As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.

/pdf/a-survey-of-positioning-systems-using-visible-led-lights-1vjjtnvbcz.pdf

A Survey of Positioning Systems Using Visible LED Lights

Advances in visible light communication (VLC) technology and the ubiquity of illumination facility have led to a growing interest in VLC-based indoor positioning. Numerous techniques have been proposed to obtain better system performance. In this paper, we survey over 100 papers ranging from pioneering papers to the state-of-the-art in the field to present the positioning technology. Not only the light emitting diode technology, modulation method and types of receivers are compared but also a novel taxonomy method is proposed. In this paper, VLC-based indoor positioning systems (VLC-based-IPSs) are classified based on the methods used: 1) mathematical method; 2) sensor-assisted method; and 3) optimization method. Different from other survey works, we emphasize and analyze the accuracy of VLC-based-IPS in the experiment and simulation environments. Meanwhile, this paper illustrates challenges, countermeasures, and lessons learned.

Indoor Positioning Systems Based on Visible Light Communication: State of the Art

While visible light communication (VLC) has become the candidate for the wireless technology of the 21st century due to its inherent advantages, VLC based positioning also has a great chance of becoming the standard approach to positioning. Within the last few years, many studies on VLC based positioning have been published, but there are not many survey works in this field. In this paper, an in-depth survey of VLC based positioning systems is provided. More than 100 papers ranging from pioneering papers to the state-of-the-art in the field were collected and classified based on the positioning algorithms, the types of receivers, and the multiplexing techniques. In addition, current issues and research trends in VLC based positioning are discussed.

https://www.mdpi.com/1424-8220/16/5/678/pdf

An in-Depth Survey of Visible Light Communication Based Positioning Systems.

Physical transceiver implementations for multiple-input multiple-output (MIMO) wireless communication systems suffer from transmit-RF (Tx-RF) impairments. In this paper, we study the effect on channel capacity and error-rate performance of residual Tx-RF impairments that defy proper compensation. In particular, we demonstrate that such residual distortions severely degrade the performance of (near-)optimum MIMO detection algorithms. To mitigate this performance loss, we propose an efficient algorithm, which is based on an i.i.d. Gaussian model for the distortion caused by these impairments. In order to validate this model, we provide measurement results based on a 4-stream Tx-RF chain implementation for MIMO orthogonal frequency-division multiplexing (OFDM).

/pdf/mimo-transmission-with-residual-transmit-rf-impairments-1shk8j9zp6.pdf

MIMO transmission with residual transmit-RF impairments

pdf/mimo-transmission-with-residual-transmit-rf-impairments-1hb7jgimx5.pdf

MIMO Transmission with Residual Transmit-RF Impairments

Sparse signal recovery finds use in a variety of practical applications, such as signal and image restoration and the recovery of signals acquired by compressive sensing. In this paper, we present two generic very-large-scale integration (VLSI) architectures that implement the approximate message passing (AMP) algorithm for sparse signal recovery. The first architecture, referred to as AMP-M, employs parallel multiply-accumulate units and is suitable for recovery problems based on unstructured (e.g., random) matrices. The second architecture, referred to as AMP-T, takes advantage of fast linear transforms, which arise in many real-world applications. To demonstrate the effectiveness of both architectures, we present corresponding VLSI and field-programmable gate array implementation results for an audio restoration application. We show that AMP-T is superior to AMP-M with respect to silicon area, throughput, and power consumption, whereas AMP-M offers more flexibility.

/pdf/vlsi-design-of-approximate-message-passing-for-signal-2860hwmkpv.pdf

VLSI Design of Approximate Message Passing for Signal Restoration and Compressive Sensing

We propose a low-cost system for indoor self-localization of mobile devices using modulated LED ceiling lamps that are fully autonomous and broadcast their identifiers without any synchronization. The proposed self-localization method is designed to handle this lack of synchronization as well as the possibility of blocked line-of-sight connections or severe attenuation in real-world environments. This robustness is achieved by applying a suitable Bayesian signal model and by taking into account the inherent sparsity in detecting the concurrently visible lamps. The proposed estimator of the location approximates optimal Bayesian estimation while maintaining low complexity. Simulation results confirm a significant gain in performance compared to a classical matched-filter approach.

/pdf/robust-asynchronous-indoor-localization-using-led-lighting-c3fmr6480n.pdf

Robust asynchronous indoor localization using LED lighting

Broadband wireless systems often operate under channel conditions that are characterized by a sparse channel impulse response When the amount of training is given by the standard, compressed sensing channel estimation can exploit this sparsity to improve the quality of the channel estimate In this paper, we analyze and compare the hardware complexity and denoising performance of three greedy algorithms for the 3GPP LTE system The complexity/performance trade-off is analyzed using parameterized designs with varying configurations One configuration of each algorithm is fabricated in a 180nm process and measured

Implementation of greedy algorithms for LTE sparse channel estimation

The emerging research field of compressed sensing (CS) promises better signal reconstruction out of fewer measurements if a sparse representation of the signal exists. Since wireless broadband channels often exhibit a sparse impulse response, CS reconstruction algorithms were proposed for channel estimation. In this paper, a hardware architecture for channel estimation using the matching pursuit algorithm is presented. The reference design targets the 3GPP LTE standard with a channel bandwidth of up to 20 MHz. Achievable performance gains over least squares channel estimation are illustrated by means of simulations. The costs in terms of chip area and reconstruction time for 180 nm CMOS technology are presented together with an analysis of the tradeoff between hardware complexity and reconstruction performance.

Matching pursuit: Evaluation and implementatio for LTE channel estimation

Passive radars using WiFi base stations as illuminators are a promising option for local area surveillance. Unfortunately, the reflections of WiFi signals from a large object can easily mask any nearby objects making it difficult to detect all targets within range. Using the fact that most target scenes are only very sparsely populated makes it possible to apply compressive sensing (CS) for target detection. We analyze how well WiFi signals fit into the CS framework, propose corresponding detection schemes, and show how detection accuracy is increased and receiver complexity is reduced. It turns out that CS enables the detection of closely spaced targets with only a small number of recorded WiFi frames.

Compressive sensing for WiFi-based passive bistatic radar

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