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.
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 [
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 [
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 [
This paper is organized as follows. Section
The structure of telemedicine monitoring system is shown in Figure
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.
Assume that the primary signal is sampled at a sampling rate
Assume that four different standard signals,
Spectra after BPS.
In order to separate the overlapping signals, a second-order BPS sampling structure is designed, as shown in Figure
Structure of the second-order BPS system.
According to the characteristics of second-order BPS [
In Figure
In area 0 to
After simplification,
From (
In area
After simplification,
From (
Using the same method, we can get the expressions for
Assume an RF signal with central frequency
Assume three bandpass signals that are received simultaneously with mirroring frequency
The relationship between signal mirroring frequency and sampling frequency.
Consider signal Aliasing by self-image spectrum around zero frequency, as shown in area A of Figure Aliasing by self-image spectrum around Aliasing by another signal, as shown in area C of Figure Aliasing by another two signals, as shown in area D of Figure
The traditional way of selecting
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
If
In the above constraint conditions,
It indicates that the sampling frequency needs to be two times greater than the maximum bandwidth of the signal (plus protection bandwidth).
It means that only one signal is allowed to have zero-bound aliasing or
It means that only two aliasing signals are allowed in the same location. There are three cases for this constraint. Case a
(a) Aliasing diagram (Case a). (b) Aliasing diagram (Case b). (c) Aliasing diagram (Case c).
Taking four input signals as examples, the parameters are shown in Table
Parameters of four input signals.
Number | Central frequency | Signal types | Frequency zone index | Frequency offset |
---|---|---|---|---|
2.428 GHz | Bluetooth | 101 | 4 MHz | |
2.452 GHz | Wi-Fi | 102 | 4 MHz | |
2.408 GHz | ZigBee | 100 | 8 MHz | |
2.464 GHz | Bluetooth | 103 | 8 MHz |
After BPS, signals
Signal spectra after BPS.
After applying the antialiasing filters designed in Section
Signal spectra after antialiasing filters
Signal spectra after antialiasing filters
Using further low-pass or high-pass filter design, the four signals can be separated.
In order to test in a real environment, hardware platform is designed using the structure as shown in Figure
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.
According to (
Considering the time delay error caused by hardware, practical phase difference can be written as
Taking three input signals as examples, the parameters are shown in Table
Parameters of three input signals.
Number | Central frequency (GHz) | Signal types | Zone index | Frequency offset (MHz) | Down sampled by 2.5 (MHz) |
---|---|---|---|---|---|
2.4275 | Bluetooth | 101 | 3.5 | 3.5 | |
2.450 | Wi-Fi | 102 | 2 | 2 | |
2.4106 | ZigBee | 100 | 10.6 | 1 |
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.
Signal spectra received by GNU Radio (channel one).
Signal spectra received by GNU Radio (channel two).
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.
The authors declare that they have no conflicts of interest.
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).