In this thesis, we design a baseband receiver for IEEE 802.15.3c SCBT system with relatively lower computation complexity than other systems. And we do the computer simulations to verify the performances of the methods that we proposed. In synchronization part, the method that we used can improve the probability of tracking on the timing of first path. And the overall probability of error (symbol timing is outside the ISI free region) is very low. Because the symbol in the synchronization sequence is either +1 or -1, then the multiplications in the algorithm can be replaced by adders and inverters to reduce the complexity of implementation. For channel estimation, we use the auto-correlation property of Golay complementary sequences to design our CE algorithm for CIRs and noise power.
The performance illustrates that the estimated CSI performs almost the same as the perfect CSI when the threshold is chosen correctly. As the same reason in synchronization, the circuit design in channel estimation part is also very simple. At last, the data detection methods we proposed improve the BER performance after FDE. We also do the simulations for different type modulations such as PSK and QAM modulations. User can choose the proper modulations according to the requirements of throughput (data rate) and BER by referring our simulation results.
In this paper, we assume the CIRs are fixed during a packet slot. In future work, we may consider the time varying channel during a data block and modify the CE algorithm on
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tracking the variation of the channel. According to the IEEE 802.15.3c standard, the Reed Solomon and LDPC codes can be use in channel coding to enhance the system performance.
The analysis of SCBT with channel coding is another potential research issue and direction. In IEEE 802.15.3c standard, the OFDM system is coexistent with SCBT system, so the transceiver design of OFDM is also a research direction. Moreover, how to implement the dual-mode system let the OFDM and the SCBT systems can operate simultaneously becomes another important issue in the future.
Appendix A. Complexity of Gibbs sampler algorithm in SCBT system
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In GS algorithm, we need to calculate the probability p x( n| , ,y x1 ",xn−1,xn+1,",xN). To simplify the equation, we define
T
where T is the vector transpose.
Assume that xn∈[m m1 2"mM] where M is the number of possible symbols for x . Then n the equation 4.30 can be rewritten as
1
After we derive the equation 4.30 and define the distance vector , we have
following relationship
The distance vector dj can be expressed as
1,
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From above equation, we know that there are P multiplications to calculate form
where P is the number of paths. Now we modify the equation A.2 as follow
dj di
According to our observation, we find that only P elements in the vector and are different. By using this property, the equation
dj di we need other P multiplications to calculate
' summarize, 2P M× ×N multiplications per iteration and 2P M× multiplications per symbol are needed, where N is the length of data symbols. If the Gibbs sampler iterates times, there are
Ns
2P M× × ×N Ns multiplications in total for this process.
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