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多用戶正交分頻多工感知無線電系統之資源分配演算法
Dissertation

多用戶正交分頻多工感知無線電系統之資源分配演算法

陳泉旭
Doctor of Philosophy (PHD), 國立清華大學, 通訊工程研究所
2010

Abstract

感知無線電 合作式通訊 多用戶正交分頻多工 資源分配
Cognitive radio (CR) is an emerging technology for maximizing spectrum utilization under the fixed spectrum management policy and can be adapted to a dynamic wireless environment to provide flexible wireless access. In a multiuser orthogonal frequency division multiplexing (MU-OFDM) CR system, one way to improve spectrum utilization is to efficiently allocate the limited resources, which are the available subcarriers of licensed users (also referred to as primary users [PUs]) and the allocated power on these subcarriers. Given the concerns of protecting PUs and conserving the energy of unlicensed users (also referred to as secondary users [SUs]), this dissertation addresses the resource allocation problem for MU-OFDM CR systems. In this dissertation, we first present a joint subcarrier and power allocation (JSPA) algorithm for MU-OFDM CR systems, where a peak power constraint is used to protect PUs. For subcarrier allocation, a rule is derived to maximize the sum capacity for a given power distribution. With this rule, we form a JSPA algorithm in which the sum capacity optimization can be alternated between the subcarrier and power. To simplify the high-complexity optimal power allocation algorithm (i.e., the iterative partitioned water-filling [IPWF] algorithm) for the joint scheme, we further develop a simple method for updating the water level when each subcarrier is allocated to an SU. The proposed method to update the water level achieves a system capacity close to that of the JSPA algorithm with optimal power distribution, and with much lower computational complexity. To study a JSPA algorithm for cooperative MU-OFDM CR systems, we simplify a three-node relay network to a two-node network with the equivalent channel gain and then formulate the resource allocation problem for a cooperative MU-OFDM CR system in a form similar to that of a non-cooperative MU-OFDM CR system. Therefore, the JSPA algorithm for MU-OFDM CR systems can be extended to the cases for cooperative MU-OFDM CR systems. The proposed algorithm achieves a system capacity close to that of the JSPA algorithm with optimal power distribution for cooperative transmission, and with much lower computational complexity. In addition, if the relay node is at the midpoint between the source and destination, the system capacity can reach its maximum value. However, when the relay is at a midway location (on the perpendicular bisector of the line segment) different from the midpoint, the capacity is sensitive to the angle of the line source-relay-destination. In contrast, when the relay is in proximity to either the source node or the destination node, the system capacity is not sensitive to the angle. Finally, we investigate an efficient power allocation algorithm based on the reliability of subcarriers for OFDM-based CR systems. The reliability of subcarriers is affected by imperfect spectrum sensing and by PU reoccupation, which introduce interference from the PU and cause rate loss to the SUs’ transmissions. To incorporate the reliability of subcarriers into a power allocation problem, we maximize the sum expected capacity using probabilities that reflect the effects of the reliability of subcarriers, instead of maximizing the sum capacity in traditional OFDM systems. A peak power constraint is also included to protect the PUs’ communication links. Based on the modeled optimization problem, we derive the optimal power distribution, which classifies the assigned subcarriers according to the peak power constraint and then allocates power using high-complexity numerical methods to solve a nonlinear equation. To reduce the computational complexity, we approximate the optimal power allocation with reliable accuracy and then propose a low-complexity power allocation algorithm for updating the water level, which is similar to the IPWF algorithm, to determine if the peak power constraint is satisfied. The proposed energy-efficient algorithm achieves a sum expected capacity close to that of the optimal solution, but with much lower computational complexity, regardless of the change in the parameters of the reliability of subcarriers.

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