arXiv subjects

Kaibin Huang

Publications and source records attributed to Kaibin Huang.

At least 217 records · Page 12Linked to original sources

arXiv2018

MIMO Over-the-Air Computation for High-Mobility Multi-Modal Sensing

In future Internet-of-Things networks, sensors or even access points can be mounted on ground/aerial vehicles for smart-city surveillance or environment monitoring. To support the high-mobility sensing with low network latency, a technique called over-the-air-computation (AirComp) was recently developed which enables an access-point to receive a desired function of sensing-data from concurrent-transmissions by exploiting the superposition property of a multi-access-channel. This work aims at further developing AirComp for next-generation multi-antenna multi-modal sensor networks. Specifically, we design beamforming and channel-feedback techniques for multi-function AirComp. Given the objective of minimizing sum-mean-squared-error of computed functions, the optimization of receive-beamforming for multi-function AirComp is a NP-hard problem. The approximate problem based on tightening transmission-power constraints, however, is shown to be solvable using differential-geometry. The solution is proved to be the weighted-centroid of points on a Grassmann-manifold, where each point represents the subspace spanned by the channel matrix of a sensor. As a by-product, the beamforming problem is found to have the same form as the classic problem of multicast-beamforming, establishing the AirComp-multicasting-duality. Its significance lies in making the said Grassmannian-centroid solution transferable to the latter problem which otherwise is solved using the computation-intensive semidefinite-relaxation-technique. Last, building on the AirComp-beamforming solution, an efficient channel-feedback technique is designed for an access-point to receive the beamformer from distributed sensor transmissions of designed signals that are functions of local channel-state-information.

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arXiv2018

V2X Downlink Coverage Analysis with a Realistic Urban Vehicular Model

As the realization of vehicular communication such as vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) is imperative for the autonomous driving cars, the understanding of realistic vehicle-to-everything (V2X) models is needed. While previous research has mostly targeted vehicular models in which vehicles are randomly distributed and the variable of carrier frequency was not considered, a more realistic analysis of the V2X model is proposed in this paper. We use a one-dimensional (1D) Poisson cluster process (PCP) to model a realistic scenario of vehicle distribution in a perpendicular cross line road urban area and compare the coverage results with the previous research that distributed vehicles randomly by Poisson Point Process (PPP). Moreover, we incorporate the effect of different carrier frequencies, mmWave and sub-6 GHz, to our analysis by altering the antenna radiation pattern accordingly. Results indicated that while the effect of clustering led to lower outage, using mmWave had even more significance in leading to lower outage. Moreover, line-of-sight (LoS) interference links are shown to be more dominant in lowering the outage than the non-line-of-sight (NLoS) links even though they are less in number. The analytical results give insight into designing and analyzing the urban V2X channels, and are verified by actual urban area three-dimensional (3D) ray-tracing simulation.

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arXiv2018

Wireless Networks for Mobile Edge Computing: Spatial Modeling and Latency Analysis (Extended version)

Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC, mobile computation offloading (MCO), is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), a MEC network can be decomposed as a radio access network (RAN) cascaded with a CS network (CSN). Based on the architecture, we investigate network constrained latency performance, namely communication latency (comm-latency) and computation latency (comp-latency) under the constraints of RAN coverage and CSN stability. To this end, a spatial random network is modeled featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Given the model and the said network constraints, we derive the scaling laws of comm-latency and comp-latency with respect to network-load parameters (density of mobiles and their task-generation rates) and network-resource parameters (bandwidth, density of APs/CSs, CS computation rate). Essentially, the analysis involves the interplay of theories of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latencies, network coverage and network stability. The results provide useful guidelines for MEC-network provisioning and planning by avoiding either of the cascaded RAN or CSN being a performance bottleneck.

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arXiv2018

Automatic Recognition of Space-Time Constellations by Learning on the Grassmann Manifold (Extended Version)

