Two key challenges in underlay dynamic spectrum access (DSA) are how to establish an interference limit from the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of the interference they create on the PN, especially when there is no exchange of information between the two networks. These challenges are addressed in this paper by presenting a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on the PN. The scheme is based on a cognitive engine with an artificial neural network that predicts, without exchanging information between the networks, the full adaptive modulation and channel coding configuration for the primary link that is received with highest power by a transmitting CR. By managing the effect of the SN on the PN, the presented technique maintains the relative average throughput change in the PN within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the PN interference limit. Simulation results show that the ability of the cognitive engine in estimating the effect of a CR transmission on the full adaptive modulation and coding (AMC) mode leads to a very fine resolution underlay transmit power control. This ability also provides higher transmission opportunities for the CRs, compared to a scheme that can only estimate the modulation scheme used at the PN link.

中文翻译：

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用于自主和分布式底层动态频谱访问的神经网络认知引擎

底层动态频谱访问（DSA）的两个主要挑战是如何建立来自主网络（PN）的干扰限制以及辅助网络（SN）中的认知无线电（CR）如何意识到它们在PN上产生的干扰，尤其是当两个网络之间没有信息交换时。本文提出了一种完全自主的分布式底层DSA方案，从而解决了这些挑战，其中，每个CR都基于预测其对PN的传输效果而运行。该方案基于具有人工神经网络的认知引擎，该认知引擎无需在网络之间交换信息即可预测由发送CR以最高功率接收的主链路的完整自适应调制和信道编码配置。通过管理SN对PN的影响，所提出的技术将PN中的相对平均吞吐量变化保持在规定的最大值之内，同时还找到了CR的发送设置，从而导致吞吐量达到PN干扰限制所允许的范围。仿真结果表明，认知引擎估计CR传输对完全自适应调制和编码（AMC）模式的影响的能力可导致分辨率非常高的底层发射功率控制。与只能估计PN链路上使用的调制方案的方案相比，此功能还为CR提供了更高的传输机会。同时找到CR的发送设置，从而导致吞吐量达到PN干扰限制所允许的范围。仿真结果表明，认知引擎估计CR传输对完全自适应调制和编码（AMC）模式的影响的能力可导致分辨率非常高的底层发射功率控制。与只能估计PN链路上使用的调制方案的方案相比，此功能还为CR提供了更高的传输机会。同时找到CR的发送设置，从而导致吞吐量达到PN干扰限制所允许的范围。仿真结果表明，认知引擎估计CR传输对完全自适应调制和编码（AMC）模式的影响的能力可导致分辨率非常高的底层发射功率控制。与只能估计PN链路上使用的调制方案的方案相比，此功能还为CR提供了更高的传输机会。