Adaptive space-time-frequency-coded UWB system for wireless body area network
- Miftadi Sudjai^{1}Email author,
- Le Chung Tran^{1} and
- Farzad Safaei^{1}
https://doi.org/10.1186/s13638-014-0235-8
© Sudjai et al.; licensee Springer. 2015
Received: 5 January 2014
Accepted: 24 December 2014
Published: 20 February 2015
Abstract
Wireless body area networks (WBAN) emerge as one of the main research streams for future wireless communications. One of the candidates for the WBAN physical layer is multiband orthogonal frequency division multiplexing ultra-wideband (MB-OFDM UWB) technology. However, despite its high data rate feature, it performs poorly in the very dispersive WBAN channel. To improve its performance, this paper proposes two novel mechanisms. First, the space-time-frequency coding (STFC) is introduced into MB-OFDM UWB system in order to enhance the diversity order, resulting in a substantial improvement in the average error performance compared to the conventional MB-OFDM UWB system. Nevertheless, the performance is very sensitive to the body orientation towards the transmitter due to the body shadowing effect. Secondly, to improve further the performance of the proposed STFC MB-OFDM UWB system in all body directions, we propose an adaptive scheme that changes the modulation, STFC coding rate, and constellation power. Simulations confirm that an additional improvement in the order of 1 to 3 dB is achieved by the adaptive system. This improvement practically means a possible 12.5% to 50% reduction of the power consumption, which may also result in smaller size of WBAN devices.
Keywords
1 Introduction
There have been active engagements in the wireless body area network (WBAN) research in recent years. One of the key factors for the emergence of WBANs is the development of advanced, tiny-sized, lightweight, and extremely low power implantable and wearable sensors [1]. In addition, body centric radio propagation measurement campaigns and WBAN channel modelling have been robustly fostering research activities on WBAN technologies and standards. In particular, WBAN is capable of alleviating the hurdle and inflexibility of cable-connected devices for, e.g., real-time monitoring of health conditions, via various implantable and wearable wireless sensors.
WBAN itself is a network of sensors or communicating devices placed in, on, or off the body to monitor physiological activities and motions and communicate the data between those devices and/or to external devices. Numerous research publications and proposals of WBAN have been put forward, e.g., [1] and [2]. In order to harmonize the development of WBAN, IEEE set up a technical group TG6 within 802.15 to standardize the WBAN in November 2007. IEEE 802.15 TG6 released the WBAN standard in February 2012 that includes the impulse radio ultra wideband (IR-UWB) as its physical layer [3]. Prior to this standard, IEEE also released the WBAN channel models that defined four different channel conditions, i.e., CM1 to CM4, in which CM4 models the body-to-external link [2].
Another competing technology for a short range, very high data-rate communication is multi band orthogonal frequency division multiplexing ultra-wideband (MB-OFDM UWB), endorsed by the WiMedia Alliance [4,5]. It combines the capability of OFDM to flatten the response of dispersive, frequency selective channels of UWB, while maintaining the benefit of high capacity of UWB. It is designed to operate at up to 1 Gbps, at low cost and with low power consumption.
Meanwhile, multiple-input multiple-output (MIMO) technology is proven to be able to significantly increase the wireless system capacity for the same total transmission power [6]. Its fundamental mechanism lies on the use of space-time coding (STC) [6-9]. In STCs, signals are coded both in spatial and temporal domains, for example, using the Alamouti code [9] or other similar codes [7-9]. Alamouti code is designed for frequency flat fading and is capable of providing full rate and full diversity for up to two Tx and two Rx antennas. As a result, it enhances the diversity order and improves the link quality and capacity.
However, its direct application for a very dispersive UWB WBAN channel may not be suitable. Therefore, in order to attain higher data rates and capitalize on the rich dispersion of UWB WBAN channels, further addition of the frequency domain processing in STC can be deployed. So, the process becomes space-time-frequency coding (STFC). Readers may refer to [10-17] for more details about STFCs and its comparison with STC. It is intuitive that a STFC MB-OFDM UWB system may provide better link performance and higher data rate and system capacity. Hence, in [18], we proposed the combination of STFC and MB-OFDM UWB, referred to as the STFC MB-OFDM UWB, as an improved physical layer for WBAN.
