arXiv subjects

Chandra R. Murthy

Publications and source records attributed to Chandra R. Murthy.

At least 37 records · Page 2Linked to original sources

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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arXiv2022

Impact of Mobility on Downlink Cell-Free Massive MIMO Systems

In this paper, we analyze the achievable downlink spectral efficiency of cell-free massive multiple input multiple output (CF-mMIMO) systems, accounting for the effects of channel aging (caused by user mobility) and pilot contamination. We consider two cases, one where user equipments (UEs) rely on downlink pilots beamformed by the access points (APs) to estimate downlink channel, and another where UEs utilize statistical channel state information (CSI) for data decoding. For comparison, we also consider cellular mMIMO and derive its achievable spectral efficiency with channel aging and pilot contamination in the above two cases. Our results show that, in CF-mMIMO, downlink training is preferable over statistical CSI when the length of the data sequence is chosen optimally to maximize the spectral efficiency. In cellular mMIMO, however, either one of the two schemes may be better depending on whether user fairness or sum spectral efficiency is prioritized. Furthermore, the CF-mMIMO system generally outperforms cellular mMIMO even after accounting for the effects of channel aging and pilot contamination. Through numerical results, we illustrate the effect of various system parameters such as the maximum user velocity, uplink/downlink pilot lengths, data duration, network densification, and provide interesting insights into the key differences between cell-free and cellular mMIMO systems.

cs.IT↗

arXiv2022

Impact of Subcarrier Allocation and User Mobility on the Uplink Performance of Multi-User Massive MIMO-OFDM Systems

This paper considers the uplink performance of a multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system with mobile users. Mobility brings two major problems to a MIMO-OFDM system: inter carrier interference (ICI) and channel aging. In practice, it is common to allot multiple contiguous subcarriers to a user as well as schedule multiple users on each subcarrier. Motivated by this, we consider a general subcarrier allocation scheme and derive expressions for the ICI power, uplink signal to interference plus noise ratio and the achievable uplink sum-rate, taking into account the ICI and the multi-user interference due to channel aging. We show that the system incurs a near-constant ICI power that depends linearly on the ratio of the number of users per subcarrier to the number of subcarriers per user, nearly independently of how the UEs distribute their power across the subcarriers. Further, we exploit the coherence bandwidth of the channel to reduce the length of the pilot sequences required for uplink channel estimation. We consider both zero-forcing and maximal-ratio combining at the receiver and compare the respective sum-rate performances. In either case, the proposed subcarrier allocation scheme leads to significantly higher sum-rates compared to previous work, owing to the near-constant ICI property as well as the reduced pilot overhead.

eess.SP↗

arXiv2022

User Activity Detection for Irregular Repetition Slotted Aloha based MMTC

Irregular repetition slotted aloha (IRSA) is a grant-free random access protocol for massive machine-type communications, where a large number of users sporadically send their data packets to a base station (BS). IRSA is a completely distributed multiple access protocol: in any given frame, a small subset of the users, i.e., the active users, transmit replicas of their packet in randomly selected resource elements (REs). The first step in the decoding process at the BS is to detect which users are active in each frame. To this end, a novel Bayesian user activity detection (UAD) algorithm is developed, which exploits both the sparsity in user activity as well as the underlying structure of IRSA-based transmissions. Next, the Cramer-Rao bound (CRB) on the mean squared error in channel estimation is derived. It is empirically shown that the channel estimates obtained as a by-product of the proposed UAD algorithm achieves the CRB. Then, the signal to interference plus noise ratio achieved by the users is analyzed, accounting for UAD, channel estimation errors, and pilot contamination. The impact of these non-idealities on the throughput of IRSA is illustrated via Monte Carlo simulations. For example, in a system with 1500 users and 10% of the users being active per frame, a pilot length of as low as 20 symbols is sufficient for accurate user activity detection. In contrast, using classical compressed sensing approaches for UAD would require a pilot length of about 346 symbols. The results also reveal crucial insights into dependence of UAD errors and throughput on parameters such as the length of the pilot sequence, the number of antennas at the BS, the number of users, and the signal to noise ratio.

