arXiv subjects

Hongbo Zhu

Publications and source records attributed to Hongbo Zhu.

At least 37 records · Page 2Linked to original sources

arXiv2022

One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR\_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR\_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime.

cs.LG↗

arXiv2022

Bond-Selective Intensity Diffraction Tomography

Recovering molecular information remains a grand challenge in the widely used holographic and computational imaging technologies. To address this challenge, we developed a computational mid-infrared photothermal microscope, termed Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost brightfield microscope with an add-on pulsed light source, BS-IDT recovers both infrared spectra and bond-selective 3D refractive index maps from intensity-only measurements. High-fidelity infrared fingerprint spectra extraction is validated. Volumetric chemical imaging of biological cells is demonstrated at a speed of ~20 seconds per volume, with a lateral and axial resolution of ~350 nm and ~1.1 micron, respectively. BS-IDT's application potential is investigated by chemically quantifying lipids stored in cancer cells and volumetric chemical imaging on Caenorhabditis elegans with a large field of view (~100 micron X 100 micron).

physics.optics↗

arXiv2022

Reynolds number effects on the bistable flows over a wavy circular cylinder

The wake of wavy cylinder has been shown to exhibit bistability. Depending on the initial condition, the final state of the wake can either develop into a steady flow (state I), or periodic shedding (state II). In this paper, we perform direct numerical simulations to reveal the Reynolds number effects on these two wake states. With increasing Reynolds number, the steady vortical structures in state I wake sways back and forth in the spanwise direction, resulting in low-frequency fluctuations in drag forces, but not in lift. For state II, the increase in Reynolds number is associated with the emergence of another spectral peak in the lift coefficient. The secondary frequency is associated with highly three-dimensional vortical structures in the wake. For both states, the wakes transition to turblent flows at higher Reynolds numbers, with the development of small-scale vortices. We further study the streamwise gust flows over the wavy cylinder. The time-varying inflow velocity results in a wide range of instantaneous Reynolds number spanning from the absolutely unstable flow regime to the bistable regime. Depending on the period of the inflow velocity variation, the wake perturbations grown at the absolutely unstable flow regime can be damped out in state I wake, or grow large enough to trigger the transition state II, resulting in loss of flow control efficacy. The above analyses reveal novel flow physics of the bistable states at unexplored Reynolds numbers, and showcase the complex transition behavior between the two states in unsteady flows. The insights gained from this study improve the understanding of the wake dynamics of the wavy cylinder.

physics.flu-dyn↗

arXiv2022

Polytopic Planar Region Characterization of Rough Terrains for Legged Locomotion

This paper studies the problem of constructing polytopic representations of planar regions from depth camera readings. This problem is of great importance for terrain mapping in complicated environment and has great potentials in legged locomotion applications. To address the polytopic planar region characterization problem, we propose a two-stage solution scheme. At the first stage, the planar regions embedded within a sequence of depth images are extracted individually first and then merged to establish a terrain map containing only planar regions in a selected frame. To simplify the representations of the planar regions that are applicable to foothold planning for legged robots, we further approximate the extracted planar regions via low-dimensional polytopes at the second stage. With the polytopic representation, the proposed approach achieves a great balance between accuracy and simplicity. Experimental validations with RGB-D cameras are conducted to demonstrate the performance of the proposed scheme. The proposed scheme successfully characterizes the planar regions via polytopes with acceptable accuracy. More importantly, the run time of the overall perception scheme is less than 10ms (i.e., > 100Hz) throughout the tests, which strongly illustrates the advantages of our approach developed in this paper.

cs.RO↗

arXiv2022

Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network

Mobile Edge Computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation-intensive and delay critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering non-stationary computation demands, user mobility, and wireless fading channels. This paper aims to study the tradeoff between energy efficiency (EE) and service delay for multi-user multi-server MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a $[O(1/V), O(V)]$ tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.

eess.SY↗

arXiv2022

Turbulence suppression by streamwise-varying wall rotation in pipe flow

Direct numerical simulations (DNSs) of turbulent pipe flow subjected to streamwise-varying wall rotation are performed. This control method is able to achieve drag reduction and even relaminarize the flow under certain control parameters at low Reynolds number. Two control parameters, which are velocity amplitude and wavelength, are considered. An annular boundary layer, called Spatial Stokes Layer (SSL), is formed by the wall rotation. Based on the thickness of SSL, two types of drag reduction scenarios are identified. When the thickness is low, the SSL acts as a spacer layer, obstructing the impact of fluids against the wall and hence reducing the shear stress. The flow structures in the outer layer are less affected and are stretched in streamwise direction due to the increased velocity gradient. Within the SSL, the turbulence intensity diminishes dramatically. When the thickness is large, a streamwise wavy pattern of near-wall low-speed streaks is formed. The orientation of streaks is found to lag behind the direction of local mean velocity in phase and conform to the direction of local resultant shear stress. The streamwise scale of near-wall flow structures is significantly reduced, resulting in the disruption of downstream development of flow structures and hence leading to the drag reduction. Besides, it is found that it requires both large enough thickness of SSL and velocity amplitude to relaminarize the turbulence. The relaminarization mechanism is that the annular SSL can continuously absorb energy from wall-normal stress due to the rotation effect, thereby the turbulence self-sustaining process could not be maintained. For the relaminarization cases, the laminar state is stable to even extremely large perturbations, which possibly makes laminar state the only fixed point for the whole system.