Recent breakthroughs in machine learning especially artificial intelligence shift the paradigm of wireless communication towards intelligence radios. One of their core operations is automatic modulation recognition (AMR). Existing research focuses on coherent modulation schemes such as QAM, PSK and FSK. The AMR of (non-coherent) space-time modulation remains an uncharted area despite its wide deployment in modern multiple-input-multiple-output (MIMO) systems. The scheme using a so called Grassmann constellation enables rate-enhancement using multi-antennas and blind detection. In this work, we propose an AMR approach for Grassmann constellation based on data clustering, which differs from traditional AMR based on classification using a modulation database. The approach allows algorithms for clustering on the Grassmann manifold, such as Grassmann K-means and depth-first search, originally developed for computer vision to be applied to AMR. We further develop an analytical framework for studying and designing these algorithms in the context of AMR. First, the maximum-likelihood Grassmann constellation detection is proved to be equivalent to clustering on the Grassmannian. Thereby, a well-known machine-learning result that was originally established only for the Euclidean space is rediscovered for the Grassmannian. Next, despite a rich literature on algorithmic design, theoretical analysis of data clustering is largely overlooked due to the lack of tractable techniques. We tackle the challenge by introducing probabilistic metrics for measuring the inter-cluster separability and intra-cluster connectivity of received space-time symbols and deriving them using tools from differential geometry and Grassmannian packing. The results provide useful insights into the effects of various parameters ranging from the signal-to-noise ratio to constellation size, facilitating algorithmic design.

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arXiv2018

Sensing Hidden Vehicles by Exploiting Multi-Path V2V Transmission

This paper presents a technology of sensing hidden vehicles by exploiting multi-path vehicle-to-vehicle (V2V) communication. This overcomes the limitation of existing RADAR technologies that requires line-of-sight (LoS), thereby enabling more intelligent manoeuvre in autonomous driving and improving its safety. The proposed technology relies on transmission of orthogonal waveforms over different antennas at the target (hidden) vehicle. Even without LoS, the resultant received signal enables the sensing vehicle to detect the position, shape, and driving direction of the hidden vehicle by jointly analyzing the geometry (AoA/AoD/propagation distance) of individual propagation path. The accuracy of the proposed technique is validated by realistic simulation including both highway and rural scenarios.

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arXiv2018

Wirelessly Powered Crowd Sensing: Joint Power Transfer, Sensing, Compression, and Transmission

Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility, mobile crowd sensing (MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named wirelessly powered crowd sensing (WPCS), which integrates MCS with wireless power transfer (WPT) for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an access point (AP) transfers energy to multiple mobile sensors (MSs), each of which performs data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the threshold-based structure with respect to a defined crowd-sensing priority function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms.

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arXiv2018

Stochastic Control of Computation Offloading to a Helper with a Dynamically Loaded CPU

Due to densification of wireless networks, there exist abundance of idling computation resources at edge devices. These resources can be scavenged by offloading heavy computation tasks from small IoT devices in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent. Thus, it is essential for a user to intelligently control the amounts of data for offloading and local computing so as to ensure a computation task can be finished in time consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU. Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally-computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper-CPU as Markov chains, the problem of offloading control is converted into a Markov-decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For different cases ranging from zero to large buffers, the low-complexity of the policies overcomes the "curse-of-dimensionality" in DP arising from joint consideration of channel, helper CPU and buffer states.

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arXiv2018

The Connectivity of Millimeter-Wave Networks in Urban Environments Modeled Using Random Lattices

Millimeter-wave (mmWave) communication opens up tens of giga-hertz (GHz) spectrum in the mmWave band for use by next-generation wireless systems, thereby solving the problem of spectrum scarcity. Maintaining connectivity stands out to be a key design challenge for mmWave networks deployed in urban regions due to the blockage effect characterizing mmWave propagation. Specifically, mmWave signals can be blocked by buildings and other large urban objects. In this paper, we set out to investigate the blockage effect on the connectivity of mmWave networks in a Manhattan-type urban region modeled using a random regular lattice while base stations (BSs) are Poisson distributed in the plane. In particular, we analyze the connectivity probability that a typical user is within the transmission range of a BS and connected by a line-of-sight. Using random lattice and stochastic geometry theories, different lower bounds on the connectivity probability are derived as functions of the buildings' size and probability of a lattice cell being occupied by a building as well as BS density and transmission range. The asymptotic connectivity probability is also derived for cases of dense buildings. Last, the results are extended to heterogeneous networks. Our study yields closed-form relations between the parameters of the building process and the BS process, providing useful guidelines for practical mmWave network deployment.

