Theses supervised by Prof. Dr. Sinan Gezici
13 theses · İhsan Doğramacı Bilkent University
Donanım hataları altında görünür ışık konumlandırma
In this thesis, we examine two distinct research problems in visible light positioning (VLP). Specifically, we explore the impact of lacking knowledge about luminous flux degradation in light-emitting diodes (LEDs) and mismatched orientations for the elements of intelligent reflecting surfaces (IRSs) on the performance of VLP systems. In the first part of the thesis, the position estimation problem based on received power measurements is investigated for visible light systems in the presence of luminous flux degradation of LEDs. When the receiver is unaware of this degradation and performs position estimation accordingly, there exists a mismatch between the true model and the assumed model. For this scenario, the misspecified Cramér-Rao bound (MCRB) and the mismatched maximum likelihood (MML) estimator are derived to quantify the performance loss due to this model mismatch. Also, the Cramér-Rao lower bound (CRB) and the maximum likelihood (ML) estimator are derived when the receiver knows the degradation formula for the LEDs but does not know the decay rate parameter in that formula. In addition, in the presence of full knowledge about the degradation formula and the decay rate parameters, the CRB and the ML estimator are obtained to specify the best achievable performance. By evaluating the theoretical limits and the estimators in these three scenarios, we reveal the effects of the information about the LED degradation model and the decay rate parameters on position estimation performance. It is shown that the model mismatch can result in significant degradation in localization performance at high signal-to-noise ratios, which can be compensated by conducting joint position and decay rate parameter estimation. Accurate localization can be performed in visible light systems in non-line-of-sight (NLOS) scenarios by utilizing IRSs, which are commonly in the form of mirror arrays with adjustable orientations. When signals transmitted from LEDs are reflected from IRSs and collected by a receiver, the position of the receiver can be estimated based on power measurements by utilizing the known parameters of the LEDs and IRSs. Since the orientation vectors of IRS elements (mirrors) cannot be adjusted perfectly in practice, it is important to evaluate the effects of mismatches between desired and true orientations of IRS elements. To this aim, in the second part of the thesis, we derive the MCRB and the MML estimator for specifying the estimation performance and the lower bound in the presence of mismatches in IRS orientations. We also provide comparisons with the conventional ML estimator and the CRB in absence of orientation mismatches for quantifying the effects of mismatches. It is shown that orientation mismatches can result in significant degradation in localization accuracy at high signal-to-noise ratios.
LED dizilerinde güç uyarlaması ile hassas görünür ışık konumlandırma
In this thesis, we develop power allocation strategies for asynchronous visible light positioning (VLP) systems employing light-emitting diode (LED) arrays with the aim of improving localization accuracy. We first review the literature on visible light positioning in indoor scenarios. Then, we formulate an optimization problem for minimizing the Cramér-Rao lower bound (CRLB) related to the estimation of the receiver location subject to practical constraints on total and individual transmission powers and required illumination levels. Due to the non-convexity of the proposed formulation, we devise a two-step methodology. First, a convex optimization problem is employed to determine the power allocation that maximizes the total received power. Subsequently, a projected gradient descent algorithm is utilized to refine the power allocation according to the CRLB metric under the power and illumination constraints. In addition, a framework for iterative power allocation and localization is developed for practical deployment, in which each step of the optimization process uses the most recent position estimate. Simulation results indicate that the proposed method can outperform both the uniform and power maximization based allocation schemes, resulting in improved localization accuracy over a wide range of operating conditions. Hence, it can provide robust and high-precision indoor positioning, even in circumstances involving asynchronous transmission and stringent illumination requirements.
