Analysis of 16-APSK modulation in AWGN and Rayleigh fading channel

Chow, Y.C.; Nix, Andrew R; McGeehan, JP

doi:10.1049/el:19921023

Cited by 71 publications

(46 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Without loss of generality, we focus our attention on the 16-level and 64-level DQAM constellations introduced in Sec. II, where the default Star QAM configurations are (M A , M P ) = (2, 8) for M = 16 as seen in [1], [2], [4]- [6], [10]- [13], [24], [28], [29], [31], [32], [39], [40] and (M A , M P ) = (4, 16) for M = 64 as demonstrated in [4], [7], [30]- [32].…”

Section: Performance Resultsmentioning

confidence: 99%

Multiple-Symbol Differential Sphere Detection and Decision-Feedback Differential Detection Conceived for Differential QAM

Hanzo

2016

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Abstract-Multiple-Symbol Differential Sphere Detection (MS-DSD) relies on the knowledge of channel correlation. More explicitly, for Differential PSK (DPSK), the transmitted symbols' phases form a unitary matrix, which can be separated from the channel's correlation matrix by the classic Multiple-Symbol Differential Detection (MSDD), so that a lower triangular matrix extracted from the inverted channel correlation matrix is utilized for the MSDSD's sphere decoding. However, for Differential QAM (DQAM), the transmitted symbols' amplitudes cannot form a unitary matrix, which implies that the MSDD's channel correlation matrix becomes amplitude-dependent and remains unknown, unless all the data-carrying symbol amplitudes are detected. In order to tackle this open problem, in this paper, we propose to determine the MSDD's non-constant amplitudedependent channel correlation matrix with the aid of a sphere decoder, so that the classic MSDSD algorithms that were originally conceived for DPSK may also be invoked for DQAM detection. As a result, our simulation results demonstrate that the MSDSD aided DQAM schemes substantially outperform their DPSK counterparts. However, the price paid is that the detection complexity of MSDSD is also significantly increased. In order to mitigate this, we then propose a reduced-complexity MSDSD search strategy specifically conceived for DQAM constellations, which separately map bits to their ring-amplitude index and phase index. Furthermore, the classic Decision-Feedback Differential Detection (DFDD) conceived for DQAM relies on a constant channel correlation matrix, which implies that these DFDD solutions are sub-optimal and they are not equivalent to the optimum MSDD operating in decision-feedback mode. With the advent for solving the open problem of MSDSD aided DQAM, we further propose to improve the conventional DFDD aided DQAM solutions in this paper.

show abstract

Section: Performance Resultsmentioning

confidence: 99%

Multiple-Symbol Differential Sphere Detection and Decision-Feedback Differential Detection Conceived for Differential QAM

Hanzo

2016

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…(46)]. The BER for 16-DAPSK is computed by numerical integration of the density function, (10), over the regions defined by the β-DSs in Fig. 2.…”

Section: Numerical Results and Spectral Efficiencymentioning

confidence: 99%

“…This approximation has been verified to be accurate to within 2% of the optimum ∆ 0 obtained by numerical solution (over the range 1.5 < R < 2.3). For R = 2, results in [7,10] (and our numerical solution of (9)) report an optimal value of ∆ 0 = 0.68; the estimate gives ∆ 0 ≈ 2/3 = 0.667.…”

Section: B Optimal Decision Scheme For 16-dapskmentioning

confidence: 99%

“…For example, to find ∆ β,1 , we would use (11b) and (11c) to determine the exact value of r d ' such that the optimum decision scheme would have transitioned halfway between the b 0 reliable region and our "transition region". For R = 2 (the optimum ring ratio for use with Rayleigh fading channels, as per [10]), we obtain the following threshold values: ∆ 3,1 = 0.818, ∆ 3,2 = 0.745, ∆ 3,3 = 0.509, ∆ 3,4 = 0.364, ∆ 2,1 = 1, ∆ 2,2 = 0.910, ∆ 2,3 = 0.179, ∆ 2,4 = 0, ∆ 1,1 = 1, ∆ 1,2 = 1, ∆ 1,3 = 0, ∆ 1,4 = 0. We note that for β = 1 and β = 2 some of our thresholds are equal to 1 or 0 indicating that some of the regions depicted in Fig.…”

Section: B Optimal Decision Scheme For 16-dapskmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Demodulation with Differentially Coherent Detection

Brown

Plataniotis

Pasupathy

2007

2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications

View full text Add to dashboard Cite

Adaptive Demodulation (ADM) is a new rate adaptive technique wherein the receiver demodulates only the most reliable bits and treats unreliable bits as erasures. This paper derives the optimum and simple near-optimum receivers for an ADM system operating without a coherent phase reference, where differential encoding is assumed at the transmitter (using 16-DPSK and 16-DAPSK). The new receivers offer the advantages of a rate-adaptive system, without requiring channel state information at the transmitter or a coherent phase reference at the receiver. Bit error analysis for the ADM system in both cases is presented along with numerical results of the spectral efficiency for the rate adaptive systems operating over a Rayleigh fading channel.

show abstract

“…Moreover, the bound deviates from the true error performance because of the overlap between error decision regions. In [4] and [5], by exploiting the symmetry properties of APSK constellations and considering only the necessary terms of pairwise probabilities, tighter union bounds are given for 16APSK and 32APSK signals. In [6], the union bound is further simplified by approximating the decision boundaries between adjacent constellation rings by concentric circles.…”

Section: Introductionmentioning

confidence: 99%

Simplified error performance analysis of APSK signals

Shi

Wang

et al. 2014

IEICE ComEX

View full text Add to dashboard Cite

The error performance of APSK signals in additive white Gaussian noise channel are analyzed in this letter. A closed-form expression for symbol error rate (SER) of 16APSK signal is derived in polar coordinates, which can be approximated by the weighted sum of the SER of the inner and the outer rings. Based on this observation, the expressions of SER for 32APSK and 64APSK signals are given. It is shown that the proposed approximated SER expressions are very close to the simulation results. It is much simpler than the results based on union bound and can be extended to high-order APSK signals directly. Giancristofaro, A. Ginesi, L. Giugno, and M. Luise, "MHOMS: high-speed ACM modem for satellite applications," IEEE Wireless Commun. Mag., vol. 12, no. 2, pp. 66-77, 2005. DOI:10.1109/MWC.2005 [2] ETSI EN 302 307: "Digital Video Broadcasting (DVB); second generation framing structure, channel coding and modulation systems for broadcasting interactive services, news gathering and other broadband satellite applications

show abstract

Analysis of 16-APSK modulation in AWGN and Rayleigh fading channel

Cited by 71 publications

References 1 publication

Multiple-Symbol Differential Sphere Detection and Decision-Feedback Differential Detection Conceived for Differential QAM

Multiple-Symbol Differential Sphere Detection and Decision-Feedback Differential Detection Conceived for Differential QAM

Adaptive Demodulation with Differentially Coherent Detection

Simplified error performance analysis of APSK signals

Contact Info

Product

Resources

About