Radio propagation in, on, and surrounding a human body is greatly affected by environment, posture, activities, and human tissue [19-24]. Numerous measurement campaigns on body centric propagations have been conducted to characterize the body centric channel, including UWB channels in the frequency bands of 3.1 to 10.6 GHz. Takada et al. show that the body centric channel varies according to the type of antennas, the position and orientation of antennas with respect to the body, the posture and motion of the body, and the variation of the human body itself [19]. Wang et al. suggest that the body shadowing is a prominent factor in short-range body-centric communications [20]. The effect of locations of on-body devices, body size as well as the movement of the body is investigated in [21-24]. Finally, the aforementioned IEEE 802.15 TG6 in [2] has summarized and proposed four channel models CM1 to CM4 for UWB WBAN channels, which comprise seven scenarios. CM1 considers implant-to-implant link for medical information and communication science (MICS). CM2 determines implant-to-body surface and implant-to-external links operating in the same frequency band as CM1. CM3 considers body-to-body link, while CM4 considers body-to-external link. Both CM3 and CM4 are proposed to operate in UWBs. Due to our focus on the UWB WBAN system, we will only consider CM3 and CM4 in this paper.
It is important to highlight the main differences between UWB channel models for wireless personal area networks (WPANs) [25,26] and the aforementioned UWB WBAN channel. The WPAN channel models are based on the Saleh-Valenzuela model and do not consider the effect of human body, while the UWB WBAN channel models do. It is clearly shown in [2] that due to the shadowing effect of human body, the UWB WBAN channel produces a larger amplitude standard deviation σ and a much greater exponential decay factor Γ compared to the UWB WPAN channel. Moreover, Γ varies significantly for different body directions with respect to the transmitter in the case of CM4 over the UWB WBAN channel. To the best of our knowledge, the performance analysis of a STFC MB-OFDM UWB system implemented in the WBAN channels has not been deeply explored. Thus, in [18] as previously mentioned, we present the performance analysis of STFC MB-OFDM UWB in CM3 and CM4 WBAN channels using the Alamouti code. One important observation drawn from this work is that the average error performance differs significantly in different body directions, i.e., the direction of the receiver placed on the surface of the body with respect to (w.r.t) the transmitter. This is due to the effects of line-of-sight (LOS), partial LOS, and body shadowing. These facts lead to the idea of adding an adaptive scheme to WBAN systems, in order to further improve their performance in all body directions.
Adaptive techniques have been employed for numerous systems and applications [27-31]. For instance, Czylwik proposed an adaptive modulation for individual subcarriers of an OFDM system [27]. Keller and Hanzo investigated adaptive OFDM with the focus on the trade-off between the performance and throughput [28]. They also presented a number of adaptive OFDM techniques and their performances [29]. In [30], the authors examined a unified adaptive modulation scheme for a general communication system where the data rate, transmitted power, and instantaneous BER are varied to maximize spectral efficiency. A cross layer adaptive modulation to minimize the transmission energy in wireless sensor networks is proposed in [31]. However, an adaptive scheme for the WBAN physical layer has not been examined. Henceforth, this paper proposes for the first time a body direction-based adaptive algorithm for STFC MB-OFDM UWB WBAN, in order to improve the average BER performance and/or reduce the power consumption of the body-to-external link for WBAN applications [32]. The core idea is that a combination of different digital modulation schemes (binary phase shift keying (BPSK), quadrature phase shift keying (QPSK)), powers of signal constellations, and different rate STFCs is adaptively selected, depending on the body direction w.r.t. the transmitter. The adaption is carried out by the measurement of angles of the body w.r.t. the transmitter, e.g., by utilize a magnetic sensor [33]. The angle information is then fed back to the transmitter via a simple feedback loop to vary its modulation, STFC coding rate, and constellation power. We demonstrate that an additional improvement in the order of 1 to 3 dB can be achieved with this scheme. The improvement practically means a possible 12.5% to 50% further reduction of the total transmitted power, compared to the non-adaptive system. In other words, the adaptive scheme can significantly reduce the power consumption and dimension of WBAN devices.
The paper is organized as follows. Section 2 reviews the UWB WBAN channel models. Section 3 analyzes the proposed system model, including the adaptive algorithm and its decoding complexity. Simulation results and analyses are presented in Section 4. Section 5 concludes the paper.
1.1 Review of IEEE UWB WBAN channel models
Yazdandoost and Sayrafian presented the final document of the IEEE 802.15 TG6 channel modelling subcommittee, providing channel models to be used in body area networks [2]. The channel models are used as a common platform for evaluating the performance of the physical layer from various proposals and measurement campaigns.
