Maximum Likelihood Detection in the Presence of Non-Gaussian Jamming

Almahorg, Khalid A.; Gohary, Ramy H.

doi:10.1109/tsp.2020.3026983

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…In these cases, our numerical results suggest that the advantage of the exact ML detector over the Gaussian approximation one can be significant. Note:The proofs of all theorems, corollaries, and lemmas are given in the extended version of this work in [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Detection in the Presence of Non-Gaussian Jamming

Almahorg

Gohary

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider a scenario in which a transmitter sends complex multidimensional symbols to a receiver in the presence of a proactive continuous jammer emitting a zero-mean complex Gaussian signal over an unknown complex Gaussian channel. The complex Gaussian signal transmitted over the unknown complex Gaussian channel induces a non-Gaussian signal at the receiver. We develop the optimal maximum likelihood (ML) detector for cases in which the receiver has full channel state information (CSI), full channel distribution information (CDI), or partial CDI about the transmitter channel. The jammer CDI is either partially or fully available at the receiver. We identify cases in which the non-Gaussian signals resulting from the jammer's transmission can be approximated by Gaussian signals to reduce the computational cost without compromising optimality of detection. Furthermore, we identify cases in which the Gaussian approximation ML detector is not equivalent to the exact ML detector. In these cases, we show that the advantage of the exact ML detector over the Gaussian approximation one can be significant.

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Section: Introductionmentioning

confidence: 99%

Optimal Detection in the Presence of Non-Gaussian Jamming

Almahorg

Gohary

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…These simulations revealed the superiority of the ML detector based on the exact likelihood functions over the one based on Gaussian approximations for the case of unit-norm constellations. This is in sharp contrast with the single antenna case wherein the two detectors are equivalent for this type of constellations [93]. Simulation results showed the significant power advantage of the exact ML detector, especially when the transmitter has a strong LOS component.…”

Section: Discussionmentioning

confidence: 81%

“…This figure illustrates the sharp contrast between the behaviour of the exact and approximate ML detectors for the cases of single and double receive antennas. In particular, as shown in [93], the performance of these detectors coincides when the signalling constellation has a unit norm and the receiver has one antenna. However, the exact ML detector offers a significant performance advantage over its approximate counterpart when the receiver has two antennas, albeit with a higher computational cost.…”

Section: Examplementioning

confidence: 87%

Optimal Detection in the Presence of Non-Gaussian Jamming

Almahrog¹

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Declaration of AuthorshipI hereby certify that this thesis is entirely my own original work except where otherwise indicated. I am aware of the University's regulations concerning plagiarism, including those concerning consequent disciplinary actions. Any use of the works of any other author, in any form, is properly acknowledged at their point of use. 1 F2 (•; •, •; •) The regularized generalized hypergeomtric function 1 F 1 (•; •; •) The confluent hypergeometric function of the first kind xiv Chapter 1 for Mobile Communications (GSM) and the Universal Mobile Telecommunications System (UMTS) networks [4] [5], the widely used Long Term Evolution (LTE) networks [6], and the evolving fifth Generation (5G) networks [7] [8] which are anticipated to provide the communication infrastructure for public safety, disaster relief, and rescue operations.• Wireless Local Area Networks (WLAN) [9] that are widely used for providing connectivity to internet gateways, printers and other shared resources in homes, office buildings, schools, universities, hospitals, malls, airports, and so on.• Low Power Wide Area Networks (LPWANs) [10] which is a group of technologies that have many attractive features such as operating at low cost, having high power efficiency, connecting large number of low rate battery-powered devices over up to 5 kilometers in urban zones and 40 kilometers in rural zones. These features make LPWANs applicable in smart agriculture through monitoring of cattle, soil moisture, levels of chemicals in soil; smart city through smart street lights, parking, waste management, metering, flood sensors; smart healthcare through tracking of patients with Alzheimer's and dementia, monitoring of refrigerators storing vaccines, medications, blood, and so on.• Low-Rate Wireless Personal Area Networks (LR-WPANs) and ZigBee Networks [11][12][13] which are employed in industrial control, environmental monitoring, health date collection, home automation, intruder and smoke warning, indoor localization, and disaster response.

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Optimal Detection in Barrage-Jammed Single-Input Double-Output Systems

Almahorg,

Gohary

2024

2024 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

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Maximum Likelihood Detection in the Presence of Non-Gaussian Jamming

Cited by 4 publications

References 26 publications

Optimal Detection in the Presence of Non-Gaussian Jamming

Optimal Detection in the Presence of Non-Gaussian Jamming

Optimal Detection in the Presence of Non-Gaussian Jamming

Optimal Detection in Barrage-Jammed Single-Input Double-Output Systems

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