eess.SP↗

arXiv2022

On the Impact of Channel Estimation on the Design and Analysis of IRSA based Systems

Irregular repetition slotted aloha (IRSA) is a distributed grant-free random access protocol where users transmit multiple replicas of their packets to a base station (BS). The BS recovers the packets using successive interference cancellation. In this paper, we first derive channel estimates for IRSA, exploiting the sparsity structure of IRSA transmissions, when non-orthogonal pilots are employed across users to facilitate channel estimation at the BS. Allowing for the use of non-orthogonal pilots is important, as the length of orthogonal pilots scales linearly with the total number of devices, leading to prohibitive overhead as the number of devices increases. Next, we present a novel analysis of the throughput of IRSA under practical channel estimation errors, with the use of multiple antennas at the BS. Finally, we theoretically characterize the asymptotic throughput performance of IRSA using a density evolution based analysis. Simulation results underline the importance of accounting for channel estimation errors in analyzing IRSA, which can even lead to 70% loss in performance in severely interference-limited regimes. We also provide novel insights on the effect of parameters such as pilot length, SNR, number of antennas at the BS, etc, on the system throughput.

cs.IT↗

arXiv2022

Performance Analysis of Irregular Repetition Slotted Aloha with Multi-Cell Interference

Irregular repetition slotted aloha (IRSA) is a massive random access protocol in which users transmit several replicas of their packet over a frame to a base station. Existing studies have analyzed IRSA in the single-cell (SC) setup, which does not extend to the more practically relevant multi-cell (MC) setup due to the inter-cell interference. In this work, we analyze MC IRSA, accounting for pilot contamination and multiuser interference. Via numerical simulations, we illustrate that, in practical settings, MC IRSA can have a drastic loss of throughput, up to $70\%$, compared to SC IRSA. Further, MC IRSA requires a significantly higher training length (about 4-5x compared to SC IRSA), in order to support the same user density and achieve the same throughput. We also provide insights into the impact of the pilot length, number of antennas, and signal to noise ratio on the performance of MC IRSA.

eess.SP↗

arXiv2022

Can Dynamic TDD Enabled Half-Duplex Cell-Free Massive MIMO Outperform Full-Duplex Cellular Massive MIMO?

We consider a dynamic time division duplex (DTDD) enabled cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is scheduled to operate in the uplink (UL) or downlink (DL) mode based on the data demands of the user equipments (UEs), with the goal of maximizing the sum UL-DL spectral efficiency (SE). We develop a new, low complexity, greedy algorithm for the combinatorial AP scheduling problem, with an optimality guarantee theoretically established via showing that a lower bound of the sum UL-DL SE is sub-modular. We also consider pilot sequence reuse among the UEs to limit the channel estimation overhead. In CF systems, all the APs estimate the channel from every UE, making pilot allocation problem different from the cellular case. We develop a novel algorithm that iteratively minimizes the maximum pilot contamination across the UEs. We compare the performance of our solutions, both theoretically and via simulations, against a full duplex (FD) multi-cell mMIMO system. Our results show that, due to the joint processing of the signals at the central processing unit, CF-mMIMO with dynamic HD AP-scheduling significantly outperforms cellular FD-mMIMO in terms of the sum SE and 90% likely SE. Thus, DTDD enabled HD CF-mMIMO is a promising alternative to cellular FD-mMIMO, without the cost of hardware for self-interference suppression.

eess.SP↗

arXiv2022

Orthogonal Delay Scale Space Modulation: A New Technique for Wideband Time-Varying Channels

