Symbols detection for frequency-selective V-BLAST OFDM systems

As the combining form of the orthogonal frequency-division multiplexing (OFDM) technique and the vertical Bell Labs layered space-time (V-BLAST) architecture, the V-BLAST OFDM system can better meet the demand of next-generation (NextG) broadband mobile wireless multimedia communications. The symbols detection problem of the V-BLAST OFDM system is investigated under the frequency-selective fading environment. The joint space-frequency demultiplexing operation is proposed in the V-BLAST OFDM system. Successively, one novel half-rate rotational invariance joint space-frequency coding scheme for the V-BLAST OFDM system is proposed. By elegantly exploiting the above rotational invariance property, we derive one direct symbols detection scheme without knowing channels state information (CSI) for the frequency-selective V-BLAST OFDM system. Extensive simulation results demonstrate the validity of the novel half-rate rotational invariance joint space-frequency coding scheme and the performance of the direct symbols detection scheme.

Training-based symbol detection with temporal convolutional neural network in single-polarized optical communication system

In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication system.Many DL-based methods have been applied to such systems to improve bit-error performance.Referring to the speech-to-text method of automatic speech recognition,this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications modes.To realize this detection method,we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal,which specifically integrates most modules of the receiver.Meanwhile,we adopt three training approaches for different signal-to-noise ratios.We also apply a parametric rectified linear unit to enhance the noise robustness of the proposed network.According to the simulation experiments,the biterror-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.

Deep Learning-Based Symbol Detection for Time-Varying Nonstationary Channels

The highly dynamic channel(HDC)in an extremely dynamic environment mainly has fast timevarying nonstationary characteristics.In this article,we focus on the most difficult HDC case,where the channel coherence time is less than the symbol period.To this end,we propose a symbol detector based on a long short-term memory(LSTM)neural network.Taking the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better performance.In addition,using the basic expansion model(BEM)as the preprocessing unit significantly reduces the number of neural network parameters.Finally,the simulation part uses the highly dynamic plasma sheath channel(HDPSC)data measured from shock tube experiments.The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

A VERTICAL LAYERED SPACE-TIME CODE AND ITS CLOSED-FORM BLIND SYMBOL DETECTION

Vertical layered space-time codes have demonstrated the enormous potential to accommodate rapid flow data. Thus far, vertical layered space-time codes assumed that perfect estimates of current channel fading conditions are available at the receiver. However, increasing the number of transmit antennas increases the required training interval and reduces the available time in which data may be transmitted before the fading coefficients change. In this paper, a vertical layered space-time code is proposed. By applying the subspace method to the layered space-time code, the symbols can be detected without training symbols and channel estimates at the transmitter or the receiver. Monte Carlo simulations show that performance can approach that of the detection method with the knowledge of the channel.

Symbol Detection Based on Temporal Convolutional Network in Optical Communications

Deep learning(DL)is one of the fastest developing areas in artificial intelligence,it has been recently gained studies and application in computer vision,automatic driving,automatic speech recognition,and communication.This paper uses the DL method to design a symbol detection algorithm in receiver for optical communication systems.The proposed DL based method is implemented by a non-causal temporal convolutional network(ncTCN),which is a convolutional neural network and appropriate for sequence processing.Meanwhile,we adopt three methods to realize the training process for multiple signal-to-noise ratios of the AWGN channel.Furthermore,we apply two nonlinear activation functions for the noise robustness to the proposed ncTCN.Without losing generality,we apply the ncTCN-based receiver to the 16-ary quadrature amplitude modulation optical communication system in the simulation experiment.According to the experiment results,the proposed method can obtain some bit error rate performance gain compared to some conventional receivers.

Joint channel estimation and symbol detection for space-time block code

The simplified joint channel estimation and symbol detection based on the EM (expectation-maximization) algorithm for space-time block code (STBC) are proposed. By assuming channel to be invariant within only one STBC word and utilizing the orthogonal structure of STBC, the computational complexity and cost of this algorithm are both very low, so it is very suitable to implementation in real systems.

Multiple symbol differential detection based on sphere decoding for unitary space-time modulation

Recently, a multiple symbol differential （MSD） sphere decoding （SD） algorithm for unitary spacetime modulation over quasi-static channel has been proved to achieve the performance of maximumlikelihood （ML） detection with relatively low complexity. However, an error floor occurs if the algorithm is applied over rapid-fading channels. Based on the assumption of continuous fading, a multiple symbol differential automatic sphere decoding （MSDASD） algorithm is developed by incorporating a recursive form of an ML metric into automatic SD （ASD） algorithm. Furthermore, two algorithms, termed as MSD approximate ASD （MSDAASD） and MSD pruning ASD （MSDPASD）, are proposed to reduce computational complexity and the number of comparisons, respectively. Compared with the existing typical algorithms, i.e., multiple symbol differential feedback detection （MS-DFD） and noncoherent sequence detection （NSD）, the performance of the proposed algorithms is much superior to that of MS-DFD and a little inferior to that of NSD, while the complexity is lower than that of MS-DFD in most cases and significantly lower than that of NSD.