# Multistandard Receiver Design for Telemedicine Monitoring System

Authors: Hongmei Wang; Chong Yao; Faguang Wang; Shiyin Li; Sanghyuk Lee
Journal: Journal of Sensors
 (2018)

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## Abstract

In short-distance wireless communications for telemedicine monitoring, different medical data measurement equipment has different wireless transmission modes. A multistandard receiver is designed that can adapt to different medical data measuring equipment. Using a second-order bandpass sampling for the design of antialiasing filters, two aliasing signals can be separated. Simultaneously, constraint conditions for sampling frequency are not as critical. The design is useful for a multistandard receiver in a telemedicine monitoring system and has the advantages such as saving spectrum resources and facilitating spectrum planning.


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## Body

## 1. Introduction

With the rapid development of Internet of things technology and short-distance wireless communications technology, telemedicine monitoring network technology has become a hot research topic [1]. This technology allows doctors to remotely diagnose the condition of patients and provide timely medical advice. In the remote medical monitoring network, the transmission of local medical data, such as heartbeat, respiration, blood oxygen, and pulse, relies mainly on short-distance wireless communications technology such as Bluetooth, Wi-Fi, and ZigBee [2] These short-distance wireless communications technologies have different applications and transmission rates. They have their own advantages and disadvantages, and each technology is constantly adapting to different types or standards of medical data transmission. The objective of our study is to design a multistandard receiver that can adapt to different communication standards so that various medical data can be flexibly received.At present, short-distance wireless communications technologies for telemedicine monitoring, such as Bluetooth, Wi-Fi, and ZigBee, all use the 2.4 GHz frequency range; therefore, spectrum resources are absent. Improving spectrum utilization is a very important problem in the receiver. To avoid aliasing caused by bandpass sampling (BPS), most researchers consider choosing the lowest possible sampling frequency in order to reduce the burden of subsequent digital processing without aliasing in the spectrum [3–5]. Many researchers have tried to find a new algorithm to simplify the frequency selection process [6–8]. However, preventing aliasing will reduce spectrum utilization and the complexity of the computation process will increase the difficulty of implementation.This work proposes a solution for aliasing in receivers that can reduce the limitations in sampling frequency, improve spectrum utilization, and realize multistandard receivers. In previous work, a sampling frequency that is twice the signal bandwidth was used to receive two-band signals [9]. This method allowed two signals to overlap with each other and separate using interplants, which eases the restriction on sampling frequency. However, this method did not provide the constraint conditions for sampling frequency and the solution for aliasing by more than two signals. Based on this method, this paper gives the constraint conditions for frequency selection and a method to process aliasing by more than two standard signals.This paper is organized as follows. Section2 gives the structure of telemedicine monitoring system and the method to design antialiasing filters to suppress aliasing. In Section 3, aliasing analysis and constraint conditions are given and analyzed. Section 4 presents the simulation results for the multistandard receiver and spectral analysis. Section 5 tests the proposed algorithm in hardware. Section 6 provides concluding remarks.


## 2. Multistandard Receiver

### 2.1. Structure of Telemedicine Monitoring System

The structure of telemedicine monitoring system is shown in Figure1.Figure 1
Structure of telemedicine monitoring system.Medical data, such as heartbeat, respiration, blood oxygen, and pulse, are measured by different equipment and transmitted by different wireless communications technology. The multistandard receiver samples the signals and removes aliasing. The received data are sent to the medical gateway and transmitted to the remote client via the Internet.The core of the system is the design of an antialiasing module that can separate the overlapped signals.


### 2.2. Antialiasing Filter Design

Assume that the primary signal is sampled at a sampling ratefs. Any signal in the frequency zone of index n expressed as [9]
(1)n−12fs<f<n+12fsis aliased into the first Nyquist zone f<fs/2, which is the frequency zone with index zero.Assume that four different standard signals,S1′, S2′, S3′, and S4′, with frequency zone index n1, n2, n3, and n4, respectively, are bandpass sampled at sampling frequency fs simultaneously. The sampled spectra are represented as S1, S2, S3, and S4, respectively, as shown in Figure 2, where signals S1 and S2 overlap in area 0 to f1, and signals S3 and S4 overlap in area f1 to fs/2.Figure 2
Spectra after BPS.In order to separate the overlapping signals, a second-order BPS sampling structure is designed, as shown in Figure3. Two analog-to-digital converters (ADCs), ADC A and ADC B, are used for BPS, where ADC B introduces time delay TΔ. The two sampled channels are referred to as channel A and channel B.Figure 3
Structure of the second-order BPS system.According to the characteristics of second-order BPS [4], the sampled signal spectra in the two channels satisfy the following equation:
(2)SiB=SiA⋅β−ni,where SiA (i = 1, 2, 3, 4) represents the signals sampled in channel A, and SiB represents the signals sampled in channel B, β=e−j2πT△fs. Because the negative and positive spectra are symmetrical, here, we discuss only the positive spectra.In Figure3, antialiasing filters FA1 and FB1 should be designed to suppress signals S2 and S4, and FA2 and FB2 should suppress signals S1 and S3.In area 0 tof1, S2 needs to be suppressed. Thus, in the frequency domain, FA1 and FB1 should satisfy
(3)S1AFA1+S1BFB1=S1A,S2AFA1+S2BFB1=0.After simplification,(4)FA1+β−n1FB1=1,FA1+β−n2FB1=0.From (2) and (3), we get
(5)FA1=−β−n2β−n1−β−n2,FB1=1β−n1−β−n2,f∈0,f1.In areaf1 to fs/2, S4 needs to be suppressed. Thus, in the frequency domain, FA1 and FB1 should satisfy
(6)S3AFA1+S3BFB1=S3A,S4AFA1+S4BFB1=0.After simplification,(7)FA1+β−n3FB1=1,FA1+β−n4FB1=0.From (2) and (7), we get
(8)FA1=−β−n4β−n3−β−n4,FB1=1β−n3−β−n4,f∈f1,fs2.Using the same method, we can get the expressions forFA2 and FB2 in the frequency domain, as shown as follows:
(9)FA2=−β−n1β−n2−β−n1,FB2=1β−n2−β−n1,f∈0,f1,FA2=−β−n3β−n4−β−n3,FB2=1β−n4−β−n3,f∈f1,fs2.


## 2.1. Structure of Telemedicine Monitoring System

The structure of telemedicine monitoring system is shown in Figure1.Figure 1
Structure of telemedicine monitoring system.Medical data, such as heartbeat, respiration, blood oxygen, and pulse, are measured by different equipment and transmitted by different wireless communications technology. The multistandard receiver samples the signals and removes aliasing. The received data are sent to the medical gateway and transmitted to the remote client via the Internet.The core of the system is the design of an antialiasing module that can separate the overlapped signals.


## 2.2. Antialiasing Filter Design

Assume that the primary signal is sampled at a sampling ratefs. Any signal in the frequency zone of index n expressed as [9]
(1)n−12fs<f<n+12fsis aliased into the first Nyquist zone f<fs/2, which is the frequency zone with index zero.Assume that four different standard signals,S1′, S2′, S3′, and S4′, with frequency zone index n1, n2, n3, and n4, respectively, are bandpass sampled at sampling frequency fs simultaneously. The sampled spectra are represented as S1, S2, S3, and S4, respectively, as shown in Figure 2, where signals S1 and S2 overlap in area 0 to f1, and signals S3 and S4 overlap in area f1 to fs/2.Figure 2
Spectra after BPS.In order to separate the overlapping signals, a second-order BPS sampling structure is designed, as shown in Figure3. Two analog-to-digital converters (ADCs), ADC A and ADC B, are used for BPS, where ADC B introduces time delay TΔ. The two sampled channels are referred to as channel A and channel B.Figure 3
Structure of the second-order BPS system.According to the characteristics of second-order BPS [4], the sampled signal spectra in the two channels satisfy the following equation:
(2)SiB=SiA⋅β−ni,where SiA (i = 1, 2, 3, 4) represents the signals sampled in channel A, and SiB represents the signals sampled in channel B, β=e−j2πT△fs. Because the negative and positive spectra are symmetrical, here, we discuss only the positive spectra.In Figure3, antialiasing filters FA1 and FB1 should be designed to suppress signals S2 and S4, and FA2 and FB2 should suppress signals S1 and S3.In area 0 tof1, S2 needs to be suppressed. Thus, in the frequency domain, FA1 and FB1 should satisfy
(3)S1AFA1+S1BFB1=S1A,S2AFA1+S2BFB1=0.After simplification,(4)FA1+β−n1FB1=1,FA1+β−n2FB1=0.From (2) and (3), we get
(5)FA1=−β−n2β−n1−β−n2,FB1=1β−n1−β−n2,f∈0,f1.In areaf1 to fs/2, S4 needs to be suppressed. Thus, in the frequency domain, FA1 and FB1 should satisfy
(6)S3AFA1+S3BFB1=S3A,S4AFA1+S4BFB1=0.After simplification,(7)FA1+β−n3FB1=1,FA1+β−n4FB1=0.From (2) and (7), we get
(8)FA1=−β−n4β−n3−β−n4,FB1=1β−n3−β−n4,f∈f1,fs2.Using the same method, we can get the expressions forFA2 and FB2 in the frequency domain, as shown as follows:
(9)FA2=−β−n1β−n2−β−n1,FB2=1β−n2−β−n1,f∈0,f1,FA2=−β−n3β−n4−β−n3,FB2=1β−n4−β−n3,f∈f1,fs2.


