Target 5G visible light positioning signal subcarrier extraction method using particle swarm optimization algorithm

Abstract

With the explosive growth of demand for Internet of Things (IoT) applications and the increasing dependence of users on wireless connections, indoor location based service(LBS) under 5G-Public-Private Partnership (5G-PPP) using cases have received more attention and get rapid development. Thanks to the safty, security and customization of 5G network pointed by 5G forum white paper, indoor positioning systems using unified 5G New Radio (NR) signals have become the focus of the next generation of visible light positioning (VLP) systems. In 5G New Radio(NR) frame, totally 192 subcarriers are used to carry positioning reference signal(PRS). In order to improve the position accuracy, the aim of this study is to investigate the method of the 5G NR subcarrier selection. A certain number of PRSs are obtained from the signal source of 192 subcarriers, and the minimum positioning error(PE) is calculated by using particle swarm optimization(PSO) algorithm. Then the relationship between the PRS with the minimum PE and the position of the subcarrier is analyzed which achieved the subcarrier extraction. The experimental results show that the average PE of this scheme is below 6 cm, and the minimum PE can reach 0.05 cm. This approach is confirmed the feasibility of high accuracy in 5G indoor VLP application and has the potential to be extended to larger and more complex 5G-PPP scenarios.

Device-Free WLAN Based Indoor Localization Scheme With Spatially Concatenated CSI and Distributed Antennas

“…In [31], orthogonal multicarrierbased proving signal designs for base station (BS)-based or mobile station (MS)-based localization are presented where non-overlapping subcarrier allocation is necessary for MS based localization to differentiate signals from different BSs. In [32], a RSSI based positioning scheme is proposed in which RSSI of a selected reference subcarrier is exploited for trilaterationbased positioning. Unlike the works described above, this paper presents consideration of simple but effective frequencydomain sampling (i.e., subcarrier selection) that is applicable to compressed CSI specified in IEEE802.11, i.e., beam-forming weights.…”

Multi-directional light-emitting diode aided visible light positioning system exploiting regular pyramid: design and demonstration

“…In this case, the traditional radio frequency based localization algorithms can be employed into achieving accurate positioning results, such as received signal strength (RSS), 10 – 13 received signal strength ratio (RSSR), 14 , 15 time-of-arrival, 16 time-difference-of-arrival (TDOA), 17 , 18 angle-of-arrival, 19 , 20 and fingerprint scheme 21 . Furthermore, to mitigate the adverse effects of inaccurate model and light intensity variation on the system performance, classical intelligent algorithms are increasingly utilized by researchers to solve the localization problem of the nVLP model, such as machine learning (ML), 22 , 23 artificial neural network (ANN), 24 – 27 and some heuristic algorithms 28 – 35 Specifically, in Ref.…”

“…33, two heuristic algorithms of grey wolf optimizer (GWO) and particle swarm optimization (PSO) were adopted to optimize the ELM parameters respectively in order to obtain higher classification accuracy, namely, EML-GWO and EML-PSO algorithms, which have a better positioning performance and robustness than the basic EML algorithm. In addition, some heuristic algorithms such as PSO algorithm, 28 fruit fly algorithm, 30 modified genetic algorithm (GA), 31 , 34 differential evolution algorithm, 32 and improved adaptive cuckoo search (IACS) algorithm 35 were also widely employed in nVLP models to enhance performance.…”

“…21 Furthermore, to mitigate the adverse effects of inaccurate model and light intensity variation on the system performance, classical intelligent algorithms are increasingly utilized by researchers to solve the localization problem of the nVLP model, such as machine learning (ML), 22,23 artificial neural network (ANN), [24][25][26][27] and some heuristic algorithms. [28][29][30][31][32][33][34][35] Specifically, in Ref. 26, the gated recurrent unit neural network (GRU-NN) was employed to deal with the nonlinearity of the nVLP system, resulting in an improvement of average localization accuracy.…”

Equivalent Modeling of Microgrids Based on Optimized Broad Learning System

“…The RBF neural network is often used, and the ELM network is often used for smart equivalent modeling of microgrids due to its simple structure, fast training speed, and strong generalization ability [13][14][15]. At the same time, in order to improve the model accuracy of intelligent modeling, enhance the generalization characteristics, and speed up the modeling speed, genetic algorithm [16,17], bacterial foraging algorithm [18,19], and particle swarm optimization algorithm [20,21] are often used for parameter optimization. The model established by the above microgrid intelligent equivalent modeling method can well describe the operation characteristics of the microgrid.…”