Computationally Efficient Maximum Likelihood Sequence Estimation and Activity Detection for $M$-PSK Signals in Unknown Flat Fading Channels

Gazor, Saeed; Derakhtian, Mostafa; Tadaion, Aliakbar

doi:10.1109/lsp.2010.2062891

Cited by 19 publications

(22 citation statements)

References 9 publications

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“…This particularity has been exploited in this article and allows us to make use of the algorithm proposed in [34]. For high SNRs between the BS and the receiver, the data symbols are drawn from a large number of constellation points such as 16-QAM or 64-QAM.…”

Section: Discussion On the Case Where The Data Symbols Are Psk Signalsmentioning

confidence: 99%

“…We provide the data {Y ν } ν∈P k to the procedure in [34] and obtain the ML estimates of the symbols c ν hereafter denoted by c ν . In this case, the ML estimate of H k is given by…”

Section: Identification Algorithmmentioning

confidence: 99%

“…Substituting (16) and c ν obtained from the procedure in [34] in (?? ), the ML estimate of σ 2 under H 1 becomes…”

Section: Identification Algorithmmentioning

confidence: 99%

“…This makes it computable as summarized in Algorithm 1 without the knowledge of these two lasts parameters. The proposed algorithm performs an N-point Fast Fourier Transform (FFT) on M frames, and then detects |P| PSK symbol by the algorithm proposed in [34]. Taking into account the complexity of each term in (20), the overall Computational Complexity (CC) is MN(2 + log 2 N) + k |P k | log 2 |P k | + |P| for M frames using the optimal algorithm in [34] and MN(2 + log 2 N) + 2|P| using the suboptimal algorithm in [35].…”

Section: Identification Algorithmmentioning

confidence: 99%

“…The proposed algorithm performs an N-point Fast Fourier Transform (FFT) on M frames, and then detects |P| PSK symbol by the algorithm proposed in [34]. Taking into account the complexity of each term in (20), the overall Computational Complexity (CC) is MN(2 + log 2 N) + k |P k | log 2 |P k | + |P| for M frames using the optimal algorithm in [34] and MN(2 + log 2 N) + 2|P| using the suboptimal algorithm in [35]. For example in [24], authors have considered a case with M = 24, 30 pilots pairs The desired system is active; 8: else if T (Y, P) < η det then 9: The desired system is inactive; 10: end if and N = 2048.…”

Section: Identification Algorithmmentioning

confidence: 99%

See 4 more Smart Citations

GLR test for OFDM system identification using pilot tones pattern

Oularbi

Gazor

Houcke

et al. 2013

J Wireless Com Network

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In the context of cognitive network architecture, an opportunistic cognitive receiver must identify the present active networks. In this article, we propose an efficient algorithm for the identification of OFDM networks exploiting the pilot patterns used in these standards which are prescribed uniquely by their underlying standards. These pilots are inserted for the channel estimation and synchronization between the base stations and their users. The proposed generalized likelihood ratio test (GLRT) not only allows a cognitive observer to detect the active networks by analyzing the observed signals but also performs channel estimation, time-frequency synchronization as well as estimation of the noise variance. These informations are of a great interest for Quality of Service estimation in the purpose of an association with the base station. The proposed solution is applicable to the existing standards (e.g., LTE, WiMAX, WiFi), doesn't require any signaling overhead to be embedded on the pilot tones, is computationally inexpensive and only requires to know the pilot patterns. An other GLRT is proposed as a pre-detector which ignores the pilot information and allows to reduce the computational cost of the system for the cases where a large number of patterns/systems are to be tested.

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Section: Discussion On the Case Where The Data Symbols Are Psk Signalsmentioning

confidence: 99%

“…We provide the data {Y ν } ν∈P k to the procedure in [34] and obtain the ML estimates of the symbols c ν hereafter denoted by c ν . In this case, the ML estimate of H k is given by…”

Section: Identification Algorithmmentioning

confidence: 99%

“…Substituting (16) and c ν obtained from the procedure in [34] in (?? ), the ML estimate of σ 2 under H 1 becomes…”

Section: Identification Algorithmmentioning

confidence: 99%

Section: Identification Algorithmmentioning

confidence: 99%

Section: Identification Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

GLR test for OFDM system identification using pilot tones pattern

Oularbi

Gazor

Houcke

et al. 2013

J Wireless Com Network

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Application of Maximum Entropy Theorem in Channel Estimation

et al. 2020

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Hybrid likelihood ratio bit-rate detectors for variable-gain multiple-access systems in unknown noise variance

2012

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We assume a linear equidistance antenna array as the receiver for a fixed frame-length multipleaccess system which employs variable-gain receiver power and repetition encoding. We propose a robust Maximum A posteriori Probability (MAP) Blind Bit-Rate Detector (BBRD). This detector considers the Rate Detection (RD) as a multi-hypothesis test and maximizes the Likelihood Functions (LF)s to find the true bit-rate, while the complex amplitude of the received signal, the noise variance and the direction of arrival are unknown parameters. First, assuming that the location parameter is known and the information sequence are independent and uniformly distributed random variables, we propose a Hybrid Likelihood Ratio Test (HLRT). The proposed HLRT requires to solve a set of nonlinear equations which have no closed form solution. Thus, we propose an iterative numerical algorithm. In addition, we propose a Quasi-HLR detector which has a significantly lower computational complexity. In the case of unknown location parameter, we develop two Quasi-HLR methods. We use fast Fourier transform and search in order to estimate the unknown location. In Q-HLRT-Method-I, the nonlinear equation similar to the one in known location parameter is iteratively solved. In Q-HLRT-Method-II, a low complexity solution is proposed. Simulation examples evaluate and compare the performances of the proposed BBRDs.

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Computationally Efficient Maximum Likelihood Sequence Estimation and Activity Detection for $M$-PSK Signals in Unknown Flat Fading Channels

Cited by 19 publications

References 9 publications

GLR test for OFDM system identification using pilot tones pattern

GLR test for OFDM system identification using pilot tones pattern

Application of Maximum Entropy Theorem in Channel Estimation

Hybrid likelihood ratio bit-rate detectors for variable-gain multiple-access systems in unknown noise variance

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