physics.flu-dyn↗

arXiv2021

A Legged Soft Robot Platform for Dynamic Locomotion

We present an open-source untethered quadrupedal soft robot platform for dynamic locomotion (e.g., high-speed running and backflipping). The robot is mostly soft (80 vol.%) while driven by four geared servo motors. The robot's soft body and soft legs were 3D printed with gyroid infill using a flexible material, enabling it to conform to the environment and passively stabilize during locomotion on multi-terrain environments. In addition, we simulated the robot in a real-time soft body simulation. With tuned gaits in simulation, the real robot can locomote at a speed of 0.9 m/s (2.5 body length/second), substantially faster than most untethered legged soft robots published to date. We hope this platform, along with its verified simulator, can catalyze the development of soft robotics.

cs.RO↗

arXiv2020

Multi-Armed Bandit Based Client Scheduling for Federated Learning

By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms.

cs.IT↗

arXiv2020

Model-Driven Beamforming Neural Networks

Beamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing (ZF) are simpler but at the expense of performance loss. Alternatively, deep learning (DL) is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data- and model-driven beamforming neural networks (BNNs), presents various possible learning strategies, and also discusses complexity reduction for the DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions.

cs.IT↗

arXiv2019

Deep Learning Cell Imaging through Anderson Localizing Optical Fibre

We demonstrate a deep-learning-based fibre imaging system which can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fibre. The cell samples are illuminated by an incoherent LED light source. A deep convolutional neural network is applied to the image reconstruction process. The network training uses data generated by a set-up with straight fibre at room temperature (~20 °C) but can be utilized directly for high fidelity reconstruction of cell images that are transported through fibre with a few degrees bend and/or fibre with segments heated up to 50 °C. In addition, cell images located several millimeters away from the bare fibre end can be transported and recovered successfully without the assistance of any distal optics. We further evidence that the trained neural network is able to reconstruct the images of cells which are never used in the training process and feature very different morphology.

physics.optics↗

arXiv2018

Power Minimization Based Joint Task Scheduling and Resource Allocation in Downlink C-RAN

In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission. The power minimization problem for transmission is a fast time-scale issue whereas the power minimization problem for computation is a slow time-scale issue. Therefore, the joint network power minimization problem is a mixed time-scale problem. To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information. In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission. Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem. In summary, this work provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.

cs.IT↗

arXiv2018

Joint Optimization of Fronthaul Compression and Bandwidth Allocation in Uplink H-CRAN with Large System Analysis

In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs). For cost-savings, only the BS is connected to the baseband unit (BBU) pool via fiber links. The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks. Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner-Ziv coding. Different decoding strategies are also considered. This work aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue. To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue because it only depends on statistical channel information. Finally, an algorithm based on Dinkelbach's algorithm is proposed to find the optimal solution to the approximate problem. In summary, this work provides an economic solution to the challenge of constrained fronthaul capacity, and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem.

cs.IT↗

arXiv2017

Outage Performance for Cooperative NOMA Transmission with an AF Relay

This letter studies the outage performance of cooperative non-orthogonal multiple access (NOMA) network with the help of an amplify-and-forward relay. An accurate closed-form approximation for the exact outage probability is derived. Based on this, the asymptotic outage probability is investigated, which shows that cooperative NOMA achieves the same diversity order and the superior coding gain compared to cooperative orthogonal multiple access. It is also revealed that when the transmit power of relay is smaller than that of the base station, the outage performance improves as the distance between the relay and indirect link user decreases.

