The Research And Realization For Several Key Technologies In Physical Layer Downlink Of Lte-Based Cognitive Network

As the explosive development of smart mobile terminal, the internet and computer companies start to participate in the communication industry and most of the mobile terminal users are in great need of high-speed internet accessing as well as handset’s mobility. On the other hand, the scarce spectrum resource can not afford high-speed internet accessing, because there are so many communication systems that the frequency spectrums are almost used by them. In fact, the spectrums authored by them are unused in most of time.To deal with the problem above, the LTE-based Cognitive Network is proposed. The LTE is able to satisfy mobile terminal users’need of high-speed internet accessing and handset’s mobility. And Cognitive Network can find the unused authored spectrums to provide enough spectrum resources for LTE. The core of LTE-Based Cognitve Network is its physical layer. Thus, the thsis focuses on the the research and realization for several key technologies in physical layer downlink of LTE-Based Cognitive Network.First of all, the thsis proposes a cooperative sensing algorithm—Eigenvector Based Algorithm (EBA). The core of EBA is the cluster head chosen algorithm, which is improved by the thsis for two times. The tree’s connectivity of graph theory is introduced to improve the EBA’s convergence speed. And the matrix-based parallel computation is introduced to improve the EBA’s computation speed and reduce the energy consumption. The computer simulation result shows that, compared with the HEED algorithm, the EBA can prolong the lifecycle of sensing devices and improve the sensing speed. Secondly, the thsis puts forth an innovative LTE physical layer design. This design focuses on the PDCCH. The DCI which is carried by PDCCH, is redesigned to support the Cognitive Network Resource Scheduling. The CR-PU (Cognitive Radio-Primary User) Spectrum is brought forword to ensure the quality of PDCCH transmission. Furmore a new physical channel PCogCCH (Physical Cognitive Control Channel) is brought up to support the Cognitive Network Resource Scheduling as the supplement of PDCCH. According to the changes of PDCCH, the physical resource mapping is redesigned as well. The computer simulation result shows that LTE which is running on the new physical layer design can gain three times more availability of frequency spectrum as the normal LTE.Last but not the least, the thsis describes how to use DSP (Digital Signal Processor) to build a hardware platform of the LTE physical layer’s downlink. The fix-point simulation is done to achieve the range and precision of the values which are needed in the hardware computation. Then the thsis presents the realization of several important phycial layer’s modules such as OFDM Modulation and DeModulation, Channel Estimation. To test the platform, an indoor and outdoor true environmental testcase is done. The result shows that the LTE-based Cognitive Network can work very well.At the end of the thsis, two future works are propounded. The LTE-based Cognitive Network’s Uplink project deserves much more researches. And the hardware platform of the LTE physical layer’s downlink should be used to test much more advanced Cognitive Network Algorithms.