Kanal değiştirme ve telsiz konumlandırma için güç tahsisi stratejileri
Optimal power allocation is an important approach for enhancing performance of both communication and localization systems. In this dissertation, optimal channel switching problems are investigated for average capacity maximization via optimization of power resources in general. In addition, power control games are designed for a wireless localization network including anchor and jammer nodes which compete for the localization performance of target nodes. First, an optimal channel switching strategy is proposed for average capacity maximization in the presence of average and peak power constraints. Necessary and sufficient conditions are derived in order to determine when the proposed optimal channel switching strategy can or cannot outperform the optimal single channel strategy, which performs no channel switching. Also, it is obtained that the optimal channel switching strategy can be realized by channel switching between at most two different channels. In addition, a low-complexity optimization problem is derived in order to obtain the optimal channel switching strategy. Furthermore, based on some necessary conditions that need to be satisfied by the optimal channel switching solution, an alternative approach is proposed for calculating the optimal channel switching strategy. Second, the optimal channel switching problem is studied for average capacity maximization in the presence of additive white Gaussian noise channels and channel switching delays. Initially, an optimization problem is formulated for the maximization of the average channel capacity considering channel switching delays and constraints on average and peak powers. Then, an equivalent optimization problem is obtained to facilitate theoretical investigations. The optimal strategy is derived and the corresponding average capacity is specified when channel switching is performed among a given number of channels. Based on this result, it is shown that channel switching among more than two different channels is not optimal. In addition, the maximum average capacity achieved by the optimal channel switching strategy is formulated as a function of the channel switching delay parameter and the average and peak power limits. Then, scenarios under which the optimal strategy corresponds to the exclusive use of a single channel or to channel switching between two channels are described. Furthermore, sufficient conditions are obtained to determine when the optimal single channel strategy outperforms the optimal channel switching strategy. Third, the optimal channel switching problem is studied for average capacity maximization in the presence of multiple receivers in the communication system. At the beginning, the optimal channel switching problem is proposed for average capacity maximization of the communication between the transmitter and the secondary receiver while fulfilling the minimum average capacity requirement of the primary receiver and considering the average and peak power constraints. Then, an alternative equivalent optimization problem is provided and it is shown that the solution of this optimization problem satisfies the constraints with equality. Based on the alternative optimization problem, it is obtained that the optimal channel switching strategy employs at most three communication links in the presence of multiple available channels in the system. In addition, the optimal strategies are specified in terms of the number of channels employed by the transmitter to communicate with the primary and secondary receivers. Last, a game theoretic framework is proposed for wireless localization networks that operate in the presence of jammer nodes. In particular, power control games between anchor and jammer nodes are designed for a wireless localization network in which each target node estimates its position based on received signals from anchor nodes while jammer nodes aim to reduce localization performance of target nodes. Two different games are formulated for the considered wireless localization network: In the first game, the average Cramér-Rao lower bound (CRLB) of the target nodes is considered as the performance metric, and it is shown that at least one pure strategy Nash equilibrium exists in the power control game. Also, a method is presented to identify the pure strategy Nash equilibrium, and a sufficient condition is obtained to resolve the uniqueness of the pure Nash equilibrium. In the second game, the worst-case CRLBs for the anchor and jammer nodes are considered, and it is shown that the game admits at least one pure Nash equilibrium.