## 3. Constraint Conditions

### 3.1. Aliasing Analysis

Assume an RF signal with central frequencyfc bandpass sampled at sampling frequency fs, the central frequency in the first Nyquist zone (called mirroring frequency) can be defined as
(10)fAF=fc−rfs2,r=fcfs/2iseven,r+1fs2−fc,r=fcfs/2isodd.where • means to round down.Assume three bandpass signals that are received simultaneously with mirroring frequencyfAF1, fAF2, and fAF3. Figure 4 shows the relationship between the mirroring frequency and sampling frequency fs.Figure 4
The relationship between signal mirroring frequency and sampling frequency.Consider signalfAF2, with different selections of fs, it has four kinds of aliasing:
(i)
Aliasing by self-image spectrum around zero frequency, as shown in area A of Figure4
(ii)
Aliasing by self-image spectrum aroundfs/2 frequency, as shown in area B of Figure 4
(iii)
Aliasing by another signal, as shown in area C of Figure4
(iv)
Aliasing by another two signals, as shown in area D of Figure4The traditional way of selectingfs is to avoid all kinds of aliasing, which limits the area of fs, and usually needs a large amount of calculations. In Section 2, aliasing caused by image spectrum and overlap with two signals can be solved by software. Hence, when we select fs, less limiting conditions are allowed.


### 3.2. Constraint Condition

According to the abovementioned rules, the sampling rate constraint conditions are given to allow the presence of two aliasing signals at the same location after sampling. The details can be expressed by the following equations:(11)fs≥2Bmax+2BG.If(12)fDFi<B2+BG,then(13)Bj2+BG<fDFj.If(14)fDFi>fs2−Bi2+BG,then(15)fDFj<fs2−Bj2+BG,where i=2,3,…,N and j=2,3,…,i−1.IffAFli>fAFlj, then fAFhk<fAFli or fAFlk>fAFhi; if fAFli<fAFlj and fAFhi>fAFhj, then fAFhk<fAFlj or fAFlk>fAFhj; if fAFli<fAFlj and fAFlj<fAFhi<fAFhj, then fAFlk<fAFlj or fAFlk>fAFhi, where i=2,3,…,N, j=1,2,3,…,i−1, k∈1,N, k≠i, and k≠j.In the above constraint conditions,Bmax represents the maximum bandwidth of N bandpass signals; BG represents protection bandwidth; Bi, fDFi, fAFli, and fAFhi represent the bandwidth, central frequency, minimum frequency, and maximum frequency of signal i, respectively, in the first Nyquist region after BPS.Constraint 1.
It indicates that the sampling frequency needs to be two times greater than the maximum bandwidth of the signal (plus protection bandwidth).Constraint 2.
It means that only one signal is allowed to have zero-bound aliasing orfs/2-bound aliasing, that is, the spectral aliasing of the image itself.Constraint 3.
It means that only two aliasing signals are allowed in the same location. There are three cases for this constraint. Case afAFli>fAFlj is shown in Figure 5(a). Case b fAFli<fAFlj and fAFhi>fAFhj is shown in Figure 5(b). Case c fAFli<fAFlj and fAFlj<fAFhi<fAFhj is shown in Figure 5(c).Figure 5
(a) Aliasing diagram (Case a). (b) Aliasing diagram (Case b). (c) Aliasing diagram (Case c).
(a)
(b)
(c)


## 3.1. Aliasing Analysis

Assume an RF signal with central frequencyfc bandpass sampled at sampling frequency fs, the central frequency in the first Nyquist zone (called mirroring frequency) can be defined as
(10)fAF=fc−rfs2,r=fcfs/2iseven,r+1fs2−fc,r=fcfs/2isodd.where • means to round down.Assume three bandpass signals that are received simultaneously with mirroring frequencyfAF1, fAF2, and fAF3. Figure 4 shows the relationship between the mirroring frequency and sampling frequency fs.Figure 4
The relationship between signal mirroring frequency and sampling frequency.Consider signalfAF2, with different selections of fs, it has four kinds of aliasing:
(i)
Aliasing by self-image spectrum around zero frequency, as shown in area A of Figure4
(ii)
Aliasing by self-image spectrum aroundfs/2 frequency, as shown in area B of Figure 4
(iii)
Aliasing by another signal, as shown in area C of Figure4
(iv)
Aliasing by another two signals, as shown in area D of Figure4The traditional way of selectingfs is to avoid all kinds of aliasing, which limits the area of fs, and usually needs a large amount of calculations. In Section 2, aliasing caused by image spectrum and overlap with two signals can be solved by software. Hence, when we select fs, less limiting conditions are allowed.