cs.IT↗

arXiv2017

Delay-Optimal Biased User Association in Heterogeneous Networks

In heterogeneous networks (HetNets), load balancing among different tiers can be effectively achieved by a biased user association scheme with which each user chooses to associate with one base station (BS) based on the biased received power. In contrast to previous studies where a BS always has packets to transmit, we assume in this paper that incoming packets intended for all the associated users form a queue in the BS. In order to find the delay limit of the network to support real-time service, we focus on the delay optimization problem by properly tuning the biasing factor of each tier. By adopting a thinned Poisson point process (PPP) model to characterize the locations of BSs in the busy state, an explicit expression of the average traffic intensity of each tier is obtained. On that basis, an optimization problem is formulated to minimize a lower bound of the network mean queuing delay. By showing that the optimization problem is convex, the optimal biasing factor of each tier can be obtained numerically. When the mean packet arrival rate of each user is small, a closed-form solution is derived. The simulation results demonstrate that the network queuing performance can be significantly improved by properly tuning the biasing factor. It is further shown that the network mean queuing delay might be improved at the cost of a deterioration of the network signal-to-interference ratio (SIR) coverage, which indicates a performance tradeoff between real-time and non-real-time traffic in HetNets.

cs.NI↗

arXiv2016

Semi-coherent Detection and Performance Analysis for Ambient Backscatter System

We study a novel communication mechanism, ambient backscatter, that utilizes radio frequency (RF) signals transmitted from an ambient source as both energy supply and information carrier to enable communications between low-power devices. Different from existing non-coherent schemes, we here design the semi-coherent detection, where channel parameters can be obtained from unknown data symbols and a few pilot symbols. We first derive the optimal detector for the complex Gaussian ambient RF signal from likelihood ratio test and compute the corresponding closed-form bit error rate (BER). To release the requirement for prior knowledge of the ambient RF signal, we next design a suboptimal energy detector with ambient RF signals being either the complex Gaussian or the phase shift keying (PSK). The corresponding detection thresholds, the analytical BER, and the outage probability are also obtained in closed-form. Interestingly, the complex Gaussian source would cause an error floor while the PSK source does not, which brings nontrivial indication of constellation design as opposed to the popular Gaussian-embedded literatures. Simulations are provided to corroborate the theoretical studies.

cs.IT↗

arXiv2016

Design and Implementation of a TDD-Based 128-Antenna Massive MIMO Prototyping System

Spurred by the dramatic mobile IP growth and the emerging Internet of Things (IoT) and cloud-based applications, wireless networking is witnessing a paradigm shift. By fully exploiting the spatial degrees of freedom, the massive multipleinput- multiple-output (MIMO) technology promises significant gains in both data rates and link reliability. This paper presents a time-division duplex (TDD)-based 128-antenna massive MIMO prototyping system designed to operate on a 20 MHz bandwidth. Up to twelve single-antenna users can be served by the designed system at the same time. System model is provided and link-level simulation corresponding to our practical TDDbased massive MIMO prototyping system is conducted to validate our design and performance of the algorithms. Based on the system hardware design demonstrated in this paper, both uplink real-time video and downlink data transmissions are realized, and the experiment results show that 268.8 Mbps rate was achieved for eight single-antenna users using QPSK modulation. The maximum spectral efficiency of the designed system will be 80.64 bit/s/Hz by twelve single-antenna users with 256-QAM modulation.

cs.IT↗

arXiv2016

Full duplex communication using visible light

In this work, we propose, fabricate and characterize a full duplex communication system using visible light on a single chip. Both the suspended p-n junction InGaN/GaN multiple quantum well (MQW) devices and the suspended waveguides are obtained on a GaN-on-silicon platform by wafer-level processing. Two suspended p-n junction InGaN/GaN MQW devices that can both emit and detect light simultaneously are connected using suspended waveguides to form an in-plane visible light communication (VLC) system. The light that is emitted from one suspended p-n junction InGaN/GaN MQW device can induce a current in the device located at the other end of the waveguide via in-plane light coupling, thus leading to full duplex communication using visible light. This proof-of-concept in-plane VLC system paves the way towards the implementation of a full duplex communications system operating at the same frequency using visible light on a single chip.

physics.ins-det↗

arXiv2016

Simultaneous light emission and detection of InGaN/GaN multiple quantum well diodes for in-plane visible light communication on a chip

This paper presents the design, fabrication, and experimental characterization of monolithically integrated p-n junction InGaN/GaN multiple quantum well diodes (MQWDs) and suspended waveguides. Suspended MQWDs can be used as transmitters and receivers simultaneously, and suspended waveguides are used for light coupling to create an in-plane visible light communication system. Compared to the waveguide with separation trench, the calculated total light efficiency is increased from 18% to 22% for the continuous waveguide. The MQWDs are characterized by their typical current-voltage performance, and the pulse excitation measurements confirm that the InGaN/GaN MQWDs can achieve the light emission and photodetection at the same time. The photocurrent measurements indicate that the photocurrent is modulated by a bias voltage and that the photons are being supplied from another transmitter. An experimental demonstration is presented showing that the proposed device works well for in-plane full-duplex communication using visible light.

physics.optics↗