Faculty - Dashboard

@inproceedings{bib_An_1_2025, AUTHOR = {Harikrishna, Kambham and Mankar, Praful and Azeemuddin, Syed }, TITLE = {An 18.87--24.19 GHz VCO with 90--164 kHz 1/f^3 Noise Corner and 189.6 dBc/Hz Peak FoM}, BOOKTITLE = {International Midwest Symposium on Circuits and Systems}. YEAR = {2025}}

This paper presents an 18.87–24.19 GHz dual-core, dual-mode voltage-controlled oscillator (VCO) using a multi-tap (MT) inductor-based 2-port resonator with capacitive coupling via a mode-switching block. The 2-port resonator introduces a gate-to-drain phase shift, suppressing flicker phase noise (PN), while the high-quality factor of the tank minimizes thermal PN. The mode-switching block enables dual resonance modes—high-frequency band (HB) and low-frequency band (LB), achieving a wide frequency tuning range (FTR) and low PN in both the 1/f^2 and 1/f^3 regions. The proposed VCO is implemented in 65 nm CMOS, and post-layout results show a PN of –68.7 dBc/Hz at a 10 kHz offset and –118.2 dBc/Hz at a 1 MHz offset from an 18.87 GHz carrier frequency, with a power consumption of 25.65 mW and an area of 0.154 mm^2. The peak figure of merit (FoM) is 189.6 dBc/Hz, and the maximum 1/f^3 noise corner frequency is 347 kHz across the FTR.

@inproceedings{bib_A_42_2025, AUTHOR = {Harikrishna, Kambham and Mankar, Praful }, TITLE = {A 42.5–58 GHz CMOS Frequency Doubler with 28.4% Drain and 13.7% Total Efficiency}, BOOKTITLE = {Asia Pacific Conference on Circuits and Systems}. YEAR = {2025}}

A conventional push–push frequency doubler (FD) is simple to implement but suffers from limited total efficiency ($eta_{text{total}}$) at millimeter-wave (mm-Wave) frequencies due to negative feedback introduced by the gate–drain capacitance ($C_{mathrm{gd}}$). To overcome this limitation, a 42.5-58 GHz mm-Wave FD is proposed, utilizing cross-coupled gate-to-source feedback. In the proposed topology, the push-push NMOS frequency doubler employs cross-coupled gate-to-source feedback, which generates a second-harmonic voltage at the gate that is in phase with the drain’s second-harmonic current. This feedback effectively mitigates the inherent feedback caused by $C_{mathrm{gd}}$, and doubles the gate-to-source voltage of the fundamental harmonic, $V_{gs}[f_{0}]$. As a result, the amplitude of the drain $2f_{0}$ current generated by the nonlinear second harmonic transconductance $g_{m2,nonlinear}$ of the NMOS increases, leading to enhanced drain efficiency ($eta_{mathrm{D}}$) and $eta_{text{total}}$. The proposed FD is designed in a 65 nm CMOS process. Post-layout simulations demonstrate a peak $eta_{mathrm{D}}$ of 28.39%, $eta_{text{total}}$ of 13.7%, and a peak $2f_0$ output power of 6.27 dBm at 47.5 GHz, with a 12 dBm local oscillator (LO) input and 14.92 mW DC power consumption. The doubler achieves a 3 dB bandwidth of 42.5–58 GHz $(30.85%)$ and an efficiency bandwidth 43.75–54.66 GHz $(20.4%)$ where $eta_{text{total}} geq 10%$.

@inproceedings{bib_Capa_2025, AUTHOR = {Manasa, M S S and Krishna, Kota Kali and Mankar, Praful and Dhillon, Harpreet S. }, TITLE = {Capacity Maximization for RIS-Assisted Multi-User Communication Systems}, BOOKTITLE = {Wireless Communications Letters}. YEAR = {2025}}

We consider a reconfigurable intelligent surface (RIS)-assisted multi-user communication system. For such a system, we aim to optimally select the RIS phase shifts and precoding vectors for maximizing the effective rank of the weighted channel covariance matrix which essentially improves the channel capacity. For a low-complexity transmitter design, we employ maximum ratio transmission (MRT) and minimum-mean square error (MMSE) precoding schemes along with water-filling algorithm-based power allocation. Further, we show that MRT and MMSE exhibit equivalent performance and become optimal when the channel effective rank is maximized by optimally configuring the RIS consisting of a large number of elements

