基于干扰温度限制的认知正交频分复用系统功率分配算法

在认知正交频分复用（OFDM）系统中，为避免对主用户（PU）的干扰，需要对认知用户（CU）基站的发射功率进行控制和分配。针对认知用户基站无法合理分配其发射功率及无法有效提高数据传输速率等问题，在传统注水功率分配算法的基础上，提出了一种双因子二分搜索最优化功率分配算法。该算法充分考虑认知用户信道上干扰温度的限制，首先，在满足总功率限制的条件下引入剩余函数；然后，利用剩余函数的单调性，通过双层二分搜索迭代方法求得拉格朗日因子的准确值；最后，通过拉格朗日因子的值求出各子信道上所分配的功率值。仿真结果表明，所提算法能有效利用主用户频带间的频谱空穴，在总功率限制和干扰温度（IT）限制下，最大化认知用户的数据传输率，其值逼近传统注水算法。同时该算法所得到的数据传输速率比总功率平均控制算法和干扰温度平均控制算法有明显的提高，在相同仿真环境下其传输总速率超出约4×105b/s。在迭代过程中所提算法处理时间较少，并体现出良好的鲁棒性。

Power allocation algorithm in cognitive orthogonal frequency division multiplexing system based on interference temperature limit

In cognitive Orthogonal Frequency Division Multiplexing (OFDM) systems, to avoid interference to Primary Users (PU), the transmission power of Cognitive Users (CU) need to be controlled and allocated. Since the transmission power can not be allocated legitimately and the data transmission rate can not be improved effectively, a power allocation algorithm of double factor binary search optimization was proposed on the basis of traditional water-filling power allocation algorithm. In the presented algorithm, the interference temperature limit on the cognitive user channel was taken into account. Firstly, a surplus function was introduced under the total power constraints. Secondly, because of the monotonicity of the surplus function, the accurate values of Lagrangian multipliers could be attained through the double binary search iteration method. Finally, the power allocation of the sub-channels was conducted through the values of Lagrangian multipliers. The simulation results show that the proposed algorithm can effectively use the spectrum hole between primary users. The data transmission rate of the cognitive users can be maximized under both total power constraints and Interference Temperature (IT) constraints. The data transmission rate is approaching to the traditional water-filling algorithm. Compared with the total power average control algorithm and the interference temperature average control algorithm, the data transmission rate of the presented algorithm is obvious higher, which exceeds about 4×105b/s under the same circumstance. Moreover, the algorithm has less processing time and reflects a good robustness.