Iterative Sparse Channel Estimation and Decoding for Underwater MIMO-OFDM
© Jie Huang et al. 2010
Received: 1 November 2009
Accepted: 8 April 2010
Published: 16 May 2010
We propose a block-by-block iterative receiver for underwater MIMO-OFDM that couples channel estimation with multiple-input multiple-output (MIMO) detection and low-density parity-check (LDPC) channel decoding. In particular, the channel estimator is based on a compressive sensing technique to exploit the channel sparsity, the MIMO detector consists of a hybrid use of successive interference cancellation and soft minimum mean-square error (MMSE) equalization, and channel coding uses nonbinary LDPC codes. Various feedback strategies from the channel decoder to the channel estimator are studied, including full feedback of hard or soft symbol decisions, as well as their threshold-controlled versions. We study the receiver performance using numerical simulation and experimental data collected from the RACE08 and SPACE08 experiments. We find that iterative receiver processing including sparse channel estimation leads to impressive performance gains. These gains are more pronounced when the number of available pilots to estimate the channel is decreased, for example, when a fixed number of pilots is split between an increasing number of parallel data streams in MIMO transmission. For the various feedback strategies for iterative channel estimation, we observe that soft decision feedback slightly outperforms hard decision feedback.
Multi-input multi-output (MIMO) techniques have been recently applied in underwater acoustic (UWA) systems to drastically improve the spectral efficiency. Experimental results have been reported in [1–9] for single-carrier systems, and in [6, 10–15] for multicarrier systems, in the form of orthogonal frequency division multiplexing (OFDM).
As we consider MIMO-OFDM in UWA channels, we specify related work: a block-by-block receiver has been developed in , where Maximum A Posteriori (MAP) and zero forcing (ZF) detectors are used for MIMO detection following least-squares- (LS-) based channel estimation. Receivers for both spatial multiplexing and differential space time coding have been developed in . Adaptive MIMO detectors have been proposed in [13, 14], where channel estimates based on the previous data block are used for demodulation of the current block after being combined with phase tracking. All the receivers in [10, 11, 13, 14] are noniterative. In , an iterative receiver has been presented for MIMO-OFDM that iterates between MIMO detection and channel decoding.
Channel estimation is included in the iteration loop so that refined channel estimates become available along the iterations.
The LS channel estimator is replaced by a more advanced channel estimator recently tested in , that exploits the sparse nature of UWA channels.
When channel estimation is included in the iteration loop, data symbols estimated in the previous round can be utilized as additional training symbols to improve the channel estimation accuracy. We investigate different feedback strategies, including hard decision feedback, soft decision feedback, and their variants that discard unreliable feedback symbols through a thresholding mechanism. We compare the performance using numerical simulation and experimental data collected from the RACE08 and SPACE08 experiments. Iterative receiver processing leads to impressive performance gains relative to a noniterative receiver.
Development of an iterative receiver for underwater MIMO-OFDM, improving upon an existing receiver .
Extensive performance testings based on experimental data, showing impressive results for underwater MIMO-OFDM with very high spectral efficiencies.
The rest of this paper is organized as follows. Section 2 introduces the system model. Section 3 presents the details on the iterative receiver. Simulation results are reported in Section 4. Experimental results are reported in Sections 5 and 6 with data collected in RACE08 and SPACE08 experiments, respectively. We conclude in Section 7.
2. System Model
2.1. MIMO-OFDM Transmission
where is the carrier frequency and subcarriers are used so that the bandwidth is .
where is the code rate, is the constellation size, and is the set of data subcarriers (excluding pilot tones). With bandwidth , the data rate is bits per second.
2.2. Receiver Preprocessing
The same receiver preprocessing as in  is applied. The received signal can be resampled to compensate a dominant Doppler effect if necessary. After resampling each receiver assumes one common Doppler shift on all transmitted data streams, and uses the energy on the null subcarriers as an objective function to search for the best Doppler shift estimate . Doppler shift compensation is done at each receiver separately.
where is the frequency response between the th transmitter and the th receiver at the th subcarrier, and is the additive noise at the demodulator output, which includes both the ambient noise and the residual intercarrier interference (ICI).
3. Iterative Sparse Channel Estimation and Decoding
3.1. Sparse Channel Estimation
where we omit the transmitter and receiver index for compact notation.
3.1.1. Overcomplete Delay Dictionary
where contains the possible delays corresponding to the dictionary columns. Since commonly is sparse, that is, it has a limited number of nonzero entries.
where is a diagonal matrix with the elements of vector on its main diagonal, and contains the possible delays corresponding to the dictionary columns for the channel from the th transmitter to the th receiver.
which depends on the pilots and known symbol estimates via the matrix .
