Power distribution method in downlink NOMA of depth deterministic strategy gradient

An allocation method and a deterministic technology, applied in the field of NOMA resource allocation, can solve the problems that it is not easy to find the optimal solution, the numerical simulation method does not have an accurate system model, and consumes a lot of time, so as to solve the problem of spectrum scarcity and improve Average transfer rate, effect of improving utilization efficiency

An allocation method and a deterministic technology, applied in the field of NOMA resource allocation, can solve the problems that it is not easy to find the optimal solution, the numerical simulation method does not have an accurate system model, and consumes a lot of time, so as to solve the problem of spectrum scarcity and improve Average transfer rate, effect of improving utilization efficiency

CN112492691APending Publication Date: 2021-03-12LIAONING TECHNICAL UNIVERSITY
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  • Power distribution method in downlink NOMA of depth deterministic strategy gradient
  • Power distribution method in downlink NOMA of depth deterministic strategy gradient
  • Power distribution method in downlink NOMA of depth deterministic strategy gradient

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] Embodiment 1: as figure 1 Shown is a structural diagram of a cellular network power allocation method according to an embodiment of the present invention. This embodiment provides a downlink NOMA system power allocation method based on a deep deterministic policy gradient algorithm. The specific steps are as follows:

[0057] 1) Initialize the downlink NOMA system simulation environment, such as Figure 4 Shown is a simulated communication system diagram, including a base station and multiple end users. Considering the complexity of decoding at the receiving end, consider the case of two users on one subchannel;

[0058] 2) Initialize the weight parameters of the two neural networks contained in the actor network module and the critic network module;

[0059] 3) Use correlation algorithms to complete the matching work between users and channels, and adopt the method of equal power distribution between sub-channels;

[0060] 4) Obtain the initialization state, first ca...

Embodiment 2

[0070] Embodiment 2: This embodiment specifically explains the small-scale fading, large-scale fading, action set, neural network structure, and parameter update method of the target network in embodiment 1.

[0071] (1) Small-scale fading, the formula is:

[0072] in, and The formula for calculating the correlation coefficient ρ is: ρ=J 0 (2πf d T s )J 0 ( ) represents the zero-order Bessel function of the first kind, f d represents the maximum Doppler frequency, T s Indicates the time interval between adjacent moments, in milliseconds.

[0073] (2) Large-scale fading, the formula is: PL -1 (d)=-120.9-37.6 log 10 (d)+10log 10 (z)

[0074] Among them, z is a random variable that obeys the logarithmic normal distribution, and the standard deviation is 8dB, and d represents the distance from the transmitting end to the receiving end, and the unit is km.

[0075] (3) The action set is a set of continuous values, ranging from 0 to 1, but not including 0 and 1. The ...

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Abstract

The invention discloses a power distribution method in a downlink NOMA system of a depth deterministic strategy gradient algorithm, and the method employs a double neural network structure and an experience pool playback mechanism, can effectively solve a problem related to a large-scale state action space, reduces the correlation between training samples, employs a deterministic strategy to select an action, and an action may be selected in a continuous action space. According to the algorithm, state information is used as input of a neural network, a state space, an action space and a rewardfunction are correspondingly designed according to a simulation downlink NOMA system situation, and signal to interference plus noise ratio information and rate information at the last moment are used as components of the state information at the current moment. According to the invention, the intelligent agent can learn and utilize the learned information to improve the behavior strategy more effectively, and an optimal power distribution strategy is obtained after multiple iterations. The method can effectively solve the problem of power distribution of multiple users in the downlink NOMA system, has good generalization performance under different user numbers and the transmitting power level of the base station, can effectively improve the rationality of power distribution, consumes less operation time, and effectively improves the efficiency of power distribution.

Description

technical field [0001] The invention relates to the field of NOMA resource allocation, in particular to a power allocation method in a downlink NOMA system based on a deep deterministic strategy gradient algorithm. Background technique [0002] With the continuous access of mobile terminal equipment and the continuous increase of user density in the wireless communication system, the amount of data in the communication system has shown an exponential growth, and the orthogonal multiple access technology has been unable to meet the needs of high system capacity. The fifth-generation mobile communication system emerged as the times require. The main focus of 5G technology is the improvement of data rate and the reduction of end-to-end delay, so as to adapt to the exponential growth of wireless business data volume. Non-orthogonal multiple access (NOMA) is considered to be a promising technology in the 5G communication system, which allows multiple users to communicate on the s...

Claims

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Application Information

Patent Timeline
12 Mar 2021
Publication
CN112492691A
IPC
H04W72/04; G06N3/04
CPC
H04W72/0473; G06N3/045; H04W72/53; Y04S10/50; Y02E40/70; Y02D30/70
Inventors
王伟; 殷爽爽