph{sparse actuator schedule}, i.e., the sequence of supports of the input vectors that ensures controllability. Next, we extend the algorithm to minimize the average control energy by simultaneously minimizing the trace of the controllability Gramian, under the sparsity constraints. We derive theoretical guarantees for both algorithms: the first algorithm ensures controllability with a minimal number of control inputs at a given sparsity level; for the second algorithm, we derive an upper bound on the average control energy under the resulting actuator schedule. Finally, we develop a novel sparse controller based on Kalman filtering and sparse signal recovery that drives the system to a desired state in the presence of process and measurement noise. We also derive an upper bound on the steady-state MSE attained by the algorithm. We corroborate our theoretical results using numerical simulations and illustrate that sparse control achieves a control performance comparable to the fully actuated systems.

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arXiv2025

Beyond Assumptions: Measuring Federated Learning over Real 5G Networks

Deploying FL using IoT devices is an area poised to significantly benefit from advances in NextG wireless. In this paper, we deploy a FL application using a 5G-NR Standalone (SA) testbed with open-source and Commercial Off-the-Shelf (COTS) components. The 5G testbed architecture consists of a network of resource-constrained edge devices, namely Raspberry Pis, and a central server equipped with a Software Defined Radio (SDR) and running O-RAN software. Our testbed allows edge devices to communicate with the server using WiFi and Ethernet in addition to 5G. FL is deployed using the Flower FL framework, extended with custom instrumentation for communication and ML metrics. We analyze the FL application across three network interfaces--5G, WiFi, and Ethernet--as well as across 5G bandwidths and uplink-downlink scheduling ratios. Our experimental results challenge some common assumptions about communication time in FL over wireless and discuss the potential pitfalls of these assumptions. We find that there is a consistent straggler in about 70% of trials, while in the other 30%, high communication time causes competing stragglers. We also compare FL performance over 5G with and without external congestion and compare our testbed to commercial 5G to validate our findings in a broader context. For reproducibility, we have open-sourced our FL application, instrumentation tools, and testbed configuration.

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arXiv2025

Performance Analysis of Multi-IRS Aided Multiple Operator Systems at mmWave Frequencies

Intelligent reflecting surfaces (IRSs) are envisioned to enhance the performance of mmWave wireless systems. In practice, multiple mobile operators (MO) coexist in an area and provide simultaneous and independent services to user-equipments (UEs) on different frequency bands. Then, if each MO deploys an IRS to enhance its performance, the IRSs also alter the channels of UEs of other MOs. In this context, this paper addresses the following questions: can an MO still continue to control its IRS independently of other MOs and IRSs? Is joint optimization of IRSs deployed by different MOs and inter-MO cooperation needed? To that end, by considering the mmWave bands, we first derive the ergodic sum spectral efficiency (SE) in a $2$-MO system for the following schemes: 1) joint optimization of an overall phase angle of the IRSs with MO cooperation, 2) MO cooperation via time-sharing, and 3) no cooperation between the MOs. We find that even with no cooperation between the MOs, the performance of a given MO is not degraded by the presence of an out-of-band (OOB) MO deploying and independently controlling its own IRS. On the other hand, the SE gain obtained at a given MO using joint optimization and cooperation over the no-cooperation scheme decreases inversely with the number of elements in the IRS deployed by the other MO. We generalize our results to a multiple MO setup and show that the gain in the sum-SE over the no-cooperation case increases at least linearly with the number of OOB MOs. Finally, we numerically verify our findings and conclude that every MO can independently operate and tune its IRS; cooperation via optimizing an overall phase only brings marginal benefits in practice.

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arXiv2024

A Probably Approximately Correct Analysis of Group Testing Algorithms

We consider the problem of identifying the defectives from a population of items via a non-adaptive group testing framework with a random pooling-matrix design. We analyze the sufficient number of tests needed for approximate set identification, i.e., for identifying almost all the defective and non-defective items with high confidence. To this end, we view the group testing problem as a function learning problem and develop our analysis using the probably approximately correct (PAC) framework. Using this formulation, we derive sufficiency bounds on the number of tests for three popular binary group testing algorithms: column matching, combinatorial basis pursuit, and definite defectives. We compare the derived bounds with the existing ones in the literature for exact recovery theoretically and using simulations. Finally, we contrast the three group testing algorithms under consideration in terms of the sufficient testing rate surface and the sufficient number of tests contours across the range of the approximation and confidence levels.

