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![Fig. 2: Block diagram of a single branch of the equalizer [12].](profile/Jianghui-Li/publication/304375505/figure/fig2/AS:401173766000647@1472658688032/Block-diagram-of-a-single-branch-of-the-equalizer-12_Q320.jpg)
In this paper, we propose a recursive least square (RLS) adaptive filter for sparse identification of underwater acoustic (UWA) channels. The adaptive filter is based on sliding window, diagonal loading, and dichotomous coordinate descent iterations. The adaptive algorithm possesses a complexity that is only linear in the filter length. The adaptiv...
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In this paper, a sparse recursive least squares (RLS) adaptive filter is investigated in application to channel estimation in underwater acoustic (UWA) channels. The adaptive filter is based on sliding-window, homotopy, and dichotomous coordinate descent iterations. It is used in a multi-antenna receiver of an UWA communication system with guard-fr...
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Citations
... In this regard, some researchers have studied a lot of nonlinear filter algorithms. For example, Kalman filters [4][5], median filters [6][7], sliding window filters [8][9]. The sliding mode variable structure control theory has received widespread attention from scholars due to its strong robustness and simple implementation [10][11][12]. ...
This paper proposes a novel sliding mode filter, which improves Emaru et al.’s filter. Compared to previous filters, the proposed filter has a faster response speed without sacrificing filtering performance, and the output signals do not experience chattering. Specifically, the proposed filter introduces feedforward compensation as an acceleration factor, enabling the system state to accelerate convergence without affecting the noise attenuation ability. Secondly, the discrete-time algorithm of the proposed filter is achieved through the implicit Euler discretization method, and an equivalent substitution of a sign function and a saturation function, and its output signals do not generate system chattering, which is a challenge faced by sliding mode technology in discrete-time implementation. The effectiveness of the proposed filter was determined through numerical simulation.
... For reliable UWA communication systems operating in fast-varying channels, the channel estimation is an essential part. RLS based adaptive filters are normally employed for this purpose [2]- [5]. However, the performance of the RLS adaptive filter is limited in fastvarying channels. ...
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In system identification scenarios, classical adaptive filters, such as the recursive least squares (RLS) algorithm, predict the system impulse response. If a tracking delay is acceptable, interpolating estimators capable of providing more accurate estimates of time-varying impulse responses can be used; channel estimation in communications is an example of such applications. The basis expansion model (BEM) approach is known to be efficient for non-adaptive (block) channel estimation in communications. In this paper, we combine the BEM approach with the sliding-window RLS (SRLS) algorithm and propose a new family of adaptive filters. Specifically, we use the Legendre polynomials, thus the name the SRLS-L adaptive filter. The identification performance of the SRLS-L algorithm is evaluated analytically and via simulation. The analysis shows significant improvement in the estimation accuracy compared to the SRLS algorithm and a good match between the theoretical and simulation results. The performance is further investigated in application to the self-interference cancellation in full-duplex underwater acoustic communications, where a high estimation accuracy is required. A field experiment conducted in a lake shows significant improvement in the cancellation performance compared to the classical SRLS algorithm.
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... For reliable UWA communication systems operating in fast-varying channels, the channel estimation is an essential part. RLS based adaptive filters are normally employed for this purpose [2]- [5]. However, the performance of the RLS adaptive filter is limited in fastvarying channels. ...
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In system identification scenarios, classical adaptive filters, such as the recursive least squares (RLS) algorithm, predict the system impulse response. If a tracking delay is acceptable, interpolating estimators capable of providing more accurate estimates of time-varying impulse responses can be used; channel estimation in communications is an example of such applications. The basis expansion model (BEM) approach is known to be efficient for non-adaptive (block) channel estimation in communications. In this paper, we combine the BEM approach with the sliding-window RLS (SRLS) algorithm and propose a new family of adaptive filters. Specifically, we use the Legendre polynomials, thus the name the SRLS-L adaptive filter. The identification performance of the SRLS-L algorithm is evaluated analytically and via simulation. The analysis shows significant improvement in the estimation accuracy compared to the SRLS algorithm and a good match between the theoretical and simulation results. The performance is further investigated in application to the self-interference cancellation in full-duplex underwater acoustic communications, where a high estimation accuracy is required. A field experiment conducted in a lake shows significant improvement in the cancellation performance compared to the classical SRLS algorithm.
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... As a further step, the matrix R(n+1) can be expressed through (8), (14) and (15) as: (16) where the first term in (16) can be expressed with the of (8) as P T X T (n)X(n)P = P T R(n)P. (17) As a result, the updated auto-correlation matrix is expressed from the previous results using the permutation matrix and the identity vector. ...
... As a further step, the matrix R(n+1) can be expressed through (8), (14) and (15) as: (16) where the first term in (16) can be expressed with the of (8) as P T X T (n)X(n)P = P T R(n)P. (17) As a result, the updated auto-correlation matrix is expressed from the previous results using the permutation matrix and the identity vector. ...
... The auto-correlation matrix can also be expressed also through dyadic product as [16], [17]: ...
... The Doppler effect estimation must be robust and resilient. A wide discussion to diminishing and compensating Doppler effect has been represented in [80]. ...
... So there is a higher spread delay. The possible approach to avoid this issue is also mentioned in [80]. Mostly described factors are caused by the chemical and physical water properties in terms of temperature, salinity, density and by their spatio-temporal variations. ...
