Xiaoya Zuo

Bio: Xiaoya Zuo is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Relay & Artificial neural network. The author has an hindex of 3, co-authored 29 publications receiving 36 citations.

Deep Learning Aided Signal Detection in OFDM Systems with Time-Varying Channels *

Abstract: In this paper, we propose a deep learning aided approach for signal detection in orthogonal frequency-division multiplexing (OFDM) systems with time-varying channels. The method simplifies the architecture of OFDM systems by treating OFDM receivers as a black box. We utilize fully-connected deep neural network (FC-DNN) properly and successfully simulate an end-to-end time-varying OFDM system. Compared with two conventional algorithms well-designed to deal with OFDM systems in time-varying environment, the proposed method does not need to estimate channel parameters to detect signals. Simulation results also demonstrate that the trained DNN model has the ability to remember the characteristics of wireless time-varying channels and provide more accurate and robust signal recovery performance.

Analytical Approximation of the Channel Rate for Massive MIMO System With Large But Finite Number of Antennas

Abstract: In practical massive multiple-input–multiple-output (MIMO) system, limited transmitted antennas installed at the base station (BS) cannot guarantee the ideal orthogonality and the theoretical channel rate cannot thus be achieved. In this paper, we consider the approximation of channel rate for the configuration of large but finite number of antennas at BS in a massive MIMO system. A perturbation matrix is introduced to model the non-ideal orthogonality. And then, Taylor expansion of determinant is utilized to derive the closed form of the achievable channel rate. The convergence of the Taylor expansion is theoretically proved and simulated. Numerical results show that our calculation of channel rate can get a better approximation compared with the practical rate, especially in the case of high expansion order and large number of antennas.

RAKE reception for IR-UWB systems in high mobile environments

Abstract: A new method of RAKE reception for Impulse Radio Ultra Wideband (IR-UWB) systems in high mobile environments is described. The conventional multipath diversity reception method for UWB is RAKE receiver, which is usually applied under static environments. In high mobile environments, due to extremely wide bandwidth of UWB signals, frequency components are shifted differently and this makes the pulse-width change in the time domain. Aiming at block-transmission UWB systems, pilot symbols are inserted to estimate the scale change factor. Template signal for RAKE receiver is resampled to compensate the Doppler effect. Simulation results demonstrate that this method could eliminate the Doppler effect and improve the bit error rate (BER) performance. Simulation results also show that the BER performance is not only related to relative velocity, but also related to block length and pilot symbol period. When pilot symbol period increases, the performance degrades. So in the practise block structure should be well designed according to the mobile environments.

A Shifted Constellation of QPSK: Constellation Design and Dynamic Demodulation Threshold Optimization ¶

Abstract: This paper proposes an improved QPSK modulation scheme which is called special QPSK. The key aspect of special QPSK is that the synchronous sequence is hidden in the information bits and does not occupy spectrum resource. In order to implement frame synchronization, correlation acquisition has been considered. Based on this approach, we derive the closed-form expression of expected peak values for the cases signals aligned or not aligned. Then the constellation of the proposed scheme is optimized by minimizing the bit error rate with the constraint for acquisition performance. Finally, at the receiver, we proposed a dynamic threshold demodulation optimization method and solved the optimal dynamic threshold. Simulation results confirm that the signal channel utilization of our proposed special QPSK is better than that of conventional $\pi/4$ -QPSK modulation scheme. At the same time, special QPSK can also achieve a good bit error rate performance which is nearly the same as that of QPSK by using the dynamic threshold demodulation method.

Intelligent Radio Signal Processing: A Survey

Abstract: Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing . This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

Vandermonde-Subspace Frequency Division Multiplexing for Two-Tiered Cognitive Radio Networks

Abstract: Vandermonde-subspace frequency division multiplexing (VFDM) is an overlay spectrum sharing technique for cognitive radio. VFDM makes use of a precoder based on a Vandermonde structure to transmit information over a secondary system, while keeping an orthogonal frequency division multiplexing (OFDM)-based primary system interference-free. To do so, VFDM exploits frequency selectivity and the use of cyclic prefixes by the primary system. Herein, a global view of VFDM is presented, including also practical aspects such as linear receivers and the impact of channel estimation. We show that VFDM provides a spectral efficiency increase of up to 1 bps/Hz over cognitive radio systems based on unused band detection. We also present some key design parameters for its future implementation and a feasible channel estimation protocol. Finally we show that, even when some of the theoretical assumptions are relaxed, VFDM provides non-negligible rates while protecting the primary system.

Secure Relay Selection for Two Way Amplify-and-Forward Untrusted Relaying Networks

Abstract: Relay assisted communication helps to extend the transmission coverage area. In more realistic scenarios, the available relays are untrusted, which try to decode the confidential signals during their main relaying function. Thus, these untrusted relays are regarded as potential eavesdroppers, and the confidential signals are vulnerable. A positive secrecy rate can be achieved at the physical layer without any cryptography, in particular, the mutual interference achieved from two-way relaying network can be used to secure the confidential signals against eavesdroppers attack. In this paper, the relay selection of two-way untrusted relays network is presented, and a directed beamforming is aligned to zero force the transmission at all other non-selected relays. Particularly, we propose a selection scheme based on max–min criterion, which requires an exhaustive search. Then, the lower bound (LB) of the secrecy rate is introduced to minimize the complexity of the selection criterion. The closed forms of the secure outage probability (SOP) for the max–min LB scheme is formulated and compared with other schemes. The simulations results validate the proposed analytical forms, and the performance of max–min LB scheme appears to be quite close to that of the max–min scheme with limited complexity.

Resource Allocation for Energy-Efficient Cellular Communications via Aerial IRS

Abstract: In this paper, we present a resource allocation framework for uplink communication in cellular networks aided with aerial intelligent reflecting surface (IRS). The main focus is on maximizing the energy efficiency by jointly optimizing the transmit powers of the users, the active beamforming at the base stations, and the passive beamforming at the IRS, while maintaining the users’ minimum rates and adhering to the power constraints. The formulated problem is a highly intractable non-convex one, with the optimization variables coupled with each other in an intricate manner. To tackle this, an iterative solution based on alternating techniques is proposed. In particular, the transmit beamforming and the phase-shift matrix are obtained by minimum mean square error and semidefinite relaxation techniques, respectively. Numerical results are provided and show that using aerial IRS has remarkable advantages compared to the system operation with conventional aerial relaying. In particular, significant energy efficiency gains are achieved when optimal transmit power and a large number of reflecting elements are implemented.

Signal Detection in Uplink Time-Varying OFDM Systems Using RNN With Bidirectional LSTM

Abstract: In this letter, we propose a deep learning-assisted approach for signal detection in uplink orthogonal frequency-division multiplexing (OFDM) systems over time-varying channels. In particular, we utilize a recurrent neural network (RNN) with bidirectional long short-term memory (LSTM) architecture to achieve signal detection. In addition, with the help of convolutional neural network (CNN) and batch normalization (BN), a new network structure CNN-BN-RNN Network (CBR-Net) is proposed to obtain better performance. The sequence feature information of the OFDM received signal is extracted from big data to successfully train a RNN-based signal detection model, which simplifies the architecture of OFDM systems and can adapt to the change of channel paths. Simulation results also demonstrate that the trained RNN model has the ability to recall the characteristics of wireless time-varying channels and provide accurate and robust signal recovery performance.