arXiv subjects

Sumit J. Darak

Publications and source records attributed to Sumit J. Darak.

At least 19 recordsLinked to original sources

arXiv2026

Demo: An RFSoC-Based Testbed for Over-the-Air Wireless Transceiver at Millimeter Wave Frequency

The millimeter wave (mmW) frequency spectrum has been explored recently for large bandwidth communication. At these frequencies, narrow directional beams are required for communication since the signal attenuation is high due to atmospheric absorption. This work presents an AMD RFSoC and Sivers Semiconductors analog front-end based hardware testbed capable of directional communication via analog beamforming at mmW. The proposed testbed comprises orthogonal frequency division multiplexing (OFDM) based baseband physical layer and digital front-end on an ARM processor and field programmable gate array (FPGA), respectively, integrated with high-speed data converters of the RFSoC. The RFSoC output at sub-6GHz is integrated with a mmW multi-antenna analog-front end for over- the-air communication at 29.8 GHz. We demonstrate end-to-end communication over the air and present bit error rate (BER) analysis in the presence of radio frequency impairments and beam misalignments in real radio channels.

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arXiv2026

Millimeter Wave Frontend for Integrated Sensing and Communication System Transceiver on Edge

IEEE 802.11ad standard uses analog beamforming for high-speed directional communication with mobile user (MU) in the millimeter wave (mmWave) spectrum. However, the lengthy beam alignment procedures involving large data packets between the base station (BS) and the MU introduce considerable overhead, deteriorating the overall throughput. Prior works have proposed 802.11ad-based integrated sensing and communication (ISAC) BS transceivers to eliminate time-consuming beam alignment. Instead, the radar and communication functionalities use the same waveform, spectrum, and millimeter wave front end (MFE) with a common spatial field of view. The radar detects and localizes the MU, enabling the subsequent directional communication with the MU. This work proposes an end-to-end IEEE 802.11ad-based ISAC BS transceiver prototype, wherein the digital baseband hardware frontend on edge is integrated with a Simulink-based MFE. The proposed prototype facilitates a systematic link budget and detailed performance analysis for different wireless channels, target motions, signal-to-noise ratios, hardware configurations, and impairments. We also investigate how these impairments affect radar performance and, in turn, the communication metrics since the performances of both systems are uniquely interrelated in an ISAC system. Our results show that even with hardware impairments, the 802.11ad-based ISAC offers 34% higher throughput than the standard with an ideal MFE.

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arXiv2026

RFSoC-Based Integrated Navigation and Sensing Using NavIC

Prior art has proposed a secondary application for Global Navigation Satellite System (GNSS) infrastructure for remote sensing of ground-based and maritime targets. Here, a passive radar receiver is deployed to detect uncooperative targets on Earth's surface by capturing ground-reflected satellite signals. This work demonstrates a hardware prototype of an L-band Navigation with Indian Constellation (NavIC) satellite-based remote sensing receiver system mounted on an AMD Zynq radio frequency system-on-chip (RFSoC) platform. Two synchronized receiver channels are introduced for capturing the direct signal (DS) from the satellite and ground-reflected signal (GRS) returns from targets. These signals are processed on the ARM processor and field programmable gate array (FPGA) of the RFSoC to generate delay-Doppler maps of the ground-based targets. The performance is first validated in a loop-back configuration of the RFSoC. Next, the DS and GRS signals are emulated by the output from two ports of the Keysight Arbitrary Waveform Generator (AWG) and interfaced with the RFSoC where the signals are subsequently processed to obtain the delay-Doppler maps. The performance is validated for different signal-to-noise ratios (SNR).

