Lab Manual
Advanced Electronics Measurement 4 (22/23)
I. Introduction
Cardiovascular diseases (CVD) are known to be the most widespread causes to death. Therefore, detecting earlier signs of cardiac anomalies is of paramount importance to ease the treatment of any cardiac complication and take immediate actions before the initiation of severe symptoms. Atrial Fibrillation is the most common cardiac rhythm disorder, accounting for one-third of hospitalizations for cardiac disturbances, and associated with increased risk of morbidity and mortality. Thanks to the development of wearable sensor devices having wireless transmission capabilities, there is a demand to develop real time systems able to accurately analyse ECG and detect cardiac irregularities. Recent technological advances in signal processing, power consumption management, sensors design and miniaturization can revolutionize the way how healthcare services are organized and regulated. While the importance of continuous monitoring of ECG signals to detect cardiac anomalies is generally accepted in preventative medicine, there remain numerous challenges to its widespread adoption. In this coursework, the proposed healthcare approach is based on implanting sensors in the human body to collect real time ECG changes in order to monitor the patient’s health status no matter where they are. The information will be immediately transmitted wirelessly to an external processing unit. This device will instantly transmit all information in real time to the doctors throughout the world. If an emergency is detected, the physicians will directly inform the patient through the computer system by sending appropriate messages or alarms. An example of a WBAN architecture for ECG monitoring is shown in figure 1. Although real-time patient monitoring field is not a new topic in wireless medical applications, researchers and industries are investing a lot of effort and research funds to it. In this coursework, experimental studies have been conducted using MIMO techniques in the industrial, scientific, and medical (ISM) band at 2.45 GHz where off- body channels will be analysed.
Fig. 1: An illustrative top view identifying TX and RX positions
Durham University
Dr Ismail Ben Mabrouk
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II. Antenna setup
2X2 MIMO textile patch antennas have been used for transmission and reception of the RF signal, at 2.4GHz. The antenna elements are spaced by half wavelength (62.5 mm at 2.4 GHz) apart from one another as shown in Fig. 2.
Figure 2: Textile antenna array and its implementation scenarios
The scattering parameters of the antenna array have been simulated using CST antenna design software based on the Finite Integration Technique. The simulation is compared to the measurements and shown in Fig. 3. There is an isolation of about 17 dB and the return loss is below the target -10 dB at 2.4 GHz.
10 0 -10 -20 -30 -40 -50 -60 -70 -80 -90
Fig.3: Measured and simulated S-parameter for the patch.
In an antenna array, the isolation between antenna elements is a critical parameter in many practical applications such as MIMO communication systems. In fact, the spacing between elements is generally
set to 𝜆#2. In this case, the impact of mutual coupling on antenna performance is no longer negligible.
Since mutual coupling affects the current distribution, which results in deformations of the radiation pattern of each antenna element and, consequently, the MIMO system’s performance will be affected due to channel correlation. Typically, in MIMO systems, independent and uncorrelated signaling between channels is required to improve channel capacity. The textile antenna provides a gain of about 8 dBi with a beam width of 92.4° and 71.3° in the azimuth and elevation planes respectively. Note that the back lobes are small and the front-to-back ratio is about 25 dB. The textile antenna has a directional radiation pattern. In fact, a backing surface is incorporated to reduces rearward radiation, which increases the gain in the forward direction.
III. Measurement Campaign
Measurement campaign are performed in frequency domain using the frequency channel sounding technique based on measuring 𝑆%& parameter using a network analyzer (Agilent E8363B). In fact, the system measurement setup, as shown in Figure 4, consists of a network analyzer (PNA), 2X2 MIMO antenna set, two switches, one power amplifier for the transmitting signal and one low noise amplifier for the receiving signal. Both amplifiers have a gain of 30 dB.
S11 simulation value S11 measured value S21 simulation value S21 measured value
2,0 2,2 2,4
2,6 2,8 3,0
Frequency (GHz)
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S-Parameter Magnitude (dB)
Fig. 4: Measurement setup.
For all experiments, the receiver (on patient chest) remained fixed, while the transmitter (base
station) changed its position along the gallery, from 1 meter up to 10 meters far from the
transmitter with intervals of 0.5 m. Parameters of the channel sounding measurements should
be carefully selected in order to assure adequate multipath resolution and at the same time
reducing the total time required for the frequency sweep. In fact, the PNA sweeps the frequency
range from 2 GHZ to 3 GHz for 6401 points and records the 6401 tones. So the frequency step
is 156.22 KHz which corresponds to time domain duration of 6401 ns. In other words, the
measurement system is capable of catching multipath components that arrive with a delay up
to 6401 ns. This duration of impulse response is found to be long enough for such indoor
environment. The calibration is performed with the transmitting (𝑇 ) and receiving (𝑅 ) ((
antenna apart 0.5m separation distance. This 0.5m T-R separation distance 𝑑+ is chosen to be the reference distance for the large-scale path loss model. Also, during the measurement, the wireless channel is assumed to be static with no significant variations and the height of the transmitting and receiving antennas were maintained at 1.5m and 1m respectively above the ground level. The transmit power was set to 10dBm.
