Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

Ballal, Tarig; Al-Naffouri, Tareq Y.; Ahmed, Syed Faraz

doi:10.1109/tcomm.2015.2480092

Cited by 15 publications

(9 citation statements)

References 25 publications

(42 reference statements)

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“…A. Extended Channel Model 1) Group or Clustered Sparsity: Next, group sparsity [25] is considered, where the few non-zero (i.e., significant) channel taps occur in clusters/blocks in a structured manner, see Fig. 1 a).…”

Section: Algorithm Developmentmentioning

confidence: 99%

Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation with Fast Convergence

De¹

2021

Preprint

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Section: Algorithm Developmentmentioning

confidence: 99%

Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation with Fast Convergence

De¹

2021

Preprint

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“…Noting that γ s i is the same in all local coordinates, ϕ j i for o 1 (ϕ 1 ij ) and o 2 (ϕ 2 ij ) should be obtained as in (6). By substituting ϕ 1 ij and ϕ 2 ij into (7), one can obtain divergence angles for transmitters located at tangent points o 1 (θ 1 ij ) and o 2 (θ 2 ij ), respectively.…”

Section: Divergence Angles Under the Absence Of Patmentioning

confidence: 99%

End-to-End Performance Analysis of Underwater Optical Wireless Relaying and Routing Techniques Under Location Uncertainty

Çelik

Saeed

Shihada

et al. 2020

IEEE Trans. Wireless Commun.

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On the contrary of low speed and high delay acoustic systems, underwater optical wireless communication (UOWC) can deliver a high speed and low latency service at the expense of short communication ranges. Therefore, multihop communication is of utmost importance to improve degree of connectivity and overall performance of underwater optical wireless networks (UOWNs). In this regard, this paper investigates relaying and routing techniques and provides their end-to-end (E2E) performance analysis under the location uncertainty. To achieve robust and reliable links, we first consider adaptive beamwidths and derive the divergence angles under the absence and presence of a pointing-acquisitioning-and-tracking (PAT) mechanism. Thereafter, important E2E performance metrics (e.g., data rate, bit error rate, transmission power, amplifier gain, etc.) are obtained for two potential relaying techniques; decode & forward (DF) and optical amplify & forward (AF). We develop centralized routing schemes for both relaying techniques to optimize E2E rate, bit error rate, and power consumption. Alternatively, a distributed routing protocol, namely Light Path Routing (LiPaR), is proposed by leveraging the range-beamwidth tradeoff of UOWCs. LiPaR is especially shown to be favorable when there is no PAT mechanism and available network information. In order to show the benefits of multihop communications, extensive simulations are conducted to compare different routing and relaying schemes under different network parameters and underwater environments.

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“…Note, (i) and (ii) are widely used in underwater communications for a range of applications; however, they have limitations, which can be addressed by adopting a third option based on optical wave propagations. The underwater optical wireless communications (UOWC) technology offers much higher data rates (i.e., in the order of Gbps) [1,7], lower latency (compared with acoustic waves) [8], lower attenuation (at the blue-green (450-580 nm) transmission band), and higher-level of security at the physical layer due to well-confined light beams in point-to-point communications [9,10].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Scattering Noise in Underwater Optical Wireless Communications

Majlesein

Ghassemlooy

2021

Sci

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In underwater optical wireless communications (UOWC), scattering of the propagating light beam results in both intensity and phase variations, which limit the transmission link range and channel bandwidth, respectively. Scattering of photons while propagating through the channel is a random process, which results in the channel-dependent scattering noise. In this work, we introduce for the first time an analytical model for this noise and investigate its effect on the bit error rate performance of the UOWC system for three types of waters and a range of transmission link spans. We show that, for a short range of un-clear water or a longer range of clear water, the number of photons experiencing scattering is high, thus leading to the increased scattering noise. The results demonstrate that the FEC limit of 3×10−3 and considering the scattering noise, the maximum link spans are 51.5, 20, and 4.6 m for the clear, coastal, and harbor waters, respectively.

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Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

Cited by 15 publications

References 25 publications

Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation with Fast Convergence

Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation with Fast Convergence

End-to-End Performance Analysis of Underwater Optical Wireless Relaying and Routing Techniques Under Location Uncertainty

Investigation of the Scattering Noise in Underwater Optical Wireless Communications

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