Machine Learning for PHY and MAC Layers

Nandana Rajatheva [email protected] Machine Learning for PHY and MAC Motivation Motivation

§ Communications technologies in beyond 5G systems are expected to be more complicated than ever, with extremely complex and sophisticated interconnected components to design and optimize § Machine learning (ML) based data-driven approaches to complement traditional model-driven algorithms are gaining interest § Main motivations for using ML based data-driven approaches in communications signal processing:

1) Modeling deficiencies

§ The traditional model-driven approach is optimal when the underlying models are accurate. § When the system operates in complex propagation environments and has unknown channel properties, or when it has hardware impairments or non-linearities, there is a model deficiency in capturing the actual system properties. § The learning capability in ML can be utilized in such unknown and varying operating conditions, using a data-driven approach to model and learn the complex system properties. 2) Algorithmic deficiencies

§ Model-driven approaches often lead to highly complex algorithms in terms of computational complexity, run-time, and the acquisition of necessary side information. § These algorithmic deficiencies can be potentially overcome by ML techniques, by utilizing their learning capability to learn fast and effective input-output relationships. § ML data-driven approaches will be more important in designing signal processing algorithms for latency-critical applications. § Joint optimization of different blocks in a communications system, which is challenging due to algorithmic complexity, can also be efficiently performed using ML approaches. Machine Learning Applications: Some Examples

Some examples and potential future research directions of ML for PHY and MAC layers:

§ End-to-end learning in communications systems § Channel decoding § Channel estimation and detection § Resource allocation in communications systems End-to-End Learning Autoencoder based End-to-End Learning

§ In conventional communications systems, transmitter and receiver are divided into a series of connected processing blocks and optimized individually.

§ Individual optimization may not achieve global end-to-end optimization. § Also, the block structure introduces higher processing complexities, processing delays and control overheads.

§ End-to-end learning using ML allows joint optimization of the transmitter and receiver components in a single process instead of having the artificial block structure, learning to minimize the end-to-end message reconstruction error [1] § Simple and straight forward signal processing, can be trained for a given network configuration and channel model

Conventional communications system model

Autoencoder performance in comparison to Equivalent autoencoder implementation conventional BPSK and QPSK schemes Extensions of Autoencoder based End-to-End Learning: § Autoencoder for SISO, MIMO systems with interference channel and flat fading conditions [2,3] § Over-the-air implementation of autoencoder based system using off-the-shelf software defined radios (SDRs) with mechanisms for continuous data transmission and receiver synchronization [4] Model Free End-to-End Communications

§ Extends the end-to-end learning when the channel response is unknown or cannot be easily modelled in a closed form analytical expression. § Using an adversarial approach for channel approximation and information encoding to learn a jointly optimum solution to perform both tasks [5] § Conditional generative adversarial net (GAN) implementation to represent channel effects and to bridge the transmitter and receiver deep neural networks (DNNs), enabling gradient backpropagation [6]

§ Overcomes the shortcoming of neural network-based autoencoders requiring a differentiable channel model to train the system and enables training with an unknown channel model or with non-differentiable components. § Receiver training using the true gradient, and transmitter training using an approximation of the gradient [7] Channel Decoding ML based Channel Decoding

§ ML based channel decoding is of major interest to reduce the processing complexity of conventional channel decoding algorithms, given the idea of DNNs being able to perform one-shot decoding. § “Curse of dimensionality” is a key challenge in channel coding. Even a short code length has too many different codewords which makes it infeasible to fully train a neural network in practice. § The DNN based channel decoding algorithm in [8] shown to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both structured and random code families for short codeword lengths. § For structured codes, neural network is shown to generalize to unseen codewords during training, showing their capability of learning from a decoding algorithm rather than a simple classifier. § A deep learning method for improving the belief propagation (BF) algorithm is presented in [9]. § A “soft” Tanner graph is obtained assigning weights to the edges of the Tanner graph that represent the given linear code, and the edges are trained using DL techniques. § The model is independent of the performance of the transmitted codeword, thus can use a single codeword for training instead of exponential number of codewords. Channel Estimation and Detection ML based Channel Estimation and Detection

§ Channel estimation, equalization, and signal detection are three inter-related crucial tasks in achieving channel capacity in wireless communication systems.

