The ubiquity, large bandwidth, and spatial diversity of the fifth-generation (5G) cellular signal render it a promising candidate for accurate positioning in indoor environments where the global navigation satellite system (GNSS) signal is absent. In this article, a joint angle and delay estimation (JADE) scheme is designed for 5G picocell base stations (gNBs), which addresses two crucial issues to make it both effective and efficient in realistic indoor environments. First, the direction dependence of the array modeling error for picocell gNB and its impact on JADE is revealed. This error is mitigated by fitting the array response measurements to a vector-valued function and precalibrating the ideal steering vector with the fit function. Second, based on the deployment reality that 5G picocell gNBs only have a small-scale antenna array but have a large signal bandwidth, the proposed scheme decouples the estimation of time-of-arrival (TOA) and direction-of-arrival (DOA) to reduce the huge complexity induced by 2-D joint processing. It employs the iterative-adaptive approach to resolve multipath signals in the TOA domain, followed by a conventional beamformer to retrieve the desired line-of-sight DOA. By further exploiting a dimension-reducing preprocessing module and accelerating spectrum computing by fast Fourier transforms, an efficient implementation is achieved for real-time JADE. Numerical simulations demonstrate the superiority of the proposed method in terms of DOA estimation accuracy. Field tests show that a triangulation positioning error of 0.44 m is achieved for 90% cases using only DOAs estimated at two separated receiving points.

As a key technology that is widely adopted in location-based services (LBS), indoor localization has received considerable attention in both research and industrial areas. Despite the huge efforts made for localization using smartphone inertial sensors, its performance is still unsatisfactory in large open areas, such as halls, supermarkets, and museums, due to accumulated errors arising from the uncertainty of users' mobility and fluctuations of magnetic field. Regarding that, this paper presents iBILL, an indoor localization approach that jointly uses iBeacon and inertial sensors in large open areas. With users' real-time locations estimated by inertial sensors through an improved particle filter, we revise the algorithm of augmented particle filter to cope with fluctuations of magnetic field. When users enter vicinity of iBeacon devices clusters, their locations are accurately determined based on received signal strength of iBeacon devices, and accumulated errors can, therefore, be corrected. Proposed by Apple Inc. for developing LBS market, iBeacon is a type of Bluetooth low energy, and we characterize both the advantages and limitations of localization when it is utilized. Moreover, with the help of iBeacon devices, we also provide solutions of two localization problems that have long remained tough due to the increasingly large computational overhead and arbitrarily placed smartphones. Through extensive experiments in the library on our campus, we demonstrate that iBILL exhibits 90% errors within 3.5 m in large open areas.

Owing to the ubiquity of cellular communication signals, positioning with the fifth-generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas. Unfortunately, although the widely employed antenna arrays in 5G remote radio units (RRUs) facilitate the measurement of the direction of arrival (DOA), DOA-based positioning performance is severely degraded by array errors. This article proposes an in situ calibration framework with a user terminal (UT) transmitting 5G reference signals at several known positions in the actual operating environment and the accessible RRUs estimating their array errors from these reference signals. Further, since sub-6 GHz small-cell RRUs deployed for indoor coverage generally have small-aperture antenna arrays, while 5G signals have plentiful bandwidth resources, this work segregates the multipath components via super-resolution delay estimation based on the maximum likelihood criteria. This differs significantly from existing in situ calibration works which resolve multipaths in the spatial domain. The superiority of the proposed method is first verified by numerical simulations. We then demonstrate via field test with commercial 5G equipment that, a reduction of 46.7% for $1{\text -}\sigma$ DOA estimation error can be achieved by in situ calibration using the proposed method.

The multiple signal classification (MUSIC) algorithm is computationally expensive in the application to joint two-dimensional (2-D) direction-of-arrival (DOA) and time-of-arrival (TOA) estimation based on uniform circular array (UCA) using orthogonal frequency-division multiplexing (OFDM) signal. This letter proposed an efficient way to compute the 3-D spatial-temporal spectrum. We extended the manifold separation technique, by which we obtained the 3-D discrete Fourier transform (DFT) form of the spectrum. On this basis, we proposed two 2-D DOA and TOA estimators called FFT-MUSIC and Two-step FFT-MUSIC. The former computed the spectrum by large size fast Fourier transform (FFT) to reduce the grid error in searching DOAs and TOAs. The latter roughly located the DOAs and TOAs by relatively small size FFT, followed by the subspace-based technique for a fine-grained searching within a local grid. Simulation results showed that both estimators can reduce the computation cost by one to two orders of magnitude, as compared to their conventional counterparts, while maintaining a similar accuracy.

The lack of sufficient spectral resources in many alternative positioning, navigation, and timing (APNT) systems for aviation has led to the problem of mutual interferences. Building APNT system with the cognitive radio and frequency-hopping (FH) techniques can resolve above problem, however, the main challenge is to achieve a long and high-precision range with high-speed FH signals. In this paper, we propose a novel ranging method called time-frequency matrix ranging (TFMR), which is based on the FH signals. Using the TFMR method, we built a jam-resistant APNT system to meet the accuracy and coverage requirements of aviation. We employed a dual-tone signal for the TFMR to estimate the pseudoranges between the ground stations and the aircrafts. One major challenge we faced was that what was good for ranging accuracy was bad for coverage. For example, increasing the dual-tone interval can improve the ranging accuracy; however, this led to a decrease in the unambiguous measurement range (UMR). To overcome this challenge, we employed different dual-tone intervals in different hops of the frequencyhopping signal. Finally, we formed a time-frequency matrix, so that a high UMR could be guaranteed without any harmful consequences on the ranging accuracy in the middle to high levels of the signal-to-noise ratio. The Cramér-Rao lower bound of TFMR is derived as the benchmark of the method. Using extensive simulations, we investigated the ranging performance of the TFMR method. The simulation results showed that the proposed TFMR could satisfy the APNT requirements of the Federal Aviation Administration.

Energy harvesting enables the wireless devices to obtain energy for communication from the ambient environment. A general theme in prior works is to investigate the power scheduling policies to increase the utility ratio of the harvested energy, which arrives at random. One key assumption is the infinite data backlog, which means that as long as there is energy, there is data to transmit. However, in real systems, the buffer size is limited, and the arrival of data is also random. When the data backlog fills up the buffer, the subsequent arrival packets will be discarded directly. Therefore, we are motivated to jointly consider the data arrival and energy arrival processes in an energy harvesting communication system (EHCS). Specifically, we first derive the maximum average throughput r̅ that EHCS can support with a simple online power scheduling scheme. Then, given a data arrival process whose average rate λ <; r̅, we characterize the average data backlog for both constant and random data arrivals. Some further analyses are conducted to the variation of data backlog. To achieve a same packet drop rate, the buffer size needed for constant data arrivals is much smaller than that for random data arrivals, which can be seen from both our theoretical and simulation results. The analysis in this paper initiates a first step towards a more dynamic energy harvesting system, where data arrivals are of importance.