Blind Modulation Classification over Fading Channels Using Expectation-Maximization

Soltanmohammadi, Erfan; Naraghi-Pour, Mort

doi:10.1109/lcomm.2013.070913.130752

Cited by 46 publications

(19 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach is robust for arbitrary cases of the co-frequency signals with different roll-off factor, sampling number per symbol, and power ratio. Furthermore, the derived cumulants in this paper can be applied in blind modulation classification of a single signal for the needless of prior knowledge and estimation of the power ratio of the mixed signal for the power control in cellular systems [26][27][28]. Further discussions could be made in the future.…”

Section: Discussionmentioning

confidence: 99%

An identification technique for the co-frequency mixed communication signals based on cumulants

Duan

Zhan

Liang

2018

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Identification of the co-frequency interference is a common problem in wireless digital communication systems. The high-order statistics (HOS) feature based on cumulants is a widely adopted solution. However, prior knowledge of the timings and the symbol period is required to extract the cumulants, which is quite difficult in a blind environment. In order to solve this problem, this paper proposed a more general calculation for the cumulants based on the over-sampled data with consideration of the inter-symbol interference (ISI). The generalized theoretical value of the cumulants is deduced in this paper. Besides, the HOS feature based on the generalized cumulants for identification is found to be robust with different roll-off factor and sampling number per symbol. Computer simulations are performed to prove the validity of the proposed method.

show abstract

Section: Discussionmentioning

confidence: 99%

An identification technique for the co-frequency mixed communication signals based on cumulants

Duan

Zhan

Liang

2018

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The main idea is that the receiver tries blindly to identify the transmission parameters. For instance in [36,37], different techniques for blind classification of the modulation format were studied, and in [38][39][40], different schemes for blindly identifying the channel code were considered. When it is known that the channel code has been chosen from a set of predefined candidate set, the receiver can use this a priori information for achieving a better blind channel code identification.…”

Section: Research Related To Signaling Overheadmentioning

confidence: 99%

Improving the Efficiency of Control Signaling in Wireless Multiple Access Systems

Moosavi¹

2013

View full text Add to dashboard Cite

To my lovely wife, my beloved parents, and my dear sisters; without them I would not be here."Even if you are on the right track, you'll get run over if you just sit there." Will Rogers AbstractPrior to the transmission of payload data in any multiple access system, there is generally a need to send control information such as scheduling assignments, transmission parameters and HARQ acknowledgments. This process is called control signaling and has a significant impact on the overall system performance. This dissertation considers different aspects of control signaling and proposes some novel schemes for improving it. The dissertation is split into two parts where in the first part the focus is on the transmission of scheduling assignments, and in the second part the focus is on improving the "blind decoding" process that is used to achieve adaptive coding and modulation in transmission of control information.More specifically, in the first part of the dissertation we first compare the two conventional schemes for control signaling using extensive system simulations. In doing so, we use practical assumptions on the scheduling algorithm as well as on the compression and transmission of the scheduling information. We then provide two schemes for reducing the amount of control signaling that concerns the transmission of scheduling assignments. The first scheme, which is reminiscent of source coding with side information, uses the knowledge that each user has about its own channel condition to compress the scheduling information more effectively. The second scheme uses the fact that in wireless multiple access systems, a user with a given channel condition can in principle decode the data intended to the users that have weaker channels. Therefore, the idea is to send the scheduling information of different terminals in a differential manner starting from the user with the weakest channel and letting all the terminals overhear the transmission of one another. Finally, in the last section of this part we use some of the recent results in information theory to form a general framework for the comparison of different control signaling schemes. We formulate an optimization problem that for a given desired error probability finds the minimum required number of channel uses for a given signaling scheme.In the second part of the thesis, we propose three schemes for reducing the complexity of the blind decoding process. The first one is a novel scheme for fast blind identification of channel codes. More precisely, we propose an efficient algorithm that for a given sequence of received symbols and a given linear channel code, finds the posterior probability that all the parity check relations of the code are satisfied. We then use this quantity to perform a sequential statistical hypotheses test that vii reduces the computational complexity of blind decoding. The idea in the second scheme is to broadcast a control message prior to the transmission of control information to instruct only a subset of the terminals (ideally only ...

show abstract

“…Therefore, the likelihood based method draws researchers' attention when the CIR and noise power are unknown in the multipath fading channel. Recently, lots of scholars used the method of expectation maximization (EM) to estimate the CIR and noise power in flat fading channel in view of MPSK and MQAM signals and then they used QHLRT to identify modulation [22][23][24].Through these analysis and studies, we found EM-QHLRT can increase the identification rate effectively and make us come up with some new ideas about modulation identification in fading channel. However, EM-QHLRT is only suitable for flat fading channels, but not for underwater acoustic multipath channels.…”

Section: Introductionmentioning

confidence: 98%

Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

et al. 2020

View full text Add to dashboard Cite

This paper presents a likelihood-based algorithm for identifying different phase shift keying (PSK) modulations, i.e., BPSK, QPSK, and 8PSK. This algorithm selects the modulation type that maximizes a loglikelihood function that is based on the known original constellation associated with the constellation of the received signals for the candidate modulation types. However, there are two problems in non-cooperative underwater acoustic Multiple Phase Shift Keying (MPSK) modulation identification based on the likelihood method. One is the original constellation, which as prior information is unknown. The other is the underwater acoustic multipath channel makes the constellation distort seriously. In this paper, we solved these problems by combining sparse bayesian learning (SBL) with expectation maximization (EM). The specific steps are as follows. Firstly, blind channel equalization can be achieved by channel impulse response (CIR), which is estimated by sparse bayesian learning in single input multi output (SIMO) underwater acoustic channel. Subsequently, we used expectation maximization to compensate amplitude attenuation and phase offset, as the original constellation of MPSK is unknown. Finally, modulation can be successfully identified by the Quasi Hybrid Likelihood Ratio Test (QHLRT). The simulation results show that the channel estimation method based on SBL can eliminate the influence of channel effectively, and the EM algorithm can make the received constellation converge to the preset constellation in the case of unknown original transmit constellation, which effectively solves these two problems. We use the proposed SBL-EM-QHLRT method to achieve an identification rate of more than 95% in underwater acoustic multipath channels with Signal to Noise Ratio (SNR) higher than 15 dB, which provides a new idea for modulation identification of non-cooperative underwater acoustic MPSK.

show abstract

Blind Modulation Classification over Fading Channels Using Expectation-Maximization

Cited by 46 publications

References 12 publications

An identification technique for the co-frequency mixed communication signals based on cumulants

An identification technique for the co-frequency mixed communication signals based on cumulants

Improving the Efficiency of Control Signaling in Wireless Multiple Access Systems

Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

Contact Info

Product

Resources

About