Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems with Few-Bit ADCs

This paper is concerned with channel estimation in MIMO systems with few-bit ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel estimator obtained in closed-form is not an optimal solution. We first consider a deep neural network (DNN) and train it as a non-linear MMSE channel estimator for few-bit MIMO systems. We then present a first attempt to use DNN in optimizing the training signal and the MMSE channel estimator concurrently. Specifically, we propose an autoencoder with a specialized first layer, whose weights embed the training signal matrix. Consequently, the trained autoencoder prompts a new training signal design that is customized for the MIMO channel model under consideration.Comment: 5 pages, 3 figures, to appear in IEEE Signal Processing Letter

Energy harvesting based two-way full-duplex relaying network over a Rician fading environment: performance analysis

Full-duplex transmission is a promising technique to enhance the capacity of communication systems. In this paper, we propose and investigate the system performance of an energy harvesting based two-way full-duplex relaying network over a Rician fading environment. Firstly, we analyse and demonstrate the analytical expressions of the achievable throughput, outage probability, optimal time switching factor, and symbol error ratio of the proposed system. In the second step, the effect of various parameters of the system on its performance is presented and investigated. In the final step, the analytical results are also demonstrated by Monte Carlo simulation. The numerical results proved that the analytical results and the simulation results agreed with each other.Web of Science68112311

Multisource power splitting energy harvesting relaying network in half-duplex system over block Rayleigh fading channel: System performance analysis

Energy harvesting and information transferring simultaneously by radio frequency (RF) is considered as the novel solution for green-energy wireless communications. From that point of view, the system performance (SP) analysis of multisource power splitting (PS) energy harvesting (EH) relaying network (RN) over block Rayleigh-fading channels is presented and investigated. We investigate the system in both delay-tolerant transmission (DTT), and delay-limited transmission (DLT) modes and devices work in the half-duplex (HD) system. In this model system, the closed-form (CF) expressions for the outage probability (OP), system throughput (ST) in DLT mode and for ergodic capacity (EC) for DTT mode are analyzed and derived, respectively. Furthermore, CF expression for the symbol errors ratio (SER) is demonstrated. Then, the optimal PS factor is investigated. Finally, a Monte Carlo simulation is used for validating the analytical expressions concerning with all system parameters (SP).Web of Science81art. no. 6

Rateless codes-based secure communication employing transmit antenna selection and harvest-to-jam under joint effect of interference and hardware impairments

In this paper, we propose a rateless codes-based communication protocol to provide security for wireless systems. In the proposed protocol, a source uses the transmit antenna selection (TAS) technique to transmit Fountain-encoded packets to a destination in presence of an eavesdropper. Moreover, a cooperative jammer node harvests energy from radio frequency (RF) signals of the source and the interference sources to generate jamming noises on the eavesdropper. The data transmission terminates as soon as the destination can receive a sufficient number of the encoded packets for decoding the original data of the source. To obtain secure communication, the destination must receive sufficient encoded packets before the eavesdropper. The combination of the TAS and harvest-to-jam techniques obtains the security and efficient energy via reducing the number of the data transmission, increasing the quality of the data channel, decreasing the quality of the eavesdropping channel, and supporting the energy for the jammer. The main contribution of this paper is to derive exact closed-form expressions of outage probability (OP), probability of successful and secure communication (SS), intercept probability (IP) and average number of time slots used by the source over Rayleigh fading channel under the joint impact of co-channel interference and hardware impairments. Then, Monte Carlo simulations are presented to verify the theoretical results.Web of Science217art. no. 70

Multi-source in DF cooperative networks with the PSR protocol based full-duplex energy harvesting over a Rayleigh fading channel: performance analysis

Due to the tremendous energy consumption growth with ever-increasing connected devices, alternative wireless information and power transfer techniques are important not only for theoretical research but also for saving operational costs and for a sustainable growth of wireless communications. In this paper, we investigate the multi-source in decode-and-forward cooperative networks with the power splitting protocol based full-duplex energy harvesting relaying network over a Rayleigh fading channel. In this system model, the multi-source and the destination communicate with each other by both the direct link and an intermediate helping relay. First, we investigate source selection for the best system performance. Then, the closed-form expression of the outage probability and the symbol error ratio are derived. Finally, the Monte Carlo simulation is used for validating the analytical expressions in connection with all main possible system parameters. The research results show that the analytical and simulation results matched well with each other.Web of Science68327526

DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.Comment: 6 pages, 8 figures, submitted for publication. arXiv admin note: text overlap with arXiv:2008.0375

Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.Comment: 12 pages, 10 figure