Hyperaccretion Disks around Neutron Stars

(Abridged) We here study the structure of a hyperaccretion disk around a neutron star. We consider a steady-state hyperaccretion disk around a neutron star, and as a reasonable approximation, divide the disk into two regions, which are called inner and outer disks. The outer disk is similar to that of a black hole and the inner disk has a self-similar structure. In order to study physical properties of the entire disk clearly, we first adopt a simple model, in which some microphysical processes in the disk are simplified, following Popham et al. and Narayan et al. Based on these simplifications, we analytically and numerically investigate the size of the inner disk, the efficiency of neutrino cooling, and the radial distributions of the disk density, temperature and pressure. We see that, compared with the black-hole disk, the neutron star disk can cool more efficiently and produce a much higher neutrino luminosity. Finally, we consider an elaborate model with more physical considerations about the thermodynamics and microphysics in the neutron star disk (as recently developed in studying the neutrino-cooled disk of a black hole), and compare this elaborate model with our simple model. We find that most of the results from these two models are basically consistent with each other.Comment: 44 pages, 10 figures, improved version following the referees' comments, main conclusions unchanged, accepted for publication in Ap

Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO

Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator