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This contribution considers turbo synchronization, that is to say, the use of soft data information to estimate parameters like carrier phase, frequency, or timing offsets of a modulated signal within an iterative data demodulator. In turbo synchronization, the receiver exploits the soft decisions computed at each turbo decoding iteration to provide a reliable estimate of some signal parameters. The aim of our paper is to show that such “turbo-estimation” approach can be regarded as a special case of the expectation-maximization (EM) algorithm. This leads to a general theoretical framework for turbo synchronization that allows to derive parameter estimation procedures for carrier phase and frequency offset, as well as for timing offset and signal amplitude. The proposed mathematical framework is illustrated by simulation results reported for the particular case of carrier phase and frequency offsets estimation of a turbo-coded 16-QAM signal.

Iterative channel estimation can improve the channel-state information (CSI) with respect to noniterative estimation. New iterative channel estimators based on the expectation-maximization (EM) algorithm are proposed in this paper. A first estimator, called the unbiased EM (UEM), is designed to unbias the EM estimates. A second estimator is then put forward, which is based on the expectation-conditional-maximization (ECM) algorithm, and its complexity is lower than that of the EM. An unbiased ECM (UECM) estimator is also proposed. Although the unbiasedness of the UEM and UECM estimators is not rigorously proved, the use of these names is explained in the paper. The new estimators are compared with well-known ones, such as the EM, the decision-directed (DD), and the data-aided (DA) estimators. Simulations are reported for a turbo receiver operating over frequency-selective multiple-input multiple-output channels. It is shown that the UEM channel estimator outperforms the EM, and that the ECM-based estimators are very close to the EM-based ones. Index Terms--Channel estimation, expectation-maximization (EM) algorithm, iterative estimation, multiple-input multiple-output (MIMO) systems, turbo detection.

The purpose is to assess the performance of bit-interleaved turbo equalization (TE) over static frequency-selective channels. The asymptotic performance is therefore first pointed out, emphasizing the fundamental role played by the constellation mapping. This specific feature is then further analyzed using the extrinsic information-transfer chart technique, leading to an efficient optimization tool. This finally enables showing that bit-interleaved TE can outperform its symbol-interleaved counterpart. Index Terms--Bit-interleaved coded modulation (BICM), extrinsic information-transfer (EXIT) charts, minimum mean-square error (MMSE)-based equalization, trellis-coded modulation (TCM), turbo demodulation (TD), turbo equalization (TE).

A soft-input soft-output (SISO) space-time fractionally spaced equalizer is designed for the minimum mean square error (MMSE) criterion. Simulation results show the ability of the receiver to account for intersymbol interference, co-antenna interference, and its capability to exploit transmit, multipath, and receive diversity.

Abstract We consider the problem of packet scheduling for the transmission of multiple video streams over a wireless local area network (WLAN). A cross-layer optimization framework is proposed to minimize the wireless transceiver energy consumption while meeting the user required visual quality constraints. The framework relies on the IEEE 802.11 standard and on the embedded bitstream structure of the scalable video coding scheme. It integrates an application-level video quality metric as QoS constraint (instead of a communication layer quality metric) with energy consumption optimization through link layer scaling and sleeping. Both energy minimization and min-max energy optimization strategies are discussed. Simulation results demonstrate significant energy gains compared to the state-of-the-art approaches.

Realizing high-quality digital image transmission via a satellite link, while optimizing resource distribution and minimizing battery consumption, is a challenging task. This paper describes a methodology to optimize a turbo-encoded wavelet-based satellite downlink progressive image transmission system with unequal error protection (UEP) techniques. To achieve that goal, we instantiate a generic UEP methodology onto the system, and demonstrate that the proposed solution has little impact on the average performance, while greatly reducing the run-time complexity. Based on a simple design-time distortion model and a low-complexity run-time algorithm, the provided solution can dynamically tune the system's configuration to any bitrate constraint or channel condition. The resulting system outperforms in terms of peak signal-to-noise ratio (PSNR), a state-of-the-art, fine-tuned equal error protection (EEP) solution by as much as 2 dB.

Multiantenna systems and more particularly those operating on multiple input and multiple output (MIMO) channels are currently a must to improve wireless links spectrum efficiency and/or robustness. There exists a fundamental tradeoff between potential spectrum efficiency and robustness increase. However, multiantenna techniques also come with an overhead in silicon implementation area and power consumption due, at least, to the duplication of part of the transmitter and receiver radio front-ends. Although the area overhead may be acceptable in view of the performance improvement, low power consumption must be preserved for integration in nomadic devices. In this case, it is the tradeoff between performance (e.g., the net throughput on top of the medium access control layer) and average power consumption that really matters. It has been shown that adaptive schemes were mandatory to avoid that multiantenna techniques hamper this system tradeoff. In this paper, we derive smartMIMO: an adaptive multiantenna approach which, next to simply adapting the modulation and code rate as traditionally considered, decides packet-per-packet, depending on the MIMO channel state, to use either space-division multiplexing (increasing spectrum efficiency), space-time coding (increasing robustness), or to stick to single-antenna transmission. Contrarily to many of such adaptive schemes, the focus is set on using multiantenna transmission to improve the link energy efficiency in real operation conditions. Based on a model calibrated on an existing reconfigurable multiantenna transceiver setup, the link energy efficiency with the proposed scheme is shown to be improved by up to 30% when compared to nonadaptive schemes. The average throughput is, on the other hand, improved by up to 50% when compared to single-antenna transmission.

This contribution considers turbo synchronization, that is to say, the use of soft data information to estimate parameters like carrier phase, frequency, or timing offsets of a modulated signal within an iterative data demodulator. In turbo synchronization, the receiver exploits the soft decisions computed at each turbo decoding iteration to provide a reliable estimate of some signal parameters. The aim of our paper is to show that such "turbo-estimation" approach can be regarded as a special case of the expectation-maximization (EM) algorithm. This leads to a general theoretical framework for turbo synchronization that allows to derive parameter estimation procedures for carrier phase and frequency offset, as well as for timing offset and signal amplitude. The proposed mathematical framework is illustrated by simulation results reported for the particular case of carrier phase and frequency offsets estimation of a turbo-coded 16-QAM signal.