3.1.2. Basis Pursuit Formulation
An efficient implementation for the complex valued version of BP has been suggested in [26, Section VI.D]. Adopting BP-based sparse channel estimation in multicarrier underwater acoustic communications has been presented in , where impressive performance gains over a LS-based channel estimator have been reported. The complexity of BP-based sparse channel estimation, specifically for underwater OFDM systems, is studied in .
3.2. MIMO Detection
where is the additive noise. We assume that the noise on different receivers is uncorrelated and Gaussian distributed.
To demodulate from (18), we use the MIMO detector of  which consists of a hybrid use of successive interference cancellation and soft minimum mean-square error (MMSE) demodulation; see  for details.
3.3. Nonbinary LDPC Decoding
With the outputs from the MMSE equalizer, nonbinary LDPC decoding as in  is performed separately for each data stream. The decoder outputs the decoded information symbols and the updated a posterior/extrinsic probabilities, which are used in the next iteration of channel estimation and equalization. During the decoding process, if all the parity check conditions of one data stream are satisfied, the decoder declares successful recovery of this data stream. In this case we assume that all symbols of this data stream are known without uncertainty.
To use feedback in channel estimation or MIMO detection, we need estimates of the unknown data and a measure of the uncertainty left in these estimates. Based on the previous round of decoding, the LDPC decoder outputs a posterior probabilities for each symbol, as well as probabilities based on extrinsic information only. While the extrinsic information is used in the MIMO symbol detection , the a posterior probabilities are used to improve channel estimation. Next we investigate different feedback strategies for channel estimation.
3.4. Feedback Strategies
We consider two categories of feedback strategies, namely, hard decision feedback and soft decision feedback. In each category we investigate full feedback and threshold-controlled feedback where the former uses all symbols for feedback and the latter uses only reliable symbols for feedback.
Let denote the a posterior probability where are the constellation symbols. There are three main feedback strategies in the literature [17–19], varying by the definition of —the estimate of for channel estimation.
Full Hard Decision Feedback
Controlled Hard Decision Feedback
where stands for the entropy calculated from which is the counterpart of the log-likelihood-ratio (LLR) of binary codes and is the threshold which lies in . In other words, only when the symbol estimate is considered reliable enough, a hard decision is made for feedback.
Full Soft Decision Feedback
In this paper, we further consider a new feedback strategy by applying a threshold on the soft information, where only symbols with the absolute value of their soft estimates larger than a threshold are used.
Controlled Soft Decision Feedback
where is the maximum absolute value of all constellation symbols and is the threshold which lies in . Here the threshold is applied to the symbols from transmitters jointly. We have also investigated the strategy when a threshold is applied on the symbols from each transmitter individually. The individually controlled feedback strategy has comparable (or worse) performance than the jointly controlled version.
4. Simulation Results
Consider an OFDM system with the following specifications: carrier frequency kHz, subcarriers, symbol duration ms, and guard time ms. The bandwidth is then kHz. It has pilot tones and null subcarriers for edge protection and Doppler estimation, leaving data subcarriers. The data within each OFDM symbol is encoded using a rate nonbinary LDPC code from , and modulated using either QPSK or 16-QAM. These parameters are used in the signal design for the SPACE08 experiment [16, 28].
We consider MIMO systems with or transmitters. With , the data rates are 10.4 kb/s and 20.8 kb/s for QPSK and 16-QAM modulations, respectively. With , the data rates are 15.6 kb/s and 31.2 kb/s for QPSK and 16-QAM modulations, respectively. The pilots are divided into nonoverlapping sets among all transmitters so that each transmitter has roughly the same number of pilots. The pilot patterns are randomly drawn, rendering irregular positioning . This is usually seen as advantageous in compressed sensing theory, as it can guarantee identifiability of active channel taps with high probability .
For the simulation scenario we generate discrete fading paths, where the interarrival times are exponentially distributed with a mean of 1 ms. The amplitude of each path is Rayleigh distributed, with decreasing variance as the delay increases. As each OFDM symbol is encoded separately, we use block-error-rate (BLER) as the figure of merit. In the simulation, each OFDM symbol experiences an independently generated channel. The pilot symbols are drawn from the QPSK constellation whereas the data symbols are drawn from QPSK or 16-QAM constellations. The pilots are scaled to ensure that about one third of the total transmission power is dedicated to channel estimation regardless of the number of transmitters. We simulate the BLER performance at different SNR levels, where SNR is the signal to noise power ratio on the data subcarriers.
"Non-iterative" receiver as in , but with the LS channel estimator replaced by the BP estimator.