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arXiv2024

Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip

Channel estimation (CE) is one of the critical signal-processing tasks of the wireless physical layer (PHY). Recent deep learning (DL) based CE have outperformed statistical approaches such as least-square-based CE (LS) and linear minimum mean square error-based CE (LMMSE). However, existing CE approaches have not yet been realized on system-on-chip (SoC). The first contribution of this paper is to efficiently implement the existing state-of-the-art CE algorithms on Zynq SoC (ZSoC), comprising of ARM processor and field programmable gate array (FPGA), via hardware-software co-design and fixed point analysis. We validate the superiority of DL-based CE and LMMSE over LS for various signal-to-noise ratios (SNR) and wireless channels in terms of mean square error (MSE) and bit error rate (BER). We also highlight the high complexity, execution time, and power consumption of DL-based CE and LMMSE approaches. To address this, we propose a novel compute-efficient LS-augmented interpolated deep neural network (LSiDNN) based CE algorithm and realize it on ZSoC. The proposed LSiDNN offers 88-90% lower execution time and 38-85% lower resource utilization than state-of-the-art DL-based CE for identical MSE and BER. LSiDNN offers significantly lower MSE and BER than LMMSE, and the gain improves with increased mobility between transceivers. It offers 75% lower execution time and 90-94% lower resource utilization than LMMSE.

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arXiv2023

Deep Neural Network Augmented Wireless Channel Estimation for Preamble-based OFDM PHY on Zynq System on Chip

Reliable and fast channel estimation is crucial for next-generation wireless networks supporting a wide range of vehicular and low-latency services. Recently, deep learning (DL) based channel estimation has been explored as an efficient alternative to conventional least-square (LS) and linear minimum mean square error (LMMSE) approaches. Most of these DL approaches have not been realized on system-on-chip (SoC), and preliminary study shows that their complexity exceeds the complexity of the entire physical layer (PHY). The high latency of DL is another concern. This paper considers the design and implementation of deep neural network (DNN) augmented LS-based channel estimation (LSDNN) for preamble-based orthogonal frequency-division multiplexing (OFDM) physical layer (PHY) on SoC. We demonstrate the gain in performance compared to the conventional LS and LMMSE approaches. Via software-hardware co-design, word-length optimization, and reconfigurable architectures, we demonstrate the superiority of the LSDNN over the LS and LMMSE for a wide range of signal-to-noise ratio (SNR), number of pilots, preamble types, and wireless channels. Further, we evaluate the performance, power, and area (PPA) of the LS and LSDNN application-specific integrated circuit (ASIC) implementations in 45 nm technology. We demonstrate that word-length optimization can substantially improve PPA for the proposed architecture in ASIC implementations.

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arXiv2022

Design and Performance Analysis of Hardware Realization of 3GPP Physical Layer for 5G Cell Search

5G Cell Search (CS) is the first step for user equipment (UE) to initiate the communication with the 5G node B (gNB) every time it is powered ON. In cellular networks, CS is accomplished via synchronization signals (SS) broadcasted by gNB. 5G 3rd generation partnership project (3GPP) specifications offer a detailed discussion on the SS generation at gNB but a limited understanding of their blind search, and detection is available. Unlike 4G, 5G SS may not be transmitted at the center of carrier frequency and their frequency location is unknown to UE. In this work, we demonstrate the 5G CS by designing 3GPP compatible hardware realization of the physical layer (PHY) of the gNB transmitter and UE receiver. The proposed SS detection explores a novel down-sampling approach resulting in a significant reduction in complexity and latency. Via detailed performance analysis, we analyze the functional correctness, computational complexity, and latency of the proposed approach for different word lengths, signal-to-noise ratio (SNR), and down-sampling factors. We demonstrate the complete CS functionality on GNU Radio-based RFNoC framework and USRP-FPGA platform. The 3GPP compatibility and demonstration on hardware strengthen the commercial significance of the proposed work.

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arXiv2022

Hardware Software Co-design of Statistical and Deep Learning Frameworks for Wideband Sensing on Zynq System on Chip

With the introduction of spectrum sharing and heterogeneous services in next-generation networks, the base stations need to sense the wideband spectrum and identify the spectrum resources to meet the quality-of-service, bandwidth, and latency constraints. Sub-Nyquist sampling (SNS) enables digitization for sparse wideband spectrum without needing Nyquist speed analog-to-digital converters. However, SNS demands additional signal processing algorithms for spectrum reconstruction, such as the well-known orthogonal matching pursuit (OMP) algorithm. OMP is also widely used in other compressed sensing applications. The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and FPGA. Experimental analysis shows a significant degradation in OMP performance for sparse spectrum. Also, OMP needs prior knowledge of spectrum sparsity. We address these challenges via deep-learning-based architectures and efficiently map them on the ZSoC platform as second contribution. Via hardware-software co-design, different versions of the proposed architecture obtained by partitioning between software (ARM processor) and hardware (FPGA) are considered. The resource, power, and execution time comparisons for given memory constraints and a wide range of word lengths are presented for these architectures.