1.2 Review of CM3 channel model
Parameters of CM3 channel [2]
Main parameter | Corresponding parameter | Values |
---|---|---|
a _{ l } | γ _{0} | −4.60 dB |
Γ | 59.7 | |
σ _{ S } | 5.02 dB | |
t _{ l } | \( \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\lambda $}\right. \) | 1.85 ns |
L | \( \overline{L} \) | 38.1 |
1.3 Review of CM4 channel model
Parameters of CM4 channel [2]
Body direction | Γ (ns) | k (∆ k (dB)) | σ (dB) |
---|---|---|---|
0° | 44.6364 | 5.111 (22.2) | 7.30 |
90° | 54.2868 | 4.348 (18.8) | 7.08 |
180° | 53.4186 | 3.638 (15.8) | 7.03 |
270° | 83.9635 | 3.983 (17.3) | 7.19 |
From Table 2, the worst link is corresponding to the 270° body direction, rather than 180°, which may be rather counter-intuitive, and the channel behaviors of the 90° and 270° are significantly different. This might possibly be due to the different surrounding environments of the 90° and 270° directions during the measurement. This observation will be reflected in our simulation results.
1.4 System model
1.4.1 STFC MB-OFDM UWB WBAN system with simple adaptive scheme
For the 3/2-rate STFC, we follow the decoding process mentioned in [7]. Generalization for the case of M-Tx and N-Rx antennas is straightforward.
1.5 Adaptive selection algorithm
- 1.
Data rate constraint: the lowest density signal constellation (BPSK) is used for the worst link (270°) in order to improve the system error performance over this direction. A higher density constellation (QPSK) is used for all other three directions. The highest rate STFC (rate 3/2) is selected for the best link (0°) while the rate 1 STFC is selected for all three remaining directions. Thus, the average spectrum efficiency over the four main directions is maintained at 2 bits/s/Hz which is exactly the same as that in the non-adaptive system.
- 2.
Power constraint: the normalized power of one is selected for the signal constellations (QPSK) for the 90° and 180° directions, similar to that in the non-adaptive system, while the highest power level (1.5) is selected for the best link (0°) to compensate for the possible performance degradation caused by the highest STFC coding rate (3/2) chosen for this direction. The above power allocation leaves the power of 0.5 for the BPSK signal constellation in the 270° direction. Thus, the total transmitted power over all four main directions is exactly the same as that in the non-adaptive system.
The core idea behind the proposed algorithm is that the best channel conveys the most information with the most power allocated while the worse link carries the least information. Hence, we define three sets of adaptive schemes (see Figure 3). Set 1 is aimed to take advantage of the best channel link by maximizing the capacity, i.e., by using the QPSK modulation, STFC code rate 3/2, and normalized power Tx 1.5. Set 2 uses QPSK, STFC code rate 1, and power Tx 1, and set 3 uses BPSK, STFC code rate 1, and power Tx 0.5. The proposed algorithm is summarized as follows:
1.6 Decoding complexity
Let P be the constellation size of the P-PSK modulator used in sets 1 and 2 (here, we used QPSK, so P is equal to 4). As a result, the constellation size of the BPSK modulator used in set 3 is P/2. The decoding process of set 1 with QPSK and 3/2 rate STFC is as follows: the decoder firstly has to determine \( {\tilde{\mathrm{x}}}_3 \) from four possible symbols in QPSK, prior to decoding \( {\tilde{\mathrm{x}}}_1 \) and \( {\tilde{\mathrm{x}}}_2 \) symbols. Then, \( {\tilde{\mathrm{x}}}_1 \) and \( {\tilde{\mathrm{x}}}_2 \) are decoded independently, given \( {\tilde{\mathrm{x}}}_3 \) is known, for every QPSK constellation. Hence, the ML decoding complexity is the decoding complexity of \( {\tilde{\mathrm{x}}}_3 \), which is in the order of P, added with the complexity of two independent decoding processes of \( {\tilde{\mathrm{x}}}_1 \) and \( {\tilde{\mathrm{x}}}_2 \) given \( {\tilde{\mathrm{x}}}_3 \) is known, which is equal to 2P. Thus, the overall decoding complexity is in the order of 3P, which is consistent with the analysis in [7]. Set 2 uses a QPSK modulator with the full rate Alamouti code as the STFC. The symbols \( {\tilde{\mathrm{x}}}_1 \) and \( {\tilde{\mathrm{x}}}_2 \) are decoded independently for every constellation, leading to a 2P -decoding complexity. Set 3 uses the full rate Alamouti code in which \( {\tilde{\mathrm{x}}}_1 \) and \( {\tilde{\mathrm{x}}}_2 \) symbols are decoded independently for every BPSK constellation point. Therefore, the ML decoding complexity in set 3 is \( 2\times \frac{P}{2}=P. \)
There are four possible body directions with three possible adaptive schemes. If the four body directions are assumed to be equiprobable, i.e., the probability of each possible direction is 0.25, with the note that set 2 is used for two body directions, the overall complexity of the ML decoding process in the proposed adaptive scheme is 0.25(3P) + 0.5(2P) + 0.25(P) or 2P, beside the body direction estimation. It can be inferred that the ML decoding complexity in our adaptive approach is in the order of P or O(P). It is obvious that the complexity of this adaptive system only linearly increases with respect to the number of signal constellations, thanks to relatively simple decoding processes.
