AI驱动的无线网络教程：神经网络在5G时代的挑战与应用

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随着5G和物联网（IoT）时代的到来，无线网络正面临前所未有的挑战，即如何在超可靠、低延迟的通信需求与实时管理大规模IoT设备的复杂性之间找到平衡。在这个背景下，机器学习（Machine Learning, ML）和人工智能（Artificial Intelligence, AI）在无线网络中的应用日益重要。本文《利用人工智能的神经网络教程：机器学习在无线网络中的应用》由Mingzhe Chen、Ursula Challita、Walid Saad、Changchuan Yin和Mérouane Debbah等专家撰写，旨在为读者提供一个全面的教程，深入探讨如何通过神经网络技术来解决这些问题。首先，文章阐述了为何机器学习是实现下一代无线网络的关键。传统的网络管理方法难以满足严格的通信服务质量（Quality of Service, QoS）需求，特别是在动态环境中，如快速变化的无线环境和海量的设备连接。通过引入机器学习，网络能够自动适应和优化，比如预测用户行为、调整传输策略和资源分配，从而提升效率并减少延迟。其次，神经网络作为AI的核心组成部分，在无线网络中的作用不可或缺。神经网络模仿人脑的结构和功能，具有自我学习和适应的能力。它们在无线网络中可以用于以下几方面： 1. **信号处理与优化**：神经网络可以帮助设计和优化无线信道编码、解码以及信号检测算法，提高通信质量。 2. **流量管理和调度**：通过深度学习，可以预测数据流量模式，实现智能的流量调度，确保关键任务得到优先处理。 3. **网络自愈和故障检测**：神经网络模型可以实时分析网络性能，识别异常行为，自动修复故障或进行预防性维护。 4. **设备定位和资源分配**：通过位置感知和机器学习，可以精确地定位用户和设备，合理分配网络资源，减少覆盖盲区。 5. **安全与隐私保护**：神经网络可以用于创建更有效的入侵检测系统，以及隐私保护技术，抵御网络攻击。 6. **边缘计算**：结合移动边缘计算（Mobile Edge Computing, MEC），神经网络可以在靠近用户的地方处理数据，进一步降低延迟并减轻核心网络压力。文章还可能详细介绍了如何设计和实施基于神经网络的无线网络解决方案，包括数据预处理、模型训练、验证和部署的最佳实践，以及可能面临的挑战和未来的研究方向。这篇教程提供了实用的指导，使无线网络工程师和技术人员能够更好地理解和应用机器学习和人工智能技术，以构建更加智能、高效和可靠的无线通信网络。

Feedforward neural network

Recurrent neural network

Modular neural network

Physical neural network

Spiking neural network

Self-organizing maps

Radial basis function

Autoencoder

Probabilistic neural network

Time delay neural network

Convolutional neural network

Fully recurrent neural network

Simple recurrent network

Echo state network

Long short-term memory

Stochastic neural network

Bidirectional neural network

Hopfield network

Boltzmann machine

Learning vector quantization

Restricted Boltzmann machine

Denoising autoencoder

Sparse autoencoder

Variational autoencoder

Contractive autoencoder

Gated recurrent units

Extreme learning machines

Deep neural network

Liquid state machine

Generative adversarial networks

Neural turing machines

All types of neural networks with multiple hidden layers

Deep belief networks

Fig. 4. Summary of a recurrent neural network..

a learning algorithm cannot capture the underlying trend of the data. Intuitively, underﬁtting

occurs when the learning algorithm does not ﬁt the data well enough. Therefore, one must

carefully choose the architecture of an ANN along with the proper training methods to avoid

overﬁtting and underﬁtting.

Using the aforementioned training algorithms, the values of the weight matrix and bias can

be updated to their optimal values, and, hence, a trained ANN can output the desired output

signal. However, each type of ANNs, as shown in Fig. 4, is suitable for a particular type of data.

For instance, RNNs are more convenient for time series data while spiking neural networks are

good at modeling continuous data. Therefore, in Section III, we will introduce speciﬁc types of

ANNs and we discuss their properties and use cases.

III. TYPES OF ARTIFICIAL NEURAL NETWORKS

Beyond the simple FNN architecture introduced earlier, in this section, we speciﬁcally dis-

cuss three more advanced types of ANNs: recurrent neural networks, spiking neural networks,

and deep neural networks. For each kind of neural network, we will present its architecture,

information and exploit dynamic temporal behaviors such as those faced in mobility prediction,

handwriting recognition, or speech recognition. For example, an RNN can be used to recognize

an individual person’s speech when they pronounce only one word at each step. Clearly, this

task cannot be done in one step without combining different words from different steps. Another

application example for RNN is mobility prediction. An RNN can be used to predict the mobility

patterns of certain vehicles. These patterns are related to the historical locations that the vehicles

have been visited. This task also cannot be done in one step without combing historical locations

from previous steps. Therefore, ANNs whose output depends only on the current input, such

as FNNs, cannot perform tasks such as speech recognition. RNNs can also be seen as an ANN

that have a “memory”, which allows RNNs to store historical information. Note that, in theory,

RNNs can make use of historical information in arbitrarily long sequences, but in practice they

are limited to only a subset of historical information [72]. Given their ability to store historical

information, RNNs can more easily perform time-related tasks such as users’ mobility pattern

predictions compared to traditional ANNs (e.g., FNNs). However, RNNs can require more time

to train since each value of the activation function depends on the series data recorded in RNNs.

In terms of architecture, the key components of a given RNN can be speciﬁed as follows:

• Input signal x

: this signal represents the input data to a given RNN at time t.

