To enhance spectrum efficiency for the future Internet-of-Things, in this paper, we investigate the joint subcarrier and power allocation problem for a full-duplex network with machine-to-machine nodes and cellular nodes. We aim to maximize the network spectral efficiency while achieving individual quality-of-service (QoS) requirements, considering imperfect and partial channel state information. The spectral efficiency maximization problem is formulated as a mixed-integer nonlinear programming problem and proved to be NP-hard. To make it more tractable, we first solve the coupling between subcarrier assignment and power allocation variables by replacing the product of two variables with one auxiliary variable. We then relax the binary subcarrier assignment variables to integer variables and introduce penalty terms to penalize the objective function. Majorization minimization method is adopted to further handle the coupling between uplink and downlink due to full-duplex transmission. We prove that the proposed joint resource allocation scheme converges to local optimal solution. Simulation results demonstrate the impact of channel estimation error on spectral efficiency and the superiority of the proposed algorithm.

中文翻译：

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不完善CSI下M2M/H2H共存全双工网络的QoS保证资源分配

为了提高未来物联网的频谱效率，在本文中，我们研究了具有机器对机器节点和蜂窝节点的全双工网络的联合副载波和功率分配问题。考虑到不完善和部分信道状态信息，我们的目标是最大化网络频谱效率，同时实现个人服务质量 (QoS) 要求。频谱效率最大化问题被表述为混合整数非线性规划问题，并被证明是 NP-hard 问题。为了使其更易于处理，我们首先通过用一个辅助变量替换两个变量的乘积来解决子载波分配和功率分配变量之间的耦合。然后，我们将二进制副载波分配变量放松为整数变量，并引入惩罚项来惩罚目标函数。采用Majorization Minimization方法进一步处理全双工传输引起的上下行耦合。我们证明了所提出的联合资源分配方案收敛于局部最优解。仿真结果证明了信道估计误差对频谱效率的影响以及所提算法的优越性。