For the body direction estimation, a simple direction sensor, e.g., by using a giant magneto resistance (GMR) thin film sensor chip [33], can be used. It is a robust magnetic sensor, which is capable to provide 360°-angular measurements. The measured body direction is not fed back directly to the transmitter. Instead, depending on which region among the three pre-defined regions shown in Figure 3 this body direction belongs to, 2-bit angular information will be fed back to the transmitter to indicate this region in order for the transmitter to select the corresponding combination of modulation, STFC structure, and constellation power. In other words, the proposed adaptive scheme could be implemented with only minor increase in system complexity.
1.7 Performance evaluations
Simulation parameters
Parameters | Value |
---|---|
FFT and IFFT size N _{fft} | 128 |
Number of ZPS N _{ZPS} | 37 |
Convolutional coder (K = 7) rate | One-half |
Convolutional decoder and mode | Viterbi, hard |
Interleaver/de-interleaver | Column-wise written, row-wise read |
Average number of paths in CM3 | 38 |
Average number of paths in CM4 | 400 |
Body directions | 0°, 90°, 180°, 270° |
The performance comparison in Figure 6 reveals a significant degradation in the BER performance when the receiver (on the body) turns away from the transmitter. The front body (0° direction) has a LOS component, which results in the best performance compared to other directions. The back of the body (180° direction) suffers from a body shadowing effect, and the receiver only receives NLOS multipath signals. Nonetheless, the performance is still reasonably good, particularly with the 2I2O configuration, compared to the 270° direction. Its performance degrades 3.5 dB at BER = 10^{−3}, compared to the front body. The 270° direction experiences the worst performance, and its performance is different from the performance of the 90° direction. This observation is consistent with the parameters of CM4 mentioned in Section 2.2. The average BER performance for each MIMO configuration shown by the dash-dotted curves in Figure 6 is calculated over four body directions. They are used as the benchmark for comparison with the adaptive system.
Figure 6 also shows the presence of error floors, where further increasing E _{ b }/N _{0} does not bring about a significant improvement. This is due to the fact that the ZPS is much shorter than the channel length in the very dispersive CM4 channel. Thus, the inter-symbol interference (ISI) cannot be overcome completely. The residual ISI is still large enough to neutralize the performance. This is the reason why the MB-OFDM technique has been proposed where consecutive MB-OFDM symbols are transmitted over different radio frequencies (RF), thus avoiding the residual ISI. For simplicity, simulations are run here in the baseband rather than in the RF band, thus the error floors can be observed. In other words, the performance provided in the paper works as the lower bound for the improvement that could be provided by the proposed system.
It is worth to note that while the particular level of performance improvement achieved by the proposed adaptive approach might vary should a different channel model be used, the overall idea of the proposed approach (probably with slight modifications) could be still valid for other channel models.
It is noted that the aforementioned average BER improvements are achieved without any increase of the total transmitted power or any sacrifice of the data rate. In other words, an improvement in the order of 1 to 3 dB means a possible reduction of 12.5% to 50% of the total transmitted power, while maintaining the same BER performance and data rate as in the non-adaptive STFC MB-OFDM UWB system. Due to the fact that power is the main constraint in WBAN applications [1], this power saving will significantly reduce the total power consumption of a WBAN system and/or reduces the dimension of WBAN devices.
2 Conclusions
This paper proposes a STFC MB-OFDM UWB system as an alternative high data-rate physical layer for a WBAN system. The results confirm that the proposed system can achieve significantly better BER performances, compared to the conventional MB-OFDM system. To improve the performance of STFC MB-OFDM UWB systems further, a simple body direction-based adaptive modulation and coding scheme is proposed. This adaptive scheme brings about an additional improvement in the order of 1 to 3 dB. Those improvements practically mean a possible 12.5% to 50% reduction of the total transmitted power, hence reducing the dimension of WBAN devices and prolonging their battery life. We conclude that, with the price of slightly increased complexity, the proposed systems could be an effective solution to achieve a power saving and better average BER performance for WBAN applications without sacrificing the data rate. Our future work will focus on the adaptive scheme driven by the measured signal quality in the receiver.
Declarations
Acknowledgements
The first author is grateful to the AusAID that provides financial support through the Australian Development Scholarship (ADS) scheme. The authors also would like to thank the anonymous reviewers for insightful feedbacks.
Authors’ Affiliations
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