• Input weight matrix W

: this matrix represents the strengths of the connections between

the neurons in the input layer and the neurons in the hidden layers.

• Output weight matrix W

out

: this matrix is used to represent the strengths of the connections

between the neurons in the output layer and the neurons in the hidden layers.

• Recurrent weight matrix W : The hidden weight matrix is deﬁned as the recurrent weight

matrix, which captures the strengths of the connections between the neurons in the hidden

layers of the RNN.

• Hidden state s

: this is effectively the hidden state of a neuron in the hidden layer at time t.

The hidden state represents the value of the activation function at time t, which is calculated

based on the previous hidden state s

t−1

and the input at time t. s

can be computed using

different methods for different recurrent neural networks. For most commonly used RNNs,

we have s

= f (W s

t−1

+ W

) where f(x) =

−e

−x

or f(x) = max (0, x). However,

in more elaborate types of RNNs, such as long short-term memory algorithm [85] which

will be detailed in Subsection III-C, each neuron needs to decide what to keep in and what

to erase from the hidden state.

• Output signal: y

is the output of a RNN at time t, representing the output signal.

Clearly, we can see that the basic architecture of RNNs is similar to that of FNNs except for

the generation of the input, output, and recurrent weight matrices. Moreover, the hidden state in

RNNs depends on both current and historical inputs, which enables RNNs to store the historical

information. However, when the architecture of an ANN changes from FNNs to RNNs, traditional

training methods may not be applicable to RNNs. Hence, next, we introduce training methods

suitable for RNNs.

2) Training in Recurrent Neural Networks: In the RNN architecture, the connections be-

tween units will form a directed cycle and, hence, the feedforward gradient descent algorithms

such as backpropagation cannot be directly used. This is due to the fact that the error backprop-

agation pertaining to a backpropagation algorithm requires no cycles in the connections between

the ANN neurons. In consequence, the backpropagation through time (BPTT) algorithm [86] is

more commonly used for training RNNs. The BPTT approach unfolds the recurrent network in

time, by stacking identical copies of it, and redirecting connections within the network to obtain

connections between subsequent copies, as shown in Fig. 6. In consequence, the BPTT algorithm

actually transforms an RNN into an FNN, which is amenable for training by a backpropagation

algorithm. Since BPTT is similar in operation to the backpropagation algorithm, it is signiﬁcantly

faster for training RNNs than general-purpose optimization algorithms such as evolutionary

optimization which some works such as in [87] have used. However, due to the cycle connections

in RNNs, BPTT may get more often trapped in numerous sub-optimal local minima compared

to the gradient descent algorithms used for training FNNs. Moreover, like backpropagation, the

gradient in BPTT is computed based on the complete training set, which may become time

consuming if the size of the training set is very large.

To overcome these drawbacks in BPTT training, real-time recurrent learning (RTRL) [88] can

be used to compute the exact error gradient at every time step, which is suitable for online

learning tasks. In contrast to the BPTT that unfolds RNNs in time, RTRL propagates error

forward in time. From (3), we can see that the gradient value with respect to w at time t is

∂E(t)

∂w

. In RTRL, the update of weight w depends not only on the gradient value at time t but

also on the gradient value at the previous time instant, i.e., w

t+1

= w

− γ

k=0

∂E(k)

∂w

. In RTRL,

the gradient of errors propagates forward in time rather than backward in time as in the BPTT

algorithm and, therefore, there is no need to unfold RNNs as needed by the BPTT algorithm.

However, the time complexity of RTRL is O (N

) where N

is the number of neurons in the

considered RNN. In contrast, BPTT has a time complexity of O (N

G) where G is length of

the input data.

Training the weights in RNNs can also be modeled as a nonlinear global optimization problem.

A target function can be formed to evaluate the error of a particular weight vector. In particular,

the sum-squared-difference between the predictions and the target values speciﬁed in the training

sequence is used to represent the error of the current weight vector. Arbitrary global optimization

techniques may then be used to minimize the sum-squared-difference. The most common global

optimization method for training RNNs is via the use of genetic algorithms [89], especially in

unstructured networks. Other global optimization algorithms may be used to seek a good set

of weights, such as simulated annealing [90] or particle swarm optimization [91]. Such global

optimization algorithms can maximize all weight values at the same time and, hence, can avoid

to reach sub-optimal local minimum. However, the complexity of these algorithms for training

RNNs can be very high, particularly, when an RNN has a large number of neurons. For instance,

when an RNN has 1000 neurons, then a typical global optimization algorithm needs to optimize

1000 × 1000 weight values at the same time, which is clearly more complex than in the case of

BPTT.

In summary, training RNNs can be done using a variety of techniques. Each technique has

its own advantages and disadvantages in terms of the particular learning tasks (e.g., supervised

vs. unsupervised), training data size, training time, and data storage space. Depending on the

scenario and target performance needed, one can select the most suitable RNN training algorithm.

3) Example RNN – Echo State Networks: Next, to shed more light on how RNNs operate,

we introduce a type of RNNs that is conceptually simple and easy to implement, called echo

state networks (ESNs) [92]. Since their inception, ESNs proved to be a highly practical type

of RNNs due to their effective approach for training the neural network [93]. In fact, ESNs

reinvigorated interest in RNNs [94] by making them accessible to wider audiences due to their

apparent simplicity. In ESN, the input weight matrix and hidden weight matrix are randomly

generated without any speciﬁc training. Therefore, ESN needs to only train the output weight

matrix. Moreover, ESNs belong to a class of algorithms in the framework of reservoir computing

[72]. In general, an ANN algorithm is considered part of the framework of reservoir computing

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