This method takes the advantage of using ... Biomedical wireless sensor networks (BWSN) can play ... advantages of IR-UWB are low power consumption, high.
PROCEEDINGS OF THE 2008 IEEE INTERNATIONAL CONFERENCE ON ULTRA-WIDEBAND (ICUWB2008), VOL. 1
Handling Unknown NBI in IR-UWB system used in Biomedical Wireless Sensor Networks Hessam Moussavinik∗ ∗
Ilangko Balasingham∗,†
Tor Ramstad∗
Department of Electronics and Telecommunications, NTNU, N-7491 Trondheim, Norway † Interventional Center, Rikshospitalet University Hospital, N-0027 Oslo, Norway Email: {hessam.moussavinik, ilangko.balasingham, tor.ramstad}@iet.ntnu.no
Abstract—Impulse radio-ultra wideband (IR-UWB) communication technique is considered as a strong candidate for short range biomedical wireless sensor networks (BWSN). Coexistence with other systems in the ISM band is important, where narrowband interference (NBI) can become a threat to this system. This paper proposes a method to increase robustness of IR-UWB system in the existence of NBI. This method takes the advantage of using both FEC coding and frequency interleaving technique. One of the features of the proposed system is that it performs well in an environment with unknown NBI. Comparisons between single-band and multi-band signaling schemes are given. Simulation results show that the proposed multi-band scheme performs well in existence of NBI and provides about 3.5 dB gain in comparison to single-band scheme with the same configuration, where the NBI to AWGN energy ratio equals 30 dB. Index Terms—Ultra Wideband, Impulse Radio, Narrowband Interference, Coexistence, Frequency Interleaving, FEC Coding
I. I NTRODUCTION Wireless sensor networks (WSN) find applications in many different areas, where health and care is one of them. Biomedical wireless sensor networks (BWSN) can play an important role in improving the health and care issues. Different types of sensors are used for applications like electroencephalography (EEG), electrocardiogram (ECG), blood pressure measurement, etc. Sensors are required to communicate with an external computer or fusion center via a wireless interface. Since some sensors are implanted in humans, systems with high reliability and safety are required. Also, there are challenges with power consumption that may affect the complexity and transmission power of the system [1]. BWSN requires medium data rate, since most of the medical signals have small bandwidth requirements [3]. Data rates up to 10 Mbps is often sufficient [5]. Range of operation in BWSN is short, typically between 2 to 5 meters [3], [5]. Ultra wideband (UWB) technology is one of the best candidates to be used in BWSN. Since UWB frequency spectrum is within the unlicensed ISM band, coexistence with other systems becomes an important issue. Coexistence is defined as “the ability of one system to perform a task in a given (shared) environment where other systems may or may not be using the same set of rules ” [6]. In other words, the capability of handling the interference from other devices, where most of them are narrowband systems, is required. Although the interfered frequencies by narrowband
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systems are a small fraction of the UWB spectrum, since narrowband signals have much higher power than the UWB spectrum, these narrowband interferences (NBI) can dramatically degrade the performance of the UWB system [2]. One of the popular and major techniques to implement UWB is impulse radio (IR) that is based on transmitting very short and low power pulses. Use of IR technique can eliminate the requirement for up- & down-conversion so that it reduces the transceiver complexity. Possible modulation schemes for IR-UWB can be OOK, PAM, PPM, and PSK, while receivers can be based on energy detector and rake receivers. Also, UWB signaling can be performed with single-band or multi-band schemes [2]. In this paper we discuss coexistence issues of UWB-WSN and propose a method to handle NBI that is unknown to the system. There are two main strategies for dealing with NBI, namely avoiding and cancelling methods [2]. The avoidance techniques are based on spectrum nulling [4], spectral-encoded UWB system [7], or neglecting the frequency band that has major NBI, e.g. in OFDM by turning off the corresponding carrier [2]. However, if the exact information about the center frequency of the NBI is not known, then its suppressing is not possible by use of the avoidance techniques [2]. In the case of cancelling techniques we can mention MMSE combining techniques that are possible by means of rake receivers [2]. In this paper we propose a new method for handling NBI that does not require any exact information about the center frequency of the NBI. This method uses a combination of forward error correction (FEC) codes and a scheduled multi-band UWB scheme. We will show that this method performs well in dealing with NBI and in environments with unknown or changing NBI. The paper is organized in the following manner: Section II explains the system and channel models. Section III explains about simulations of IR-UWB with single- and multi-band schemes in AWGN channels with NBI. Section IV provides the simulation results of the proposed method with cooperative FEC codes and frequency interleaving. Conclusion is given in Section V.