@inproceedings{bib_Spar_2024, AUTHOR = {Motwani, Yash and Krishna, Kota Kali and Mankar, Praful }, TITLE = {Sparsity-based Channel Estimation for RIS-aided MIMO mmWave Communication Systems}, BOOKTITLE = {International Symposium on Personal, Indoor and Mobile Radio Communications}. YEAR = {2024}}

Reconfigurable intelligent surfaces (RIS) are emerging as a promising technology for 6G networks due to their ability to shape the radio propagation environment. This ability helps to overcome propagation challenges, such as high path loss, absorption loss, etc., that are particularly needed at millimetre wave (mmWave) bands. Thus, RIS can enhance the capacity of next-generation mmWave communication networks. However, one critical aspect in the design of RIS-aided systems is channel estimation, as it involves estimating two channels (i.e., base stationRIS and RIS-user terminal) separately based on the compound channel observed by the receiver. In this paper, we address this problem by proposing an algorithm that estimates both channels separately by leveraging the advantages of sparsity of the mmWave channel. Specifically, the proposed algorithm uses the parallel factor (PARAFAC) decomposition of the tensor, which is constructed using the received signal matrices observed under different RIS phase shift configurations. Our numerical analysis shows that the proposed algorithm provides significantly smaller normalized mean square error (NMSE) than the widely used alternating least squares (ALS) algorithm, particularly when the number of RIS phase shift configurations is smaller than the number of RIS elements.

@inproceedings{bib_RIS-_2024, AUTHOR = {Mankar, Praful and Krishna, Kota Kali }, TITLE = {RIS-NOMA integrated low-complexity transceiver architecture: Sum rate and energy efficiency perspective}, BOOKTITLE = {Technical Report}. YEAR = {2024}}

This paper aims to explore RIS integration in a millimeter wave (mmWave) communication system with low-complexity transceiver architecture under imperfect channel state information (CSI) assump- tion. Motivated by this, we propose a RIS-aided system with a fully analog architecture at the base station (BS). However, to overcome the drawback of single-user transmission due to the single RF chain in the analog architecture, we propose to employ NOMA to enable multi-user transmission. For such a system, we formulate two problems to obtain the joint transmit beamformer, RIS phase shift matrix, and power allocation solutions that maximize sum rate and energy efficiency such that the minimum rate for each user is satisfied. However, both problems are intractable due to 1) the fractional objective, 2) non-convex minimum rate and unit modulus RIS phase shift constraints, and 3) the coupled optimization variables. Hence, we first tackle the fractional objectives of both problems by reformulating the sum rate and energy efficiency maximization problems into equivalent quadratic forms using the quadratic transform. On the other hand, we employ successive convex approximation and the semi-definite relaxation technique to handle the non-convex minimum rate and unit modulus constraint of the RIS phase shifts, respectively. However, the problems remain non-convex due to the coupled optimization variables. Thus, we propose an alternating optimization-based algorithm that iterates over the transmit beamformer, power allocation, and RIS phase shift subproblems. Further, we also show that the quadratic reformulation is equivalent to the weighted mean square error-based reformulation for the case of sum rate maximization problem. Our numerical results show that the proposed RIS-NOMA integrated analog architecture system outperforms the optimally configured fully digital architecture in terms of sum rate at low SNR and energy efficiency for a wide range of SNR while still maintaining low hardware complexity and cost. Finally, we present the numerical performance analysis of the RIS-NOMA integrated low-complexity system for various system configuration parameters.

@inproceedings{bib_Maxi_2024, AUTHOR = {Pradipsingh, Parihar Nikhilsingh and Mankar, Praful and Chaudhari, Sachin }, TITLE = {Maximum Eigenvalue Detection based Spectrum Sensing in RIS-aided System with Correlated Fading}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2024}}

Robust spectrum sensing is crucial for facilitating opportunistic spectrum utilization for secondary users (SU) in the absense of primary users (PU). However, propagation environment factors such as multi-path fading, shadowing, and lack of line of sight (LoS) often adversely affect detection performance. To deal with these issues, this paper focuses on utilizing reconfig- urable intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein both the multi-path fading and noise are correlated. In particular, to leverage the spatially correlated fading, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypothesis. Next, utilizing these results, we present the exact closed-form expressions for the false alarm and detection probabilities. In addition, we also optimally configure the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving the detection performance. Our numerical analysis demonstrates that the MED’s receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS. Index Terms—Reconfigurable Intelligent Surfaces, Spectrum Sensing, Maximum Eigenvalue Detector, Correlated Fading, e

