Noncoherent demodulation techniques for trellis coded M-DPSK signals

Giallorenzi, T.R.; Wilson, Stephen G.

doi:10.1109/26.403770

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…Additional simulations with other delay profiles and vehicle speeds are necessary for a more complete evaluation of these algorithms. Further work in several areas may lead to improved overall system performance; for example, multiple symbol differential detection [16] or even coherent modulation should in general yield better performance at a price of additional complexity. Our results above are given for systems without a first adjacent interference canceler, but they can be interpreted as results with a nonideal canceler with residual interference.…”

Section: Discussionmentioning

confidence: 99%

“…For simplicity, we consider differential demodulation based on only two adjacent received symbols because this case allows us to employ the Viterbi algorithm for decoding the CPPC code. Multiple-symbol differential detection methods [16] can offer coding gains of up to 3 dB over this approach, but are considerably more complex. Also shown in Fig.…”

Section: Differential Encodingmentioning

confidence: 99%

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Soft selection combining for terrestrial digital audio broadcasting in the FM band

Laneman

Sundberg²

2001

IEEE Trans. on Broadcast.

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Methods of adaptive soft combining and channel decoding are developed to combat the effects of multipath fading and nonuniform interference channels, with particular application to digital reception in hybrid in-band on-channel (HIBOC) digital audio broadcast (DAB) systems in the FM band. These systems transmit near CD quality digital audio and analog FM simultaneously within the same license band, requiring the digital audio to be protected with powerful channel codes and sophisticated decoding algorithms to provide broad coverage under a variety of fading and potentially severe interference conditions created by first adjacent FM stations. In an example HIBOC DAB system, digital transmissions are DQPSK/OFDM modulated in two sidebands of the analog FM host signal, and a complementary punctured pair convolutional (CPPC) inner coding scheme allows for higher diversity benefit than code combining when both sidebands are interference-free as well as full recovery of the audio information when one of the sidebands is severely corrupted by first adjacent interference. For the intermediate cases in which one of the sidebands is partially useful, we demonstrate via simulations that an unmodified receiver designed for a Gaussian channel and corresponding to equal-gain combining performs ineffectively for moderate to high interference levels. Motivated by a clear need for more sophisticated receivers, we examine soft combiners derived from the maximum-likelihood principle and provide simulated performance bounds for the case in which perfect channel parameter estimates are available. We then discuss more practical methods for performing adaptive soft combining and channel decoding, focusing in particular on an appealing soft selection combining technique, based on successive erasures and Viterbi decoding, that requires only coarse estimates of the channel parameters. An outer code used for error concealment may be further utilized to perform the selection function. The performance of this soft selection combining scheme under a variety of interference scenarios is also evaluated via simulation. Further improvements may be obtained with a list Viterbi decoder. Index Terms-Digital audio broadcasting (DAB), diversity combining, fading channel, interference mitigation, selection combining, successive erasure decoding. I. INTRODUCTION D IGITAL AUDIO BROADCAST (DAB) in the FM radio band promises near CD-quality audio, data services, and more robust coverage than existing analog FM transmission. However, before all-digital broadcasting can be made a reality,

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Section: Discussionmentioning

confidence: 99%

Section: Differential Encodingmentioning

confidence: 99%

Soft selection combining for terrestrial digital audio broadcasting in the FM band

Laneman

Sundberg²

2001

IEEE Trans. on Broadcast.

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“…Then the k th received symbol r k is expressed as

r_{k} = h α s_{k} + n_{k},

where h denotes a channel impulse response, which is a zero‐mean circularly symmetric complex Gaussian random variable with normalized power, and black n k is the zero mean complex additive white Gaussian noise (AWGN) whose variance per dimension is N 0 /2. Note that h is invariant to time because we only assume slow or static channel where channel is almost static over several frames . This assumption is commonly applicable for short‐range and low‐data rate communication systems, such as WBAN or IoT applications, where both transmitter and receiver are fixed or slowly move.…”

Section: Decoding Criteria Of Repeated Dpsk Symbolmentioning

confidence: 99%

“…Note that h is invariant to time because we only assume slow or static channel where channel is almost static over several frames. 22 This assumption is commonly applicable for short-range and low-data rate *Note that when R is 1, Figure 1 is simply the typical DPSK modulator without repetition.…”

Section: Decoding Criteria Of Repeated Dpsk Symbolmentioning

confidence: 99%

Performance analysis on DPSK modulation using symbol repetition and interleaving

Han

Lee

Kwon

et al. 2018

Int J Communication

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Summary It is well known that the differential phase shift keying (DPSK) transmission does not require a reference phase and reduces the computational complexity and power consumption of the receiver at the cost of the transmission reliability. To improve the reliability of transmission under such scenario, we use a symbol repetition scheme to the DPSK modulation, such as the IEEE 802.15.6 wireless body area network and the ITU‐T G.9903. However, the message symbol repetition with interleaving in conventional manner as in coherent modulation schemes may degrade the receiver performance because of the noise characteristic of the differential demodulation structure. In this work, we analyze the performance of repeated DPSK system with and without the interleaver and demonstrate which scenario is suitable for each modulation order. We propose a proper transmission setting, which achieves noticeable performance improvement at the receiver as well as enhances the energy efficiency of the communication system. Since it can be implemented with very small modification to existing systems, our results are highly applicable with negligible modification of the hardware structure and thus enable substantially low‐cost implementation in future communication systems including wireless body area network internet of things applications.

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“…In order to reduce the gap between scalar quantization and RVQ, we also consider encoding of each beam using trellis coded quantization (TCQ), which is analogous to trellis coded modulation [9], [10]. Using the Viterbi algorithm to search the codebook, the TCQ scheme entails only linear complexity in the number of feedback bits per coherent block.…”

Section: Introductionmentioning

confidence: 99%

MIMO Precoding with Limited Rate Feedback: Simple Quantizers Work Well

Guo

Honig

2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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Transmitter precoding is a crucial technique for harnessing the potential of multiple-input multiple-output (MIMO) fading channels. In many practical wireless systems, a limited amount of feedback from the receiver is available at the transmitter, which can be used to direct the choice of the precoder from a codebook to match the channel state. Assuming noiseless, limited-rate feedback, this work studies the design of simple, efficient quantization and feedback schemes which achieve nearoptimal ergodic channel capacity. In the case the precoder takes the form of a beamforming vector for modulating a single symbol stream, it is found that simple scalar quantization of the elements of the vector is nearly optimal over a wide range of feedback rates; it typically costs a fraction of a dB higher SNR to achieve the same capacity as that of far more sophisticated vector quantization schemes. In the case a precoding matrix consisting of multiple beams is used to modulate multiple symbol streams, separate encoding of the beams using scalar quantization also performs well. Roughly speaking, the rate loss due to separate encoding of the beams increases linearly with the number of beams but appears to be constant over a wide range of SNRs. The loss can be reduced substantially by more sophisticated encoding of each beam, e.g., two-state trellis coded quantization. The complexity of such quantization schemes is linear in the number of antennas and the number of feedback bits.

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Noncoherent demodulation techniques for trellis coded M-DPSK signals

Cited by 12 publications

References 13 publications

Soft selection combining for terrestrial digital audio broadcasting in the FM band

Soft selection combining for terrestrial digital audio broadcasting in the FM band

Performance analysis on DPSK modulation using symbol repetition and interleaving

MIMO Precoding with Limited Rate Feedback: Simple Quantizers Work Well

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