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PROCEEDINGS OF THE 2008 IEEE INTERNATIONAL CONFERENCE ON ULTRA-WIDEBAND (ICUWB2008), VOL. 1 II. S YSTEM AND C HANNEL M ODELS For the implementation of a UWB system, an impulse radio technique is selected due to some of its features. In impulse radio techniques, baseband pulses of very short time duration, in the order of nano seconds, are transmitted. The system becomes interesting since it is a carrierless system and its implementation cost is low [7]. Some of the potential advantages of IR-UWB are low power consumption, high immunity against interference, low power spectral density, high data rate, adaptability to the electromagnetic environment in the human body, etc [10]. Data bits are modulated by binary pulse position modulation (BPPM). The reason for selection of BPPM is that under weak channel conditions, lower order modulation is preferred for low data rate applications [2]. In BPPM the first slot of the frame is reserved for bit value 1 and the last slot is reserved for bit value 0, where the proper pulse is transmitted over the corresponding slot. The BPPM signal modulation can be represented as following: 0 (b = 1) x(t) = ptr (t − δdb ), db = (1) 1 (b = 0) where x(t) is the modulated signal to be transmitted, ptr is the pulse shape of our UWB signal, δ is PPM fixed parameter that specifies the amount of shift in position and db gets its value according to the bit, b, to be transmitted [11]. We designed and used pulse shapes according to the algorithm described in [8], where the spectrum of the designed pulse fits with the frequency mask, according to the rules provided by the FCC.
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III. S INGLE -BAND AND M ULTI -BAND IR-UWB S CHEMES A. Single-band IR-UWB In single-band signaling scheme, the spectrum of the impulse signal fits with the entire frequency mask of UWB.
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Assume that the transmitted signal is sampled at the receiver with N points. Thus we represent the transmitted signal by a vector of its samples as x and the observed signal is given by y =x+w+s
(2)
where y, w and s are the vectors of samples of the observed signal, AWGN and NBI, respectively. The frame duration of BPPM is set to 5 ns, and the pulse duration is 1 ns. There are 5 time slots in each frame. Since the transmitted pulse shape is completely known at the receiver, matched filtering is used. By means of matched filters we find the inhabited energy in the first and the last slots of each frame. By comparing the found energies, decision on the received bit is made. For simplicity of notation, let us denote the observed signal at the first and the last slot of the kth frame by y1k and y0k , respectively. Assume b = 1 is transmitted at the kth frame. Then we have F1k =
The channel is assumed to be AWGN with existence of NBI. This NBI can have any center frequency within the UWB band, since our proposed system works irrespective of the position of NBI. We assumed NBI to be in the UNII frequency band, since 802.11a WLAN devices work in this band and these devices can be found almost everywhere. At the receiver, matched filtering and energy detection followed by a hard decision are used. It is assumed to have perfect passband filters at the receiver. Fig. 1 shows a general block diagram of this system model. In case of using forward error correction (FEC) codes, an encoder is used before the modulator at the transmitter side, and a decoder is used after the demodulator at the receiver side. The hard decision block will also be removed, where the decision is made at the end of the decoding procedure on the log-likelihood ratio (LLR) values. Fig. 2 shows a general block diagram of the system model when FEC codes are used. Receivers are noncoherent and synchronized.
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i=0
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where sums are on N sample points of a symbol, ˜bk represents received bit at the kth frame, F1k and F0k are the amount of the inhabited energy found in the first and the last slot of the kth frame, respectively. NBI is assumed to be provoked by 802.11a WLAN devices that operate in the frequency band [5.15,5.35] GHz [12]. Fig. 3 shows the simulation results for different values of NBI to AWGN energy ratio. It can be seen that the performance degrades by increasing the NBI energy. When the NBI to AWGN energy ratio is 30 dB the performance reduces a lot.
