Search

Non-orthogonal multiple access (NOMA) has great potential to implement the fifth-generation (5G) requirements of wireless communication. For a NOMA traditional detection method, successive interference cancellation (SIC) plays a vital role at the receiver side for both uplink and downlink transmission. Due to the complex multipath channel environment and prorogation of error problems, the traditional SIC method has a limited performance. To overcome the limitation of traditional detection methods, the deep-learning method has an advantage for the highly efficient tool. In this paper, a deep neural network which has bi-directional long short-term memory (Bi-LSTM) for multiuser uplink channel estimation (CE) and signal detection of the originally transmitted signal is proposed. Unlike the traditional CE schemes, the proposed Bi-LSTM model can directly recover multiuser transmission signals suffering from channel distortion. In the offline training stage, the Bi-LTSM model is trained using simulation data based on channel statistics. Then, the trained model is used to recover the transmitted symbols in the online deployment stage. In the simulation results, the performance of the proposed model is compared with the convolutional neural network model and traditional CE schemes such as MMSE and LS. It is shown that the proposed method provides feasible improvements in performance in terms of symbol-error rate and signal-to-noise ratio, making it suitable for 5G wireless communication and beyond. [ABSTRACT FROM AUTHOR]

An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication. [ABSTRACT FROM AUTHOR]

A combining of MIMO signal processing with OFDM is regarded as a promising solution of enhancing the performance of next generation wireless system. Therefore, in the paper, an OFDM-based wireless system employing layered space-time architecture is considered for a high-rate transmission. With an emphasis on a preamble design for multi-channel separation, we address a channel estimation based on the time-domain windowing and its imperfectness in MIMO-OFDM system. By properly designing each preamble for multiple antennas to be orthogonal in the time domain, the channel estimation can be executed based on semi-blind processing in the case of more than two transmitting antennas. And also we evaluate PAR performance in the MIMO-OFDM system using the SLM and PTS approaches. The investigated SLM and PTS schemes for MIMO-OFDM signals select the transmitted sequence with lowest average PAR over all transmitting antennas and retrieve the side information very accurately at the expense of a slight degradation of the PAR performance. The low probability of false side information can improve the overall detection performance of the MIMO-OFDM system with erroneous side information compared to the ordinary SLM and PTS approaches, respectively. Also, we provide closed form of the average BER performance in MIMO-OFDM system using analytic approach. [ABSTRACT FROM AUTHOR]

This paper proposes an improved pilot-based algorithm for joint estimation of carrier frequency offset (CFO) and sampling frequency offset (SFO) in ultrawideband orthogonal frequency division multiplexing (UWBOFDM) systems with cyclic delay diversity. By proper selection of cyclic delay time and a frequency band with maximum channel power, a joint estimation of CFO and SFO is derived. Via computer simulation and analysis, the proposed frequency estimator is shown to benefit from the properly selected delay parameter and frequency band. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved and low-cost integer frequency offset (IFO) estimation method by partitioning pilot symbols effectively in an orthogonal frequency division multiplexing based digital radio mondiale (DRM) system. To this end, the time reference cell (TRC) symbol for frequency estimation is grouped into a number of pilot clusters, so that the TRC subcarriers in each cluster are closely spaced to show approximately frequencynonselective characteristics. The performance of the proposed IFO estimator is compared with that of the conventional estimator, and shows that the proposed technique can effectively achieve lower estimation errors in frequency offset estimation as well as can be implemented with reduced computational complexity. [ABSTRACT FROM AUTHOR]