认知网络中基于中继的频谱资源分配(4)

图3 3种算法在不同误比特率下的功耗比较

图5 3种算法在不同用户数下的功耗比较

JOA、HCS和DFA-TPA这3种算法在不同用户功率下的吞吐量比较如图6所示。当用户功率为 4dBW时，JOA算法的吞吐量比HCS算法高出5%左右，比DFA-TPA算法高出10%左右；当用户功率10dBW时，JOA算法的吞吐量要比HCS算法高出17%左右，比DFA-TPA算法高出13%左右。这说明用户功率越高，JOA算法在吞吐量方面的优势

图4给出在误比特率为10 4时，不同用户传输速率情况下3种算法的单位比特功耗性能比较。虽然JOA算法、HCS算法和DFA-TPA算法的单位比特功耗均随着用户速率要求的增加而递升，但JOA算法所需的单位比特功率始终低于HCS算法和DFA-TPA算法2dB左右，并且随着用户速率要求的增加，JOA算法的改善效果更加明显。

第11期 张士兵等：认知网络中基于中继的频谱资源分配 ·9·

越明显。另一方面，随着用户功率的增加，3种算法的吞吐量将趋于平稳。因此，不能只靠增加用户发射功率来增加系统的吞吐量，由此说明频谱资源分配算法对吞吐量具有相当的影响。

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图6 3种算法在不同用户功率下的吞吐量比较

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从图3~图6的仿真结果中不难看出，联合信道增益差值因子和最优配对的JOA算法减小了系统总发射功率，很大程度上降低了对PU产生有害干扰的可能性，提高了系统的QoS。

5 结束语

本文研究了CR网络的频谱资源分配，根据子信道增益差值因子和源节点、中继节点的最小化功率，最优配对网络的上、下行链路，提出了上、下行链路子载波联合优化的频谱分配算法。该算法根据信道增益差值因子分配下行链路的子载波，采用最优配对分配上行链路的子载波，并对上、下行链路的比特、功率进行最优分配，有效避免了衰落严重的子载波分配给用户的情况，减小了系统发射功率，降低了对主用户的干扰，提高了系统的吞吐量。 参考文献：

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