Görünür ışık konumlandırma sistemleri: Temel sınırlar, algoritmalar ve kaynak tahsisi yaklaşımları
Visible light communication (VLC) is an emerging paradigm that enables multiple functionalities to be accomplished concurrently, including illumination, high-speed data communications, and localization. Based on the VLC technology, visible light positioning (VLP) systems aim to estimate locations of VLC receivers by utilizing light-emitting diode (LED) transmitters at known locations. VLP presents a viable alternative to radio frequency (RF)-based positioning systems by providing inexpensive and accurate localization services. In this dissertation, we consider the problem of localization in visible light systems and investigate distance and position estimation approaches in synchronous and asynchronous scenarios, focusing on both theoretical performance characterization and algorithm development aspects. In addition, we design optimal resource allocation strategies for LED transmitters in VLP systems for improved localization performance. Moreover, we propose a cooperative localization framework for VLP systems, motivated by vehicular VLC networks involving vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. First, theoretical limits and estimators are studied for {distance estimation in} synchronous and asynchronous VLP systems. Specifically, the Cram\'{e}r-Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) are investigated based on time-of-arrival (TOA) and/or received signal strength (RSS) parameters. Hybrid TOA/RSS based distance estimation is proposed for VLP systems, and its CRLB is compared analytically against the CRLBs of TOA based and RSS based distance estimation. In addition, to investigate effects of sampling, asymptotic performance results are obtained under sampling rate limitations as the noise variance converges to zero. A modified hybrid TOA/RSS based distance estimator is proposed to provide performance improvements in the presence of sampling rate limitations. Moreover, the Ziv-Zakai bound (ZZB) is derived for synchronous VLP systems. The proposed ZZB extracts ranging information from the prior information, the time delay parameter, and the channel attenuation factor based on the Lambertian pattern. In addition to the ZZB, the Bayesian Cram\'{e}r-Rao bound (BCRB) and the weighted CRB (WCRB) are calculated for synchronous VLP systems. Furthermore, a closed-form expression is obtained for the expectation of the conditional CRB (ECRB). Numerical examples are presented to compare the bounds against each other and against the maximum a-posteriori probability (MAP) estimator. It is observed that the ZZB can provide a reasonable lower limit on the performance of MAP estimators. On the other hand, the WCRB and the ECRB converge to the ZZB in regions of low and high source optical powers, respectively; however, they are not tight in other regions. Second, direct and two-step positioning approaches are investigated for both synchronous and asynchronous VLP systems. {In particular}, the CRLB and the direct positioning based ML estimator are derived for three-dimensional localization of a VLC receiver in a synchronous scenario by utilizing information from both time delay parameters and channel attenuation factors. Then, a two-step position estimator is designed for synchronous VLP systems by exploiting the asymptotic properties of TOA and RSS estimates. The proposed two-step estimator is shown to be asymptotically optimal, i.e., converges to the direct estimator at high signal-to-noise ratios (SNRs). In addition, the CRLB and the direct and two-step estimators are obtained for positioning in asynchronous VLP systems. It is proved that the two-step position estimation is optimal in asynchronous VLP systems for practical pulse shapes. Various numerical examples are provided to illustrate the improved performance of the proposed estimators with respect to the current state-of-the-art and to investigate their robustness against model uncertainties in VLP systems. Third, the problem of optimal power allocation among LED transmitters in a VLP system is considered for the purpose of improving localization performance of VLC receivers. Specifically, the aim is to minimize the CRLB on the localization error of a VLC receiver by optimizing LED transmission powers in the presence of practical constraints such as individual and total power limitations and illuminance constraints. The formulated optimization problem is shown to be convex and thus can efficiently be solved via standard tools. We also investigate the case of imperfect knowledge of localization parameters and develop robust power allocation algorithms by taking into account both overall system uncertainty and individual parameter uncertainties related to the location and orientation of the VLC receiver. In addition, we address the total power minimization problem under predefined accuracy requirements to obtain the most energy-efficient power allocation vector for a given CRLB level. Numerical results illustrate the improvements in localization performance achieved by employing the proposed optimal and robust power allocation strategies over the conventional uniform and non-robust approaches. In the final part of the dissertation, we propose to employ cooperative localization for visible light networks by designing a VLP system configuration that involves multiple LED transmitters with known locations and VLC units equipped with both LEDs and photodetectors (PDs) for the purpose of cooperation. In the proposed cooperative scenario, we derive the CRLB and the MLE for the localization of VLC units. To tackle the nonconvex structure of the MLE, we adopt a set-theoretic approach by formulating the problem of cooperative localization as a quasiconvex feasibility problem, where the aim is to find a point inside the intersection of convex constraint sets constructed as the sublevel sets of quasiconvex functions resulting from the Lambertian formula. {Then}, we devise two feasibility-seeking algorithms based on iterative gradient projections to solve the feasibility problem. Both algorithms are amenable to distributed implementation, thereby avoiding high-complexity centralized approaches. Capitalizing on the concept of quasi-Fej\'er convergent sequences, we carry out a formal convergence analysis to prove that the proposed algorithms converge to a solution of the feasibility problem in the consistent case. Numerical examples illustrate the improvements in localization performance achieved via cooperation among VLC units and evidence the convergence of the proposed algorithms to true VLC unit locations in both the consistent and inconsistent cases.