§ Conventionally, implemented and optimized individually.

§ ML based approaches allow individual optimization of these blocks, as well as the optimal joint design which is a substantially complicated task in conventional systems. ML based Channel Estimation

§ ML techniques used in image processing, computer vision and natural language processing are adapted in channel estimation, where correlations among time, frequency and space of channels are exploited in learning. § Image super-resolution (SR) and image restoration (IR) approach for channel interpolation and noise suppression, treating the time-frequency response of a fading channel as a low resolution 2D image [10]

Proposed DL-based channel estimation in [8] Channel estimation MSE in for SUI5 channel model [8] Joint Channel Estimation and Detection

§ Deep learning based joint channel estimation and signal detection algorithm for OFDM systems shown to have a better channel estimation performance with a reduced signaling overhead (fewer training pilots and no cyclic prefix) and capable of dealing with nonlinear clipping noise [11]

System model in [11] Performance comparison with different pilot lengths [11] Channel Estimation and Detection

Challenges and Potential Research Directions

§ Overcoming the challenge of offline model training which causes high-computational complexity and performance degradation due to the discrepancies between the real channels and training channels

§ Online fully complex extreme learning machine (C-ELM)-based channel estimation and equalization scheme with a single hidden layer feedforward network (SLFN) proposed in [12]. Shown to have a better channel estimation performance than conventional approaches for OFDM systems against fading channel conditions and nonlinear distortions from a high-power amplifier.

§ Constructing training data to match real-world channel conditions. § Using transfer learning approaches to account for the difference between the training data and real-world data. Resource Allocation ML based Resource Allocation

§ Radio resource management is important for improved performance and efficiency in communications systems. § Often involves highly complex optimization-based techniques or exhaustive/greedy approaches to find the optimal resource allocations. § ML based techniques can reduce the processing complexity of resource allocation tasks by learning the optimal resource allocations in a data-driven manner. § Examples: Deep learning-based massive MIMO beamforming optimization Deep learning-based power control for cellular and cell-free massive MIMO Joint beam forming and power control

§ Potential research directions: reinforcement learning frameworks and transfer learning techniques to learn, adapt, and optimize for varying conditions over time to perform resource management tasks with minimal supervision. Deep Learning based Beamforming Optimization

§ Integrated machine learning and coordinated beamforming solution approach to enable highly-mobile mmWave applications with a reliable coverage, low latency, and negligible training overhead [13] § The deep learning model learns and predicts the optimum beamforming vectors at the BSs, using a single pilot sequence from user, received using omni or quasi-omni beam patterns. § Simulation results have shown that the proposed approach achieves higher rates in high- mobility large-array scenarios compared to the traditional beamforming solutions. Learning and prediction phases of the proposed deep learning implementation in [13] Deep Learning based Beamforming Optimization

§ Convolutional neural network (CNN) framework for joint design of precoder and combiners in a single user MIMO system using an imperfect channel matrix, in order to maximize the user spectral efficiency [14]

Joint Beamforming Optimization, Power Control and Interference Coordination § Deep reinforcement learning framework to solve the non-convex optimization problem of maximizing SINR for joint beamforming, power control and interference coordination [15] Deep Learning based MIMO Power Control

§ Power control in cellular and cell-free massive MIMO improves the spectral efficiency of the network, yet is a complex task given the network complexity and involves complex optimization or exhaustive search algorithms. § DNNs can be used to learn the mapping between UE positions and optimal power allocations obtained from solving an optimization problem, and to predict the power profiles of a set of new UE positions[16] § Improved complexity-performance trade-off of power allocation compared to traditional optimization-oriented methods § Max-min power control for cell-free massive MIMO in [17] also uses a DNN to learn the input (UE positions/ channel statistics) and output power allocation mapping of users in cell-free network References

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