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arXiv2024

Exploiting Beam-Split in IRS-aided Systems via OFDMA

In wideband systems operating at mmWave frequencies, intelligent reflecting surfaces (IRSs) equipped with many passive elements can compensate for channel propagation losses. Then, a phenomenon known as the beam-split (B-SP) occurs in which the phase shifters at the IRS elements fail to beamform at a desired user equipment (UE) over the total allotted bandwidth (BW). Although B-SP is usually seen as an impairment, in this paper, we take an optimistic view and exploit the B-SP effect to enhance the system performance via an orthogonal frequency division multiple access (OFDMA). We argue that due to the B-SP, when an IRS is tuned to beamform at a particular angle on one frequency, it also forms beams in different directions on other frequencies. Then, by opportunistically scheduling different UEs on different subcarriers (SCs), we show that, almost surely, the optimal array gain that scales quadratically in the number of IRS elements can be achieved on all SCs in the system. We derive the achievable throughput of the proposed scheme and deduce that the system also enjoys additional multi-user diversity benefits on top of the optimal beamforming gain over the full BW. Finally, we verify our findings via numerical simulations.

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arXiv2024

Sparse Actuator Scheduling for Discrete-Time Linear Dynamical Systems

We consider the control of discrete-time linear dynamical systems using sparse inputs where we limit the number of active actuators at every time step. We develop an algorithm for determining a sparse actuator schedule that ensures the existence of a sparse control input sequence, following the schedule, that takes the system from any given initial state to any desired final state. Since such an actuator schedule is not unique, we look for a schedule that minimizes the energy of sparse inputs. For this, we optimize the trace of the inverse of the resulting controllability Gramian, which is an approximate measure of the average energy of the inputs. We present a greedy algorithm along with its theoretical guarantees. Finally, we empirically show that our greedy algorithm ensures the controllability of the linear system with a small number of active actuators per time step without a significant average energy expenditure compared to the fully actuated system.

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arXiv2024

On the Impact of an IRS on the Out-of-Band Performance in Sub-6 GHz & mmWave Frequencies

Intelligent reflecting surfaces (IRSs) were introduced to enhance the performance of wireless communication systems. However, from a service provider's viewpoint, a concern with the use of an IRS is its effect on out-of-band (OOB) quality of service. Specifically, if two operators, say X and Y, provide services in a given geographical area using non-overlapping frequency bands, and if operator X uses an IRS to enhance the spectral efficiency (SE) of its users (UEs), does it degrade the performance of UEs served by operator Y? We answer this by analyzing the average and instantaneous performances of the OOB operator considering both sub-6 GHz and mmWave bands. Specifically, we derive the ergodic sum SE achieved by the operators under round-robin scheduling. We also derive the outage probability and analyze the change in the SNR caused by the IRS at an OOB UE using stochastic dominance theory. Surprisingly, even though the IRS is randomly configured from operator Y's point of view, the OOB operator still benefits from the presence of the IRS, witnessing a performance enhancement for free in both sub-6 GHz and mmWave bands. This is because the IRS introduces additional paths between the transmitter and receiver, increasing the overall signal power arriving at the UE and providing diversity benefits. Finally, we show that the use of opportunistic scheduling schemes can further enhance the benefit of the uncontrolled IRS at OOB UEs. We numerically illustrate our findings and conclude that an IRS is always beneficial to every operator, even when the IRS is deployed & controlled by only one operator.

