System Modeling and Analysis of the IEEE 802.15.4

Physical Layer Design Jikang Xia*, Lan Chen, Ying Li, Yinhao Zhou

Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China * Email: xiajikang@ime.ac.cn

Abs tract

Physical layer structure usually is fIrst considered to design a standard SoC in wireless sensor networks, while few practical works can be referenced. This paper presents detailed functional system modeling which obeys IEEE 802.15.4 protocol and performance analysis of three physical layer bands. Emphatically, the system includes a novel algorithm of de-spreading with minimum hamming distance and an implementation of half-sine pulse shaping fIlters. According to the simulation results, error control coding like BCH is recommended to use to enhance the data accuracy, and the low complexity fIlters can effectively improve the system performance. The contribution of this paper is providing valuable reference for the further design on wireless sensor network baseband chips.

1. In troducti on

IEEE 802.15.4[1] is a well-known standard developed for low cost, low data rate, low power consumption, and short-range radio frequency (RF) transmissions in a wireless personal area network (WPAN). In the past couple of years it has become a popular protocol for wireless sensor networks (WSN). Typical applications are various including remote medical care, home/office intelligence, modem military, etc.

Several studies of WSN protocols were built on network simulators, such as NS, OPNET or OMNET [2] [3]. However, to our best knowledge, there are few complete and practical model-based evaluations and analysis for the physical (PHY) layer. The authors of [4][5] just build the most common models in Simulink without considering the half-sine pulse shaping fIlter, PHY packet format and error control coding. Our goal is to establish a multi-purpose wireless sensor network application system as the fIrst step of an efficient baseband SoC design. Baseband chip is the core of the entire node for signal acquisition, transmission and processing, thus we present a detailed PHY architecture by providing functional modeling and performance analysis. The simulations are used to test the feasibility and validity of this system architecture.

The rest of the paper is organized as follows. Section

978-1-61284-193-9/11/$26.00 2011 IEEE

2 provides a brief description of the physical layer design. Section 3 presents the implementation of the modules in the system. The simulation and analysis are described in Section 4. Section 5 gives the summary.

2. Overview of the physicaJJayer design

We focus on the physical layer of WSN SoC (system on chip) node design, while the effects of the Medium Access Control (MAC) layer will be tackled in the future. The main functions of the physical layer are spreading, de-spreading, modulation and demodulation, pulse shaping and de-pulse shaping of the signal. At the physical layer, the devices operate in the ISM (Industrial, ScientifIc, and Medical) bands within three different frequency ranges. In the digital baseband part, the chip sequences use direct sequence spread spectrum (DSSS) to represent each data and the offset quadrature phase-shift keying (O-QPSK) with half-sine pulse shaping to be modulated onto the carrier. In the 868/915 MHz band instead, the DSSS chip sequences are modulated onto the carrier by binary phase-shift keying (BPSK) with raised cosine pulse shaping, and differential encoding is used for data symbol encoding.

The general system structure of the physical layer bands is shown in Figure 1. The data from Information Source are expressed as the PPDU (PHY Protocol Data Unit) packet in fIxed format. Then, all binary data contained in the PHY PDU shall be encoded using the spreading, modulation and pulse shaping functions. Through the noisy channel, the received data using the inverse transformation method is compared with the original one at the Error Rate Calculator. Since most WSN nodes working under the harsh environment, it is necessary to add the Error Control Coding in this system for transmission accuracy and reliability. The detail modeling description is presented in Section 3.

Figure 1. The general system structure

3. M odels in sys tem

According to the structure, we build the system model and run the simulation in Simulink. By using the pre-installed internal built-in module blocks, we build the OQPSK Modulator and De-Modulator, additive white Gaussian noise (AWGN) channel, Error Rate Calculator, Error Control Encoder and Decoder of the 2.4 GHz model. The Error Control Coding can either be BCH EncoderlDecoder, or Convolutional Encoder and Viterbi Decoder. Other blocks including PPDU packet, spreading, pulse shaping filters are customized designed for this system separately. At the end of this section, we introduce the method of building 868/915 MHz band system model based on the forenamed model. (1) Information source and PRY PDU Receiver

The format of PPDU is shown in the Table 1[1]. The synchronization header (SHR), PHY header (PHR) and payload are packed to form the PPDU using the block of Vector Concatenate. The PHY payloads are randomly generated integers. In the PHY PDU Receiver the PHY payloads are divided from the package and displayed.

(2) Spreader and De-Spreader The direct sequence spread spectrum communication

system extends the useful information to a very wide band by means of pseudo-random sequences, in order to gain strong anti-interference and high security. In the spreader, the lookup table block which stores the list of the symbol to chip mapping is substantial. And in the de-spreader, the chip to symbol mapping is decoded by the method of minimum hamming distance defmed as an embedded MATLAB function. The Fig. 2 describes the flow vividly. Firstly, the 32-bit-chip stream is singly compared with the relevant chip sequences from 0 to 15 by XOR operation, and then the chip to symbol de-spreading block outputs the matching symbol which has smallest hamming distance. The experiments prove this method by checking the fault-tolerant and error correction capability. For lower complexity, setting a threshold of the hamming distance can be valuable, as it can reduce the number of 32-bit XOR operations. The other mappings are done by the block of Integer to Bit Converter and Bit to Integer Converter. (3) Pulse Shaping and De-Pulse Shaping Filters

The digital baseband shaping filter can effectively suppress the inter-symbol interference (lSI), increase data rates, and reduce transmission distortion. According to the protocol, that the chip rate is 2.0 Mchip/s predicates Tc is 0.5J.ls. The chip sequences representing