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arXiv2018

Inference from Randomized Transmissions by Many Backscatter Sensors

Attaining the vision of Smart Cities requires the deployment of an enormous number of sensors for monitoring various conditions of the environment. Backscatter-sensors have emerged to be a promising solution due to the uninterruptible energy supply and relative simple hardwares. On the other hand, backscatter-sensors with limited signal-processing capabilities are unable to support conventional algorithms for multiple-access and channel-training. Thus, the key challenge in designing backscatter-sensor networks is to enable readers to accurately detect sensing-values given simple ALOHA random access, primitive transmission schemes, and no knowledge of channel-states. We tackle this challenge by proposing the novel framework of backscatter sensing featuring random-encoding at sensors and statistical-inference at readers. Specifically, assuming the on/off keying for backscatter transmissions, the practical random-encoding scheme causes the on/off transmission of a sensor to follow a distribution parameterized by the sensing values. Facilitated by the scheme, statistical-inference algorithms are designed to enable a reader to infer sensing-values from randomized transmissions by multiple sensors. The specific design procedure involves the construction of Bayesian networks, namely deriving conditional distributions for relating unknown parameters and variables to signals observed by the reader. Then based on the Bayesian networks and the well-known expectation-maximization principle, inference algorithms are derived to recover sensing-values.

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arXiv2018

Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio

Multimedia content especially videos is expected to dominate data traffic in next-generation mobile networks. Caching popular content at the network edge has emerged to be a solution for low-latency content delivery. Compared with the traditional wireless communication, content delivery has a key characteristic that many signals coexisting in the air carry identical popular content. They, however, can interfere with each other at a receiver if their modulation-and-coding (MAC) schemes are adapted to individual channels following the classic approach. To address this issue, we present a novel idea of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures that all signals carry identical content are encoded using an identical MAC scheme, achieving spatial MAC alignment. Consequently, interference can be harnessed as signals, to improve the reliability of wireless delivery. In the remaining part of the paper, we focus on quantifying the gain CAMAC can bring to a content-delivery network using a stochastic-geometry model. Specifically, content helpers are distributed as a Poisson point process, each of which transmits a file from a content database based on a given popularity distribution. It is discovered that the successful content-delivery probability is closely related to the distribution of the ratio of two independent shot noise processes, named a shot-noise ratio. The distribution itself is an open mathematical problem that we tackle in this work. Using stable-distribution theory and tools from stochastic geometry, the distribution function is derived in closed form. Extending the result in the context of content-delivery networks with CAMAC yields the content-delivery probability in different closed forms. In addition, the gain in the probability due to CAMAC is shown to grow with the level of skewness in the content popularity distribution.

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arXiv2017

Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data

To overcome the limited coverage in traditional wireless sensor networks, \emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To achieve longer battery lives of user devices and incentive human involvement, this paper presents a novel approach that seamlessly integrates MCS with wireless power transfer, called \emph{wirelessly powered crowd sensing} (WPCS), for supporting crowd sensing with energy consumption and offering rewards as incentives. The optimization problem is formulated to simultaneously maximize the data utility and minimize the energy consumption for service operator, by jointly controlling wireless-power allocation at the \emph{access point} (AP) as well as sensing-data size, compression ratio, and sensor-transmission duration at \emph{mobile sensor} (MS). Given the fixed compression ratios, the optimal power allocation policy is shown to have a \emph{threshold}-based structure with respect to a defined \emph{crowd-sensing priority} function for each MS. Given fixed sensing-data utilities, the compression policy achieves the optimal compression ratio. Extensive simulations are also presented to verify the efficiency of the contributed mechanisms.

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arXiv2017

Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing

Scavenging the idling computation resources at the enormous number of mobile devices can provide a powerful platform for local mobile cloud computing. The vision can be realized by peer-to-peer cooperative computing between edge devices, referred to as co-computing. This paper considers a co-computing system where a user offloads computation of input-data to a helper. The helper controls the offloading process for the objective of minimizing the user's energy consumption based on a predicted helper's CPU-idling profile that specifies the amount of available computation resource for co-computing. Consider the scenario that the user has one-shot input-data arrival and the helper buffers offloaded bits. The problem for energy-efficient co-computing is formulated as two sub-problems: the slave problem corresponding to adaptive offloading and the master one to data partitioning. Given a fixed offloaded data size, the adaptive offloading aims at minimizing the energy consumption for offloading by controlling the offloading rate under the deadline and buffer constraints. By deriving the necessary and sufficient conditions for the optimal solution, we characterize the structure of the optimal policies and propose algorithms for computing the policies. Furthermore, we show that the problem of optimal data partitioning for offloading and local computing at the user is convex, admitting a simple solution using the sub-gradient method. Last, the developed design approach for co-computing is extended to the scenario of bursty data arrivals at the user accounting for data causality constraints. Simulation results verify the effectiveness of the proposed algorithms.

