High-Performance Beamformer and Low-Complexity Detector for DF-Based Full-Duplex MIMO Relaying Networks

In this paper, we consider a full.duplex multiple.input multiple.output(MIMO) relaying network with the decode.and.forward(DF) protocol. Due to the full.duplex transmissions, the self.interference from the relay transmitter to the relay receiver degrades the system performance. We thus propose an iterative beamforming structure(IBS) to mitigate the self.interference. In this method, the receive beamforming at the relay is optimized to maximize the signal.to.interference.plus.noise.ratio(Max.SINR), while the transmit beamforming at the relay is optimized to maximize the signal.to.leakage.plusnoise.ratio(Max.SLNR). To further improve the performance, the receive and transmit beamforming matrices are optimized between Max.SINR and Max.SLNR in an iterative manner. Furthermore, in the presence of the residual self.interference, a low.complexity whitening.filter(WF) maximum likelihood(ML) detector is proposed. In this detector, a WF is designed to transform a colored interference.plus.noise to a white noise, while the singular value decomposition is used to convert coupled spatial subchannels to parallelindependent ones. From simulations, we find that the proposed IBS performs much better than the existing schemes. Also, the proposed low.complexity detector significantly reduces the complexity of the conventional ML(CML) detector from exponential time(an exponential function of the number of the source transmit antennas) to polynomial one while achieving a slightly better BER performance than the CML due to interference whitening.

Diversity order of decode-and-forward MIMO relaying with transmit antenna selection

This article analyzes the diversity order of several proposed schemes, where the transmit antenna selection （TAS） strategies are combined with low-complexity decode-and-forward （DF） protocols in the multiple-input multiple-output （MIMO） relaying scenario. Although antenna selection is a suboptimal form of beamforming, it enjoys the advantages of tractable optimization and low feedback overhead. Specifically, this article proposes schemes that combine TAS strategies with fixed decode-and-forward （FDF） and selection decode-and-forward （SDF） protocols. Following that, the asymptotic expressions of outage probabilities are derived and the diversity order of the proposed schemes analyzed. These kinds of combination of transmit antenna selection strategies and low-complexity decode-and-forward protocols can achieve partial diversity order in the MIMO relaying scenario. The numerical simulations verify the analysis.

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.