Sparse Channel Impulse Response Research Articles

Active Terminal Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA-OTFS in LEO Satellite Internet-of-Things

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial–satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.

An improved channel estimation for underwater acoustic OFDM and COFDM systems

Recently, there has been a tremendous increase in research and development of underwater communication systems. Orthogonal frequency division multiplexing has been widely employed for underwater acoustic communication because of its numerous advantages. Coding as well as interleaving techniques can be used along with orthogonal frequency division multiplexing for improving the error correction capability of the receiver. For demodulating the signal with fairly acceptable accuracies, the channel parameters need to be estimated at the receiver. Channel estimation can be performed by inserting pilots into the sub-carriers of an OFDM symbol. Underwater channels exhibit sparse channel impulse response and hence, the channel can be estimated using the compressive sensing techniques for properly reconstructing the received signals. Dictionary-based sparse channel estimation is presented in this paper which outperforms the channel estimation by LS and MMSE estimations. The results have been compared for various environmental conditions as well as for various numbers of pilots. It has been observed that the number of pilots is to be chosen as a trade-off between channel estimation accuracy and bandwidth efficiency.

Toward underwater channel impulse response estimation using sources of opportunity

Passive acoustic tomography exploits the acoustic energy generated by sources with unknown spectral content such as sources of opportunity (e.g., cargo ships) to study the ocean. However, separating the channel impulse response (CIR) from a possibly moving, random radiator is non-trivial for coherent processing and the amount of unknown variables may leave the problem intractable. Using an incremental approach, we study the sparse CIR estimation problem for a single receiver. Second, we add multiple receivers, relax source spectrum knowledge, and study processor performance in resolving possibly coherent multipath arrivals across the vertical line array. Third, we discuss the extension to include multiple VLAs and sequential processing for a source with known location. Application of the Noise Correlation 2009 Experiment and the Santa Barbara Channel Experiment 2016 are discussed.

Compressive sensing‐based differential channel feedback for massive MIMO

Massive multiple-input multiple-output (MIMO) is becoming a key technology for future 5G wireless communications. Channel feedback for massive MIMO is challenging due to the substantially increased dimension of MIMO channel matrix. A compressive sensing (CS)-based differential channel feedback scheme to reduce the feedback overhead is proposed. Specifically, the temporal correlation of time-varying channels is exploited to generate the differential channel impulse response (CIR) between two CIRs in neighbouring time slots, which enjoys a much stronger sparsity than the original sparse CIRs. Thus, the base station can recover the differential CIR from the highly compressed differential CIR under the framework of CS theory. Simulations show that the proposed scheme reduces the feedback overhead by about 20% compared with the direct CS-based scheme.

Sparse channel estimation and pilot optimization for underwater acoustic orthogonal frequency division multiple access uplink communications

Considering that the conventional channel interpolation method with sparse and irregular spaced pilots will lead to an error floor in underwater acoustic (UWA) orthogonal frequency division multiple access (OFDMA) uplink communications, a method for sparse channel estimation and pilot optimization is proposed in this paper. A compressed sensing (CS) algorithm is utilized for sparse channel impulse response estimation, which performs well in sparse and irregular spaced pilots and significantly decreases the channel estimation error. Besides, the pilots’ pattern and power joint optimization algorithm based on the random search technique is proposed for the minimum mutual coherence criterion in CS theory, which further improves the performance of CS estimation algorithm. During each iteration step, we randomly pick a pilots’ pattern from the subcarrier index set and a pilots’ power subset from the available power set. Then we perform this step iteratively within a certain searching time. Finally, the local optimal solution of the objective function for minimizing mutual coherence is considered as the feasible pilots’ pattern and power. Simulation results show that the convergence performance of the pilots’ pattern and power joint optimization algorithm is much better than that of the pilots’ pattern optimization algorithm. Furthermore, the channel estimation error of the proposed method is much lower than that of conventional least-squares channel estimator based on linear interpolation, CS channel estimator without pilot optimization, and CS channel estimator merely with pilots’ pattern optimization in channels of different multipath delay spreads. Finally, performance of the proposed method is demonstrated in the UWA uplink OFDMA systems with interleaved and generalized carrier assignment schemes respectively in the two-user case in a pool experiment. Experimental results show that the proposed method decreases dramatically the bit error rate in both carrier assignment schemes, and simultaneous reception for two users is achieved when signal noise ratio is larger than 10 dB.