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arXiv2017

Cache-Enabled Heterogeneous Cellular Networks: Optimal Tier-Level Content Placement

Caching popular contents at base stations (BSs) of a heterogeneous cellular network (HCN) avoids frequent information passage from content providers to the network edge, thereby reducing latency and alleviating traffic congestion in backhaul links. In general, the optimal strategies for content placement in HCNs remain largely unknown and deriving them forms the theme of this paper. To this end, we adopt the popular random HCN model where $K$ tiers of BSs are modelled as independent Poisson point processes distributed in the plane with different densities. Further, the random caching scheme is considered where each of a given set of $M$ files with corresponding popularity measures is placed at each BS of a particular tier with a corresponding probability, called placement probability. The probabilities are identical for all BSs in the same tier but vary over tiers, giving the name tier-level content placement. We consider the network performance metric, hit probability, defined as the probability that a file requested by the typical user is delivered successfully to the user. We maximize the hit probability over content placement probabilities, which yields the optimal tier-level placement policies. For the case of uniform received signal-to-interference thresholds for successful transmissions for BSs in different tiers, the policy is in closed-form where the placement probability for a particular file is proportional to the square-root of the corresponding popularity measure with an offset depending on BS caching capacities. For the general case of non-uniform SIR thresholds, the optimization problem is non-convex and a sub-optimal placement policy is designed by approximation, which has a similar structure as in the case of uniform SIR thresholds and shown by simulation to be close-to-optimal.

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arXiv2017

A Survey on Mobile Edge Computing: The Communication Perspective

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

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arXiv2017

Full-Duplex Backscatter Interference Networks Based on Time-Hopping Spread Spectrum

Future Internet-of-Things (IoT) is expected to wirelessly connect billions of low-complexity devices. For wireless information transfer (WIT) in IoT, high density of IoT devices and their ad hoc communication result in strong interference which acts as a bottleneck on WIT. Furthermore, battery replacement for the massive number of IoT devices is difficult if not infeasible, making wireless energy transfer (WET) desirable. This motivates: (i) the design of full-duplex WIT to reduce latency and enable efficient spectrum utilization, and (ii) the implementation of passive IoT devices using backscatter antennas that enable WET from one device (reader) to another (tag). However, the resultant increase in the density of simultaneous links exacerbates the interference issue. This issue is addressed in this paper by proposing the design of full-duplex backscatter communication (BackCom) networks, where a novel multiple-access scheme based on time-hopping spread-spectrum (TH-SS) is designed to enable both one-way WET and two-way WIT in coexisting backscatter reader-tag links. Comprehensive performance analysis of BackCom networks is presented in this paper, including forward/backward bit-error rates and WET efficiency and outage probabilities, which accounts for energy harvesting at tags, non-coherent and coherent detection at tags and readers, respectively, and the effects of asynchronous transmissions.

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arXiv2017

Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems

Despite its promising performance gain, the realization of mmWave massive MIMO still faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds of RF chains matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of hybrid-beamforming where MIMO processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients, rendering the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the AoA of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed-form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length and the spatial separation between paths.

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arXiv2017

Effects of Base-Station Spatial Interdependence on Interference Correlation and Network Performance

The spatial-and-temporal correlation of interference has been well studied in Poisson networks where the interfering base stations (BSs) are independent of each other. However, there exists spatial interdependence including attraction and repulsion among the BSs in practical wireless networks, affecting the interference distribution and hence the network performance. In view of this, by modeling the network as a Poisson clustered process, we quantify the effects of spatial interdependence among BSs on the interference correlation and analytically prove that BS clustering increases the level of interference correlation. In particular, it is shown that the level increases as the attraction between the BSs increases. Furthermore, we study the effects of spatial interdependence among BSs on network performance with a retransmission scheme via considering heterogeneous cellular networks in which small-cell BSs exhibit a clustered topology in practice. It is shown that the interference correlation degrades the network performance and the degradation increases as the attraction between BSs increases. Finally, a correlation-aware retransmission scheme is proposed to improve the network performance by taking advantage of the interference correlation and avoiding the blind retransmissions.

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arXiv2017

Wirelessly Powered Backscatter Communications: Waveform Design and SNR-Energy Tradeoff

This paper shows that wirelessly powered backscatter communications is subject to a fundamental tradeoff between the harvested energy at the tag and the reliability of the backscatter communication, measured in terms of SNR at the reader. Assuming the RF transmit signal is a multisine waveform adaptive to the channel state information, we derive a systematic approach to optimize the transmit waveform weights (amplitudes and phases) in order to enlarge as much as possible the SNRenergy region. Performance evaluations confirm the significant benefits of using multiple frequency components in the adaptive transmit multisine waveform to exploit the nonlinearity of the rectifier and a frequency diversity gain.

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