Kestirim kuramsal güvenli haberleşme için optimal parametre kodlama stratejileri
Physical layer security has gained a renewed interest with the advances in modern wireless communication technologies. In estimation theoretic security, secrecy levels are measured via estimation theoretic tools and metrics, such as mean-squared error (MSE), where the objective is to perform accurate estimation of the parameter at the intended receiver while keeping the estimation error at the eavesdropper above a certain level. This framework proves useful both for analyzing the achievable performance under security constraints in parameter estimation problems, and for designing low-complexity, practical methods to enhance security in communication systems. In this dissertation, we investigate optimal deterministic encoding of random scalar and vector parameters in the presence of an eavesdropper, who is unaware of the encoding operation. We also analyze optimal stochastic encoding of a random parameter under secrecy constraints in a Gaussian wiretap channel model, where the eavesdropper is aware of the encoding strategy at the transmitter. In addition, we perform optimal parameter design for secure broadcast of a parameter to multiple receivers with fixed estimators. First, optimal deterministic encoding of a scalar parameter is investigated in the presence of an eavesdropper. The aim is to minimize the expectation of the conditional Cramer-Rao bound (ECRB) at the intended receiver while keeping the MSE at the eavesdropper above a certain threshold. The eavesdropper is modeled to employ the linear minimum mean-squared error (LMMSE) estimator based on the encoded version of the parameter. First, the optimal encoding function is derived in the absence of secrecy constraints for any given prior distribution on the parameter. Next, an optimization problem is formulated under a secrecy constraint and various solution approaches are proposed. Also, theoretical results on the form of the optimal encoding function are provided. Furthermore, a robust parameter encoding approach is developed. In this case, the objective is to maximize the worst-case Fisher information of the parameter at the intended receiver while keeping the MSE at the eavesdropper above a certain level. The optimal encoding function is derived when there exist no secrecy constraints. Next, to obtain the solution of the problem in the presence of the secrecy constraint, the form of the encoding function that maximizes the MSE at the eavesdropper is explicitly derived for any given level of worst-case Fisher information. Then, based on this result, a low-complexity algorithm is provided to calculate the optimal encoding function for the given secrecy constraint. Numerical examples are presented to illustrate the theoretical results for both the ECRB and worst-case Fisher information based designs. Second, optimal deterministic encoding of a vector parameter is investigated in the presence of an eavesdropper. The objective is to minimize the ECRB at the intended receiver while satisfying an individual secrecy constraint on the MSE of estimating each parameter at the eavesdropper. The eavesdropper is modeled to employ the LMMSE estimator based on the noisy observation of the encoded parameter without being aware of encoding. First, the problem is formulated as a constrained optimization problem in the space of vector-valued functions. Then, two practical solution strategies are developed based on nonlinear individual encoding and affine joint encoding of parameters. Theoretical results on the solutions of the proposed strategies are provided for various scenarios on channel conditions and parameter distributions. Finally, numerical examples are presented to illustrate the performance of the proposed solution approaches. Third, estimation theoretic secure transmission of a scalar random parameter is investigated in the presence of an eavesdropper. The aim is to minimize the estimation error at the receiver under a secrecy constraint at the eavesdropper; or, alternatively, to maximize the estimation error at the eavesdropper for a given estimation accuracy limit at the receiver. In the considered setting, the encoder at the transmitter is allowed to use a randomized mapping between two one-to-one and continuous functions and the eavesdropper is fully aware of the encoding strategy at the transmitter. For small numbers of observations, both the eavesdropper and the receiver are modeled to employ LMMSE estimators, and for large numbers of observations, the ECRB metric is employed for both the receiver and the eavesdropper. Optimization problems are formulated and various theoretical results are provided in order to obtain the optimal solutions and to analyze the effects of encoder randomization. In addition, numerical examples are presented to corroborate the theoretical results. It is observed that stochastic encoding can bring significant performance gains for estimation theoretic secrecy problems. Finally, estimation theoretic secure broadcast of a random parameter is investigated. In the considered setting, each receiver device employs a fixed estimator and carries a certain security risk such that its decision can be available to a malicious third party with a certain probability. The encoder at the transmitter is allowed to use a random mapping to minimize the weighted sum of the conditional Bayes risks of the estimators under secrecy and average power constraints. After formulating the optimal parameter design problem, it is shown that the optimization problem can be solved individually for each parameter value and the optimal mapping at the transmitter involves a randomization among at most three different signal levels. Sufficient conditions for improvability and nonimprovability of the deterministic design via stochastic encoding are obtained. Numerical examples are provided to corroborate the theoretical results.