"Turbo-equalization" receiver as in , but with the LS channel estimator replaced by the BP estimator.
The proposed iterative receiver with "controlled soft decision feedback" with different thresholds.
The proposed iterative receiver with "full hard decision feedback" (in all subsequent figures, "Non-iterative," "Turbo-equalization," "Soft feedback," and "Hard feedback" are used as legends for different receivers).
Also we include a case with full channel state information (CSI) which still iterates between MIMO detection and LDPC decoding, but has a perfect channel estimate.
Figures 3–6 show that employing a turbo equalization receiver gains about 0.5–1 dB over a noniterative receiver, Including channel estimation in the iteration loop leads to gains of about 1 dB for and 1.5 dB for . This seems intuitive, as with an increasing number of transmitters there are less pilots available per data stream, making the "additional pilots" from feedback more valuable. The gap between the proposed receivers and the full CSI case is approximately between 0.5 dB and 1 dB.
In Figure 4 the iterative receiver with full hard decision feedback performs slightly worse than the iterative receivers with soft decision feedback. This gap gets more pronounced when the number of transmitters increases as shown in Figures 5 and 6.
5. Experimental Results: RACE08
The RACE08 experiment was held in the Narragansett Bay, Rhode Island, in March 2008. The water depth in the area is between 9 and 14 meters. The system parameters are the same as in the numerical simulation, except for a different bandwidth of kHz. The corresponding symbol duration and subcarrier spacing are ms and Hz, respectively. More detailed description of the RACE08 experiment can be found in [12, 29].
During the experiment, each transmission file was transmitted twice every four hours, leading to 12 transmissions each day. A total of 124 data sets were successfully recorded on each array within 13 days from the Julian date 073 to the Julian date 085. We focus on three days of the experiment, Julian dates 81–83, and receiver S3, which was located 400 m away from the transmitter. We consider 16-QAM and two MIMO setups: one with two transmitters and one with three transmitters. These setups have also been studied in  with the turbo-equalization receiver.
In Table I, we also include results for two setups not available in : (i) , 64-QAM and (ii) , 16-QAM, having spectral efficiencies of 5.28 and 4.69 bits/s/Hz, respectively. The results are based on Julian date 83 only, and receive-elements are used. Although data stream one performs poorly due to a transducer issue (see discussion in ), the other data streams can be decoded at reasonable levels.
Performance results with high data rates from RACE08; twelve receivers used.
6. Experimental Results: SPACE08
The SPACE08 experiment was held off the coast of Martha's Vineyard, MA, from Oct. 14 to Nov. 1, 2008. The water depth is about 15 meters. The spacing between consecutive hydrophones is 12 cm. The detailed description of the SPACE08 experiment can be found in [28, 29].
We focus on receivers S3 and S5 that were located 200 m and We consider recorded data from three consecutive days, Julian date 297 to Julian date 299. For each day, there are twelve recorded files consisting of twenty OFDM symbols each. On the Julian date 298, the five files recorded during the afternoon were severely distorted and therefore unusable; we focus on the remaining seven files recorded during the morning and evening. Due to the more challenging environment, we only consider the small-size QPSK constellation. The transmission signal model for SPACE08 has the same setup as the simulation setup in Section 4. The data rates for the MIMO system using QPSK modulation are 10.4 kb/s and 15.6 kb/s, when and , respectively.
For the simulation results in Section 4, we plot the BLER performance as a function of SNR. For the experimental results in Sections 5 and 6, we plot the BLER performance as a function of the number of phones used at the receiver. One common practice to show the performance dependance on SNR based on experimental data is to add recorded ambient noise to the received signals. In this paper, we have not pursued such an approach, which could be explored in the near future.
In this paper, we have developed an iterative receiver for underwater MIMO-OFDM that couples sparse channel estimation, MIMO detection, and channel decoding. Various types of feedback information have been considered to improve the sparse channel estimator using the Basis Pursuit algorithm. We tested the proposed receiver extensively using numerical simulation and experimental data for MIMO-OFDM with very large spectral efficiencies. We find that including channel estimation in the iterative loop leads to significant gains in performance. These gains are more pronounced if less pilots are available for channel estimation, for example, when a fixed number of pilots is split between parallel data streams. For the various feedback strategies for iterative channel estimation, we observe that soft decision feedback slightly outperforms hard decision feedback in most cases.
This work was supported by the NSF Grant CNS-0721834, the ONR Grants N00014-07-1-0805 (YIP), and N00014-09-1-0704 (PECASE). Part of this work was presented at the MTS/IEEE OCEANS Conference, Biloxi, MS, USA, Oct. 2009.
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