arXiv subjects

Kaibin Huang

Publications and source records attributed to Kaibin Huang.

At least 181 records · Page 10Linked to original sources

arXiv2021

Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing

Wireless power transfer (WPT) is an emerging paradigm that will enable using wireless to its full potential in future networks, not only to convey information but also to deliver energy. Such networks will enable trillions of future low-power devices to sense, compute, connect, and energize anywhere, anytime, and on the move. The design of such future networks brings new challenges and opportunities for signal processing, machine learning, sensing, and computing so as to make the best use of the RF radiations, spectrum, and network infrastructure in providing cost-effective and real-time power supplies to wireless devices and enable wireless-powered applications. In this paper, we first review recent signal processing techniques to make WPT and wireless information and power transfer as efficient as possible. Topics include power amplifier and energy harvester nonlinearities, active and passive beamforming, intelligent reflecting surfaces, receive combining with multi-antenna harvester, modulation, coding, waveform, massive MIMO, channel acquisition, transmit diversity, multi-user power region characterization, coordinated multipoint, and distributed antenna systems. Then, we overview two different design methodologies: the model and optimize approach relying on analytical system models, modern convex optimization, and communication theory, and the learning approach based on data-driven end-to-end learning and physics-based learning. We discuss the pros and cons of each approach, especially when accounting for various nonlinearities in wireless-powered networks, and identify interesting emerging opportunities for the approaches to complement each other. Finally, we identify new emerging wireless technologies where WPT may play a key role -- wireless-powered mobile edge computing and wireless-powered sensing -- arguing WPT, communication, computation, and sensing must be jointly designed.

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arXiv2020

Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it is with rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and internet of things (IoT) devices. In this paper, we present an infrastructure to perform ML tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criterions are to maximize the throughput, we aim at maximizing the learning performance. Specifically, we minimize the maximum learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS. An alternating optimization (AO)-based framework is proposed to optimize the three terms iteratively, where a successive convex approximation (SCA)-based algorithm is developed to solve the power allocation problem, closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching (ELS) framework to effectively solve the challenging nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified communication-training-inference platform is developed based on the CARLA platform and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform.

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arXiv2020

One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis

Federated edge learning (FEEL) is a popular framework for model training at an edge server using data distributed at edge devices (e.g., smart-phones and sensors) without compromising their privacy. In the FEEL framework, edge devices periodically transmit high-dimensional stochastic gradients to the edge server, where these gradients are aggregated and used to update a global model. When the edge devices share the same communication medium, the multiple access channel (MAC) from the devices to the edge server induces a communication bottleneck. To overcome this bottleneck, an efficient broadband analog transmission scheme has been recently proposed, featuring the aggregation of analog modulated gradients (or local models) via the waveform-superposition property of the wireless medium. However, the assumed linear analog modulation makes it difficult to deploy this technique in modern wireless systems that exclusively use digital modulation. To address this issue, we propose in this work a novel digital version of broadband over-the-air aggregation, called one-bit broadband digital aggregation (OBDA). The new scheme features one-bit gradient quantization followed by digital quadrature amplitude modulation (QAM) at edge devices and over-the-air majority-voting based decoding at edge server. We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme. The analysis shows that the hostilities slow down the convergence of the learning process by introducing a scaling factor and a bias term into the gradient norm. However, we show that all the negative effects vanish as the number of participating devices grows, but at a different rate for each type of channel hostility.

