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

Source enumeration in large arrays using moments of eigenvalues and relatively few samples,”

Abstract-This paper presents a method based on Minimum Description Length (MDL) criterion to enumerate the incident waves impinging on a large array using a relatively small number of samples. The proposed scheme exploits the statistical properties of eigenvalues of the Sample Covariance Matrix (SCM) of Gaussian processes. We use a number of moments of noise eigenvalues of the SCM in order to separate noise and signal subspaces more accurately. In particular, we assume a MarcenkoPastur probability density function (pdf) for the eigenvalues of SCM associated with the noise subspace. We also use an enhanced noise variance estimator to reduce the bias leakage between the subspaces. Numerical simulations demonstrate that the proposed method estimates the true number of signals for large arrays and a relatively small number of snapshots. In particular, the proposed method requires less number of samples to achieve the same correct enumeration probability compared to the state-ofthe-art methods. We evaluated the assumed pdf in order to justify the limitation and the behavior of the proposed method for small number of snapshots and array sizes. Index Terms-Array signal processing, Minimum Description Length (MDL), Random Matrix Theory

Designing bike networks using the concept of network clusters

Abstract In this paper, a novel method is proposed for designing a bike network in urban areas. Based on the number of taxi trips within an urban area, a weighted network is abstracted. In this network, nodes are the origins and destinations of taxi trips and the number of trips among them is abstracted as link weights. Data is extracted from the Taxi smart card system of a real city. Then, Communities i.e. clusters of this network are detected using a modularity maximization method. Each community contains the nodes with highest number of trips within the cluster and lowest number of trips with other clusters. Within each community, the nodes close enough to each other for being traveled by bicycle are detected as key points and some non-dominated bike network connecting these nodes are enumerated using a bi-objective optimization model. The total travel cost (distance or time) on the network and the path length are considered as objectives. The method is applied to Isfahan city in Iran and a total of seven regions with some non-dominated bike networks are proposed

CD44-specific short peptide A6 boosts cellular uptake and anticancer efficacy of PEGylated liposomal doxorubicin in vitro and in vivo

Abstract Although liposomes have improved patient safety and the pharmacokinetic profile of free drugs, their therapeutic efficacy has only shown marginal improvement. The incorporation of active-targeted ligands to enhance cellular uptake has shown promise in preclinical studies. However, no active-targeted liposomes have successfully translated into clinical use thus far. This study aimed to evaluate the targeting ability and antitumor efficiency of A6, a specific short peptide (KPSSPPEE) when incorporated into PEGylated liposomal doxorubicin (PLD). The results revealed significantly enhanced cellular uptake. The cytotoxicity of the formulations was determined by 3 h and 6 h incubation of formulations with cells, followed by 48 h incubation to evaluate the targeted ability of the formulations and the results indicated the higher cytotoxicity of A6-PLD (IC50 of 7.52 µg/mL after 6 h incubation) in the CD44 overexpressing C26 cell line compared to non-targeted PLD (IC50 of 15.02 µg/mL after 6 h incubation). However, CD44-negative NIH-3T3 cells exhibited similar uptake and in vitro cytotoxicity for both A6-PLD (IC50 of 38.05 µg/mL) and PLD (IC50 of 34.87 µg/mL). In animal studies, A6-PLD demonstrated significantly higher tumor localization of doxorubicin (Dox) (~ 8 and 15 µg Dox/g tumor for 24 and 48 after injection) compared to PLD (~ 6 and 8 µg Dox/g tumor for 24 and 48 after injection), resulting in effective inhibition of tumor growth. The median survival time (MST) for Dextrose 5% was 10, PLD was 14 and A6-PLD was 22 days. In conclusion, A6-PLD, a simple and effective targeted liposome formulation, exhibits high potential for clinical translation. Its improved targetability and antitumor efficacy make it a promising candidate for future clinical applications