Non-convex distributed power allocation games in cognitive radio networks

In this thesis, we explore interweave communication systems in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio user by optimizing jointly both the detection operation based on sensing and the power allocation across channels, taking into account the influence of the sensing accuracy and the interference limitation to the primary users. The optimization problem is addressed in single and multiuser cognitive radio networks for both single-input single-output and multi-input multi-output channels. Firstly, we study the resource allocation optimization problem for single-input single-output single user cognitive radio networks, wherein the cognitive radio aims at maximizing its own sum-rate by jointly optimizing the sensing information and power allocation over all the channels. In this framework, we consider an opportunistic spectrum access model under interweave systems, where a cognitive radio user detects active primary user transmissions over all the channels, and decides to transmit if the sensing results indicate that the primary user is inactive at this channel. However, due to the sensing errors, the cognitive users might access the channel when it is still occupied by active primary users, which causes harmful interference to both cognitive radio users and primary users. This motivates the introduction of a novel interference constraint, denoted as rate-loss gap constraint, which is proposed to design the power allocation, ensuring that the performance degradation of the primary user is bounded. The resulting problem is non-convex, thus, an exhaustive optimization algorithm and an alternating direction optimization algorithm are proposed to solve the problem efficiently. Secondly, the resource allocation problem for a single-input single-output multiuser cognitive radio network under a sensing-based spectrum sharing scheme is analyzed as a strategic non-cooperative game, where each cognitive radio user is selfish and strives to use the available spectrum in order to maximize its own sum-rate by considering the effect of imperfect sensing information. The resulting game-theoretical formulations belong to the class of non-convex games. A distributed cooperative sensing scheme based on a consensus algorithm is considered in the proposed game, where all the cognitive radio users can share their sensing information locally. We start with the alternating direction optimization algorithm, and prove that the local Nash equilibrium is achieved by the alternating direction optimization algorithm. In the next step, we use a new relaxed equilibrium concept, namely, quasi-Nash equilibrium for the non-convex game. The analysis of the sufficient conditions for the existence of the quasi-Nash equilibrium for the proposed game is provided. Furthermore, an iterative primal-dual interior point algorithm that converges to a quasi-Nash equilibrium of the proposed game is also proposed. From the simulation results, the proposed algorithm is shown to yield a considerable performance improvement in terms of the sum-rate of each cognitive radio user, with respect to previous state-of-the-art algorithms. Finally, we investigate a multiple-input multiple-output multiuser cognitive radio network under the opportunistic spectrum access scheme. We focus on the throughput of each cognitive radio user under correct sensing information, and exclude the throughput due to the erroneous decision of the cognitive radio users to transmit over occupied channels. The optimization problem is analyzed as a strategic non-cooperative game, where the transmit covariance matrix, sensing time, and detection threshold are considered as multidimensional variables to be optimized jointly. We also use the new relaxed equilibrium concept quasi-Nash equilibrium and prove that the proposed game can achieve a quasi-Nash equilibrium under certain conditions, by making use of the variational inequality method. In particular, we prove theoretically the sufficient condition of the existence and the uniqueness of the quasi-Nash equilibrium for this game. Furthermore, a possible extension of this work considering equal sensing time is also discussed. Simulation results show that the iterative primal-dual interior point algorithm is an efficient solution that converges to the quasi-Nash equilibrium of the proposed game

Quasi-Nash Equilibria for Non-Convex Distributed Power Allocation Games in Cognitive Radios

In this paper, we consider a sensing-based spectrum sharing scenario in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio user by optimizing jointly both the detection operation based on sensing and the power allocation, taking into account the influence of the sensing accuracy and the interference limitation to the primary users. The resulting optimization problem for each cognitive user is non-convex, thus leading to a non-convex game, which presents a new challenge when analyzing the equilibria of this game where each cognitive user represents a player. In order to deal with the non-convexity of the game, we use a new relaxed equilibria concept, namely, quasi-Nash equilibrium (QNE). A QNE is a solution of a variational inequality obtained under the first-order optimality conditions of the player's problems, while retaining the convex constraints in the variational inequality problem. In this work, we state the sufficient conditions for the existence of the QNE for the proposed game. Specifically, under the so-called linear independent constraint qualification, we prove that the achieved QNE coincides with the NE. Moreover, a distributed primal-dual interior point optimization algorithm that converges to a QNE of the proposed game is provided in the paper, which is shown from the simulations to yield a considerable performance improvement with respect to an alternating direction optimization algorithm and a deterministic game

Human Papillomavirus Infection in Relation to Vaginal Microflora and Immune Factors