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arXiv2020

Over-the-Air Computing for Wireless Data Aggregation in Massive IoT

Wireless data aggregation (WDA), referring to aggregating data distributed at devices (e.g., sensors and smartphone), is a common operation in 5G-and-beyond machine-type communications to support Internet-of-Things (IoT), which lays the foundation for diversified applications such as distributed sensing, learning, and control. Conventional WDA techniques that are designed based on a separated-communication-and-computation principle encounter difficulty in accommodating the massive access under the limited radio resource and stringent latency constraints imposed by emerging applications (e.g, auto-driving). To address this issue, over-the-air computation (AirComp) is being developed as a new WDA solution by seamlessly integrating computation and communication. By exploiting the waveform superposition property of a multiple-access channel, AirComp turns the air into a computer for computing and communicating functions of distributed data at many devices, thereby allowing low-latency WDA over massive devices. In view of growing interests on AirComp, this article provides a timely overview of the technology by introducing basic principles, discussing advanced techniques and applications, and identifying promising research opportunities.

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arXiv2020

Cooperative Multi-Point Vehicular Positioning Using Millimeter-Wave Surface Reflection (Extended version)

Multi-point vehicular positioning is one essential operation for autonomous vehicles. However, the state-of-the-art positioning technologies, relying on reflected signals from a target (i.e., RADAR and LIDAR), cannot work without line-of-sight. Besides, it takes significant time for environment scanning and object recognition with potential detection inaccuracy, especially in complex urban situations. Some recent fatal accidents involving autonomous vehicles further expose such limitations. In this paper, we aim at overcoming these limitations by proposing a novel relative positioning approach, called Cooperative Multi-point Positioning (COMPOP). The COMPOP establishes cooperation between a target vehicle (TV) and a sensing vehicle (SV) if a LoS path exists, where a TV explicitly lets an SV to know the TV's existence by transmitting positioning waveforms. This cooperation makes it possible to remove the time-consuming scanning and target recognizing processes, facilitating real-time positioning. One prerequisite for the cooperation is a clock synchronization between a pair of TV and SV. To this end, we use a phase-differential-of-arrival based approach to remove the TV-SV clock difference from the received signal. With clock difference correction, the TV's position can be obtained via peak detection over a 3D power spectrum constructed by a Fourier transform (FT) based algorithm. The COMPOP also incorporates nearby vehicles, without knowing their locations, into the above cooperation for the case without a LoS path. The effectiveness of the COMPOP is verified by several simulations concerning practical channel parameters.

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arXiv2020

Capacity of Remote Classification Over Wireless Channels

Wireless connectivity creates a computing paradigm that merges communication and inference. A basic operation in this paradigm is the one where a device offloads classification tasks to the edge servers. We term this remote classification, with a potential to enable intelligent applications. Remote classification is challenged by the finite and variable data rate of the wireless channel, which affects the capability to transfer high-dimensional features and thus limits the classification resolution. We introduce a set of metrics under the name of classification capacity that are defined as the maximum number of classes that can be discerned over a given communication channel while meeting a target classification error probability. The objective is to choose a subset of classes from a library that offers satisfactory performance over a given channel. We treat two cases of subset selection. First, a device can select the subset by pruning the class library until arriving at a subset that meets the targeted error probability while maximizing the classification capacity. Adopting a subspace data model, we prove the equivalence of classification capacity maximization to Grassmannian packing. The results show that the classification capacity grows exponentially with the instantaneous communication rate, and super-exponentially with the dimensions of each data cluster. This also holds for ergodic and outage capacities with fading if the instantaneous rate is replaced with an average rate and a fixed rate, respectively. In the second case, a device has a preference of class subset for every communication rate, which is modeled as an instance of uniformly sampling the library. Without class selection, the classification capacity and its ergodic and outage counterparts are proved to scale linearly with their corresponding communication rates instead of the exponential growth in the last case.