İkili hipotez testi için bütçe kısıtlı sensör seçimi ve tasarımı
We consider a sensor selection problem for binary hypothesis testing with cost-constrained measurements. Random observations related to a parameter vector of interest are assumed to be generated by a linear system corrupted with Gaussian noise. The aim is to decide on the state of the parameter vector based on a set of measurements collected by a limited number of sensors. The cost of each sensor measurement is determined by the number of amplitude levels that can reliably be distinguished. By imposing constraints on the total cost and the maximum number of sensors that can be employed, a sensor selection problem is formulated in order to maximize the detection performance for binary hypothesis testing. By characterizing the form of the solution corresponding to a relaxed version of the optimization problem, a computationally efficient algorithm with near optimal performance is proposed. In addition to the case of fixed sensor measurement costs, we also consider the case where they are subject to design. In particular, the problem of allocating the total cost budget to a limited number of sensors is addressed by designing the measurement accuracy (i.e., the noise variance) of each sensor to be employed in the detection procedure. The optimal solution is obtained in closed form. Numerical examples are presented to corroborate the proposed methods.
Fısher bilgisi tabanlı objektiflerle vektör parametre kestirimi için optimal güç dağıtım teknikleri
In this thesis, optimal power allocation problems are investigated for vector parameter estimation according to various Fisher information based optimality criteria. By considering a generic observation model involving a linear/nonlinear transformation of the parameter vector and an additive noise component with an arbitrary joint probability distribution, six different optimal power allocation problems are formulated based on Fisher information based objective functions. Various closed-form solutions are derived for the proposed problems using optimization theoretic approaches for the cases in which the transformation acting on the parameter vector is linear. Also, the results are extended to cases in which nuisance parameters exist in the system model, and to the cases when the transformation acting on the parameter vector is nonlinear. It is shown that the proposed methods are {also} valid for the provided extensions {under certain conditions}. Numerical examples are presented to investigate performance of the proposed power allocation strategies, and it is shown that they provide significant performance gains over the equal power allocation strategy.
5G PDSCH: mm-dalga için kanal ve faz gürültüsü kestiriminde DMRS ve ptrs tasarımlarının performans analizi
The mm-Wave is one of the main enablers for the performance requirements of 5G. Although it provides communication systems with huge bandwidth and data rates, it also has some disadvantages as the carrier frequencies can significantly exceed 6 GHz and go up to 300 GHz. For example, there are significant challenges such as propagation loss and severe phase noise (PN). The PN can be observed in two parts: common phase error (CPE) and inter-carrier interference (ICI). In the literature, there are algorithms for the estimation and compensation of PN for OFDM-based systems. We apply both CPE and ICI compensation algorithms for 5G PDSCH at the carrier frequency of 70 GHz. Detailed performance analysis is performed for demodulation reference signal (DMRS) based channel estimation and phase-tracking reference signal (PTRS) based PN estimation. We observe the effects of different reference signal parameters in 5G for each PN compensation algorithm. For this purpose, we use up-to-date power spectral density (PSD) models for PN modeling and show uncoded bit error rate (BER) graphs obtained via extensive simulations for MATLAB's tapped delay line (TDL) channels. We also analyze the system performance under very high Doppler, where PTRS based channel estimation is compared with DMRS based channel estimation.