## 3.2. Constraint Condition

According to the abovementioned rules, the sampling rate constraint conditions are given to allow the presence of two aliasing signals at the same location after sampling. The details can be expressed by the following equations:(11)fs≥2Bmax+2BG.If(12)fDFi<B2+BG,then(13)Bj2+BG<fDFj.If(14)fDFi>fs2−Bi2+BG,then(15)fDFj<fs2−Bj2+BG,where i=2,3,…,N and j=2,3,…,i−1.IffAFli>fAFlj, then fAFhk<fAFli or fAFlk>fAFhi; if fAFli<fAFlj and fAFhi>fAFhj, then fAFhk<fAFlj or fAFlk>fAFhj; if fAFli<fAFlj and fAFlj<fAFhi<fAFhj, then fAFlk<fAFlj or fAFlk>fAFhi, where i=2,3,…,N, j=1,2,3,…,i−1, k∈1,N, k≠i, and k≠j.In the above constraint conditions,Bmax represents the maximum bandwidth of N bandpass signals; BG represents protection bandwidth; Bi, fDFi, fAFli, and fAFhi represent the bandwidth, central frequency, minimum frequency, and maximum frequency of signal i, respectively, in the first Nyquist region after BPS.Constraint 1.
It indicates that the sampling frequency needs to be two times greater than the maximum bandwidth of the signal (plus protection bandwidth).Constraint 2.
It means that only one signal is allowed to have zero-bound aliasing orfs/2-bound aliasing, that is, the spectral aliasing of the image itself.Constraint 3.
It means that only two aliasing signals are allowed in the same location. There are three cases for this constraint. Case afAFli>fAFlj is shown in Figure 5(a). Case b fAFli<fAFlj and fAFhi>fAFhj is shown in Figure 5(b). Case c fAFli<fAFlj and fAFlj<fAFhi<fAFhj is shown in Figure 5(c).Figure 5
(a) Aliasing diagram (Case a). (b) Aliasing diagram (Case b). (c) Aliasing diagram (Case c).
(a)
(b)
(c)


## 4. Simulation Results for Multistandard Receiver

Taking four input signals as examples, the parameters are shown in Table1. The sampling frequency is 24 MHz. Time delay between two sampling channels is 1200 ps.Table 1
Parameters of four input signals.
Number
Central frequency
Signal types
Frequency zone index
Frequency offset
S1
2.428 GHz
Bluetooth
101
4 MHz
S2
2.452 GHz
Wi-Fi
102
4 MHz
S3
2.408 GHz
ZigBee
100
8 MHz
S4
2.464 GHz
Bluetooth
103
8 MHzAfter BPS, signalsS1 and S2 overlap at around 4 MHz, and signals S3 and S4 overlap at around 8 MHz, as shown in Figure 6.Figure 6
Signal spectra after BPS.After applying the antialiasing filters designed in Section2, the overlapping signals can be separated. In Figure 7, S2 and S4 are suppressed, and only S1 and S3 are left. In Figure 8, S1 and S3 are suppressed, and S2 and S4 are left. The suppressed effects are more than 30 dB.Figure 7
Signal spectra after antialiasing filtersFA1 and FB1.Figure 8
Signal spectra after antialiasing filtersFA2 and FB2.Using further low-pass or high-pass filter design, the four signals can be separated.


## 5. Hardware Test

### 5.1. Platform Design

In order to test in a real environment, hardware platform is designed using the structure as shown in Figure9.Figure 9
Structure of test platform.Signal generator is used to generate input RF signals. Two ADS5463 ADCs are used to realize second-order BPS. LMK03002C clock generator contributes time delay to ADC B. Antifilters are implemented in FPGA. Digital down conversion also should be implemented in FPGA. Low speed digital signals are transmitted to PC and received by GNU Radio.


### 5.2. Time Delay Compensation

According to (2), the theoretical phase difference between two ADC outputs can be described as
(16)θt=2πnTΔfsConsidering the time delay error caused by hardware, practical phase difference can be written as(17)θr=2πnTΔ+T'fs+2πTΔ+T'fwhere T' is time error which can be estimated by fitting the measured data. f is the frequency offset. 2πTΔ+T′f denotes the compensation for group delay. In real environment, (17) is used to design antialiasing filters.


