A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays.The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values.

In recent years, the number of electric vehicles in China has continued to rise, and the charging network for electric vehicles has developed rapidly. This paper studies the development characteristics of the electric vehicle charging network business market and forms a charging network policy environment that includes national and local policies. Then, use policy tools and divide policy tools into supply, environment and demand. Finally, based on the policy influence from the perspective of policy tools, the corresponding development suggestions are put forward.

We consider uplink multiuser multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication. The transmit (Tx) side of the envisaged system consists of several single-antenna users or/and several multiple-antenna users. At the receive side, a multiple-antenna access point employs compressive sensing techniques to estimate the channel impulse response from the preamble portion of the observed packets. The traditional approach is that of orthogonal pilot allocation: during a short training period, each OFDM subcarrier is assigned exclusively to a single Tx antenna. In this case, the channel state information can conveniently be acquired on a per Tx antenna basis. To the best of our knowledge, all related research imposes that all Tx antennas are allocated the same amount of pilots (which must then be tailored for the most extreme channel conditions). However, in the considered system, Tx antennas may experience totally different channel conditions. Under these circumstances, the use of a fixed number of pilots per Tx antenna results in a lot of unnecessary overhead. To tackle this problem, our work addresses the design of efficient algorithms for adaptive orthogonal pilot allocation. The following design principles are applied: orthogonal pilot allocation, constant-modulus modulation, minimum measurement matrix mutual coherence optimization, and the condition that the number of pilot subcarriers allocated to each Tx antenna is adjusted to the channel conditions experienced by that Tx antenna. The paper tackles the problem of determining the optimal number of pilot subcarriers as well as the optimal positions of the pilots. To facilitate adaptive operation, we propose a reduced-complexity method to determine the optimal pilot positions. The performance of our algorithms is demonstrated by means of computer simulations, using both theoretical channel models and results from our own channel measurement campaign.

The power amplifier (PA) is an indispensable part of ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) systems. Nevertheless, it will produce nonlinear distortions, degrading the accuracy of channel estimation and thus the performance of the system. To improve the system performance, it is essential to investigate channel estimation using distorted signals. Taking advantage of the sparsity of the UWB channel, a sparse channel estimation method using distorted signals is proposed in this letter, referred to as sparse channel estimation combined with power amplifier nonlinear cancellation (SCE-PANC). Within SCE-PANC, the measurement matrix is constructed only with pilot subcarriers and nonlinear distortions will be eliminated iteratively. The numerical results show that the proposed method has a better performance than the state-of-the-art methods.

Nonlinear distortions generated by the power amplifier (PA) will affect the performance of channel estimation thus the veracity of signal detection for ultra-wideband (UWB) orthogonal frequency-division multiplexing (OFDM) systems. The existing nonlinear cancellation techniques for tackling this problem reduce the overhead of the system due to the large number of pilot subcarriers required. As the UWB channel can usually be modeled as sparse, an iterative method that estimates the sparse channel and recovers the original signal, referred to as SCE-DSD, is proposed in this letter. Involving the nonlinear distortion in the measurement matrix construction, SCE-DSD estimates the channel and the original signal sequentially. The numerical results show that, compared to the state-of-the-art method, the proposed method has a better performance using a smaller number of pilot subcarriers.