@inproceedings{bib_Stat_2023, AUTHOR = {Krishna, Kota Kali and Manasa, M S S and Mankar, Praful and Dhillon, Harpreet S. }, TITLE = {Statistically Optimal Beamforming and Ergodic Capacity for RIS-Aided MISO Systems}, BOOKTITLE = {IEEE Access}. YEAR = {2023}}

This paper focuses on optimal beamforming to maximize the mean signal-to-noise ratio (SNR) for a passive reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) downlink system. We consider a realistic setting where both the direct and indirect (through RIS) links to the user equipment (UE) experience correlated Rician fading. Such a general fading model is particularly important to capture the impact of line-of-sight (LoS) and correlated multipath fading that may occur due to the compact placement of a large number of RIS elements. The assumption of passive RIS imposes the unit modulus constraint, which makes the beamforming problem non-convex. To tackle this issue, we apply semidefinite relaxation (SDR) to obtain the optimal phase-shift matrix and propose an iterative algorithm to obtain a statistically optimal solution for the transmit beamforming vector and RIS-phase shift matrix. Further, to measure the performance of the proposed beamforming scheme, we analyze key system performance metrics such as outage probability (OP) and ergodic capacity (EC). Similar to the existing works, the OP and EC evaluations rely on the numerical computation of the proposed iterative algorithm. However, this is not conducive to revealing the functional dependence of system performance on key parameters such as line-of-sight (LoS) components, correlated fading, number of reflecting elements, number of antennas at the base station (BS), and fading factor. To overcome this major limitation, we derive closed-form expressions for the optimal beamforming vector and phase shift matrix for various special cases of the above general fading model. These fixed-point solutions aid in deriving a closed-form solution for OP that further provides a direct evaluation of EC. These mathematical expressions are then used to gain useful insights into the system’s performance. We analytically establish the fact that correlated fading is more beneficial than the independent and identically distributed (i.i.d.) case when the LoS components are blocked. Further, we also analytically demonstrate that the maximum mean SNR improves linearly/quadratically with the number of RIS elements in the absence/presence of LoS component under i.i.d. fading.

@inproceedings{bib_Stat_2023, AUTHOR = {Krishna, Kota Kali and Manasa, MSS and Mankar, Praful and Manasa, MSS }, TITLE = {Statistically Optimal Beamforming and Ergodic Capacity for RIS-aided MISO Systems}, BOOKTITLE = {Technical Report}. YEAR = {2023}}

This paper focuses on optimal beamforming to maximize the mean signal-to-noise ratio (SNR) for a reconfigurable intelligent surface (RIS)-aided MISO downlink system under correlated Rician fading. The beamforming problem becomes non-convex because of the unit modulus constraint of passive RIS elements. To tackle this, we propose a semidefinite relaxation-based iterative algorithm for obtaining statistically optimal transmit beamforming vector and RIS-phase shift matrix. Further, we analyze the outage probability (OP) and ergodic capacity (EC) to measure the performance of the proposed beamforming scheme. Just like the existing works, the OP and EC evaluations rely on the numerical computation of the iterative algorithm, which does not clearly reveal the functional dependence of system performance on key parameters. Therefore, we derive closed-form expressions for the optimal beamforming vector

@inproceedings{bib_Age__2023, AUTHOR = {Sinha, Ashirwad and Mankar, Praful and Pappas, Nikolaos and Dhillon, Harpreet S. }, TITLE = {Age of Information with On-Off Service}, BOOKTITLE = {Information Theory Workshop}. YEAR = {2023}}

This paper considers a communication system where a source sends time-sensitive information to its destination. We assume that both arrival and service processes of the messages are memoryless and the source has a single server with no buffer. Besides, we consider that the service is interrupted by an independent random process, which we model using the OnOff process. For this setup, we study the age of information for two queueing disciplines: 1) non-preemptive, where the messages arriving while the server is occupied are discarded, and 2) preemptive, where the in-service messages are replaced with newly arriving messages in the Off states. For these disciplines, we derive closed-form expressions for the mean peak age and mean age. Index Terms—Age of information, Peak age, On-Off process, preemptive discipline, non-preemptive discipline