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arXiv2020

Optimized Power Control for Over-the-Air Computation in Fading Channels

In this paper, we study the power control problem for Over-the-air computation (AirComp) over fading channels. Our objective is to minimize the computation error by jointly optimizing the transmit power at the power-constrained devices and a signal scaling factor (called denoising factor) at the fusion center (FC). The problem is generally non-convex due to the coupling of the transmit power over devices and denoising factor at the FC. To tackle the challenge, we first consider the special case with static channels, for which we derive the optimal solution in closed form. The optimal power control exhibits a threshold-based structure. Specifically, for each device, if the product of the channel quality and power budget, called quality indicator, exceeds an optimized threshold, this device applies channel-inversion power control; otherwise, it performs full power transmission. Building on the results, we proceed to consider the general case with time-varying channels. To solve the more challenging non-convex power control problem, we use the Lagrange-duality method via exploiting its "time-sharing" property. The derived optimal power control exhibits a regularized channel inversion structure, where the regularization has the function of balancing the tradeoff between the signal-magnitude alignment and noise suppression. Moreover, for the special case with only one device being power limited, we show that the optimal power control for the power-limited device has an interesting channel-inversion water-filling structure, while those for other devices (with sufficient power budgets) reduce to channel-inversion power control over all fading states.

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arXiv2020

Cooperative Interference Management for Over-the-Air Computation Networks

This paper considers a multi-cell AirComp network and investigates the optimal power control policies over multiple cells to regulate the effect of inter-cell interference. First, we consider the scenario of centralized multi-cell power control, where we characterize the Pareto boundary of the multi-cell MSE region by minimizing the sum MSE subject to a set of constraints on individual MSEs. Though the sum-MSE minimization problem is non-convex and its direct solution intractable, we optimally solve this problem via equivalently solving a sequence of convex second-order cone program feasibility problems together with a bisection search. Next, we consider distributed power control in the other scenario without a centralized controller, for which an alternative IT-based method is proposed to characterize the same MSE Pareto boundary, and enable a decentralized power control algorithm. Accordingly, each AP only needs to individually control the power of its associated devices, but subject to a set of IT constraints on their interference to neighboring cells, while different APs can cooperate in iteratively updating the IT levels by pairwise information exchange, to achieve a Pareto-optimal MSE tuple. Last, simulation results demonstrate that cooperative power control using the proposed algorithms can substantially reduce the sum MSE of AirComp networks.

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arXiv2020

Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing

Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to train artificial intelligence (AI) models. Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation. FEEL coordinates global model training at an edge server and local model training at edge devices that are connected by wireless links. This work contributes to the energy-efficient implementation of FEEL in wireless networks by designing joint computation-and-communication resource management ($\text{C}^2$RM). The design targets the state-of-the-art heterogeneous mobile architecture where parallel computing using both a CPU and a GPU, called heterogeneous computing, can significantly improve both the performance and energy efficiency. To minimize the sum energy consumption of devices, we propose a novel $\text{C}^2$RM framework featuring multi-dimensional control including bandwidth allocation, CPU-GPU workload partitioning and speed scaling at each device, and $\text{C}^2$ time division for each link. The key component of the framework is a set of equilibriums in energy rates with respect to different control variables that are proved to exist among devices or between processing units at each device. The results are applied to designing efficient algorithms for computing the optimal $\text{C}^2$RM policies faster than the standard optimization tools. Based on the equilibriums, we further design energy-efficient schemes for device scheduling and greedy spectrum sharing that scavenges "spectrum holes" resulting from heterogeneous $\text{C}^2$ time divisions among devices. Using a real dataset, experiments are conducted to demonstrate the effectiveness of $\text{C}^2$RM on improving the energy efficiency of a FEEL system.

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arXiv2020

Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning

To leverage data and computation capabilities of mobile devices, machine learning algorithms are deployed at the network edge for training artificial intelligence (AI) models, resulting in the new paradigm of edge learning. In this paper, we consider the framework of partitioned edge learning for iteratively training a large-scale model using many resource-constrained devices (called workers). To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets. Then, the local updates are uploaded to and cascaded by the server for updating a global model. To reduce resource usage by minimizing the total learning-and-communication latency, this work focuses on the novel joint design of parameter (computation load) allocation and bandwidth allocation (for downloading and uploading). Two design approaches are adopted. First, a practical sequential approach, called partially integrated parameter-and-bandwidth allocation (PABA), yields two schemes, namely bandwidth aware parameter allocation and parameter aware bandwidth allocation. The former minimizes the load for the slowest (in computing) of worker groups, each training a same parametric block. The latter allocates the largest bandwidth to the worker being the latency bottleneck. Second, PABA are jointly optimized. Despite its being a nonconvex problem, an efficient and optimal solution algorithm is derived by intelligently nesting a bisection search and solving a convex problem. Experimental results using real data demonstrate that integrating PABA can substantially improve the performance of partitioned edge learning in terms of latency (by e.g., 46%) and accuracy (by e.g., 4%).