Güç ve aydınlatma kısıtları altında görünür ışık konumlandırma için optimal sinyal tasarımı
The optimal design of transmit signals for light-emitting diodes (LEDs) in a visible light positioning (VLP) system is analyzed with the objectives of improvements in localization accuracy and power efficiency. Specifically, the localization performance maximization problem is addressed for asynchronous and synchronous VLP systems where certain system limitations including power consumption, illumination, and effective bandwidth are considered, and the localization performance is quantified using the Cramér-Rao lower bound (CRLB). The formulated signal design problems are demonstrated to be convex optimization problems and some properties of the optimal signal design parameters are found. On the other hand, the signal design problem is also formulated for achieving the lowest possible power consumption while guaranteeing a certain localization accuracy. Then, the optimal signal design parameters resulted from the solution of these optimization problems are used to construct the optimal transmit signals in the LEDs. The advantages of the optimal signal design approach is demonstrated through the numerical experiments while also presenting a comparison with the state-of-the-art optimal power allocation method in the literature.
Kısıtlanmış bayes çerçevesinde korelasyon belirsizliği altında en iyi doğrusal mmse kestirimi
A restricted Bayes approach is proposed for the linear estimation of a scalar random parameter based on a scalar observation under uncertainty regarding the correlation between the parameter and the observation. In particular, the optimal linear estimator that minimizes the average mean-squared error (MSE) is derived under a constraint on the worst-case MSE by considering possible values of the correlation coefficient and its probability distribution. A closed-form expression is derived for the optimal linear estimator in the proposed restricted Bayesian framework by considering a generic statistical characterization of the correlation coefficient. The performance of the proposed estimator is evaluated via numerical examples and its benefits are illustrated in various scenarios. The proposed framework is also extended to the case of vector-valued observation and the properties of the optimal linear estimator are characterized.
Kuramsal ve öğrenme tabanlı yaklaşımlar kullanarak kesintikapasitesi ve veri hızı maksimizasyonu
This thesis explores two research problems in wireless communications: the optimal channel switching and randomization problem in flat-fading Gaussian noise channels, and channel selection and switching approaches based on the upper confidence bound (UCB) bandit algorithm. In the first part of the thesis, the optimal channel switching and randomization problem is formulated and its solution is characterized for flat-fading Gaussian noise channels with the aim of outage capacity maximization under average power and outage probability constraints. For the single user scenario, it is proved that the optimal solution can always be realized by performing one of the following strategies: (1) Transmission over a single channel with no randomization. (2) Channel switching between two channels with no randomization. (3) Randomization between two parameter sets over a single channel. Hence, the solution can easily be obtained by considering only these three strategies. However, for the multiuser scenario, obtaining the optimal solution can have very high computational complexity. Therefore, an algorithm is proposed to calculate an approximately optimal channel switching and randomization solution (with adjustable approximation accuracy) based on the solution of a linearly constrained linear optimization problem. In the second part of the thesis, we consider the case of unknown channel statistics at the transmitter, and propose channel selection and channel switching approaches based on the UCB bandit algorithm for communications between a transmitter and a receiver over a block fading channel. In the absence of channel switching in a block, we propose a UCB bandit algorithm for selecting the best channel among the possible set of channels for maximizing the number of correctly received symbols per unit of time. In the presence of channel switching, we first define a set of virtual channels by considering all possible channel pairs with various power levels and time sharing factors. Then, a UCB bandit algorithm is utilized to determine the best virtual channel; hence, to find the optimal channel switching strategy. Also, a low complexity version of this algorithm is proposed for efficient convergence to the optimal solution when a high number of virtual channels exists. In addition, for comparison purposes, theoretical limits are presented when the channel statistics are available at the transmitter. Simulation results indicate that the proposed UCB bandit algorithms can achieve very close performance to theoretical limits over a sufficiently large number of blocks, and make benefits of channel switching be realized.