@inproceedings{bib_Codi_2023, AUTHOR = {Singhvi, Shubhransh and Mankar, Praful }, TITLE = {Coding Gain for Age of Information in a Multi-source System with Erasure Channel}, BOOKTITLE = {Information Theory Workshop}. YEAR = {2023}}

In our work, we study the age of information (AoI) in a multi-source system where K sources transmit updates of their time-varying processes via a common-aggregator node to a destination node through a channel with packet delivery errors. We analyze AoI for an (α, β, 0, 1)-Gilbert-Elliot (GE) packet erasure channel with a round-robin scheduling policy. We employ maximum distance separable (MDS) scheme at aggregator for encoding the multi-source updates. We characterize the mean AoI for the MDS coded system for the case of large blocklengths. We further show that the optimal coding rate that achieves maximum coding gain over the uncoded system is n(1 − P) − O(n), where P , β α+β 0 + α α+β 1, and this maximum coding gain is (1 + P)/(1 + O(1)). In our work, we study the age of information (AoI) in a multi-source system where K sources transmit updates of their time-varying processes via a common-aggregator node to a destination node through a channel with packet delivery errors. We analyze AoI for an (α, β, 0, 1)-Gilbert-Elliot (GE) packet erasure channel with a round-robin scheduling policy. We employ maximum distance separable (MDS) scheme at aggregator for encoding the multi-source updates. We characterize the mean AoI for the MDS coded system for the case of large blocklengths. We further show that the optimal coding rate that achieves maximum coding gain over the uncoded system is n(1 − P) − O(n), where P , β α+β 0 + α α+β 1, and this maximum coding gain is (1 + P)/(1 + O(1)).

@inproceedings{bib_On_t_2022, AUTHOR = {Stana, Pranava C. and Reddy, Duggireddy Chinnellugari Siva Durga and Mankar, Praful and Dhillon, Harpreet S. and Dhillon, Harpreet S. }, TITLE = {On the Properties of Time-Varying SNR Process in Cellular-Enabled UAV Networks}, BOOKTITLE = {International Conference on Communications}. YEAR = {2022}}

The unmanned aerial vehicle (UAV) based communication is expected to play an important role in enabling a variety of applications in future cellular networks. However, because of the mobility of the UAVs, the communications links involving UAVs undergo large-scale temporal variations in the received signal quality, which may affect the quality-of-service of the underlying application. Therefore, it is crucial to characterize the time-varying process of signal quality observed by the UAVs. In this paper, we consider a scenario in which a cellular-connected UAV acts as a user equipment (UAV-UE), where the locations of base stations (BSs) follow a Poisson point process (PPP) and the UAV-UE is moving along a 3GPP-inspired straight-line trajectory. For this setting, we study the properties of the time-varying successful transmission process that is defined in terms of the time-varying signal-to-noise ratio (SNR) observed at the UAV. In particular, we show that this process is a wide sense stationary (WSS) process and derive its first- and second-order statistics. Finally, we establish an equivalence between the successful transmission processes observed by a UAV-UE served by terrestrial BSs and a terrestrial user served by UAV mounted BSs (UAV-BSs) each moving along an independent straight-line trajectory.

@inproceedings{bib_Deep_2022, AUTHOR = {Mondal, Washim Uddin and Mankar, Praful and Das, Goutam and Aggarwal, Vaneet and Ukkusuri, Satish V. }, TITLE = {Deep Learning-Based Coverage and Rate Manifold Estimation in Cellular Networks}, BOOKTITLE = {IEEE Transactions on Cognitive Communications and Networking}. YEAR = {2022}}

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SGbased model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40% and 25% respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals. Index Terms—Network Performance Prediction, Convolutional Neural Network, Stochastic Geometry, Network Design

@inproceedings{bib_Opti_2022, AUTHOR = {Krishna, Kota Kali and Mankar, Praful and Dhillon, Harpreet S. }, TITLE = {Optimal Beamforming and Outage Analysis for Max Mean SNR under RIS-aided Communication}, BOOKTITLE = {Technical Report}. YEAR = {2022}}

