No abstract

Traditional channel estimation algorithms such as minimum mean square error (MMSE) are widely used in massive multiple-input multiple-output (MIMO) systems, but require a matrix inversion operation and an enormous amount of computations, which result in high computational complexity and make them impractical to implement. To overcome the matrix inversion problem, we propose a computationally efficient hybrid steepest descent Gauss–Seidel (SDGS) joint detection, which directly estimates the user’s transmitted symbol vector, and can quickly converge to obtain an ideal estimation value with a few simple iterations. Moreover, signal detection performance was further improved by utilizing the bit log-likelihood ratio (LLR) for soft channel decoding. Simulation results showed that the proposed algorithm had better channel estimation performance, which improved the signal detection by 31.68% while the complexity was reduced by 45.72%, compared with the existing algorithms.

This chapter aims to look at the current status of poverty and existing social policies in Pakistan. Poverty is one of the concerns for the governments of almost all countries including Pakistan. There is a continuous research on the policy measurements by national and international organizations in Pakistan, which demonstrated the decline in poverty. The government has launched many social policies in the past three decades to help the nation in reducing the poverty. Apart from government, many national and international organizations have also contributed a lot in the effort of reducing the poverty. However, there is very little research available on the effectiveness of these social policies, and on the need of social policy areas in particular. Disparity among the urban and rural population is another important factor, which has been discussed in almost every research on poverty. Still, very few social policies in Pakistan are focusing on rural population. Therefore, the issue of social policy needs fresh exploration in the country, which is necessary to make new social policies that can benefit all citizens.

The provision of high-quality food is a primary factor in ensuring adequate nourishment and preventing malnourishment-related diseases in Pakistan. This study, therefore, aimed to quantify the impact of income on nutrient consumption in Pakistan, with the hypothesis that income has a primary role in reducing malnourishment in the developing world. To do this, we estimated nutrient–income elasticity—defined as the proportion of change in nutrient consumption in response to a change in income—for total calories, macronutrients, and micronutrients, using the nationally representative Household Integrated Economic Survey data (2010–2011) for Pakistan. Nutrient–income elasticity values were derived using several parametric regression approaches. We also assessed the non-linearity and endogeneity of the relationship. Calorie–income elasticity was found to be significantly different from zero, irrespective of the estimation technique used. Income elasticity for macronutrients and micronutrients was also found to be significantly different from zero, ranging from 0.29 to 0.65. This study, therefore, supports the hypothesis that increased household income likely improves nutrient consumption.

Different models for prediction of blast loading, response of masonry structure against blast load, and various mitigation strategies are discussed. Variation of peak positive incident pressure with scale distance in free field spherical burst and surface burst scenarios, proposed by different researchers, is presented and compared. The variation is found significant in the region of small scaled distances. Blast wave parameters in urban environment have been found different from the free field scenario. Effects of geometry, boundary conditions, and material properties on response of masonry buildings were found significant. Different mitigation strategies such as blast wall, landscaping, architecture, and retrofitting techniques are presented.