Application of Bayesian Compressive Sensing in IR-UWB Channel Estimation

Due to the sparse nature of the impulse radio ultra-wideband(IR-UWB)communication channel in the time domain,compressive sensing(CS)theory is very suitable for the sparse channel estimation. Besides the sparse nature,the IR-UWB channel has shown more features which can be taken into account in the channel estimation process,such as the clustering structures. In this paper,by taking advantage of the clustering features of the channel,a novel IR-UWB channel estimation scheme based on the Bayesian compressive sensing(BCS)framework is proposed,in which the sparse degree of the channel impulse response is not required. Extensive simulation results show that the proposed channel estimation scheme has obvious advantages over the traditional scheme,and the final demodulation performance,in terms of Bit Error Rate(BER),is therefore greatly improved.

Joint 2D DOA and Doppler frequency estimation for L-shaped array using compressive sensing

A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.

Cooperative Compressive Spectrum Sensing in Cognitive Underw ater Acoustic Communication Networks

Because of the specific of underwater acoustic channel,spectrum sensing entails many difficulties in cognitive underwater acoustic communication( CUAC) networks, such as severe frequency-dependent attenuation and low signal-to-noise ratios. To overcome these problems, two cooperative compressive spectrum sensing( CCSS) schemes are proposed for different scenarios( with and without channel state information). To strengthen collaboration among secondary users( SUs),cognitive central node( CCN) is provided to collect data from SUs. Thus,the proposed schemes can obtain spatial diversity gains and exploit joint sparse structure to improve the performance of spectrum sensing. Since the channel occupancy is sparse,we formulate the spectrum sensing problems into sparse vector recovery problems,and then present two CCSS algorithms based on path-wise coordinate optimization( PCO) and multi-task Bayesian compressive sensing( MT-BCS),respectively.Simulation results corroborate the effectiveness of the proposed methods in detecting the spectrum holes in underwater acoustic environment.

Stochastic analysis of load-transfer mechanism of energy piles by random finite difference model

The surge in demand for renewable energy to combat the ever-escalating climate crisis promotes development of the energy-saving,carbon saving and reduction technologies.Shallow ground-source heat pump(GSHP)system is a promising carbon reduction technology that can stably and effectively exploit subsurface geothermal energy by taking advantage of load-bearing structural elements as heat transfer medium.However,the transformation of conventional geo-structures(e.g.piles)into heat exchangers between the ground and superstructures can potentially induce variable thermal axial stresses and displacements in piles.Traditional energy pile analysis methods often rely on deterministic and homogeneous soil parameter profiles for investigating thermo-mechanical soil-structure interaction,without consideration of soil spatial variability,model uncertainty or statistical uncertainty associated with interpolation of soil parameter profiles from limited site-specific measurements.In this study,a random finite difference model(FDM)is proposed to investigate the thermo-mechanical load-transfer mechanism of energy piles in granular soils.Spatially varying soil parameter profile is interpreted from limited site-specific measurements using Bayesian compressive sensing(BCS)with proper considering of soil spatial variability and other uncertainties in the framework of Monte Carlo simulation(MCS).Performance of the proposed method is demonstrated using an illustrative example.Results indicate that the proposed method enables an accurate evaluation of thermally induced axial stress/displacement and variation in null point(NP)location with quantified uncertainty.A series of sensitivity analyses are also carried out to assess effects of the pile-superstructure stiffness and measurement data number on the performance of the proposed method,leading to useful insights.