Sparse Adaptive Frequency Domain Equalizers for Mode-Group Division Multiplexing

In this paper, we develop low complexity sparse frequency domain equalizers (FDEs) that exploit the sparsity that we observe in the graded index multimode fiber multiple-input multiple-output channel. The sparse channel impulse response is caused by the strong crosstalk at the mode MUX/DEMUX and weak coupling in the fiber between different mode groups. Two sparse FDE designs are proposed in order to compensate the crosstalk with relatively low computational complexity. The first method is based on a priori knowledge of the channel impulse response, which is used to generate a mask of taps with significant magnitudes. The second method is based on the improved proportionate normalized least-mean-square algorithm, where the active and inactive taps are adjusted at different rates of convergence. The computational complexity and the system performance of the proposed algorithms are analyzed. It is shown that the sparse FDEs offer low complexity relative to the sparse equalizers that use delay buffers, while maintaining improved performance over non-sparse equalizers in the presence of noise.

Adaptive OFDM Modulation for Underwater Acoustic Communications: Design Considerations and Experimental Results

In this paper, we explore design aspects of adaptive modulation based on orthogonal frequency-division multiplexing (OFDM) for underwater acoustic (UWA) communications, and study its performance using real-time at-sea experiments. Our design criterion is to maximize the system throughput under a target average bit error rate (BER). We consider two different schemes based on the level of adaptivity: in the first scheme, only the modulation levels are adjusted while the power is allocated uniformly across the subcarriers, whereas in the second scheme, both the modulation levels and the power are adjusted adaptively. For both schemes we linearly predict the channel one travel time ahead so as to improve the performance in the presence of a long propagation delay. The system design assumes a feedback link from the receiver that is exploited in two forms: one that conveys the modulation alphabet and quantized power levels to be used for each subcarrier, and the other that conveys a quantized estimate of the sparse channel impulse response. The second approach is shown to be advantageous, as it requires significantly fewer feedback bits for the same system throughput. The effectiveness of the proposed adaptive schemes is demonstrated using computer simulations, real channel measurements recorded in shallow water off the western coast of Kauai, HI, USA, in June 2008, and real-time at-sea experiments conducted at the same location in July 2011. We note that this is the first paper that presents adaptive modulation results for UWA links with real-time at-sea experiments.

Frequency domain response estimation of underwater acoustic channel and noise estimation for single-carrier frequency domain equalization

Single-carrier frequency domain equalization (SC-FDE) has drawn increasing researches because of its lower computational complexity and better performance of mitigating intersymbol interference (ISI) as compared with the time domain equalization. The practical SC-FDE systems, however, require the knowledge of the channel frequency domain response and the noise power to calculate filter coefficients of the frequency domain equalizer. Improper channel and noise estimations have been shown to result in error floor at high signal-to-noise ratio (SNR), which may impede the utilization of SC-FDE in the high-rate underwater acoustic (UWA) communications. In this paper, we discuss the possible influence of inaccurate noise estimation on the error floor at high SNR, and propose a joint estimation scheme for the channel time domain and frequency domain responses and also the noise power based on Chu sequence. The channel energy concentration region is determined by the threshold and the sparse channel impulse response (CIR) of UWA channels, used in denoising process of the CIR and the noise power computation. The bite error ratio performance of the SC-FDE variation versus the threshold is simulated via the UWA models, and accurate noise power estimation is shown to be desirable to improve the SC-FDE performance. Numerical examples also indicate that our joint estimation scheme can eliminate the error floor and improve the performance of SC-FDE at high SNR.

