Cognition anti-interference communication method based on reinforcement learning algorithm
A communication method and reinforcement learning technology, applied in wireless communication, transmission monitoring, network traffic/resource management, etc., can solve problems such as difficulty in learning effective anti-interference strategies, and achieve the effect of maximizing throughput
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[0030] Step (1): Set iteration time t = 0, time range T = 20000, according to perceived interference-free channels {1, 2} and power {4, 8, 12, 16}, form different frequency channels and transmit power combined subset {f u ,p v}, where f u ∈{1,2},f v ∈{4,8,12,16}, the index of each subset is marked as k∈{1,...,8}, all subsets constitute the set {{1,4},...,{2,16 }};
[0031] Step (2): At the initial time t=0, for any node j, traverse all subsets, calculate the metric value of node j for each subset, and obtain the set of metric values of all subsets corresponding to wireless network node j
[0032] As an example, computing the metric for the k=1th subset of nodes j=1 The specific steps are as follows:
[0033] Step (2.1), first calculate the signal-to-interference-noise ratio of the k=1 subset selected by the wireless network node j=1 according to the selected channel and power Among them, node j=1 selects the received power of the k=1th subset as P j,k -249.4150dBm...
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