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Asymptotically Optimal Resource Allocation in OFDM-Based Cognitive Networks with Multiple Relays

机译:具有多个中继的基于OFDM的认知网络中的渐近最优资源分配

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摘要

In this letter, the problem of resources allocation in decode-and-forward (DF) relayed OFDM based cognitive system is considered. The dual decomposition technique is adopted to obtain an asymptotically optimal subcarrier pairing, relay selection, and power allocation. The resources are optimized under the individual power constraints in source and relays so that the sum rate is maximized while the interference induced to the primary system is kept below a pre-specified interference temperature limit. Moreover, a sub-optimal scheme is presented to avoid the high computational complexity of the optimal scheme. The sub-optimal algorithm allocates jointly the different resources taking into account the channel qualities, the DF-relaying strategy, the interference induced to the primary system ,and the individual power budgets. The performance of the different schemes and the impact of the constraints values are discussed through the numerical simulation results.
机译:在本文中,考虑了基于解码转发(DF)中继OFDM的认知系统中的资源分配问题。采用双重分解技术以获得渐近最佳子载波配对,中继选择和功率分配。在源和继电器的各个功率约束下优化资源,以使总速率最大化,同时对主系统产生的干扰保持在预定的干扰温度极限以下。此外,提出了次优方案以避免最优方案的高计算复杂度。次优算法考虑了信道质量,DF中继策略,对主系统的干扰以及各个功率预算,共同分配了不同的资源。通过数值仿真结果讨论了不同方案的性能以及约束值的影响。

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