Objective: Clarify the vaginal microflora and immune factors in women with human papilloma virus (HPV) infection, and explore its association with HPV infection. Methods: This study collected vaginal secretions and blood from 160 women initially diagnosed as HPV positive in our hospital from June 2020 to December 2020 and 80 healthy women with HPV negative physical examination in the same period. The vaginal microflora of the patients were detected by 16S rDNA sequencing and the expression of immune factors was measured by a high-performance liquid phase chip. Results: The different types of HPV were HPV mix (64,40%), HPV52 (39,24.375%), HPV16 (30,18.750%), HPV58 (18,11.250%), HPV18 (6,3.750%), HPV53 (1,0.625%), HPV55 (1,0.625%), and HPV68 (1,0.625%).α diversity analysis showed that there was no significant difference in vaginal microflora between different HPV types (P=0.733). The genus level abundance of vaginal microflora in each group was mainly Lactobacillus, followed by Gardnerella and Prevotella. LEfSe Analysis showed that the mix group was Gardnerella and the type HPV16 group was Streptococcus. The immune comparison showed that MIP-1β was significantly upregulated in the HPV-positive group, but EGF in the HPV-negative group. Conclusion: This study revealed that HPV infection can change the proportion of vaginal microbial bacteria and the expression of immune factors, which provides a basis for local vaginal treatment and prevention of HPV infection after HPV infection

Non-convex power allocation games in MIMO cognitive radio networks

Consideramos un escenario de reparto del espectro, basado en la detección, en una red de radio cognitiva MIMO donde el objetivo general es maximizar el rendimiento total de cada usuario de radio cognitiva optimizando conjuntamente la operación de detección y la asignación de potencia en todos los canales, bajo una restricción de interferencia para los usuarios primarios. Los problemas de optimización resultantes conducen a un juego no convexo, que presenta un nuevo desafío a la hora de analizar los equilibrios de este juego. Con el fin de hacer frente a la no convexidad del juego, utilizamos un nuevo concepto relajado de equilibrio, el equilibrio cuasi-Nash (QNE). Se demuestran las condiciones suficientes para la existencia y la unicidad de un QNE. El trabajo también presenta un método de optimización de punto interior primal-dual que converge a un QNE. Los resultados de la simulación muestran que el juego propuesto puede lograr una considerable mejora del rendimiento con respecto a un juego determinista.TEC2010- 19545-C04-04 “COSIMA”,CONSOLIDER-INGENIO 2010 CSD2008-00010 “COMONSENS”“HYDROBIONETS” FP7 Grant no. 287613 FP7We consider a sensing-based spectrum sharing scenario in a MIMO cognitive radio network where the overall objective is to maximize the total throughput of each cognitive radio user by jointly optimizing both the detection operation and the power allocation over all the channels, under a interference constraint bound to primary users. The resulting optimization problems lead to a non-convex game, which presents a new challenge when analyzing the equilibria of this game. In order to deal with the non-convexity of the game, we use a new relaxed equilibria concept, namely, quasi-Nash equilibrium (QNE). We show the sufficient conditions for the existence and the uniqueness of a QNE. A primal-dual interior point optimization method that converges to a QNE is also discussed in this paper. Simulation results show that the proposed game can achieve a considerable performance improvement with respect to a deterministic game

Disrupted neural variability during propofol‐induced sedation and unconsciousness

Variability quenching is a widespread neural phenomenon in which trial‐to‐trial variability (TTV) of neural activity is reduced by repeated presentations of a sensory stimulus. However, its neural mechanism and functional significance remain poorly understood. Recurrent network dynamics are suggested as a candidate mechanism of TTV, and they play a key role in consciousness. We thus asked whether the variability‐quenching phenomenon is related to the level of consciousness. We hypothesized that TTV reduction would be compromised during reduced level of consciousness by propofol anesthetics. We recorded functional magnetic resonance imaging signals of resting‐state and stimulus‐induced activities in three conditions: wakefulness, sedation, and unconsciousness (i.e., deep anesthesia). We measured the average (trial‐to‐trial mean, TTM) and variability (TTV) of auditory stimulus‐induced activity under the three conditions. We also examined another form of neural variability (temporal variability, TV), which quantifies the overall dynamic range of ongoing neural activity across time, during both the resting‐state and the task. We found that (a) TTM deceased gradually from wakefulness through sedation to anesthesia, (b) stimulus‐induced TTV reduction normally seen during wakefulness was abolished during both sedation and anesthesia, and (c) TV increased in the task state as compared to resting‐state during both wakefulness and sedation, but not anesthesia. Together, our results reveal distinct effects of propofol on the two forms of neural variability (TTV and TV). They imply that the anesthetic disrupts recurrent network dynamics, thus prevents the stabilization of cortical activity states. These findings shed new light on the temporal dynamics of neuronal variability and its alteration during anesthetic‐induced unconsciousness.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146388/1/hbm24304_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146388/2/hbm24304.pd

A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly

On Mediation of Equipment Contract Dispute

The mediation is one of the basic ways to solve the equipment contract disputes. The mediation involves the good offices of a third party to facilitate the parties to the dispute to voluntarily reach a settlement agreement. The whole process of the equipment contract dispute settlement can be mediated. And the mediation of the equipment contract disputes should follow the principle of legality, seeking truth from facts and objectivity and justice