Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO

Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intraframe sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity.Comment: arXiv admin note: substantial text overlap with arXiv:2210.1721

The Determinants of FDI in BRIC countries

The combined economies of Brazil, Russia, India, and China (BRIC) have attracted more and more Foreign Direct Investment (FDI) and developed rapidly. This Study examines the determinant factors of FDI inflows into BRIC countries using a set of Panel data from 2001 to 2011. The study conducts panel data analysis and finds the variables as Market Size, Inflation Rate, Labor Cost, Trade Openness, Currency Value and Political Risk are potential determining factors of FDI inflows to BRIC countries. The result suggests that Market Size (measured by GDP per capita), Labor Cost (measured by Wages), and Trade Openness (measured by the ratio of FDI inflows to GDP) are positive and significant in relation to FDI inflows. Inflation Rate and Currency Value (measured by exchange rate) have negative significant effects on FDI inflows to BRIC countries. Trade Openness is an insignificant determinant factor of FDI inflows to BRIC economies

Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit

This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high sensing accuracy in real time. These schemes aim to reliably recover the spectrum by adaptively adjusting the number of required measurements without inducing unnecessary sampling redundancy. To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound. Then, a CSS scheme is proposed by tightening the derived lower bound, where the key is the design of a nonlinear exponential indicator through a general-purpose sampling-controlled algorithm (SCA). In particular, a sampling-controlled BOMP (SC-BOMP) is developed through a holistic integration of the existing BOMP and the proposed SCA. For fast implementation, a modified version of SC-BOMP is further developed by exploring the block orthogonality in the form of sub-coherence of measurement matrices, which allows more compressive sampling in terms of smaller lower bound of the number of measurements. Such a fast SC-BOMP scheme achieves a desired tradeoff between the complexity and the performance. Simulations demonstrate that the two SC-BOMP schemes outperform the other benchmark algorithms.Comment: 15 figures, accepted by IEEE Transactions on Communication