Orthogonal Time Frequency Space (OTFS) modulation is a recently proposed scheme for time-varying narrowband channels in terrestrial radio-frequency communications. Underwater acoustic (UWA) and ultra-wideband (UWB) communication systems, on the other hand, confront wideband time-varying channels. Unlike narrowband channels, for which time contractions or dilations due to Doppler effect can be approximated by frequency-shifts, the Doppler effect in wideband channels results in frequency-dependent non-uniform shift of signal frequencies across the band. In this paper, we develop an OTFS-like modulation scheme -- Orthogonal Delay Scale Space (ODSS) modulation -- for handling wideband time-varying channels. We derive the ODSS transmission and reception schemes from first principles. In the process, we introduce the notion of $ω$-convolution in the delay-scale space that parallels the twisted convolution used in the time-frequency space. The preprocessing 2D transformation from the Fourier-Mellin domain to the delay-scale space in ODSS, which plays the role of inverse symplectic Fourier transform (ISFFT) in OTFS, improves the bit error rate performance compared to OTFS and Orthogonal Frequency Division Multiplexing (OFDM) in wideband time-varying channels. Furthermore, since the channel matrix is rendered near-diagonal, ODSS retains the advantage of OFDM in terms of its low-complexity receiver structure.

eess.SP↗

arXiv2021

Evaluation Of Orthogonal Chirp Division Multiplexing For Automotive Integrated Sensing And Communications

We consider a bistatic vehicular integrated sensing and communications (ISAC) system that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multicarrier waveform. As a stand-alone communications waveform, OCDM has been shown to be robust against the interference in time-frequency selective channels. In a bistatic ISAC, we exploit this property to develop efficient receive processing algorithms that achieve high target resolution as well as high communications rate. We derive statistical bounds for our proposed Sequential symbol decoding and radar parameter estimation (SUNDAE) algorithm and compare its competitive performance with other multicarrier waveforms through numerical experiments.

cs.IT↗

arXiv2021

On the Support Recovery of Jointly Sparse Gaussian Sources using Sparse Bayesian Learning

In this work, we provide non-asymptotic, probabilistic guarantees for successful recovery of the common nonzero support of jointly sparse Gaussian sources in the multiple measurement vector (MMV) problem. The support recovery problem is formulated as the marginalized maximum likelihood (or type-II ML) estimation of the variance hyperparameters of a joint sparsity inducing Gaussian prior on the source signals. We derive conditions under which the resulting nonconvex constrained optimization perfectly recovers the nonzero support of a joint-sparse Gaussian source ensemble with arbitrarily high probability. The support error probability decays exponentially with the number of MMVs at a rate that depends on the smallest restricted singular value and the nonnegative null space property of the self Khatri-Rao product of the sensing matrix. Our analysis confirms that nonzero supports of size as high as O($m^2$) are recoverable from $m$ measurements per sparse vector. Our derived sufficient conditions for support consistency of the proposed constrained type-II ML solution also guarantee the support consistency of any global solution of the multiple sparse Bayesian learning (M-SBL) optimization whose nonzero coefficients lie inside a bounded interval. For the case of noiseless measurements, we further show that a single MMV is sufficient for perfect recovery of the $k$-sparse support by M-SBL, provided all subsets of $k + 1$ columns of the sensing matrix are linearly independent.

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arXiv2021

Multiple Support Recovery Using Very Few Measurements Per Sample

In the problem of multiple support recovery, we are given access to linear measurements of multiple sparse samples in $\mathbb{R}^{d}$. These samples can be partitioned into $\ell$ groups, with samples having the same support belonging to the same group. For a given budget of $m$ measurements per sample, the goal is to recover the $\ell$ underlying supports, in the absence of the knowledge of group labels. We study this problem with a focus on the measurement-constrained regime where $m$ is smaller than the support size $k$ of each sample. We design a two-step procedure that estimates the union of the underlying supports first, and then uses a spectral algorithm to estimate the individual supports. Our proposed estimator can recover the supports with $m<k$ measurements per sample, from $\tilde{O}(k^{4}\ell^{4}/m^{4})$ samples. Our guarantees hold for a general, generative model assumption on the samples and measurement matrices. We also provide results from experiments conducted on synthetic data and on the MNIST dataset.

cs.IT↗

arXiv2021

Phase Transitions for Support Recovery from Gaussian Linear Measurements

We study the problem of recovering the common $k$-sized support of a set of $n$ samples of dimension $d$, using $m$ noisy linear measurements per sample. Most prior work has focused on the case when $m$ exceeds $k$, in which case $n$ of the order $(k/m)\log(d/k)$ is both necessary and sufficient. Thus, in this regime, only the total number of measurements across the samples matter, and there is not much benefit in getting more than $k$ measurements per sample. In the measurement-constrained regime where we have access to fewer than $k$ measurements per sample, we show an upper bound of $O((k^{2}/m^{2})\log d)$ on the sample complexity for successful support recovery when $m\ge 2\log d$. Along with the lower bound from our previous work, this shows a phase transition for the sample complexity of this problem around $k/m=1$. In fact, our proposed algorithm is sample-optimal in both the regimes. It follows that, in the $m\ll k$ regime, multiple measurements from the same sample are more valuable than measurements from different samples.

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arXiv2021

Uplink Performance Analysis of Cell-Free mMIMO Systems under Channel Aging

In this paper, we investigate the impact of channel aging on the uplink performance of a cell-free~(CF) massive multiple-input multiple-output (mMIMO) system with a minimum mean squared error (MMSE) receiver. To this end, we present a new model for the temporal evolution of the channel, which allows the channel to age at different rates at different access points (APs). Under this setting, we derive the deterministic equivalent of the per-user achievable signal-to-interference-plus-noise ratio (SINR). In addition to validating the theoretical expressions, our simulation results reveal that, {at low user mobilities,} the SINR of CF-mMIMO is nearly 5 dB higher than its cellular counterpart with the same number of antennas, and about 8 dB higher than that of an equivalent small-cell network with the same number of APs. {On the other hand, at very high user velocities, and when the channel between the UEs the different APs age at same rate, the relative impact of aging is higher for CF-mMIMO compared to cellular mMIMO. However, when the channel ages at the different APs with different rates, the effect of aging on CF-mMIMO is marginally mitigated, especially for larger frame durations.

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arXiv2019

Controllability of Linear Dynamical Systems Under Input Sparsity Constraints

In this work, we consider the controllability of a discrete-time linear dynamical system with sparse control inputs. Sparsity constraints on the input arises naturally in networked systems, where activating each input variable adds to the cost of control. We derive algebraic necessary and sufficient conditions for ensuring controllability of a system with an arbitrary transfer matrix. The derived conditions can be verified in polynomial time complexity, unlike the more traditional Kalman-type rank tests. Further, we characterize the minimum number of input vectors required to satisfy the derived conditions for controllability. Finally, we present a generalized Kalman decomposition-like procedure that separates the state-space into subspaces corresponding to sparse-controllable and sparse-uncontrollable parts. These results form a theoretical basis for designing networked linear control systems with sparse inputs.

eess.SY↗

arXiv2019

Fundamental Limitations in Biomarker Based Early Disease Diagnosis

Clinical biosensors with low detection limit hold significant promise in the early diagnosis of debilitating diseases. Recent progress in sensor development has led to the demonstration of detection capable of detecting target molecules even down to single-molecule level. One crucial performance parameter which is not adequately discussed is the issue of measurement fidelity in such sensors. We define measurement fidelity based on the false positive rate of the system as we expect systems with higher sensitivity to concomitantly respond more to interfering molecules thus increasing the false-positive rates. We present a model which allows us to estimate the limit of detection of a biosensor system constrained by a specified false-positive rate. Two major results emerging from our model is that a) there is a lower bound to the detection limit for a target molecule determined by the variation in the concentration of background molecules interfering with the molecular recognition process and b) systems which use a secondary label, such as a fluorophore, can achieve lower detection limits for a given false positive rate. We also present data collected from literature to support our model. The insights from our model will be useful in the systematic design of future clinical biosensors to achieve relevant detection limits with assured fidelity.

