Employing Antenna Selection to Improve Energy-Efficiency in Massive MIMO Systems

Massive MIMO systems promise high data rates by employing large number of antennas, which also increases the power usage of the system as a consequence. This creates an optimization problem which specifies how many antennas the system should employ in order to operate with maximal energy efficiency. Our main goal is to consider a base station with a fixed number of antennas, such that the system can operate with a smaller subset of antennas according to the number of active user terminals, which may vary over time. Thus, in this paper we propose an antenna selection algorithm which selects the best antennas according to the better channel conditions with respect to the users, aiming at improving the overall energy efficiency. Then, due to the complexity of the mathematical formulation, a tight approximation for the consumed power is presented, using the Wishart theorem, and it is used to find a deterministic formulation for the energy efficiency. Simulation results show that the approximation is quite tight and that there is significant improvement in terms of energy efficiency when antenna selection is employed.Comment: To appear in Transactions on Emerging Telecommunications Technologies, 12 pages, 8 figures, 2 table

A Sequential MUSIC algorithm for Scatterers Detection 2 in SAR Tomography Enhanced by a Robust Covariance 3 Estimator

Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information of urban infrastructures. Due to the widespread applications of the MUSIC algorithm in source localization, it is a suitable solution in TomoSAR when multiple snapshots (looks) are available. While the classical MUSIC algorithm aims to estimate the whole reflectivity profile of scatterers, sequential MUSIC algorithms are suited for the detection of sparse point-like scatterers. In this class of methods, successive cancellation is performed through orthogonal complement projections on the MUSIC power spectrum. In this work, a new sequential MUSIC algorithm named recursive covariance canceled MUSIC (RCC-MUSIC), is proposed. This method brings higher accuracy in comparison with the previous sequential methods at the cost of a negligible increase in computational cost. Furthermore, to improve the performance of RCC-MUSIC, it is combined with the recent method of covariance matrix estimation called correlation subspace. Utilizing the correlation subspace method results in a denoised covariance matrix which in turn, increases the accuracy of subspace-based methods. Several numerical examples are presented to compare the performance of the proposed method with the relevant state-of-the-art methods. As a subspace method, simulation results demonstrate the efficiency of the proposed method in terms of estimation accuracy and computational load

Energy-Efficient Resource Allocation for Multi-IRS-Aided Indoor 6G Networks

In this paper, we propose a distributed intelligent reflecting surface (IRS) assisted single-user and multi-user millimeter wave (mmWave) system. Then, we formulate the resource allocation problem as an optimization to maximize energy efficiency under individual quality of service (QoS) constraints. We first propose a centralized algorithm, and further, a low-complexity distributed one where the access point (AP) and IRSs independently adjust the transmit beamforming of AP, the phase shifts, and the on-off status of IRSs in an alternating manner until the convergence is reached. In a multi-user scenario, in the first stage, the successive convex approximation (SCA) and fractional programming (FP) approaches are applied to achieve a solution for optimization subproblems of the phase-shift coefficients and element on-off status of IRSs. Then, for the beamforming subproblem, a modified nested FP approach is proposed that finds an optimal solution for the beamforming vectors of AP. Our performance analysis on a practical scenario shows that the proposed centralized and distributed approach respectively enhances the energy efficiency by up to 55%, 42% for single-user, and up to 984% for multi-user scenarios, in comparison to the case where the on-off status and phase-shift coefficients of IRS elements are not selected optimally

Interference alignment for one-hop and two-hops MIMO systems with uncoordinated interference

Providing higher data rate is a momentous goal for wireless communications systems, while interference is an important obstacle to reach this purpose. To cope with this problem, interference alignment (IA) has been proposed. In this paper, we propose two rank minimization methods to enhance the performance of IA in the presence of uncoordinated interference, i.e., interference that cannot be properly aligned with the rest of the network and thus is a crucial issue. In this scenario, perfect and imperfect channel state information (CSI) cases are considered. Our proposed approaches employ the l2 and the Schatten-p norms to approximate the rank function, due to its non-convexity. Also, we propose a new convex relaxation to expand the feasible set of our optimization problem, providing lower rank solutions compared to other IA methods from the literature. In addition, we propose a modified weighted-sum method to deal with interference in the relay-aided MIMO interference channel, which employs a set of weighting parameters in order to find more solutions