IV. Measurement results samples 1. Power Delay Profile
A wireless channel can be described by its impulse response. For any fixed location between the transmitter and the receiver, under static conditions, the overall average of the magnitude squared of the impulse response is referred to as the power delay profile (PDP) and is given by:
PDP(t) = 〈|h(𝑡)|%〉 (1) The scattering parameter 𝑆%& was measured over a certain bandwidth and the Inverse Discrete
Fourier Transformation (IDFT) was applied.
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Fig. 5: PDP samples of MIMO subchannel H11 computed for all Tx-Rx distances and normalized with respect to 0.5m reference.
Fig. 6: MIMO subchannel H11 of all f all Tx-Rx distances normalized with respect to 0.5m reference. 2. RMS Delay (𝝉𝑹𝑴𝑺)
The RMS delay spread roughly characterizes the multipath propagation in the delay domain. It is the square root of the second central moment of the averaged power and it is defined as :
whereτ>isthemeanexcessdelay,τ istheaveragepowerandPisthereceivedpower(inlinearscale)
at τL corresponding arrival time of the 𝑖NO path. The RMS delay spread should be calculated in terms of
the separation distance 𝑑P(QR(. Threshold level of about 10 dB, 15 dB or 20 dB should be chosen in Durham University Dr Ismail Ben Mabrouk
∑BCDF ∑BCD %
𝜏:;<==𝜏 −(𝜏̅) ==E E −GE EH (2)
∑E BE ∑E BE
order to suppress the noise effect on the statistics of multipath arrival times. Such threshold level is considered as a relevant choice for reliable channel-parameter estimation.
3. Path Loss
Path loss in the channel is normally distributed in decibel (dB) with a linearly increasing mean and is modeled as :
𝑃𝐿UV(𝑑+) = 𝑃𝐿UV (𝑑+) + 10αlog (UW) + X (3)
where 𝑃𝐿UV (𝑑+) is the mean path loss at the reference distance 𝑑+, 10αlog (d/𝑑+) is the mean
path loss referenced to 𝑑+, and X is a zero mean Gaussian random variable expressed in dB.
The mean path loss at 𝑑+ and the path loss exponent α were determined through least square
regression analysis. The difference between this fit and the measured data is represented by the
Gaussian random variable X. Talble 2 lists the values obtained for α and 𝜎 (standard deviation
4. Channel Capacity
If we consider a system composed on m transmitting antennas and n receiving antennas, the maximum capacity of a memoryless MIMO narrow band channel expressed in bits/s/Hz, with a uniform power allocation constraint and in the presence of additional white Gaussian noise is given by Foschini et al.:
C = log2 det (Im + s.HHH) (4)
where s is the average signal to noise ratio per receiving antenna; Im denotes the identity matrix of size m, the upper script H represents the hermitian conjugate of the matrix and det(X) means the determinant of a matrix X. The average channel capacity should be calculated while setting a constant signal to noise ratio (SNR) of 10 dB, whatever the position of the receiver and the noise floor is estimated around - 90dBm. The relationship between the channel capacity C and the distance 𝑑P(QR( based on equation (4) is shown in Fig.7.
Durham University
Dr Ismail Ben Mabrouk
Fig. 7: TX–RX range-dependent MIMO capacity
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III. REQUIRED TASQS
Based on the provided measured data and the description and samples provided in the lab manual, each student has to submit a complete report where the following sections should be presented.
Section 1. Introduction: Discuss the importance of Wireless Body Area Networks (WBANs) in modern healthcare applications. Provide information about the experimental measurement setup and about the provided datasets. (10%)
Section 2. State of the Art: Please discuss the literature review related to the integration of wearable sensors in order to monitor patient health within a hospital room. Make sure to cite a couple of IEEE papers as references. (10%)
Section 3. Post-Processing Results: Please calculate and thoroughly discuss:
1-Channel Frequency Responses and Power Delay Profiles (PDPs) at 3m and 10m distances.
Compare them. (20%)
2-RMS Delay Spread and Coherence bandwidth with respect to at all distances. (10%)
3-Path Loss exponent (n) and standard deviation (std). (20%)
4-Channel Capacity (20%)
Section 4. Conclusion: Please summarise all presented results. Discuss whether you think if your wireless link is reliable or not based on your results. Why? (10%)
Durham University Dr Ismail Ben Mabrouk
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