physics.med-ph↗

arXiv2019

Learned-SBL: A Deep Learning Architecture for Sparse Signal Recovery

In this paper, we present a computationally efficient sparse signal recovery scheme using Deep Neural Networks (DNN). The architecture of the introduced neural network is inspired from sparse Bayesian learning (SBL) and named as Learned-SBL (L-SBL). We design a common architecture to recover sparse as well as block sparse vectors from single measurement vector (SMV) or multiple measurement vectors (MMV) depending on the nature of the training data. In the MMV model, the L-SBL network can be trained to learn any underlying sparsity pattern among the vectors including joint sparsity, block sparsity, etc. In particular, for block sparse recovery, learned-SBL does not require any prior knowledge of block boundaries. In each layer of the L-SBL, an estimate of the signal covariance matrix is obtained as the output of a neural network. Then a maximum a posteriori (MAP) estimator of the unknown sparse vector is implemented with non-trainable parameters. In many applications, the measurement matrix may be time-varying. The existing DNN based sparse signal recovery schemes demand the retraining of the neural network using current measurement matrix. The architecture of L-SBL allows it to accept the measurement matrix as an input to the network, and thereby avoids the need for retraining. We also evaluate the performance of Learned-SBL in the detection of an extended target using a multiple-input multiple-output (MIMO) radar. Simulation results illustrate that the proposed approach offers superior sparse recovery performance compared to the state-of-the-art methods.

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arXiv2017

On Distributed Power Control for Uncoordinated Dual Energy Harvesting Links: Performance Bounds and Near-Optimal Policies

In this paper, we consider a point-to-point link between an energy harvesting transmitter and receiver, where neither node has the information about the battery state or energy availability at the other node. We consider a model where data is successfully delivered only in slots where both nodes are active. Energy loss occurs whenever one node turns on while the other node is in sleep mode. In each slot, based on their own energy availability, the transmitter and receiver need to independently decide whether or not to turn on, with the aim of maximizing the long-term time-average throughput. We present an upper bound on the throughput achievable by analyzing a genie-aided system that has noncausal knowledge of the energy arrivals at both the nodes. Next, we propose an online policy requiring an occasional one-bit feedback whose throughput is within one bit of the upper bound, asymptotically in the battery size. In order to further reduce the feedback required, we propose a time-dilated version of the online policy. As the time dilation gets large, this policy does not require any feedback and achieves the upper bound asymptotically in the battery size. Inspired by this, we also propose a near-optimal fully uncoordinated policy. We use Monte Carlo simulations to validate our theoretical results and illustrate the performance of the proposed policies.

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arXiv2016

On the Capacity of the 2-User Interference Channel with Transmitter Cooperation and Secrecy Constraints

This paper studies the value of limited rate cooperation between the transmitters for managing interference and simultaneously ensuring secrecy, in the 2-user Gaussian symmetric interference channel (GSIC). First, the problem is studied in the symmetric linear deterministic IC (SLDIC) setting, and achievable schemes are proposed, based on interference cancelation, relaying of the other user's data bits, and transmission of random bits. In the proposed achievable scheme, the limited rate cooperative link is used to share a combination of data bits and random bits depending on the model parameters. Outer bounds on the secrecy rate are also derived, using a novel partitioning of the encoded messages and outputs depending on the relative strength of the signal and the interference. The inner and outer bounds are derived under all possible parameter settings. It is found that, for some parameter settings, the inner and outer bounds match, yielding the capacity of the SLDIC under transmitter cooperation and secrecy constraints. In some other scenarios, the achievable rate matches with the capacity region of the 2-user SLDIC without secrecy constraints derived by Wang and Tse [1]; thus, the proposed scheme offers secrecy for free, in these cases. Inspired by the achievable schemes and outer bounds in the deterministic case, achievable schemes and outer bounds are derived in the Gaussian case. The proposed achievable scheme for the Gaussian case is based on Marton's coding scheme and stochastic encoding along with dummy message transmission. One of the key techniques used in the achievable scheme for both the models is interference cancelation, which simultaneously offers two seemingly conflicting benefits: it cancels interference and ensures secrecy. Many of the results derived in this paper extend to the asymmetric case also.

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