0000000000212870
AUTHOR
Xiaoge Huang
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 th…
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 condicio…
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 equilib…
Power allocation optimization in OFDM-based cognitive radios based on sensing information
Owing to the non-zero probability of the missed detection and false alarm of active primary transmission, a certain degree of performance degradation of the primary user (PU) from cognitive radio users (CRs) is unavoidable. In this paper, we consider OFDM-based communication systems and present efficient algorithms to maximize the total rate of the CR by optimizing jointly both the detection operation and the power allocation, taking into account the influence of the probabilities of missed detection and false alarm, namely, the sensing accuracy. The optimization problem can be formulated as a two-variable non-convex problem, which can be solved approximately by using an alternating directi…
Non-cooperative power allocation game with imperfect sensing information for cognitive radio
In this paper, we consider a sensing-based spectrum sharing scenario and present an efficient decentralized algorithm to maximize the total throughput of the cognitive radio users by optimizing jointly both the detection operation and the power allocation, taking into account the influence of the sensing accuracy. This optimization problem can be formulated as a distributed non-cooperative power allocation game, which can be solved by using an alternating direction optimization method. The transmit power budget of the cognitive radio users and the constraint related to the rate-loss of the primary user due to the interference are considered in the scheme. Finally, we use variational inequal…