This paper considers beamforming for a reconfigurable intelligent surface (RIS)-aided multiple input single output (MISO) communication system in the presence of Rician multipath fading. Our aim is to jointly optimize the transmit beamformer and RIS phase shift matrix for maximizing the mean signal-to-noise (SNR) of the combined signal received over direct and indirect links. While numerical solutions are known for such optimization problems, this is the first paper to derive closedform expressions for the optimal beamformer and the phase shifter for a closely related problem. In particular, we maximize a carefully constructed lower bound of the mean SNR, which is more conducive to analytical treatment. Further, we show that effective channel gain under optimal beamforming follows Rice distribution. Next, we use these results to characterize a closedform expression for the outage probability under the proposed beamforming scheme, which is subsequently employed to derive an analytical expression for the ergodic capacity. Finally, we numerically demonstrate the efficacy of the proposed beamformer solution in comparison with the existing algorithmically obtained optimal solution for the exact mean SNR maximization. Index Terms—RIS, Optimal Beamforming, Rician Channel, Outage Analysis, Ergodic Capacity, and Maximum Mean SNR.

@inproceedings{bib_Deep_2022, AUTHOR = {Mondal, Washim Uddin and Mankar, Praful and Das, Goutam and Aggarwal, Vaneet and Ukkusuri, Satish V. }, TITLE = {Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks}, BOOKTITLE = {IEEE Transactions on Cognitive Communications and Networking}. YEAR = {2022}}

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG-based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40 and 25 percents respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.

@inproceedings{bib_Deep_2022, AUTHOR = {Mondal, Washim Uddin and Mankar, Praful and Das, Goutam and Aggarwal, Vaneet and Ukkusuri, Satish V }, TITLE = {Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks}, BOOKTITLE = {Technical Report}. YEAR = {2022}}

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG- based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40% and 25% respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.

@inproceedings{bib_A_Sp_2021, AUTHOR = {Mankar, Praful and Chen, Zheng and Abd-Elmagid, Mohamed A. and Pappas, Nikolaos and Dhillon, Harpreet S. }, TITLE = {A Spatio-temporal Analysis of Cellular-based IoT Networks under Heterogeneous Traffic}, BOOKTITLE = {IEEE Global Communications Conference}. YEAR = {2021}}

In this paper, we consider a cellular-based Internet of things (IoT) network consisting of IoT devices that can communicate directly with each other in a device-to-device (D2D) fashion as well as send real-time status updates about some underlying physical processes observed by them. We assume that such real-time applications are supported by cellular networks where cellular base stations (BSs) collect status updates over time from a subset of the IoT devices in their vicinity. We characterize two performance metrics: i) the network throughput which quantifies the performance of D2D communications, and ii) the Age of Information which quantifies the performance of the real-time IoT-enabled applications. Concrete analytical results are derived using stochastic geometry by modeling the locations of IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another Independent PPP. Our results provide useful design guidelines on the efficient deployment of future IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

@inproceedings{bib_A_Sp_2021, AUTHOR = {Mankar, Praful and Chen, Zheng and Abd-Elmagid, Mohamed A. and Pappas, Nikolaos and Dhillon, Harpreet S. }, TITLE = {A Spatio-temporal Analysis of Age of Information and Throughput in Cellular-based IoT Networks}, BOOKTITLE = {International Conference on Communications}. YEAR = {2021}}

In this paper, we consider a cellular-based Internet of things (IoT) network consisting of IoT devices that can communicate directly with each other in a device-to-device (D2D) fashion as well as send real-time status updates about some underlying physical processes observed by them. We assume that such real-time applications are supported by cellular networks (owing to their ubiquity) where cellular base stations (BSs) collect status updates over time from some subset of the IoT devices in their vicinity. For this setup, we characterize two performance metrics: i) the network throughput which quantifies the performance of D2D communications, and ii) the Age of Information (AoI) which quantifies the performance of the realtime IoT-enabled applications. Concrete analytical results are derived using stochastic geometry by modeling the locations of IoT devices as a bipolar Poisson Point process (PPP) and that of the BSs as another Independent PPP. Our results provide useful design guidelines on the efficient deployment of future IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

@inproceedings{bib_Spat_2021, AUTHOR = {Mankar, Praful and Abd-Elmagid, Mohamed A. and Dhillon, Harpreet S. }, TITLE = {Spatial distribution of the mean peak age of information in wireless networks}, BOOKTITLE = {IEEE Transactions on Wireless Communications}. YEAR = {2021}}