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arXiv2022

Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks

In wireless networks, the rate achieved depends on factors like level of interference, hardware impairments, and channel gain. Often, instantaneous values of some of these factors can be measured, and they provide useful information about the instantaneous rate achieved. For example, higher interference implies a lower rate. In this work, we treat any such measurable quality that has a non-zero correlation with the rate achieved as side-information and study how it can be exploited to quickly learn the channel that offers higher throughput (reward). When the mean value of the side-information is known, using control variate theory we develop algorithms that require fewer samples to learn the parameters and can improve the learning rate compared to cases where side-information is ignored. Specifically, we incorporate side-information in the classical Upper Confidence Bound (UCB) algorithm and quantify the gain achieved in the regret performance. We show that the gain is proportional to the amount of the correlation between the reward and associated side-information. We discuss in detail various side-information that can be exploited in cognitive radio and air-to-ground communication in $L-$band. We demonstrate that correlation between the reward and side-information is often strong in practice and exploiting it improves the throughput significantly.

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arXiv2021

Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?

Multi-armed Bandit (MAB) algorithms identify the best arm among multiple arms via exploration-exploitation trade-off without prior knowledge of arm statistics. Their usefulness in wireless radio, IoT, and robotics demand deployment on edge devices, and hence, a mapping on system-on-chip (SoC) is desired. Theoretically, the Bayesian approach-based Thompson Sampling (TS) algorithm offers better performance than the frequentist approach-based Upper Confidence Bound (UCB) algorithm. However, TS is not synthesizable due to Beta function. We address this problem by approximating it via a pseudo-random number generator-based approach and efficiently realize the TS algorithm on Zynq SoC. In practice, the type of arms distribution (e.g., Bernoulli, Gaussian, etc.) is unknown and hence, a single algorithm may not be optimal. We propose a reconfigurable and intelligent MAB (RI-MAB) framework. Here, intelligence enables the identification of appropriate MAB algorithms for a given environment, and reconfigurability allows on-the-fly switching between algorithms on the SoC. This eliminates the need for parallel implementation of algorithms resulting in huge savings in resources and power consumption. We analyze the functional correctness, area, power, and execution time of the proposed and existing architectures for various arm distributions, word-length, and hardware-software co-design approaches. We demonstrate the superiority of the RI-MAB over TS and UCB only architectures.

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arXiv2020

Towards Intelligent Reconfigurable Wireless Physical Layer (PHY)

Next-generation wireless networks are getting significant attention because they promise 10-factor enhancement in mobile broadband along with the potential to enable new heterogeneous services. Services include massive machine type communications desired for Industrial 4.0 along with ultra-reliable low latency services for remote healthcare and vehicular communications. In this paper, we present the design of an intelligent and reconfigurable physical layer (PHY) to bring these services to reality. First, we design and implement the reconfigurable PHY via a hardware-software co-design approach on system-on-chip consisting of the ARM processor and field-programmable gate array (FPGA). The reconfigurable PHY is then made intelligent by augmenting it with online machine learning (OML) based decision-making algorithm. Such PHY can learn the environment (for example, wireless channel) and dynamically adapt the transceivers' configuration (i.e., modulation scheme, word-length) and select the wireless channel on-the-fly. Since the environment is unknown and changes with time, we make the OML architecture reconfigurable to enable dynamic switch between various OML algorithms on-the-fly. We have demonstrated the functional correctness of the proposed architecture for different environments and word-lengths. The detailed throughput, latency, and complexity analysis validate the feasibility and importance of the proposed intelligent and reconfigurable PHY in next-generation networks.