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arXiv2024

Half-Duplex APs with Dynamic TDD vs. Full-Duplex APs in Cell-Free Systems

In this paper, we present a comparative study of half-duplex (HD) access points (APs) with dynamic time-division duplex (DTDD) and full-duplex (FD) APs in cell-free (CF) systems. Although both DTDD and FD CF systems support concurrent downlink (DL) transmission and uplink (UL) reception capability, the sum spectral efficiency (SE) is limited by various cross-link interferences. We first present a novel pilot allocation scheme that minimizes the pilot length required to ensure no pilot contamination among the user equipments (UEs) served by at least one common AP. Then, we derive the sum SE in closed form, considering zero-forcing combining and precoding along with the signal-to-interference plus noise ratio optimal weighting at the central processing unit. We also present a provably convergent algorithm for joint UL-DL power allocation and UL/DL mode scheduling of the APs (for DTDD) to maximize the sum SE. Further, the proposed algorithms are precoder and combiner agnostic and come with closed-form update equations for the UL and DL power control coefficients. Our numerical results illustrate the superiority of the proposed pilot allocation and power control algorithms over several benchmark schemes and show that the sum SE with DTDD can outperform an FD CF system with similar antenna density. Thus, DTDD combined with CF is a promising alternative to FD that attains the same performance using HD APs, while obviating the burden of intra-AP interference cancellation.

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arXiv2024

Distributed IRSs Always Benefit Every Mobile Operator

We investigate the impact of multiple distributed intelligent reflecting surfaces (IRSs), which are deployed and optimized by a mobile operator (MO), on the performance of user equipments (UEs) served by other co-existing out-of-band (OOB) MOs that do not control the IRSs. We show that, under round-robin scheduling, in mmWave frequencies, the ergodic sum spectral efficiency (SE) of an OOB MO increases logarithmically in the total number of IRS elements with a pre-log factor that increases with the ratio of the number of OOB paths through the IRS to the number of elements at an IRS. We further show that the maximum achievable SE of the OOB MO scales log-linearly with the total IRS elements, with a pre-log factor of $1$. Then, we specify the minimum number of IRSs as a function of the channel parameters and design a distributed IRS system in which an OOB MO almost surely obtains the maximum SE. Finally, we prove that the outage probability at an OOB UE decreases exponentially as the number of IRSs increases, even though they are randomly configured from the OOB UE's viewpoint. We numerically verify our theory and conclude that distributed IRSs always help every MO, but the MO controlling the IRSs benefits the most.

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arXiv2023

Joint State and Sparse Input Estimation in Linear Dynamical Systems

Sparsity constraints on the control inputs of a linear dynamical system naturally arise in several practical applications such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the problem of jointly estimating the states and sparse inputs of such systems from low-dimensional (compressive) measurements. Due to the low-dimensional measurements, conventional Kalman filtering and smoothing algorithms fail to accurately estimate the states and inputs. We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy. Sparsity in the input estimates is promoted by using different prior distributions on the input. We investigate two main approaches: regularizer-based MAP, and {Bayesian learning-based estimation}. We also extend the approaches to handle control inputs with common support and analyze the time and memory complexities of the presented algorithms. Finally, using numerical simulations, we show that our algorithms outperform the state-of-the-art methods in terms of accuracy and time/memory complexities, especially in the low-dimensional measurement regime.

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arXiv2023

Tradeoff of age-of-information and power under reliability constraint for short-packet communication with block-length adaptation

In applications such as remote estimation and monitoring, update packets are transmitted by power-constrained devices using short-packet codes over wireless networks. Therefore, networks need to be end-to-end optimized using information freshness metrics such as age of information under transmit power and reliability constraints to ensure support for such applications. For short-packet coding, modelling and understanding the effect of block codeword length on transmit power and other performance metrics is important. To understand the above optimization for short-packet coding, we consider the optimal tradeoff problem between age of information and transmit power under reliability constraints for short packet point-to-point communication model with an exogenous packet generation process. In contrast to prior work, we consider scheduling policies that can possibly adapt the block-length or transmission time of short packet codes in order to achieve the optimal tradeoff. We characterize the tradeoff using a semi-Markov decision process formulation. We also obtain analytical upper bounds as well as numerical, analytical, and asymptotic lower bounds on the optimal tradeoff. We show that in certain regimes, such as high reliability and high packet generation rate, non-adaptive scheduling policies (fixed transmission time policies) are close-to-optimal. Furthermore, in a high-power or in a low-power regime, non-adaptive as well as state-independent randomized scheduling policies are order-optimal. These results are corroborated by numerical and simulation experiments. The tradeoff is then characterized for a wireless point-to-point channel with block fading as well as for other packet generation models (including an age-dependent packet generation model).

