Improving link quality of UWB communication links by means of PSWF-Basis persuit denoising

Dullaert, Wouter; Rogier, Hendrik; Camillis, L. De; Dhaene, Tom

doi:10.1109/apwc.2011.6046748

Cited by 9 publications

(6 citation statements)

References 5 publications

(7 reference statements)

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“…† The gamma distribution was used for average fading duration. † The permissible parameters were adopted of IEEE802.15.3, IEEE802.15.5 and IEEE802.16e protocols which support low power communication in WBANs [25]. † The MFCs characteristics were simulated in both time and frequency domains.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this section, MFCs’ features such as PL, PDP and detectable delay are simulated. The following assumptions were made for simulation: The MFCs’ parameters extracted from measured channel transfer functions (CTFs) for various frequency bands permissible of UWB between 400 MHz and 11 GHz. The MFCs in this band cover the ISI band as well as whole frequency bands of the UWB. The biomedical data was adopted from MIT‐BIH database. The finite difference time domain procedure was used to simulate MFCs in UWB close to the human body. The log‐normal distribution was used to investigate SSF of MFCs. The exponential distribution was used for arrival paths at GW. The gamma distribution was used for average fading duration. The permissible parameters were adopted of IEEE802.15.3, IEEE802.15.5 and IEEE802.16e protocols which support low power communication in WBANs [25]. The MFCs characteristics were simulated in both time and frequency domains. To simulate the MFCs, the channel links in WBANs are divided into two main parts: the channel between the WNs on/in the body to the GW and the channel between the GW to the AP [26].…”

Section: Simulation Resultsmentioning

confidence: 99%

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New channel model for wireless body area network with compressed sensing theory

Balouchestani

Raahemifar

Krishnan

2013

IET wirel. sens. syst.

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Wireless body area networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body to collect vital biomedical data for providing a Continuous Health Monitoring System for diagnostic and therapeutic purposes. To fully exploit the benefits of WBANs the power consumption and sampling rate should be restricted to a minimum. The power usage can be minimised by optimising the features of multipath fading channels (MFCs) such as the number of arrival paths. That is why an improving of MFCs as well as a simple and generic channel model is inevitably required. With this in mind, compressed sensing (CS) theory, as a new sampling procedure, is employed to MFCs. Advance WBANs with the authors new model for MFCs based on CS theory will be able to deliver healthcare not only to patients in hospital and medical centres; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. The authors simulation results illustrate 20% reduction for path loss and 10% for bit-error rate at gate way (GW). The simulation results also confirm that signal amplitude at GW increases by 25%, which will result in an increase, in the distance, between transmitter and receiver sensors.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

New channel model for wireless body area network with compressed sensing theory

Balouchestani

Raahemifar

Krishnan

2013

IET wirel. sens. syst.

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“…The collected data are then transmitted to APs using specific types of wireless networks such as WPAN or WLAN for diagnostic and therapeutic purposes. Some protocols are given for supported communication of biomedical data between sensors with APs [125]. The IEEE802.11 protocol supports WLANs for hospital and home cases; the IEEE802.16 can be employed for the ambulance helicopter; and the IEEE802.11P is proposed for the ambulance van case [126].…”

Section: Channels In Wbansmentioning

confidence: 99%

Wireless Body Area Networks Based on Compressed Sensing Theory

Asli¹

2021

Preprint

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In this research, the effective sampling method known as Compressed Sensing (CS) theory is applied to Wireless Body Area Networks (WBANs) to provide low power and low sampling-rate wireless healthcare systems and intelligent emergency care management systems. The fundamental contribution of this work can be divided into three areas. 1) We propose two new algorithms in the sensing, measurement, and processing area to compress biomedical data. 2) In the communication area, one new channel model based on CS theory is defined to transmit compressed data to the receiver side. 3) In the receiver side or reconstruction area, two new algorithms for recovering the original biomedical data are presented to recover the original data. Our results will be divided into three areas. 1) We employ the proposed algorithms to WBANs with a single biomedical signal (i.e. Electroencephalography [ECG] signals as a sample signal). In this area, the simulation results illustrate an increment of 10% improved for sensitivity in receiving compressed ECG signals. The simulation results also illustrate a 25% reduction of Percentage Root-mean-square Difference (PRD) for ECG signals on the receiver side. In addition, they confirm the ability of CS to maximize the prediction level for received the ECG signal at either Gate Ways (GWs) or Access Points (APs). 2) We illustrate that the proposed algorithms can be employed in WBANs with multiple biomedical signals to enhance current health care systems into low-power wireless healthcare systems. In this area, the simulation results confirm that for a particular WBAN, including N biomedical signals, the sampling-rate can be reduced by 25-35% and power consumption by 35-40%, without sacrificing the network’s performance. 3) Here improvements for wireless channel feature between BWSs and either GWs or APs are shown. In this area, the results demonstrate that CS is able to maximize signal amplitude to 25-30% at the receiver as well as distance between transmitter and receiver BWS to 30%. Moreover, these results confirm that path loss can be reduced to 25%.

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“…In the step 2, the initial square matrix is used for each of the sensing matrices in the experiments [19]. In the step 3, a Row Selection Scheme (RSS) is applied to reduce the number of rows from N to M [20]. Two RSSs approaches are compared: (1) matrix.…”

Section: Performance Measurementioning

confidence: 99%

Robust Low-Power Algorithm for Random Sensing Matrix for Wireless ECG Systems Based on Low Sampling-Rate Approach

Balouchestani

Raahemifar

Krishnan

2013

JSIP

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The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient's mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 25% decrease in sampling-rate and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best compression performance with the highest energy efficiency for random sensing matrix.

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Improving link quality of UWB communication links by means of PSWF-Basis persuit denoising

Cited by 9 publications

References 5 publications

New channel model for wireless body area network with compressed sensing theory

New channel model for wireless body area network with compressed sensing theory

Wireless Body Area Networks Based on Compressed Sensing Theory

Robust Low-Power Algorithm for Random Sensing Matrix for Wireless ECG Systems Based on Low Sampling-Rate Approach

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