Kötücül LED vericileri veya akıllı yansıtıcı yüzeyler varlığında görünür ışık konumlandırma
Visible light positioning (VLP) is a recent solution to the localization problem in indoor environments which involves the use of light emitting diodes (LEDs) as transmitters and photodetectors (PDs) as receivers. VLP systems have increasingly been popular as LEDs are employed for illumination purposes over conventional light bulbs nowadays due to their various advantages. In this thesis, we develop VLP algorithms for two main scenarios. In the first scenario, we assume that the system is not completely secure, meaning that the transmit power of some LEDs can be controlled by a third unknown party, i.e., hijacked, to degrade the positioning accuracy. In the second scenario, we assume the deployment of intelligent reflecting surfaces (IRSs) into the system to improve the positioning accuracy in the absence of line-of-sight (LOS) signals from of a subset of LED transmitters. First, we consider a VLP system in which a receiver performs position estimation based on signals emitted from a number of LED transmitters. Each LED transmitter can be malicious and transmit at an unknown power level with a certain probability. A maximum likelihood (ML) position estimator is derived based on the knowledge of probabilities that LED transmitters can be malicious. In addition, in the presence of training measurements, decision rules are designed for detection of malicious LED transmitters, and based on detection results, various ML based location estimators are proposed. To evaluate the performance of the proposed estimators, Cramér-Rao lower bounds (CRLBs) are derived for position estimation in scenarios with and without a training phase. Moreover, an ML estimator is derived when the probabilities that the LED transmitters can be malicious are unknown. The performances of all the proposed estimators are evaluated via numerical examples and compared against the CRLBs. Second, we formulate and analyze a received power based position estimation problem for VLP systems in the presence of IRSs. In the proposed problem formulation, a visible light communication (VLC) receiver collects signals from a number of LED transmitters via LOS paths and/or via reflections from IRSs. We derive the CRLB expression and the ML estimator for generic three-dimensional positioning in the presence of IRSs with arbitrary configurations. In addition, we consider the problem of optimizing the orientations of IRSs when LOS paths are blocked, and propose an optimal adjustment approach for maximizing the received powers from IRSs based on analytic expressions, which can be solved in closed form or numerically. Since the optimal IRS orientations depend on the actual position of the VLC receiver, an N-step localization algorithm is proposed to perform adjustment of IRS orientations in the absence of any prior knowledge about the position of the VLC receiver. Performance of the proposed approach is evaluated via simulations and compared against the CRLB. It is deduced that although IRSs do no provide critical improvements in positioning accuracy in the presence of LOS signals from a sufficient number of LED transmitters, they can be very important in achieving accurate positioning when all or most of LOS paths are blocked.
İşbirlikçi görünür ışık konumlandırma sistemleri
Light emitting diode (LED) based visible light positioning (VLP) systems offer an alternative approach to commonly used radio frequency (RF) based positioning systems. In the literature, VLP studies are performed only for noncooperative systems. In this thesis, received signal strength (RSS) based cooperative localization is proposed for visible light systems. The effects of cooperation on the localization accuracy of visible light positioning systems are illustrated based on a Cramer-Rao lower bound expression. The obtained expression is generic for any three-dimensional configuration and covers all possible cooperation scenarios via definitions of connectivity sets. In addition, a low-complexity positioning algorithm is proposed for cooperative location estimation. Numerical results are presented to investigate the significance of cooperation and to evaluate performance of the proposed algorithm in various scenarios.