— This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates, to their corresponding destinations in a time-slotted fashion. We employ age of information (AoI) for quantifying the freshness of the status updates measured at the destination nodes under the preemptive and non-preemptive queueing disciplines with no storage facility. The non-preemptive queue drops the newly arriving updates until the update in service is successfully delivered, whereas the preemptive queue replaces the current update in service with the newly arriving update, if any. As the update delivery rate for a given link is a function of the interference field seen from the receiver, the temporal mean AoI can be treated as a random variable over space. Our goal in this paper is to characterize the spatial distribution of the mean AoI observed by the SD pairs by modeling them as a bipolar Poisson point process (PPP). Towards this objective, we first derive accurate bounds on the moments of success probability while efficiently capturing the interference-induced coupling in the activities of the SD pairs. Using this result, we then derive tight bounds on the moments as well as the spatial distribution of peak AoI (PAoI). Our numerical results verify our analytical findings and demonstrate the impact of various system design parameters on the mean PAoI.

@inproceedings{bib_Stoc_2021, AUTHOR = {Mankar, Praful and Abd-Elmagid, Mohamed A. and Dhillon, Harpreet S. }, TITLE = {Stochastic Geometry-based Analysis of the Distribution of Peak Age of Information}, BOOKTITLE = {International Conference on Communications}. YEAR = {2021}}

In this paper, we consider a large-scale wireless network consisting of source-destination (SD) pairs where the source nodes frequently send status updates about some underlying physical processes (observed by them) to their corresponding destination nodes. For this setup, we employ age of information (AoI) as a performance metric to quantify freshness of the status updates when they reach the destination nodes. While most of the existing works are focused on the analysis of the temporal mean AoI in deterministic network topologies, we aim to characterize the spatial AoI performance disparity that is inherently present in wireless networks. In particular, we treat the temporal mean AoI as a random variable over space as the update delivery rate of a wireless link is a function of the interference field observed by its receiver. Our objective is to characterize the spatial distribution of the temporal mean AoI observed by the SD pairs by modeling them as a Poisson bipolar process. We first derive accurate bounds on the moments of the successful transmission probability of a status update which are then used to derive tight bounds on the moments as well as the spatial distribution of the temporal mean peak AoI. Our results provide useful design guidelines on the appropriate selection of different system parameters to minimize the mean peak AoI.

@inproceedings{bib_Thro_2021, AUTHOR = {Mankar, Praful and Chen, Zheng and Abd-Elmagid, Mohamed A. and Pappas, Nikolaos and Dhillon, Harpreet S. }, TITLE = {Throughput and age of information in a cellular-based IoT network}, BOOKTITLE = {IEEE Transactions on Wireless Communications}. YEAR = {2021}}

— This paper studies the interplay between device-todevice (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the Johnson-Mehl (JM) tessellation, such that the IoT devices located in the JM cells are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

@inproceedings{bib_Adap_2021, AUTHOR = {Sreya, G. and Saigadha, S. and Mankar, Praful and Das, Goutam and Dhillon, Harpreet S. }, TITLE = {Adaptive Rate NOMA for Cellular IoT Networks}, BOOKTITLE = {Wireless Communications Letters}. YEAR = {2021}}

—Internet-of-Things (IoT) technology is envisioned to enable a variety of real-time applications by interconnecting billions of sensors/devices. These IoT devices rely on low-power wide-area wireless connectivity for transmitting, mostly fixed- but small-size, status updates of the random processes observed by them. Owing to their ubiquity, cellular networks are seen as a natural candidate for providing reliable wireless connectivity to IoT devices. Given the massive number of IoT devices, enabling non-orthogonal multiple access (NOMA) for the mobile users and IoT devices is appealing in terms of the efficient utilization of spectrum compared to the orthogonal multiple access (OMA). For instance, the uplink NOMA can also be configured such that the mobile users adapt their transmission rates depending upon the channel conditions while the IoT devices transmit at a fixed rate. For this setting, we analyze the ergodic capacity of the mobile users and the mean local delay of IoT devices using stochastic geometry. Our analysis demonstrates that the aforementioned NOMA configuration provides better ergodic capacity for mobile users compared to OMA when delay constraint of IoT devices is strict. We also show that NOMA supports a larger packet size at IoT devices than OMA under the same delay constraint.