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arXiv2020

Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.

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arXiv2020

Scheduling for Mobile Edge Computing with Random User Arrivals: An Approximate MDP and Reinforcement Learning Approach

In this paper, we investigate the scheduling design of a mobile edge computing (MEC) system, where active mobile devices with computation tasks randomly appear in a cell. Every task can be computed at either the mobile device or the MEC server. We jointly optimize the task offloading decision, uplink transmission device selection and power allocation by formulating the problem as an infinite-horizon Markov decision process (MDP). Compared with most of the existing literature, this is the first attempt to address the transmission and computation optimization with the random device arrivals in an infinite time horizon to our best knowledge. Due to the uncertainty in the device number and location, the conventional approximate MDP approaches addressing the curse of dimensionality cannot be applied. An alternative and suitable low-complexity solution framework is proposed in this work. We first introduce a baseline scheduling policy, whose value function can be derived analytically with the statistics of random mobile device arrivals. Then, one-step policy iteration is adopted to obtain a sub-optimal scheduling policy whose performance can be bounded analytically. The complexity of deriving the sub-optimal policy is reduced dramatically compared with conventional solutions of MDP by eliminating the complicated value iteration. To address a more general scenario where the statistics of random mobile device arrivals are unknown, a novel and efficient algorithm integrating reinforcement learning and stochastic gradient descent (SGD) is proposed to improve the system performance in an online manner. Simulation results show that the gain of the sub-optimal policy over various benchmarks is significant.

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arXiv2020

V2X-Based Vehicular Positioning: Opportunities, Challenges, and Future Directions

Vehicle-to-Everything (V2X) will create many new opportunities in the area of wireless communications, while its feasibility on enabling vehicular positioning has not been explored yet. Vehicular positioning is a crucial operation for autonomous driving. Its complexity and stringent safety requirement render conventional technologies like RADAR and LIDAR inadequate. This article aims at investigating whether V2X can help vehicular positioning from different perspectives. We first explain V2X's critical advantages over other approaches and suggest new scenarios of V2X-based vehicular positioning. Then we review the state-of-the-art positioning techniques discussed in the ongoing 3GPP standardization and point out their limitations. Lastly, some promising research directions for V2X-based vehicular positioning are presented, which shed light on realizing fully autonomous driving by overcoming the current barriers.

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arXiv2020

Simultaneous Signal-and-Interference Alignment for Two-Cell Over-the-Air Computation

The next-generation wireless networks are envisioned to support large-scale sensing and distributed machine learning, thereby enabling new intelligent mobile applications. One common network operation will be the aggregation of distributed data (such as sensor observations or AI-model updates) for functional computation (e.g., averaging) so as to support large-scale sensing and distributed machine learning. An efficient solution for data aggregation, called "over-the-air computation" (AirComp), embeds functional computation into simultaneous access by many edge devices. Such schemes exploit the waveform superposition of a multi-access channel to allow an access point to receive a desired function of simultaneous signals. In this work, we aim at realizing AirComp in a two-cell multi-antenna system. To this end, a novel scheme of simultaneous signal-and-interference alignment (SIA) is proposed that builds on classic IA to manage interference for multi-cell AirComp. The principle of SIA is to divide the spatial channel space into two subspaces with equal dimensions: one for signal alignment required by AirComp and the other for inter-cell IA. As a result, the number of interference-free spatially multiplexed functional streams received by each AP is maximized (equal to half of the available spatial degrees-of-freedom). Furthermore, the number is independent of the population of devices in each cell. In addition, the extension to SIA for more than two cells is discussed.