Application of Glycyrrhiza glabra

The aim of this paper is to investigate the removal of toluene from gaseous solution through Glycyrrhiza glabra root (GGR) as a waste material. The batch adsorption experiments were conducted at various conditions including contact time, adsorbate concentration, humidity, and temperature. The adsorption capacity was increased by raising the sorbent humidity up to 50 percent. The adsorption of toluene was also increased over contact time by 12 h when the sorbent was saturated. The pseudo-second-order kinetic model and Freundlich model fitted the adsorption data better than other kinetic and isotherm models, respectively. The Dubinin-Radushkevich (D-R) isotherm also showed that the sorption by GGR was physical in nature. The results of the thermodynamic analysis illustrated that the adsorption process is exothermic. GGR as a novel adsorbent has not previously been used for the adsorption of pollutants

Lessons learned from SARS-CoV and MERS-CoV : FDA-approved Abelson tyrosine-protein kinase 2 inhibitors may help us combat SARS-CoV-2

SARS-CoV-2 is a newly emerging infectious disease, which originated from Wuhan in the Hubei province of China in late December 2019 [1]. Since then, it has rapidly spread all over the world, and at the time of writing this letter, WHO statistics show more than 1,696,588 cases and 105,952 deaths confirmed across the world [2]. Although there is no specific therapy for SARS-CoV-2 infection [3], combination therapy with antiviral and anti-inflammatory drugs accompanied by supportive treatment have been used for SARS-CoV-2 patients [4]. The combination of well-known HIV protease inhibitors, such as ritonavir with lopinavir, has also been a common approach to treat SARS-CoV-2. Insufficient outcome in severe cases is, however, one of the main challenges associated with the current antiviral-based therapy for SARS-CoV-2 [5]. In view of the long period required for novel drug discovery and the desperate need for a prompt response to this pandemic infection, one must resort to repurposing FDA-approved drugs. In this direction, our experience with other close members of coronaviruses such as SARS and MERS has taught us that repurposing the current drugs is a reasonable strategy. Abelson tyrosine-protein kinase 2 (Abl2), the imatinib target, was required for efficient SARS-CoV and MERS-CoV replication in vitro [6]. Coleman et al. have shown that the imatinib target Abl2 is indispensable for efficient replication of SARS-CoV and MERS-CoV in vitro

National, sub-national, and risk-attributed burden of thyroid cancer in Iran from 1990 to 2019

An updated exploration of the burden of thyroid cancer across a country is always required for making correct decisions. The objective of this study is to present the thyroid cancer burden and attributed burden to the high Body Mass Index (BMI) in Iran at national and sub-national levels from 1990 to 2019. The data was obtained from the GBD 2019 study estimates. To explain the pattern of changes in incidence from 1990 to 2019, decomposition analysis was conducted. Besides, the attribution of high BMI in the thyroid cancer DALYs and deaths were obtained. The age-standardized incidence rate of thyroid cancer was 1.57 (95% UI: 1.33–1.86) in 1990 and increased 131% (53–191) until 2019. The age-standardized prevalence rate of thyroid cancer was 30.19 (18.75–34.55) in 2019 which increased 164% (77–246) from 11.44 (9.38–13.85) in 1990. In 2019, the death rate, and Disability-adjusted life years of thyroid cancer was 0.49 (0.36–0.53), and 13.16 (8.93–14.62), respectively. These numbers also increased since 1990. The DALYs and deaths attributable to high BMI was 1.91 (0.95–3.11) and 0.07 (0.04–0.11), respectively. The thyroid cancer burden and high BMI attributed burden has increased from 1990 to 2019 in Iran. This study and similar studies’ results can be used for accurate resource allocation for efficient management and all potential risks’ modification for thyroid cancer with a cost-conscious view