The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper proposes a backscatter-assisted wireless powered communication network that includes a hybrid access point and multiple users. In conventional wireless powered communication networks with only harvest-then-transmit (HTT) mode, urgent data transmission is not possible since users need to first harvest sufficient energy before transmitting information. Backscatter communication depends on...
Broadband frequency-selective fading channels usually exhibit the inherent sparse structure distribution in spread time-domain. By exploiting the sparsity, adaptive sparse channel estimation (ASCE) algorithms, e.g., least mean square with reweighted L1-norm constraint (LMS-RL1) algorithm, can bring a considerable performance gain under the assumption of additive white Gaussian noise (AWGN). In the...
In this paper, by exploiting the special features of temporal correlations of dynamic sparse channels that path delays change slowly over time but path gains evolve faster, we propose the structured matching pursuit (SMP) algorithm to realize the reconstruction of dynamic sparse channels. Specifically, the SMP algorithm divides the path delays of dynamic sparse channels into two different parts to...
The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with traditional dense-assumption based linear channel estimation methods, e.g., least mean square/fourth (LMS/F) algorithm, exploiting sparse structure information can get extra performance gain. By introducing ℓ1-norm penalty,...
Cluster-sparse channels often exist in frequency-selective fading broadband communication systems. The main reason is received scattered waveform exhibits cluster structure which is caused by a few reflectors near the receiver. Conventional sparse channel estimation methods have been proposed for general sparse channel model which without considering the potential cluster-sparse structure information...
Compared with standard cyclic prefix OFDM (CP-OFDM), time domain synchronous OFDM (TDS-OFDM) can achieve a higher spectrum efficiency by using the known training sequence instead of CP as the guard interval. However, TDS-OFDM suffers from reduced energy efficiency and performance loss due to the existing mutual inferences. In this paper, based on the newly emerging theory of structured compressive...
Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., zero-attracting LMS (ZA-LMS), reweighted zero-attracting LMS (RZA-LMS) and Lp - norm sparse LMS (LP-LMS), have also been proposed. To take full advantage...
Sparse multi-path channels (MPC) are often encountered in several wireless communications. Exploiting the sparse property of MPC, several estimation methods have been proposed in recent years. Unlike the previous methods based on either dense MPC or sparse MPC models, we propose a novel MPC estimation method based on a generalized hybrid MPC model. The existed least squares (LS) and sparse component...
Sparse multi-path is encountered in OFDM and Ultra-wideBand (UWB) communications. Conventional channel estimation methods ignore the prior knowledge of the sparseness, and estimates have higher MSE. We introduced two approaches: Lp norm constraint method and LASSO method which are related to Bayesian model. The two approaches exploit the sparse a priori and obtain better performance.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.