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PROCEEDINGS OF THE 2008 IEEE INTERNATIONAL CONFERENCE ON ULTRA-WIDEBAND (ICUWB2008), VOL. 1 B. Multi-band IR-UWB For multi-band signaling scheme the entire spectrum of UWB is divided into smaller bands of the minimum allowed bandwidth of 500 MHz [13]. The number of sub-bands and their bandwidth distribution is arbitrary and can be selected according to the application. We choose to have each subband with 500 MHz bandwidth. Thus, for the UWB spectrum, which is from 3.1 to 10.6 GHz, 15 subbands are obtained. Since we are interested in impulse radio signaling 15 different pulse shapes are required, where spectrum of each one fits with one of the subband frequency masks. Here, the frame duration of BPPM is 30 ns, which includes 5 equal time slots. Data bits can be sent in arbitrary ways over these subbands. We propose to send data bits serially in subsequent subbands. It means that bits can be packed in strings of length 15 bits, and for each string we transmit the first, the second, and up to the fifteenth bit through the first, the second, and up to the fifteenth subband, respectively. This scheduling provides frequency interleaving. Assume the NBI to be same as explained before. It is seen that the assumed NBI is within the frequency band of fifth subband, [5.1,5.6] GHz. In a different configuration it is possible to have NBI that can overlap with two subbands and can disturb both of them. Simulation results for this system is shown in Fig. 3. It is obvious that in this situation the performance of the multi-band scheme is better than single-band scheme. This is because the NBI disturbs only one subband out of 15, or one bit out of every 15 bits. Equations (3) and (4) are valid only for the observed signal that occurs in the disturbed subband, here it is in the fifth one, and for the rest of the subbands we have s = 0. We can also see that the performance of this scheme decreases when the NBI to AWGN energy ratio increases, but it does 0
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not decrease as much as the performance of the single-band scheme, e.g. for 30 dB of NBI to AWGN energy ratio. This may be the advantage of using frequency interleaving.
IV. IR-UWB, FEC AND F REQUENCY I NTERLEAVING We use forward error correction (FEC) coding to increase the system robustness further. Turbo product codes (TPC) with a posteriori probability (APP) iterative decoding algorithm is selected in the FEC coding system. To test the efficiency of FEC coding, a small Hamming(8, 4, 4)2 product code is chosen to keep the whole system simple. The rate of this code is 14 with minimum Hamming distance of 16 [9]. Fig. 2 shows the system model with FEC coding. The information bits are encoded by the selected product code and the output of the encoder goes to the modulator as mentioned in Section III. In some cases it may be useful to add an interleaver after the encoder. This has not been included in our model since there will be marginal increase of performance. Our decoding algorithm is a soft-in soft-out (SISO) algorithm. Therefore, we cannot do hard decision after demodulation as before. Proper soft input for the decoder is required. The mentioned decoder works for BPSK modulation, where the observed data would be the input to the decoder. In BPPM, similar signals are sent for both b ={0,1} and only their positions are different within a frame, so they should be treated in another way. The decoder needs to consider two different phases for b = 0 and b = 1. A differential energy detection is considered. We transmit b = 1 at the first and b = 0 at the last slot of a frame in BPPM and subtract the energy of the observed signal in the last slot from the energy of the observed signal in the first slot of each frame. This will give us a proper input for the decoder. But, the use of simple energy detection is not a good choice and it may result in a poor performance for the decoder. That is why matched filters are used. Assume b = 1 is transmitted at the kth frame. Then from (3) and (4) the differential energy can be derived:
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−2
10
F1k − F0k = ||xk ||2 + N −1 N −1 (w1ki − w0ki ).xki + (s1ki − s0ki ).xki
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i=0
Similarly, if we assume that b = 0 is transmitted, we get F1k − F0k = −||xk ||2 + N −1 N −1 (w1ki − w0ki ).xki + (s1ki − s0ki ).xki
−4
10
−4
−2
0
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i=0
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6
8
i=0
10
Fig. 3. Performance of single- and multi-band (SB, MB, respectively) signaling schemes in AWGN channel with NBI. NAR: NBI to AWGN energy Ratio. Eb : Energy per information bit. N0 : AWGN energy.
(8)
i=0
This differential energy detection provides proper primary input data for the decoder. It is worth mentioning that the decoder uses LLR values to perform the iterations and with the final LLR values, the decoder decides the received bits.
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PROCEEDINGS OF THE 2008 IEEE INTERNATIONAL CONFERENCE ON ULTRA-WIDEBAND (ICUWB2008), VOL. 1 A. Single-band IR-UWB with FEC We perform simulations for a single-band scheme as shown in Fig. 2. The performance of the simulation is shown in Fig.4. We can see that the result is better than the case without FEC. This is however not surprising, since there is the coding gain. Also by increasing the NBI to AWGN energy ratio the BER performance will degrade. In the case without FEC, when the NBI to AWGN energy ratio becomes equal 30 dB, poor performances are obtained. B. Multi-band IR-UWB with FEC and Frequency Interleaving The proposed method tends to increase the robustness of the system in existence of NBI. The system model is shown in Fig. 2 with a multi-band signaling scheme. Information bits are encoded and then transmitted by BPPM within subsequent frequency bands as explained in Section III-B. The multi-band transmitting schedule provides frequency interleaving. Fig. 4 shows the simulation results of the system. The performance slowly decreases with increasing NBI to AWGN energy ratio compared to a single-band scheme. If we compare both graphs, where the NBI to AWGN energy ratio is equal to 30 dB, then we see that there is a big gap between these two performance curves, which is about 3.5 dB gain in terms Eb of N . The multi-band scheme is much stronger and robust 0 against NBI than the single-band scheme. The robustness is a result of the cooperation between FEC coding and frequency interleaving. A combination of these two techniques makes the system less sensitive to the position of the NBI. It is also expected that the maximum effect of FEC coding becomes visible when there are more than one source of NBI. This cooperation makes the system much more robust to the NBI with higher system reliability.
