Energy and spectral efficiency tradeoff with user association and power coordination in massive MIMO enabled HetNets

In this letter, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) while ensuring proportional rate fairness in massive multiple-input multiple-output enabled heterogenous networks, where user association and power coordination are jointly considered. It is first formulated as a multi-objective optimization problem, and then transformed into a single-objective optimization problem. To solve this mixed-integer non-convex problem, an effective algorithm is developed, where the original problem is separated into lower level power coordination problem and master user association problem. Simulation results verify that our proposed algorithm can significantly improve the performance of EE-SE tradeoff and obtain higher rate fairness compared with other algorithms

On the Energy and Spectral Efficiency Tradeoff in Massive MIMO Enabled HetNets with Capacity-Constrained Backhaul Links

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive MIMO enabled HetNets while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously.With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared to other algorithms

Robust Multi-Objective Optimization for EE-SE Tradeoff in D2D Communications Underlaying Heterogeneous Networks

In this paper, we concentrate on the robust multiobjective optimization (MOO) for the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in device-to-device (D2D) communications underlaying heterogeneous networks (HetNets). Different from traditional resource optimization, we focus on finding robust Pareto optimal solutions for spectrum allocation and power coordination in D2D communications underlaying HetNets with the consideration of interference channel uncertainties. The problem is formulated as an uncertain MOO problem to maximize EE and SE of cellular users (CUs) simultaneously while guaranteeing the minimum rate requirements of both CUs and D2D pairs.With the aid of "-constraint method and strict robustness, we propose a general framework to transform the uncertain MOO problem into a deterministic single-objective optimization problem. As exponential computational complexity is required to solve this highly non-convex problem, the power coordination and the spectrum allocation problems are solved separately, and an effective two-stage iterative algorithm is developed. Finally, simulation results validate that our proposed robust scheme converges fast and significantly outperforms the non-robust scheme in terms of the effective EE-SE tradeoff and the quality of service satisfying probability of D2D pairs

Classification of colon adenocarcinoma based on immunological characterizations: Implications for prognosis and immunotherapy

Accurate immune molecular typing is pivotal for screening out patients with colon adenocarcinoma (COAD) who may benefit from immunotherapy and whose tumor microenvironment (TME) was needed for reprogramming to beneficial immune-mediated responses. However, little is known about the immune characteristic of COAD. Here, by calculating the enrichment score of immune characteristics in three online COAD datasets (TCGA-COAD, GSE39582, and GSE17538), we identified 17 prognostic-related immune characteristics that overlapped in at least two datasets. We determined that COADs could be stratified into three immune subtypes (IS1–IS3), based on consensus clustering of these 17 immune characteristics. Each of the three ISs was associated with distinct clinicopathological characteristics, genetic aberrations, tumor-infiltrating immune cell composition, immunophenotyping (immune “hot” and immune “cold”), and cytokine profiles, as well as different clinical outcomes and immunotherapy/therapeutic response. Patients with the IS1 tumor had high immune infiltration but immunosuppressive phenotype, IS3 tumor is an immune “hot” phenotype, whereas those with the IS2 tumor had an immune “cold” phenotype. We further verified the distinct immune phenotype of IS1 and IS3 by an in-house COAD cohort. We propose that the immune subtyping can be utilized to identify COAD patients who will be affected by the tumor immune microenvironment. Furthermore, the ISs may provide a guide for personalized cancer immunotherapy and for tumor prognosis

Investigation of Variation in Gene Expression Profiling of Human Blood by Extended Principle Component Analysis

BACKGROUND: Human peripheral blood is a promising material for biomedical research. However, various kinds of biological and technological factors result in a large degree of variation in blood gene expression profiles. METHODOLOGY/PRINCIPAL FINDINGS: Human peripheral blood samples were drawn from healthy volunteers and analysed using the Human Genome U133Plus2 Microarray. We applied a novel approach using the Principle Component Analysis and Eigen-R(2) methods to dissect the overall variation of blood gene expression profiles with respect to the interested biological and technological factors. The results indicated that the predominating sources of the variation could be traced to the individual heterogeneity of the relative proportions of different blood cell types (leukocyte subsets and erythrocytes). The physiological factors like age, gender and BMI were demonstrated to be associated with 5.3% to 9.2% of the total variation in the blood gene expression profiles. We investigated the gene expression profiles of samples from the same donors but with different levels of RNA quality. Although the proportion of variation associated to the RNA Integrity Number was mild (2.1%), the significant impact of RNA quality on the expression of individual genes was observed. CONCLUSIONS: By characterizing the major sources of variation in blood gene expression profiles, such variability can be minimized by modifications to study designs. Increasing sample size, balancing confounding factors between study groups, using rigorous selection criteria for sample quality, and well controlled experimental processes will significantly improve the accuracy and reproducibility of blood transcriptome study

Interference-Aware Resource Optimization for Device-to-Device Communications in 5G Networks

In this paper, we investigate the resource optimization problem for device-to-device (D2D) communications in the fifth-generation cellular networks, where multiple D2D links and cellular links share the same spectrum. A general framework is proposed to jointly optimize the energy efficiency (EE), spectral efficiency (SE), and queuing delay. Specifically, we formulate the problem as a stochastic optimization model aiming at maximizing the EE and SE concurrently under the network stability constraint, where subchannel allocation and power control are jointly optimized. Afterwards, with the help of Lyapunov techniques and weighted sum method, it is then transformed into a single-objective optimization problem, which is a mixedinteger and non-convex problem. Therefore, to solve this challenging subchannel allocation and power control problem with low computational complexity, we separate it into two levels of problems, and a twostage iterative algorithm is proposed, which only requires polynomial computational complexity. Through theoretical analysis and numerical results, the effectiveness, convergence, and optimality of the proposed algorithm are validated

Energy- and spectral-efficiency tradeoff with α\alpha-fairness in downlink OFDMA systems

In this letter, we adopt multi-objective optimization to investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in downlink orthogonal frequency division multiple access (OFDMA) systems. In the proposed model, α-fair utility function is applied to take account of the rate fairness among users. We then transfer the original multi-objective optimization into a single objective optimization employing the weighted sum method to obtain the solution set characterized as a Pareto set. The obtained Pareto set demonstrates the tradeoff between EE and SE while α-fairness guarantee is in place. We further consider price of fairness, as a metric to quantify the loss of EE due to enforcing fairness requirements. Such a metric enables the network operators to determine an acceptable operation point in terms of EE-SE tradeoff when certain level of fairness is required. Simulation results indicate that higher fairness results in lower system EE, and the price of fairness is significantly raised with the increase of overall SE