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arXiv2020

Reconfigurable and Intelligent Ultra-Wideband Angular Sensing: Prototype Design and Validation

The emergence of beyond-licensed spectrum sharing in FR1 (0.45-6 GHz) and FR2 (24 - 52 GHz) along with the multi-antenna narrow-beam based directional transmissions demand a wideband spectrum sensing in temporal as well as spatial domains. We referred to it as ultra-wideband angular spectrum sensing (UWAS), and it consists of digitization followed by characterization of the wideband spectrum. In this paper, we design and develop state-of-the-art UWAS prototype using USRPs and LabVIEW NXG for the validation in the real-radio environment. Since 5G is expected to co-exist with LTE, the transmitter generates the multi-directional multi-user wideband traffic via LTE specific single carrier frequency division multiple access (SC-FDMA) approach. At the receiver, the first step of wideband spectrum digitization is accomplished using a novel approach of integrating sparse antenna-array with reconfigurable sub-Nyquist sampling (SNS). The reconfigurable SNS allows the digitization of non-contiguous spectrum via low-rate analog-to-digital converters, but it needs intelligence to choose the frequency bands for digitization. We explore the multi-play multi-armed bandit based learning algorithm to embed intelligence. Compared to previous works, the proposed characterization (frequency band status and direction-of-arrival estimation) approach does not need prior knowledge of received signal distribution. The detailed experimental results for various spectrum statistics, power gains and antenna array arrangements along with lower complexity validate the functional correctness, superiority and feasibility of the proposed UWAS over state-of-the-art approaches.

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arXiv2020

Novel Deep Learning Framework for Wideband Spectrum Characterization at Sub-Nyquist Rate

Introduction of spectrum-sharing in 5G and subsequent generation networks demand base-station(s) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.) of occupied bands. Such characterization at Nyquist sampling is area and power-hungry due to the need for high-speed digitization. Though sub-Nyquist sampling (SNS) offers an excellent alternative when the spectrum is sparse, it suffers from poor performance at low signal to noise ratio (SNR) and demands careful design and integration of digital reconstruction, tunable channelizer and characterization algorithms. In this paper, we propose a novel deep-learning framework via a single unified pipeline to accomplish two tasks: 1)~Reconstruct the signal directly from sub-Nyquist samples, and 2)~Wideband spectrum characterization. The proposed approach eliminates the need for complex signal conditioning between reconstruction and characterization and does not need complex tunable channelizers. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based approaches and characterization performance approaches to Nyquist sampling-based framework with an increase in SNR. Easy to design and integrate along with a single unified deep learning framework make the proposed architecture a good candidate for reconfigurable platforms.

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arXiv2020

Distributed Algorithm for Dynamic Cognitive Ad-hoc Networks

Cognitive ad-hoc networks allow users to access an unlicensed/shared spectrum without the need for any coordination via a central controller and are being envisioned for futuristic ultra-dense wireless networks. The ad-hoc nature of networks require each user to learn and regularly update various network parameters such as channel quality and the number of users, and use learned information to improve the spectrum utilization and minimize collisions. For such a learning and coordination task, we propose a distributed algorithm based on a multi-player multi-armed bandit approach and novel signaling scheme. The proposed algorithm does not need prior knowledge of network parameters (users, channels) and its ability to detect as well as adapt to the changes in the network parameters thereby making it suitable for static as well as dynamic networks. The theoretical analysis and extensive simulation results validate the superiority of the proposed algorithm over existing state-of-the-art algorithms.

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arXiv2020

Distributed Learning in Ad-Hoc Networks: A Multi-player Multi-armed Bandit Framework

Next-generation networks are expected to be ultra-dense with a very high peak rate but relatively lower expected traffic per user. For such scenario, existing central controller based resource allocation may incur substantial signaling (control communications) leading to a negative effect on the quality of service (e.g. drop calls), energy and spectrum efficiency. To overcome this problem, cognitive ad-hoc networks (CAHN) that share spectrum with other networks are being envisioned. They allow some users to identify and communicate in `free slots' thereby reducing signaling load and allowing the higher number of users per base stations (dense networks). Such networks open up many interesting challenges such as resource identification, coordination, dynamic and context-aware adaptation for which Machine Learning and Artificial Intelligence framework offers novel solutions. In this paper, we discuss state-of-the-art multi-armed multi-player bandit based distributed learning algorithms that allow users to adapt to the environment and coordinate with other players/users. We also discuss various open research problems for feasible realization of CAHN and interesting applications in other domains such as energy harvesting, Internet of Things, and Smart grids.

