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Energy-Efficient Resource Management in OFDM-Based Cognitive Radio Networks Under Channel Uncertainty

机译：信道不确定性下基于OFDM的认知无线电网络中的节能资源管理

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摘要

In this paper, we investigate the energy consumption issue in cognitive radio (CR) networks. We aim to maximize the energy efficiency of the CR network while considering practical restrictions, including the power budget of the system, the interference thresholds of the primary users (PUs), the rate requirements of the secondary users, and the fairness among them. Particularly, due to the lack of explicit support from the PU system, perfect channel state information may not be acquired. Thus, the interference constraint is posed as chance-constrained form and tackled by Bernstein approximation. Then, we convert the optimization task into a quasi-convex problem via relaxing the integer variables, followed by a simple rounding technique to yield feasible subchannels assignment. We derive a fast algorithm to distribute power among subchannels by exploiting the structure of the power-allocation problem. Moreover, we give an efficient heuristic algorithm for subchannels assignment, which reduces the computation load dramatically. Simulation results show that both our proposed resource allocation schemes perform well in practical scenarios. The energy efficiency obtained by the integer subchannels assignment and the fast power distribution achieves more than 98% of the upper bound. On the other hand, the proposed heuristic subchannels assignment with optimal power allocation achieves a good tradeoff between computation complexity and energy efficiency.
机译：在本文中，我们调查了认知无线电（CR）网络中的能耗问题。我们的目标是在考虑实际限制的同时，最大化CR网络的能源效率，包括系统的功率预算，主要用户（PU）的干扰阈值，次要用户的速率要求以及它们之间的公平性。特别地，由于缺乏来自PU系统的明确支持，可能无法获取完美的信道状态信息。因此，干扰约束被表示为机会约束形式，并通过伯恩斯坦近似法解决。然后，我们通过放宽整数变量将优化任务转换为准凸问题，然后采用简单的舍入技术产生可行的子通道分配。通过利用功率分配问题的结构，我们得出了一种在子信道之间分配功率的快速算法。此外，我们给出了一种有效的启发式算法进行子信道分配，从而大大降低了计算负荷。仿真结果表明，我们提出的两种资源分配方案在实际情况下均具有良好的性能。通过整数子信道分配和快速功率分配获得的能效达到了上限的98％以上。另一方面，所提出的具有最佳功率分配的启发式子信道分配在计算复杂度和能量效率之间实现了良好的折衷。

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