### 5.3. Test Results

Taking three input signals as examples, the parameters are shown in Table2. The sampling frequency is 24 MHz. Theoretical time delay between two sampling channels is 1200 ps, that is, clock generate is set with delay 1200 ps. By fitting the measured data, time delay error can be estimated as T′≈129ps. Then, time delay 1329 ps is used to design antialiasing filters in FPGA.Table 2
Parameters of three input signals.
Number
Central frequency (GHz)
Signal types
Zone index
Frequency offset (MHz)
Down sampled by 2.5 (MHz)
S1r
2.4275
Bluetooth
101
3.5
3.5
S2r
2.450
Wi-Fi
102
2
2
S3r
2.4106
ZigBee
100
10.6
1After BPS, three signals lies in 3.5 MHz, 2 MHz, and 10.6 MHz, respectively. To decrease the signal speed, signals are down sampled by 2.5, that is, signals send to PC with a sampling rate of 9.6 MHz. After down sampled, three signal lies in 3.5 MHz, 2 MHz, and 1 MHz, respectively.S2r and S3r are too closed to separate, so we design antialiasing filters to separate them. After applying the antialiasing filters designed in FPGA, S2r and S3r can be separated in channel one and channel two. In Figure 10, S2r is suppressed, and only S3r and S1r are left. In Figure 11, S3r is suppressed, and S2r and S1r are left.Figure 10
Signal spectra received by GNU Radio (channel one).Figure 11
Signal spectra received by GNU Radio (channel two).


## 5.1. Platform Design

In order to test in a real environment, hardware platform is designed using the structure as shown in Figure9.Figure 9
Structure of test platform.Signal generator is used to generate input RF signals. Two ADS5463 ADCs are used to realize second-order BPS. LMK03002C clock generator contributes time delay to ADC B. Antifilters are implemented in FPGA. Digital down conversion also should be implemented in FPGA. Low speed digital signals are transmitted to PC and received by GNU Radio.


## 5.2. Time Delay Compensation

According to (2), the theoretical phase difference between two ADC outputs can be described as
(16)θt=2πnTΔfsConsidering the time delay error caused by hardware, practical phase difference can be written as(17)θr=2πnTΔ+T'fs+2πTΔ+T'fwhere T' is time error which can be estimated by fitting the measured data. f is the frequency offset. 2πTΔ+T′f denotes the compensation for group delay. In real environment, (17) is used to design antialiasing filters.


## 5.3. Test Results

Taking three input signals as examples, the parameters are shown in Table2. The sampling frequency is 24 MHz. Theoretical time delay between two sampling channels is 1200 ps, that is, clock generate is set with delay 1200 ps. By fitting the measured data, time delay error can be estimated as T′≈129ps. Then, time delay 1329 ps is used to design antialiasing filters in FPGA.Table 2
Parameters of three input signals.
Number
Central frequency (GHz)
Signal types
Zone index
Frequency offset (MHz)
Down sampled by 2.5 (MHz)
S1r
2.4275
Bluetooth
101
3.5
3.5
S2r
2.450
Wi-Fi
102
2
2
S3r
2.4106
ZigBee
100
10.6
1After BPS, three signals lies in 3.5 MHz, 2 MHz, and 10.6 MHz, respectively. To decrease the signal speed, signals are down sampled by 2.5, that is, signals send to PC with a sampling rate of 9.6 MHz. After down sampled, three signal lies in 3.5 MHz, 2 MHz, and 1 MHz, respectively.S2r and S3r are too closed to separate, so we design antialiasing filters to separate them. After applying the antialiasing filters designed in FPGA, S2r and S3r can be separated in channel one and channel two. In Figure 10, S2r is suppressed, and only S3r and S1r are left. In Figure 11, S3r is suppressed, and S2r and S1r are left.Figure 10
Signal spectra received by GNU Radio (channel one).Figure 11
Signal spectra received by GNU Radio (channel two).


## 6. Conclusions

Antialiasing filters were designed to separate more than two aliasing signals. The test results show that the antialiasing filters can suppress more than 30 dB. Based on this method, constraint conditions for selecting sampling rates are given. The algorithm is proven to be correct by hardware analysis. Compared with existing receivers in telemedicine monitoring systems, the proposed structure can solve the problem of aliasing and realize a multistandard receiver that can receive different standard signals simultaneously. This receiver can significantly improve spectrum utilization as well as the flexibility of receivers for telemedicine monitoring systems.



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