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arXiv2020

Intelligent and Reconfigurable Architecture for KL Divergence Based Online Machine Learning Algorithm

Online machine learning (OML) algorithms do not need any training phase and can be deployed directly in an unknown environment. OML includes multi-armed bandit (MAB) algorithms that can identify the best arm among several arms by achieving a balance between exploration of all arms and exploitation of optimal arm. The Kullback-Leibler divergence based upper confidence bound (KLUCB) is the state-of-the-art MAB algorithm that optimizes exploration-exploitation trade-off but it is complex due to underlining optimization routine. This limits its usefulness for robotics and radio applications which demand integration of KLUCB with the PHY on the system on chip (SoC). In this paper, we efficiently map the KLUCB algorithm on SoC by realizing optimization routine via alternative synthesizable computation without compromising on the performance. The proposed architecture is dynamically reconfigurable such that the number of arms, as well as type of algorithm, can be changed on-the-fly. Specifically, after initial learning, on-the-fly switch to light-weight UCB offers around 10-factor improvement in latency and throughput. Since learning duration depends on the unknown arm statistics, we offer intelligence embedded in architecture to decide the switching instant. We validate the functional correctness and usefulness of the proposed architecture via a realistic wireless application and detailed complexity analysis demonstrates its feasibility in realizing intelligent radios.

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arXiv2019

Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous Networks

We consider an ad hoc network where multiple users access the same set of channels. The channel characteristics are unknown and could be different for each user (heterogeneous). No controller is available to coordinate channel selections by the users, and if multiple users select the same channel, they collide and none of them receive any rate (or reward). For such a completely decentralized network we develop algorithms that aim to achieve optimal network throughput. Due to lack of any direct communication between the users, we allow each user to exchange information by transmitting in a specific pattern and sense such transmissions from others. However, such transmissions and sensing for information exchange do not add to network throughput. For the wideband sensing and narrowband sensing scenarios, we first develop explore-and-commit algorithms that converge to near-optimal allocation with high probability in a small number of rounds. Building on this, we develop an algorithm that gives logarithmic regret, even when the number of users changes with time. We validate our claims through extensive experiments and show that our algorithms perform significantly better than the state-of-the-art CSM-MAB, dE3 and dE3-TS algorithms.

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arXiv2018

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiments.

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arXiv2018

Distributed Learning Algorithms for Opportunistic Spectrum Access in Infrastructure-less Networks

An opportunistic spectrum access (OSA) for the infrastructure-less (or cognitive ad-hoc) network has received significant attention thanks to emerging paradigms such as the Internet of Things (IoTs) and smart grids. Research in this area has evolved from the \r{ho}rand algorithm requiring prior knowledge of the number of active secondary users (SUs) to the musical chair (MC) algorithm where the number of SUs are unknown and estimated independently at each SU. These works ignore the number of collisions in the network leading to wastage of power and bring down the effective life of battery operated SUs. In this paper, we develop algorithms for OSA that learn faster and incurs fewer number of collisions i.e. energy efficient. We consider two types of infrastructure-less decentralized networks: 1) static network where the number of SUs are fixed but unknown, and 2) dynamic network where SUs can independently enter or leave the network. We set up the problem as a multi-player mult-armed bandit and develop two distributed algorithms. The analysis shows that when all the SUs independently implement the proposed algorithms, the loss in throughput compared to the optimal throughput, i.e. regret, is a constant with high probability and significantly outperforms existing algorithms both in terms of regret and number of collisions. Fewer collisions make them ideally suitable for battery operated SU terminals. We validate our claims through exhaustive simulated experiments as well as through a realistic USRP based experiments in a real radio environment.

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