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Journal of Sensors
Volume 2018, Article ID 7501914, 8 pages
https://doi.org/10.1155/2018/7501914
Research Article

Multistandard Receiver Design for Telemedicine Monitoring System

1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
2Collaborative Innovation Center of Intelligent Mining Equipment, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou 221116, China
3Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China
4Ton Duc Thang University, Go Chi Minh, Vietnam

Correspondence should be addressed to Faguang Wang; nc.ude.tmuc@gnaugafw

Received 23 December 2017; Accepted 26 March 2018; Published 30 April 2018

Academic Editor: Mucheol Kim

Copyright © 2018 Hongmei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

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 [35]. Many researchers have tried to find a new algorithm to simplify the frequency selection process [68]. 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. Section 2 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 Figure 1.

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 rate . Any signal in the frequency zone of index expressed as [9] is aliased into the first Nyquist zone , which is the frequency zone with index zero.

Assume that four different standard signals, , , , and , with frequency zone index , , , and , respectively, are bandpass sampled at sampling frequency simultaneously. The sampled spectra are represented as , , , and , respectively, as shown in Figure 2, where signals and overlap in area 0 to , and signals and overlap in area to .

Figure 2: Spectra after BPS.

In order to separate the overlapping signals, a second-order BPS sampling structure is designed, as shown in Figure 3. Two analog-to-digital converters (ADCs), ADC A and ADC B, are used for BPS, where ADC B introduces time delay . 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: where (i = 1, 2, 3, 4) represents the signals sampled in channel A, and represents the signals sampled in channel B, . Because the negative and positive spectra are symmetrical, here, we discuss only the positive spectra.

In Figure 3, antialiasing filters and should be designed to suppress signals and , and and should suppress signals and .

In area 0 to , needs to be suppressed. Thus, in the frequency domain, and should satisfy

After simplification,

From (2) and (3), we get

In area to , needs to be suppressed. Thus, in the frequency domain, and should satisfy

After simplification,

From (2) and (7), we get

Using the same method, we can get the expressions for and in the frequency domain, as shown as follows:

3. Constraint Conditions

3.1. Aliasing Analysis

Assume an RF signal with central frequency bandpass sampled at sampling frequency , the central frequency in the first Nyquist zone (called mirroring frequency) can be defined as where means to round down.

Assume three bandpass signals that are received simultaneously with mirroring frequency , , and . Figure 4 shows the relationship between the mirroring frequency and sampling frequency .

Figure 4: The relationship between signal mirroring frequency and sampling frequency.

Consider signal , with different selections of , it has four kinds of aliasing: (i)Aliasing by self-image spectrum around zero frequency, as shown in area A of Figure 4(ii)Aliasing by self-image spectrum around frequency, as shown in area B of Figure 4(iii)Aliasing by another signal, as shown in area C of Figure 4(iv)Aliasing by another two signals, as shown in area D of Figure 4

The traditional way of selecting is to avoid all kinds of aliasing, which limits the area of , 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 , 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:

If

then

If

then where and .

If , then or ; if and , then or ; if and , then or , where , , , , and .

In the above constraint conditions, represents the maximum bandwidth of bandpass signals; represents protection bandwidth; , , , and represent the bandwidth, central frequency, minimum frequency, and maximum frequency of signal , 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 or -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 a is shown in Figure 5(a). Case b and is shown in Figure 5(b). Case c and is shown in Figure 5(c).

Figure 5: (a) Aliasing diagram (Case a). (b) Aliasing diagram (Case b). (c) Aliasing diagram (Case c).

4. Simulation Results for Multistandard Receiver

Taking four input signals as examples, the parameters are shown in Table 1. The sampling frequency is 24 MHz. Time delay between two sampling channels is 1200 ps.

Table 1: Parameters of four input signals.

After BPS, signals and overlap at around 4 MHz, and signals and overlap at around 8 MHz, as shown in Figure 6.

Figure 6: Signal spectra after BPS.

After applying the antialiasing filters designed in Section 2, the overlapping signals can be separated. In Figure 7, and are suppressed, and only and are left. In Figure 8, and are suppressed, and and are left. The suppressed effects are more than 30 dB.

Figure 7: Signal spectra after antialiasing filters FA1 and FB1.
Figure 8: Signal spectra after antialiasing filters FA2 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 Figure 9.

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

Considering the time delay error caused by hardware, practical phase difference can be written as where is time error which can be estimated by fitting the measured data. is the frequency offset. 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 Table 2. 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 . Then, time delay 1329 ps is used to design antialiasing filters in FPGA.

Table 2: Parameters of three input signals.

After 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.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61601464 and 61771474) and in part by the Fundamental Research Funds for the Central Universities (2013QNA49).

References

  1. M. Aguas, J. Del Hoyo, R. Faubel, B. Valdivieso, and P. Nos, “Telemedicine in the treatment of patients with inflammatory bowel disease,” Gastroenterología y Hepatología, vol. 40, no. 9, pp. 641–647, 2017. View at Publisher · View at Google Scholar · View at Scopus
  2. N. Jiang, Y. Zhuang, and D. K. W. Chiu, “Multiple transmission optimization of medical images in recourse-constraint mobile telemedicine systems,” Computer Methods and Programs in Biomedicine, vol. 145, pp. 103–113, 2017. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Cao, J. Li, Y. Dai, F. Yin, and K. Xu, “Multiband phase-modulated RoF link with coherent detection and bandpass sampling,” IEEE Photonics Technology Letters, vol. 27, no. 21, pp. 2308–2311, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Baek, “Design of second-order BPS systems for the cancellation of multiple aliasing,” Journal of the Institute of Electronics and Information Engineers, vol. 52, no. 3, pp. 162–170, 2015. View at Publisher · View at Google Scholar
  5. M. A. Messaoud, F. M. Ghannouchi, R. Barrak, and A. Ghazel, “SDR based multi-band subsampling receivers for GNSS applications,” in 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 497–501, Sousse, Tunisia, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. J.-M. Munoz-Ferreras, R. Gomez-Garcia, and F. Perez-Martinez, “RF front-end concept and implementation for direct sampling of multiband signals,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 58, no. 3, pp. 129–133, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Choe and K. Kim, “Bandpass sampling algorithm with normal and inverse placements for multiple RF signals,” IEICE Transactions on Communications, vol. E88-B, no. 2, pp. 754–757, 2005. View at Publisher · View at Google Scholar
  8. S. Sarwana, D. E. Kirichenko, V. V. Dotsenko, A. F. Kirichenko, S. B. Kaplan, and D. Gupta, “Multi-band digital-RF receiver,” IEEE Transactions on Applied Superconductivity, vol. 21, no. 3, pp. 677–680, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. H.-m. Wang, J.-h. Kim, F.-g. Wang, S.-h. Lee, and X.-s. Wang, “Design of BPS digital frontend for software defined radio receiver,” Journal of Central South University, vol. 22, no. 12, pp. 4709–4716, 2015. View at Publisher · View at Google Scholar · View at Scopus