7 research outputs found
Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems
The goal of 6G communication networks requires higher transmission speeds, tremendous data processing, and low-latency communication. Orthogonal frequency-division multiplexing (OFDM), which is widely utilized in 5G communication systems, may be a viable alternative for 6G. It significantly reduces inter symbol interference (ISI) in the frequency-selective fading environment. Channel estimation is critical in OFDM to optimize system performance. Deep learning has been employed as an appealing alternative for channel estimation and signal detection in OFDM-based communication systems due to its better potential for feature learning and representation. In this study, we examine the deep neural network (DNN) layers created from long-short term memory (LSTM) for detecting the signals by learning the received signal as well as channel information. We investigate the performance of the system under various conditions. The simulation results show that the signal bit error (SER) is equivalent to and better than that of the minimum mean squared error (MMSE) and least square (LS) methods
Deep Learning for Over-the-Air Non-Orthogonal Signal Classification
Non-cooperative communications, where a receiver can automatically
distinguish and classify transmitted signal formats prior to detection, are
desirable for low-cost and low-latency systems. This work focuses on the deep
learning enabled blind classification of multi-carrier signals covering their
orthogonal and non-orthogonal varieties. We define two signal groups, in which
Type-I includes signals with large feature diversity while Type-II has strong
feature similarity. We evaluate time-domain and frequency-domain convolutional
neural network (CNN) models in simulation with wireless channel/hardware
impairments. Simulation results reveal that the time-domain neural network
training is more efficient than its frequency-domain counterpart in terms of
classification accuracy and computational complexity. In addition, the
time-domain CNN models can classify Type-I signals with high accuracy but
reduced performance in Type-II signals because of their high signal feature
similarity. Experimental systems are designed and tested, using software
defined radio (SDR) devices, operated for different signal formats to form full
wireless communication links with line-of-sight and non-line-of-sight
scenarios. Testing, using four different time-domain CNN models, showed the
pre-trained CNN models to have limited efficiency and utility due to the
mismatch between the analytical/simulation and practical/real-world
environments. Transfer learning, which is an approach to fine-tune learnt
signal features, is applied based on measured over-the-air time-domain signal
samples. Experimental results indicate that transfer learning based CNN can
efficiently distinguish different signal formats in both line-of-sight and
non-line-of-sight scenarios with great accuracy improvement relative to the
non-transfer-learning approaches
Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends
Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output (CM-MIMO) systems. This introduces the concept of intelligent massive MIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO-enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends