Expectation–Maximization-Based Passive Localization Relying on Asynchronous Receivers: Centralized Versus Distributed Implementations

Yuan, Weijie; Wu, Nan; Etzlinger, Bernhard; Li, Yonghui; Yan, Chaoxing; Hanzo, Lajos

doi:10.1109/tcomm.2018.2866478

Cited by 50 publications

(33 citation statements)

References 44 publications

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“…The distance information between different nodes was considered in our scenario [37,38]. It was assumed that the MBS exactly knew the location [39,40] of CUEs and VUEs present in its cell. In order to guarantee acceptable average interference between CUEs/MBS and VUEs, we assumed that the distance information between different nodes satisfied the following conditions:…”

Section: System Modelmentioning

confidence: 99%

Stackelberg Game-Based Power Allocation for V2X Communications

Zhang

Yin

2019

Sensors

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Ultra-reliable low-latency communication (URLLC) is one of the three usage scenarios anticipated for 5G, which plays an important role in advanced applications of vehicle-to-everything (V2X) communications. In this paper, the Stackelberg game-based power allocation problem was investigated in V2X communications underlaying cellular networks. Assuming that the macro-cellular base station (MBS) sets the interference prices to protect itself from the V2X users (VUEs), the Stackelberg game was adopted to analyze the interaction between MBS and VUEs, where the former acts as a leader and the latter act as followers. For MBS, we aimed at maximizing its utility from interference revenue while considering the cost of interference. Meanwhile, the VUEs aimed at maximizing their utilities per unit power consumption. We analyzed the Stackelberg model and obtained the optimal prices for MBS and optimal transmit powers for VUEs. Simulation results demonstrated the superiority of the proposed Stackelberg game-based power allocation scheme in comparison with the traditional power allocation strategy. Meanwhile, the proposed scheme achieved a better trade-off between economic profit and power consumption.Recently, there have been volumes of existing literature on V2X communications [5][6][7][8][9][10]. In [5], a cooperative automated driving (CAD) system based on collective perception and cooperative maneuver coordination was proposed, which could be applied to several use cases. The authors of [6] investigated the impact of the new radio (NR) flexible numerology on the cellular-vehicle-to-anything (C-V2X) autonomous access mode. The authors of [7] examined the possibility of using the V2X systems at sea and discussed the challenges. In [8,9], the performance comparison of IEEE 802.11p and C-V2X was presented. Moreover, the temporal and spatial dynamics of the V2X network were investigated in [10].In spite of the above attractive features and application scenarios, there are also a great number of problems in the coexistence of V2X communication and other networks, such as the resource allocation (RA), interference management on account of co-channel interference (CCI) caused by spectrum reuse. In order to solve these problems and achieve technical breakthroughs, several related works have been done in terms of RA, power control, congestion control, link scheduling, interference coordination, and so on [11][12][13][14][15][16][17][18][19][20][21][22][23]. The authors of [11] focused on the existing RA algorithms for V2X communications, and these algorithms were classified and compared with each other according to selected criteria. In [12,13], an RA problem among safety VUEs, non-safety VUEs, and conventional cellular UEs (CUEs) was studied. The authors of [14] proposed a novel hybrid scheme based on C-V2X technology to improve latency and reliability performance. A cooperative solution for V2X communications was proposed in [15], which could guarantee reliability and latency requirements for 5G enhanced V2X services. In [16], th...

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Section: System Modelmentioning

confidence: 99%

Stackelberg Game-Based Power Allocation for V2X Communications

Zhang

Yin

2019

Sensors

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“…The centralized method needs a fusion center which may involve high operating costs and increase the risk of failure if the fusion center break down [37]. To this ends, we propose a distributed implementation of the proposed method.…”

Section: Distributed Sage With Sensor Position Uncertaintymentioning

confidence: 99%

Distributed Target Localization with Inaccurate Collaborative Sensors in Multipath Environments

2019

KSII TIIS

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Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

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“…Taking the close relationship between synchronization and localization into account, sophisticated schemes have been proposed for simultaneous synchronization and active localization [16]- [18]. In passive localization, Zhou et al [19] exploited the time difference of arrival (TDOA) to eliminate impact of imperfect clock.In [20], a maximum likelihood method relying on Tinkhonov regularization was proposed for quasi-synchronous networks, while an expectation maximization based method was devised in [21], which treated the clock offsets of the receivers as hidden variables. In these treatises, the clock information is time invariant and the inherent clock skew is not considered.…”

Section: Introductionmentioning

confidence: 99%

TOA-Based Passive Localization Constructed Over Factor Graphs: A Unified Framework

Yuan

Guo

et al. 2019

IEEE Trans. Commun.

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Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a "passive" target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios, such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér-Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation.

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Expectation–Maximization-Based Passive Localization Relying on Asynchronous Receivers: Centralized Versus Distributed Implementations

Cited by 50 publications

References 44 publications

Stackelberg Game-Based Power Allocation for V2X Communications

Stackelberg Game-Based Power Allocation for V2X Communications

Distributed Target Localization with Inaccurate Collaborative Sensors in Multipath Environments

TOA-Based Passive Localization Constructed Over Factor Graphs: A Unified Framework

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