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arXiv2023

Channel State Information Based User Censoring in Irregular Repetition Slotted Aloha

Irregular repetition slotted aloha (IRSA) is a massive random access protocol which can be used to serve a large number of users while achieving a packet loss rate (PLR) close to zero. However, if the number of users is too high, then the system is interference limited and the PLR is close to one. In this paper, we propose a variant of IRSA in the interference limited regime, namely Censored-IRSA (C-IRSA), wherein users with poor channel states censor themselves from transmitting their packets. We theoretically analyze the throughput performance of C-IRSA via density evolution. Using this, we derive closed-form expressions for the optimal choice of the censor threshold which maximizes the throughput while achieving zero PLR among uncensored users. Through extensive numerical simulations, we show that C-IRSA can achieve a 4$\times$ improvement in the peak throughput compared to conventional IRSA.

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arXiv2023

Does an IRS Degrade Out-of-Band Performance?

Intelligent reflecting surfaces (IRSs) were introduced to enhance the performance of wireless systems. However, from a cellular service provider's view, a concern with the use of an IRS is its effect on out-of-band (OOB) quality of service. Specifically, given two operators, say X and Y, providing services in a geographical area using non-overlapping frequency bands, if operator-X uses an IRS to optimally enhance the throughput of its users, does the IRS degrade the performance of operator-Y? We answer this by deriving the ergodic sum spectral efficiency (SE) of both operators under round-robin scheduling. We also derive the complementary cumulative distribution function of the change in effective channel at an OOB user with and without the IRS, which provides deeper insights into OOB performance. Surprisingly, we find that even though the IRS is randomly configured from operator-Y's view, the OOB operator still benefits from the IRS, witnessing a performance enhancement for free. This happens because the IRS introduces additional paths between the nodes, increasing the signal power at the receiver and providing diversity benefits. We verify our findings numerically and conclude that an IRS is beneficial to every operator, even when the IRS is deployed to optimally serve only one operator.

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arXiv2023

Multi-Carrier Wideband OCDM-Based THz Automotive Radar

Automotive radars at the Terahertz (THz) frequency band have the potential to be compact and lightweight while providing high (nearly-optical) angular resolution. In this paper, we propose a bistatic THz automotive radar that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multi-carrier waveform. As a stand-alone communications waveform, OCDM has been investigated for robustness against interference in time-frequency selective channels. The THz-band path loss, and, hence, radar signal bandwidth, are range-dependent. We address this unique feature through a multi-carrier wideband OCDM sensing transceiver that exploits the coherence bandwidth of the THz channel. We develop an optimal scheme to combine the returns at different range/bandwidths by assigning weights based on the Cramer-Rao lower bound on the range and velocity estimates. Numerical experiments demonstrate improved target estimates using our proposed combined estimation from multiple varied-attenuation THz frequencies.

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arXiv2022

Performance Analysis of Intelligent Reflecting Surface Assisted Opportunistic Communications

Intelligent reflecting surfaces (IRSs) are a promising technology for enhancing coverage and spectral efficiency, both in the sub-6 GHz and the millimeter wave (mmWave) bands. Existing approaches to leverage the benefits of IRS involve the use of a resource-intensive channel estimation step followed by a computationally expensive algorithm to optimize the reflection coefficients at the IRS. In this work, focusing on the sub-6 GHz band of communications, we present and analyze several alternative schemes, where the phase configuration of the IRS is randomized and multi-user diversity is exploited to opportunistically select the best user at each point in time for data transmission. We show that the throughput of an IRS assisted opportunistic communication (OC) system asymptotically converges to the optimal beamforming-based throughput under fair allocation of resources, as the number of users gets large. We also introduce schemes that enhance the rate of convergence of the OC rate to the beamforming rate with the number of users. For all the proposed schemes, we derive the scaling law of the throughput in terms of the system parameters, as the number of users gets large. Following this, we extend the setup to wideband channels via an orthogonal frequency division multiplexing (OFDM) system and discuss two OC schemes in an IRS assisted setting that clearly elucidate the superior performance that IRS aided OC systems can offer over conventional systems, at very low implementation cost and complexity.

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