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arXiv2019

Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labelling

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The realistic adoption of this technique however entails labelling of the enormous data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new perspective of wireless crowd labelling that is capable of downloading data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. In this cross-disciplinary area, the integration of the rate-distortion theory and the principle of repetition labelling for accuracy improvement gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios. Thereby, the optimal solution can be found by an efficient tree search. Next, the solution is simplified by applying truncated channel inversion. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, exact polices are derived for the annotators constrained and spectrum constrained cases. Last, simulation results demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.

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arXiv2019

An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning

The 5G network connecting billions of Internet-of-Things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization architecture will enable cloud computing at the (network) edge. The availability of both rich data and computation power at the edge has motivated Internet companies to deploy artificial intelligence (AI) there, creating the hot area of edge-AI. Edge learning, the theme of this project, concerns training edge-AI models, which endow on IoT devices intelligence for responding to real-time events. However, the transmission of high-dimensional data from many edge devices to servers can result in excessive communication latency, creating a bottleneck for edge learning. Traditional wireless techniques deigned for only radio access are ineffective in tackling the challenge. Attempts to overcome the communication bottleneck has led to the development of a new class of techniques for intelligent radio resource management (RRM), called data-importance aware RRM. Their designs feature the interplay of active machine learning and wireless communication. Specifically, the metrics that measure data importance in active learning (e.g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers. This article aims at providing an introduction to the emerging area of importance-aware RRM. To this end, we will introduce the design principles, survey recent advancements in the area, discuss some design examples, and suggest some promising research opportunities.

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arXiv2019

An Introduction to Communication Efficient Edge Machine Learning

In the near future, Internet-of-Things (IoT) is expected to connect billions of devices (e.g., smartphones and sensors), which generate massive real-time data at the network edge. Intelligence can be distilled from the data to support next-generation AI-powered applications, which is called edge machine learning. One challenge faced by edge learning is the communication bottleneck, which is caused by the transmission of high-dimensional data from many edge devices to edge servers for learning. Traditional wireless techniques focusing only on efficient radio access are ineffective in tackling the challenge. Solutions should be based on a new approach that seamlessly integrates communication and computation. This has led to the emergence of a new cross-disciplinary paradigm called communication efficient edge learning. The main theme in the area is to design new communication techniques and protocols for efficient implementation of different distributed learning frameworks (i.e., federated learning) in wireless networks. This article provides an overview of the emerging area by introducing new design principles, discussing promising research opportunities, and providing design examples based on recent work.

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arXiv2019

High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning

Edge machine learning involves the deployment of learning algorithms at the wireless network edge so as to leverage massive mobile data for enabling intelligent applications. The mainstream edge learning approach, federated learning, has been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for updating a global AI model. Since each stochastic gradient is typically high-dimensional (with millions to billions of coefficients), communication overhead becomes a bottleneck for edge learning. To address this issue, we propose in this work a novel framework of hierarchical stochastic gradient quantization and study its effect on the learning performance. First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively. Subsequently, the quantized normalized block gradients are scaled and cascaded to yield the quantized normalized stochastic gradient using a so-called hinge vector designed under the criterion of minimum distortion. The hinge vector is also efficiently compressed using another low-dimensional Grassmannian quantizer. The other feature of the framework is a bit-allocation scheme for reducing the quantization error. The scheme determines the resolutions of the low-dimensional quantizers in the proposed framework. The framework is proved to guarantee model convergency by analyzing the convergence rate as a function of the quantization bits. Furthermore, by simulation, our design is shown to substantially reduce the communication overhead compared with the state-of-the-art signSGD scheme, while both achieve similar learning accuracies.

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