To facilitate 5G-based positioning applications, Release 16 of the 3GPP 5G standard has defined the Positioning Reference Signal (PRS), which can be used to measure Time of Arrival (TOA) for downlink positioning. However, Orthogonal Frequency Division Multiplexing (OFDM) signals are sensitive and vulnerable to synchronization errors. Moreover, the highly configurable 5G PRS in Release 16 calls for a unique allocation pattern on the subcarriers. Existing timing recovery methods that have been employed for reference signals, which are evenly inserted in the subcarrier symbols, may not perform well. To solve the timing recovery issue of the OFDM signal through 5G standard-compliant PRS, we propose a three-stage timing recovery scheme. We use the 5G PRS as pilot symbols to estimate the path time delay and complete receiver sampling clock synchronization. We propose a generalized path time delay estimation method that can correct timing errors larger than one sample. In addition, we incorporate a delay-locked loop (DLL) that can track the PRS code-phase when the phase errors are within one sample, which showcases the precise positioning possible with a standard-compliant 5G New Radio (NR) signal.
Intelligent transportation system significantly relies on accurate positioning information of land vehicles for both safety and non-safety related applications, such as hard-braking ahead warning and red-light violation warning. However, existing Global Navigation Satellite System (GNSS) based solutions suffer from positioning performance degradation in challenging environments, such as urban canyons and tunnels. In this paper, we focus on the positioning performance enhancement of land vehicles via cooperative positioning under a partial GNSS environment in a Vehicular Ad-hoc NETwork (VANET). The availability of Time-of-Flight (ToF) based inter-vehicle or vehicle-to-infrastructure ranges is verified via 5.9 GHz Dedicated Short-Range Communication (DSRC) vehicle-to-everything communication with RTS/CTS unicast mechanism. An inertial navigation sensor aided, tightly coupled integration approach for land vehicle cooperative positioning using DSRC ToF ranges and carrier frequency offset range-rates is proposed, where a digital map is used to constrain the position estimates. If available, the GNSS pseudorange and Doppler shift under partial GNSS environment can also be incorporated. A Rao-Blackwellized particle filter is utilized to estimate the unknown variables allowing for reduced computational complexity in comparison with the conventional particle filter. The posterior Cramer-Rao lower bound is also derived to give a theoretical performance guideline. Both simulation and experimental results show the validity of our proposed approach.
In this paper, we propose a multi-level localization algorithm that breaks a centralized localization problem into a cluster-level distributed localization problem, where each cluster is a centralized unit. In contrast to fully distributed localization, the cluster-level distributed scheme results in reduction in contention, communication overheads, convergence time and energy consumption because cluster heads are responsible for the intracluster positioning on behalf of the whole cluster. To generate a global map, the cluster heads communicate with their direct neighbors to carry out inter-cluster ranging and positioning. The proposed method is suitable for large ad-hoc networks where most agents are low-cost, low-power RF transceivers used for ranging only while some agents are integrated with microcomputers such as Raspberry Pis capable of running intra and inter-cluster localization algorithms. The proposed system can work without anchor nodes and thus it can be deployed in the environments such as urban canyon, inside multi-story buildings, airports, and underground shopping malls where access to anchors or Global Navigation Satellite System (GNSS) is limited or prohibitive. We exploit a hybrid of two well-known methods: multidimensional scaling (MDS) and extended Kalman filtering (EKF) to effectively construct local and global position maps, even in the absence of GNSS information, anchors, or a complete ranging matrix.
Cognitive radio (CR) is emerging as a field essential to the progress of wireless communications. With spectral efficiency at the forefront in protocol design, CR aims in combating the supposed spectrum crisis due to inefficiency in spectral utilization. Thus a lot more spectrum-hungry services can be supported by the limited frequency bands through opportunistic access or sharing of licensed spectrum. As spectrum sensing precedes any possible access or sharing of spectrum, it is crucial that the sensing result is accurate when the initial sensing decision is made. Most of the current sensing techniques are passive in nature (i.e. they do not involve any participation of the CR) and hence can incur sensing errors when eavesdropping in the midst of high-noise or fading environments. Thus we consider a novel proactive spectrum sensing scheme that increases the certainty in the spectrum decision while combating the hidden-node problem. This is achieved when the CR sends out a probing signal prior to the sensing process. We study the results of applying power constraints to the power available for probing, and see how this scheme proves beneficial to the licensed user (LU) in terms of greater protection while still maintaining the throughput needs of the CR. After proactive sensing we focus our attention on the issues raised with spectrum sharing in the field of CR communication. As outlined by most work in literature, cross-channel estimation still remains an issue. Assumptions regarding cross-channel information are common amongst work that deals with a spectrum sharing protocol. In actuality this is impractical, and there is no way for the CR to ascertain the actual interference it deals to the LU. Thus we propose a supervised probing and sensing model that enables a CR to estimate the magnitude of the cross-channel link between itself and the LU, thus allowing for interference-shielding. With probing power playing an essential role in cross-channel estimation, we optimize the incremental probing step-size to allow for the highest estimation success and study its effect on cross-channel estimation error. Finally we take the conventional spectrum sharing protocols one step further to design proactive spectrum sharing. With a protocol established with bandwidth efficiency in mind, we see how cooperation x Abstract between a proactive CR and a willing LU can prove doubly beneficial to both parties. Typically the LU would have to deal with the dynamic fading channel on its own, in meeting a target outage probability. But with a proactive scenario, the CR can take the initiative by sending out a cooperation request signal, to help relay the LU signal thus lowering the outage probability. For the CR, this opportunity means spectrum access, through superimposition of its own signal atop the forwarded LU signal. We also study the optimum power allocation strategy used by the CR for maximum LU power saving in maintaining the target outage probability at the licensed receiver (LR). Our results show that through innovations ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.