By the FCC’s (U.S. Federal Communications Commission) definition, UWB communication systems use signals with -10dB fractional bandwidth that is larger than 20% of the center frequency, or greater than 500 MHz. Besides, FCC gives the rigid spectrum mask which the power spectrum density of the UWB signal should be within. With the low power spectrum density of UWB systems, the UWB signals could be viewed as noise by other narrow-band devices. On the other hand, for UWB systems with a wide PSD, the disturbance from narrow-band systems occupies merely a small fraction of its bandwidth; therefore, the data carried by UWB signal can be easily recovered with FEC.
Synchronization is an essential task for any digital communication system.
Without effective synchronization algorithms, it is not possible to reliably receive the transmitted data. From the digital base-band algorithm design engineer’s perspective, synchronization algorithms impose a major design problem that has to be solved to build a successful product.
OFDM can be used in the context of continuous mode and packet-mode transmission networks. The two transmission schemes require somewhat different approaches to the synchronization problem. Continuous-mode systems transmit data continuously, so a typical receiver can initially spend a relatively long time to acquire the signal and then switch to tracking mode. On the other hand, the synchronization has to be acquired within a very short time after the start of the packet to archive high
data rates for packet-mode systems.
In MB-OFDM UWB systems, the transmitted signals are OFDM symbols carried by a sequence of carrier frequencies depending on the TFC. Beside the usual OFDM synchronization tasks including packet detection, carrier frequency synchronization, and timing recovery, the MB-OFDM UWB system also needs the band alignment procedure which aligns the transmitter time-frequency sequences.
Signal Detection
In MB-OFDM UWB systems, there are 30 preambles as training sequences to train the receiver, as mentioned in 2.2. The four patterns for each channel have low cross correlation property to each other. And for each of the preamble, the
autocorrelation with time shift is almost equal to zero. Just like the direct sequence spread spectrum, the preamble can be used to channelize piconets. So the proposed signal detection scheme is a match filter (MF), as shown in figure 5. For the
simplicity of implement, the match filter coefficients are the signs of the preamble, there for there will be no multiplier in the match filter. The MF is proposed to match the preamble pattern corresponding to the channel which the receiver is going to access. The match filter output is compare to a threshold to determine if the signal comes. For power saving purpose, a constant threshold is preferred in the system. If a proper threshold is setting, the signal detection will be robust to interference and noise.
D D D
pN pN−1 p1
Σ
March filter
•••
•••
channel on the depending
±1
i = p
Figure 5
Interferences
When there are two or more piconets operating at the same time, collision will happen in some band. Unfortunately, sometimes the power of interference is stronger than the desired signal. To simplify the detection algorithm, the MF collects the signal of one band, and there will be a probability of 1/3 that the MF output is interfered by the signal of other piconet. The signal detection algorithm must be designed robustly to the interference.
First of all, the interference should be modeled in mathematical form to formulate the statistic property of MF output. Take a look at the OFDM symbol,
∑
−−
=
= /2 1
2 /
2 0
) (
N
N n
t nf j ne a t
s π
, which can be represented as
∑
−−
=
= /2 1
2 /
) (
N
N n
n nw a t
s
, a weighting sum of modulated data. If the data are taken as random variables and N is large, s(t) is a Gaussian process based on the central limit theorem (CLT).
The OFDM symbol of MB-OFDM systems is composed of 128 tones and each tone carries a QPSK symbol of power equal to unity and tones of indexes {0 64 65 66 67 68} are null tones. The autocorrelation function is approximately equal to delta function, which means the approximate Gaussian process is also near to white.
Therefore, the OFDM signal can be modeled as white Gaussian noise with zero mean because the DC tone is null. It will simplify the analysis of the MF output statistic property while the interference can be modeled as white Gaussian.
Simulation Platforms
There are two kinds of simulation platforms; one is take the SOP interference as white noise and pass through a multipath channel then add to the received signal. In the second kind model, the received signal is the desired signal incorporated with thermal noise and the signal generated from another piconet. As shown in figure 6.
MB-OFDM packet generator
Frequency Hopping
Multipath channel +
Piconet 2 Multipath channel
WGN
Packet detection
Figure 6
Threshold Settings
The threshold level is an import parameter of the detection algorithm. A simple setting method based on AWGN channel is derivate.
The received signal after sampling is ri =si +ni, where si is the transmitted
The value of Ai depends on the cross-correlation of the symbol and pattern, it is
reasonable to assume Ai equal to 0 if it is not the desired signal sequence. And wi is a random variable with distribution N(0,σv2), where σv2 =Nσ. So defined two hypotheses, H0 and H1, where
H0 : mi =wi
H1 : mi = A+wi
Based on the proposal of MB-OFDM system, the value of Ai is 1300 when the received signal matches the pattern. So the value of mi is a random variable of Gaussian distribution with variance Nσ2 and bias 0 or 1300 depending on the excess of boundary. The threshold can be set according to the desired false alarm rate or missing rate, as shown in figure 7. In figure 7, the SNR is -10 dB, and
77 . 404
163840 10
, 10
2
10 / 2
2 10 / 2
=
=
=
×
=
×
= − −
σ σ
σ σ
N
N N
N
v
SNR SNR
False alarm rate Missing rate
Threshold
77 .
=404 σv
77 .
=404 σv
Figure 7
Due to the co-band interference of SOP, the SINR might get negative value in dB which means the power of interference is stronger than that of desired signal. The packet detection algorithm in MB-OFDM systems should be robust to this kind of
interference. It is important to how much noise the algorithm can tolerate. A plot of the missing rate versus SNR is shown in figure 8. As shown in this figure, the false alarm rate and missing rate is greatly increasing when SNR lower than -3dB. It is desired to design another algorithm more robust.
Figure 8
Figure 9