Octets: 4 Preamble

SHR

Table 1 Format of the PPDU 1

SFD 1

Frame length I Reserved (7 bits) (1 bit) PHR

variable PSDU

payload

each data symbol are modulated onto the carrier using O-QPSK with half-sine pulse shaping. Even-indexed chips are modulated onto the in-phase (I) carrier, while odd-indexed chips are modulated onto the quadrature -phase (Q) carrier. The half-sine pulse shape used to represent each baseband chip is defmed by Equation (1):

pet) = {sin(1i 2c ),0 s, t s, 2Tc (1) 0, otherwise

In the Half-sine Pulse Shaping Transmit Filter, the output signal is switched between positive and negative discrete half-sine wave with upsampling based on the value of chip sequences. In the receiver, an optimized low-order lowpass filter is designed to impair the channel noise on the effective signal and a Rate Transition block is used for downsampling. Trading off the performance and complexity, we choose 10 as the sampling factor and 30 as the De-Pulse Shaping filter's order after a number of experiments.

The raised-cosine filter is also a low-pass filter commonly used for pulse-shaping in digital modulation. To select the customized filter scheme, we compare the performance of the two shaping filters in the same system model. From the Figure 3, the half-sine pulse shaping filters is mostly better than the raised cosine filters with lO-upsampling and 10 I-order which are provided by Matlab/ Simulink. (4) 868/915 MHz band system model

The model of the physical layer in the 868/915 MHz band is similar to the 2.4G one except a few modifications. The code we used in the embedded MATLAB spreader/de-spreader function change to only two value of I5-chip pseudo-random noise (PN) sequence in bit-to-chip mapping after differential encoding. The OQPSK modulator and demodulator are replaced by the BPSK modulator and demodulator. The half-sine pulse shaping filters are also replaced by the raised cosine pulse shaping filters.

4. Simula ti on and Resul ts analysis

The 2.4GHz band model is shown in Figure 4.

the relevant chip sequences from 0 to 15

L-...L..I CO I =:=CI I =='" ICl l l 0 l2-bit-chip-Slream {r--TI co I CI I _ ICl l 1 i0} m

m

,ntCh i ng SY

om bO I

I CO,CI,U-,C31 1 XOR _ . . . . L.J rol c:::::::::::> :

I co I CI I ICl l 1 G Figure 2. The flow of de-spreading with minimum

hamming distance

a:: w '"

1i _____ _ ====_=====_==== - _____ ____ _ :: :: :: :: :: c :: = = = = 1= = = = = = :: = = = = ......- Half-sine pulse shaping - - - - - - - - - = = -: = = = = = I: = = = = Raised cosine shaping -----r - -- ---- r ---- '---:---'-------=-:=-=--=-'=--::-'l -----r----- --- - ----- T ----- T -----I I I I I

:i

I _____ L _____ L _____ L ___ _ _ ___ ____ _

I I I I I 1

_ = = :::::C:::::C:::::I::::: ::: :I::::: ----- r ----- r ----- r ----- I --- -T---------- r ----- r ----- r ----- T ----- T -----

I I I I I

! :

1051L,-5 ----1:'::0----:-5-----;O;----7---:!;,10:-------:;15 SNR

Figure 3 Comparison of Half-sine pulse shaping filters and Raised cosine shaping filters BER versus SNR

under AWGN

Simulations are performed to study the bit error rate (BER) versus signal to noise ratio (SNR) of the designed models of various parameters. The first simulation case runs on the 2.4 GHz model band to investigate the effect of varying the input signal power in the AWGN channel. The data rate is fixed at 250kbps, while the input signal power is varied as 0.1, 0.2, 0.5 and 1 Watts. The results are shown in Figure 5. It shows that at the same SNR, the higher the input signal power, the higher the probability of error. The reason is that input signal power is the actual power of the samples at the AWGN input. According to the Matlab Equation(2), changing the input signal power affects the variance of the noise added per sample, which causes a change in the final error rate.

(Tsy",1T,amp)xSNR Noise Variance = PxT.ym IT.amp xlO 20 (2)

Where: P is input signal power, Tsym is the symbol period, Tsamp is the inherited sample time.

The second simulation case runs on the 868/915 MHz model band to investigate the effect of varying the input signal power in the AWGN channel. The data rate is fixed at 20/40 kbps, while the input signal power is

Figure 4. Simulink model

0: w OJ

__P=O.lW -6-P=0.2W __ P=0.5W -+-P=lW

101'5 -------:-1;;- 0 -----;_5-----;;----7---'10 SNR

Figure 5. BER versus SNR for different input signal power values in the 2.4 GHz band

varied as 0.1, 0.2, 0.5 and 1 Watts. The results are shown in Figure 6. It is similar to the results in the 2.4 GHz band that at the same SNR, the higher the input signal power, the higher the probability of error. When the SNR exceeds 5dB, the BER tends to be zero.

The third simulation case runs on the 2.4 GHz model band to investigate the effect of varying error control coding. The input signal power on the AWGN channel is 1 watts and the data rate is 250 kbps. The results are shown in Figure 7. The performances of BCH and convolutional coding behave almost. When the SNR exceeds -5dB, the BER of the communication system with error control coding is better than the original one. Considering the restriction of the hardware complexity, BCH coding is easier to decode than convolutional coding. So in the actual noisy channel, it is necessary to introduce the error control coding like BCH.

'" w OJ

101 '6 ---+.14:------:-12;;--:---1'!;;0-----;;_a-----;-6;--_4-;---:!;-2:------:i;---;-----; SNR

Figure 6. BER versus SNR for different input signal power values in the 868/915 MHz band

0: W 6dB, the BER