随着无线接入设备数的增长及各种移动业务的快速发展，静态的频谱资源分配方式己经不能满足当下的通信需求。认知无线电能够在无线用户之间智能调度空闲频谱，从而提高其利用率。频谱感知与功率控制技术是认知无线电系统功能得以实现的关键。传统认知无线电单节点频谱感知方法中，频谱感知只发生于特定时段，忽略了其他时段次用户所接收信息对频谱感知的支持作用。多目标粒子群优化算法是一种高效的多目标优化算法，很适合应用于解决衬底式认知无线电功率控制优化问题，但会陷于局部最优。

针对以上问题，本文主要完成了以下研究工作：一，机会式认知无线电系统中，利用传输阶段信息进行频谱感知的能量检测算法的研究与仿真，及一种最优检测门限的计算方法。二，将高斯变异机制引入传统多目标粒子群算法的粒子位置更新中，并采用一种基于密度的参考线法对算法解集档案进行维护。将改进后的算法应用于解决衬底式认知无线电次用户功率控制优化问题。

经仿真验证表明，相较于传统频谱感知的能量检测算法，本文所述算法通过对传输阶段所接收信息的二次利用进行频谱感知，从系统机制层面节约了频谱感知所用时间，有效提高次用户间通信效率。应用改进后的粒子群优化算法对衬底式认知无线电系统中次用户发射功率进行控制，同等条件下次用户可用发射功率及信噪比明显优于标准粒子群算法。

With the increase in the number of wireless access devices and the rapid development of various mobile services, static spectrum resource control methods can no longer meet the current communication needs. Cognitive radio can intelligently schedule idle spectrum among wireless users, thereby improving its utilization rate. Spectrum sensing and power control technology is the key to the realization of cognitive radio system functions. In the traditional, the spectrum sensing of cognitive radio single-node only occurs in a specific time period, ignoring the support effect of the information received by the secondary users in other time periods on the spectrum sensing. The multi-objective particle swarm optimization algorithm is an efficient multi-objective optimization algorithm, which is very suitable for solving the power control optimization problem of substrate-based cognitive radio, but it is easier to fall into the local optimum.

In view of the above problems, this thesis mainly completes the following research work: First, the research and simulation of the energy detection algorithm based on the transmission stage information for spectrum sensing in the opportunistic cognitive radio system, and an optimal detection threshold calculation method. Secondly, Gaussian mutation mechanism is introduced into the particle position update of the traditional multi-objective particle swarm optimization algorithm, and a density-based reference line method is used to maintain the solution set file of the algorithm. The improved algorithm is applied to solve the secondary user power control optimization problem of cognitive radio.

Simulation verification shows that compared with the traditional spectrum sensing energy detection algorithm, the algorithm described in this article performs spectrum sensing through the secondary utilization of the information received in the transmission phase, which saves the time spent on spectrum sensing from the system mechanism level and effectively improve the communication efficiency between secondary users. The improved particle swarm optimization algorithm is used to control the transmission power of the secondary user in the substrate-based cognitive radio system. Under the same conditions, the secondary user available transmission power and signal-to-noise ratio are significantly better than the traditional particle swarm algorithm under the same conditions

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