More than 75% of the Earth surface is covered by water in the form of oceans. The oceans are unexplored and very far-fetched to investigate due to distinct phenomenal activities in the underwater environment. Underwater wireless communication (UWC) plays a significant role in observation of marine life, water pollution, oil and gas rig exploration, surveillance of natural disasters, naval tactical operations for coastal securities and to observe the changes in the underwater environment. In this regard, the widespread adoption of UWC has become a vital field of study to envisage various military and commercial applications that have been growing interest to explore the underwater environment for numerous applications. Acoustic, Optical and RF wireless carriers have been chosen to be used for data transmission in an underwater environment. The internet of underwater things (IoUT) and next-generation (5G) networks have a great impact on UWC as they support the improvement of the data rate, connectivity, and energy efficiency. In addition to the potential emerging UWC techniques, assisted by 5G network and improve existing work is also focusing in this study. This survey presents a comprehensive overview of existing UWC techniques, with possible future directions and recommendations to enable the next generation wireless networking systems in the underwater environment. The current project schemes, applications and deployment of latest amended UWC techniques are also discussed. The main initiatives and contributions of current wireless communication schemes in underwater for improving quality of service and quality of energy of the system over long distances are also mentioned.
... As compared to existing works, our main contributions are summarized below: 1) A low complexity RLS-type algorithm for SISO sparse system identification with Homotopy, dichotomous coordinate descent (DCD) and reweighting iterations, exponential-weighted Homotopy RLS-DCD (EW-HRLS-DCD) algorithm [28], [29], is extended to estimate time-varying sparse MIMO UWA channels. The proposed adaptive channel estimator based on the EW-HRLS-DCD algorithm, can capture the inherent sparsity of the MIMO UWA channel, leading to significant improvement in the performance compared with the classical RLS algorithm and other sparse RLS algorithms [30]. Its complexity is only linear in the length of the estimated channel. ...
... with respect to the matrix ∆H(k), where W ∈ R M×L + is a weight matrix formed by reshaping the M L × 1 vector w, and C(k|k − 1) is given by (30). ...
Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output (SISO) system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori softdecision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes.
... It is seen that the proposed DoA estimator and beamformer (introduced in Section IV-B and used in SFs 3, 4, and 5), reduce the receiver complexity by 8-15 times compared to the receiver with the SF using fractional delays (SF 6). Note that a single equalizer branch requires about 8.3 × 10 7 MACs (see the complexity analysis in [25] and [40]). ...
Underwater acoustic (UWA) communication channels are characterized by the spreading of received signals in space (direction of arrival) and in time (delay). The spread is often limited to a small number of space-time clusters. In this paper, the spacetime clustering is exploited in a proposed receiver designed for guard-free orthogonal frequency-division multiplexing (OFDM) with superimposed data and pilot signals. For separation of space clusters, the receiver utilizes a vertical linear array (VLA) of hydrophones, whereas for combining delay-spread signals within a space cluster, a time-domain equalizer is used. We compare a number of space-time processing techniques, including a proposed reduced-complexity spatial filter, and show that techniques exploiting the space-time clustering demonstrate an improved detection performance. The comparison is done using signals transmitted by a moving transducer, and recorded on a 14-element non-uniform VLA in sea trials at distances of 46 km and 105 km.
... To obtain good channel estimation performance, adaptive algorithms have been employed extensively [2], e.g., classic recursive least square (RLS) adaptive filters [3]. However, the classic RLS adaptive filters have low performance and high complexity when estimating channels with large delay spreads [4], [5]. For improving the performance, sparse adaptive filters were proposed [6]- [8]. ...
... In [5] and [8], the convergence of exponential-window was compared with that of a sliding-window, and the comparison results showed that the sliding-window provides a faster convergence to the steady-state. In [10], an exponentialwindow homotopy RLS-DCD adaptive filter possessing a high performance and low complexity was proposed. ...
In this paper, a sparse recursive least squares (RLS) adaptive filter is investigated in application to channel estimation in underwater acoustic (UWA) channels. The adaptive filter is based on sliding-window, homotopy, and dichotomous coordinate descent iterations. It is used in a multi-antenna receiver of an UWA communication system with guard-free orthogonal frequency division multiplexing (OFDM) signals and superimposed pilot symbols. More specifically, it is used for channel estimation in the channel-estimate-based equalizer. We compare the sliding-window homotopy RLS adaptive filter with the exponential-window homotopy and classic RLS algorithms. The results show that the proposed algorithm provides an improved performance compared to the other adaptive filters. The comparison is based on signals recorded on a 14-element vertical antenna array in sea trials at a distance of 105 km transmitted by a fast moving transducer. In these conditions, error-free transmission is achieved with a spectral efficiency of 0.33 bit/s/Hz.
... A possible approach to avoid this complexity increment is to employ the knowledge that the acoustic channel is intrinsically sparse. For references which exploit this property for estimating the channel impulse response, see [47], [58]- [63]. ...
The increasing exploitation of natural resources under water, particularly in the sea, has ignited the development of many technological advances in the domains of environmental monitoring, oil and gas exploration, warfare, among others. In all these domains, underwater wireless communications play an important role, where the technologies available rely on radio-frequency, optical, and acoustic transmissions. This paper surveys key features inherent to these communication technologies, putting into perspective their technical aspects, current research challenges, and to-be-explored potential. In the end we list several technical challenges that are addressed with the aid of signal processing tools.