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10
−4
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−5
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−4
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4
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Fig. 4. Performance of single- and multi-band (SB, MB, respectively) signaling schemes in AWGN channel with NBI. FEC codes with the APP decoding algorithm are used. Decoder performed 3 iterations. NAR: NBI to AWGN energy Ratio. Eb : Energy per information bit. N0 : AWGN energy.
Another important contribution of the method is that the system can perform well in situations without any knowledge of NBI. This is defined as the unknown NBI situation. Furthermore, in a random NBI situation and without loss of generality, similar performances can be expected for any source of NBI with any given center frequency. Use of the method for one user per transmission period seems to be wasteful in terms of using available resources or degrees of freedom. But, in a multiuser scenario, this technique becomes useful. Different types of multiuser scenarios can be defined. However, it is not an issue of this paper. V. C ONCLUSION This paper proposed a technique to increase robustness of IR-UWB system in coexistence of narrowband interference (NBI). Simulation results show that cooperation of FEC coding and frequency interleaving using multi-band scheduling scheme, improves the robustness of the system, where complexity of the system has not been considered in this paper. In the proposed technique, the BER performances are much better than the single-band scheme while increasing the NBI to AWGN energy ratio. And, when this ratio is equal to 30 dB Eb . Another important there is about 3.5 dB gain in terms of N 0 contribution of this technique is that it does not require any prior information about the center frequency of NBI. Thus it performs well in situations with unknown NBI or situations with changing NBI. R EFERENCES [1] L. Schwiebert, S. Gupta, J. Weinmann, Research Challenges in Wireless Networks of Biomedical Sensors, ACM, 2001 [2] H. Arsalan, Z.N.Chen, M.G. Di Benedetto, Ultra Wideband Wireless Communication, John Wiley & Sons, Inc., 2006 [3] C. Lee, C. Toumazou, Ultra-low Power UWB for Real Time Biomedical Wireless Sensing, IEEE, 2005 [4] Z. Luo, H. Gao, Y. Liu, J. Gao, A New UWB Pulse Design Method for Narrowband Interference Suppression, IEEE Comm. Society, Globecom 2004 [5] Huan-Bang Li, Ultra-Wideband Technology and Its Standardization for Wireless Personal Area Networks, National Institute of Information and Communications Technology (NICT), Japan, 2007 [6] D. Cypher, NIST Co., IEEE 802.15.2 def. of coexistence, Coexistence, Interoperability and Other Terms, IEEE 802.15-99/134r1, Mar. 2000 [7] C. Silva, L. B. Milstein, Spectral-Encoded UWB Communication Systems: Real-Time Implementation and Interference Suppression, IEEE Trans. on Comm., vol. 53, no. 8, Aug. 2005 [8] B. Parr, et. al., A Novel UWB Pulse Design Algorithm, IEEE Comm. Letters, vol.7, no.5, May 2003 [9] Hessam Moussavinik, TPC iterative decoding with Flooding and RowColumn updating schedule, M.S. thesis, Institutionen f¨or signaler och system, Chalmers tekniska h¨ogskola, ISSN 99-2747920-4, no. EX048/2006, 2006 [10] T. Guo, L. Zhang, et. al., A Novel Solution to Power Problems in Implanted Biosensor Networks, Proc. of the 28th IEEE EMBS Annual International Conference, 2006 [11] I. Oppermann, M. H¨am¨al¨ainen, J. Iinatti, UWB: Theory and Applications, John Wiley and Sons, Inc., ISBN: 0470869178, 2004 [12] M. Gast, 802.11 Wireless Networks: The Definitive Guide, ISBN: 0596001835, O’Reilly, Apr. 2002 [13] G. Roberto Aiello, G. D. Rogerson, UWB Wireless Systems, IEEE Micro. Mag., 2003
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