Compressed sensing based deconvolution algorithm for time-domain UWB channel modeling

Extracting the parameters of the multipath with high accuracy can be achieved by using high-resolution algorithm for time-domain ultra wideband (UWB) channel modeling. The CLEAN algorithm has been used as such a high-resolution algorithm for UWB time-domain characterization. This paper presents a compressed sensing (CS) based high-resolution deconvolution algorithm for time-domain UWB channel modeling. UWB wireless channels are a prime example of long and sparse channel impulse response (CIR). Furthermore, the dictionary of parameterized waveforms that closely matches the waveform of multipath leads to that the UWB channel measurement signal is more compactly represented. By adjusting the parameter of dictionary, CIRs of different resolutions can be obtained. The matching pursuit (MP) algorithm is used as the signal reconstruction method for CS and outputs the CIR directly. We also demonstrated that if the dictionary of CS is designed specifically, MP is an equivalent of single template CLEAN. Finally, the computation complexity of CS-MP is analyzed and comparison of MP and CLEAN is performed. Simulation results show that compared to CLEAN, the proposed CS-MP deconvolution algorithm can achieve a comparable performance with much fewer samplings.

Reed-Solomon code-based sparse channel estimation for OFDM systems

A novel efficient algorithm for the estimation of sparse channel impulse response (CIR) is addressed for OFDM systems. The innovation of this algorithm comes from the fact that it equivalently sees the CIR estimation problem as a decoding one. To do so, it exploits first the channel sparsity through the modelling of the sparse CIR as a Bernoulli-Gaussian process. Then, using the relationship between the Reed-Solomon codes and the OFDM modulator it efficiently estimates the sparse CIR using directly the decoding of the OFDM received signal. The obtained simulation results highlight that using the proposed algorithm gives good estimation performance in terms of mean squares error on the sparse CIR estimates.

Sparsity level-aware threshold-based channel structure detection in OFDM systems

The structure detection of sparse channel impulse response (CIR) is addressed in the frame of OFDM systems. Tap-tuned thresholds, exploiting a priori knowledge about the channel sparsity level (CSL), are designed to optimise the detection error probability. Compared to former proposed thresholds, the CSL-aware thresholds lead to higher CIR structure true detection rates and to lower channel response mean squares error.

Estimation of Rapidly Time-Varying Sparse Channels

The estimation of sparse shallow-water acoustic communication channels and the impact of estimation performance on the equalization of phase coherent communication signals are investigated. Given sufficiently wide transmission bandwidth, the impulse response of the shallow-water acoustic channel is often sparse as the multipath arrivals become resolvable. In the presence of significant surface waves, the multipath arrivals associated with surface scattering fluctuate rapidly over time, in the sense that the complex gain, the arrival time, and the Dopplers of each arrival all change dynamically. A sparse channel estimation technique is developed based on the delay-Doppler-spread function representation of the channel. The delay-Doppler-spread function may be considered as a first-order approximation to the rapidly time-varying channel in which each channel component is associated with Doppler shifts that are assumed constant over an averaging interval. The sparse structure of the delay-Doppler-spread function is then exploited by sequentially choosing the dominant components that minimize a least squares error. The advantage of this approach is that it captures both the channel structure as well as its dynamics without the need of explicit dynamic channel modeling. As the symbols are populated with the sample Dopplers, the increase in complexity depends on the channel Doppler spread and can be significant for a severely Doppler-spread channel. Comparison is made between nonsparse recursive least squares (RLS) channel estimation, sparse channel impulse response estimation, and estimation using the proposed approach. The results are demonstrated using experimental data. In training mode, the proposed approach shows a 3-dB reduction in signal prediction error. In decision-directed mode, it improves significantly the robustness of the performance of the channel-estimate-based equalizer against rapid channel fluctuations.