Research On Key Technologies Of Massive MIMO Based Cognitive Wireless Networks

Mobile Internet and Internet of things are the major trends in the development of information industry. The widespread use of such networks pose a higher challenge for mobile communication network. With the repid development of wireless communication technologies and the successful commercialization of the fourth generation communication system (4G),domestic and foreign research institutes are studing 5G with bigger pace. Through the use of large-scale antenna (Massive MIMO), ultra-dense network, new multiple access, high frequency band access, flexible spectrum sharing, new network architecture and other key technologies, 5G will support 1000 times more network capacity, more than 100 billion connections, 5 to 15 times improvement on spectrum efficiency, Gbps experience data rate and millisecond delay.Then, during the implementation of 5G, the traditional spectrum resource allocation causes insufficient resource utilization. Based on cognitive science,information theory and control theory, cognitive wireless network is proposed.Cognitive wireless network can dynamically allocate radio resources to realize the efficient use of resources in the mobile communication system. At the same time, massive MIMO is widely recognized in the academia and industry. It is bound to be deployed in 5G. The massive MIMO system with spectrum sharing becomes a hot research topic. Therefore, research on massive MIMO based cognitive wireless network is imperative. In this paper, the key technologies of massive MIMO based cognitive wireless network are studied in depth.The main contributions of this paper are as follows:Firstly, this paper studies the spectrum sensing technology in massive MIMO based cognitive wireless network. Through the actual measurement by the radio spectrum monitoring system, it shows that there is a large number of unused time-frequency-space resources in radio environment. In this paper, based on probability theory and wireless channel fading model, we propose a method to maximize the detection probability by determining the optimal antenna positions of the secondary nodes. Through simulation results, we shaow that the proposed method improves the system detection probability. Moreover, we also propose a noval mechanism of cooperative spectrum sensing based on geographical clustering is proposed. The mechanism consists of two steps. First, a single node detection strategy based on signal propagation model is derived. Then, the cooperative node allocation is completed by using clustering and confidence factor. The new method improves the detection precision in the cognitive wireless network. This paper analyzes the detection fairness among different frequency bands. An iterative Hungarian algorithm which can derive the optimal sensing fairness is proposed. The bow shape sensor allocation scheme is proposedwith low complexity and good fairness. The simulation results give the optimal antenna positions of the secondary nodes and show the improvement of the system detection probability and fairness.Secondly, this paper studies the energy harvesting strategy in massive MIMO based cognitive wireless network. The theoretical SINR and user rate of downlink transmission in two-hop relay network with massive MIMO are obtained. When the numbers of base station antennas and relay antennas tend to infinity, the asymptotic SINR and sum rate is independent of fast fading. In the case of perfect channel estimation, the user power is inversely proportional to the number of antennas. When the ratio between the number of base station antennas and relay antennas tends to infinity, the transmitting rate of two-hop relay network is only related to the transmission parameters of K users, regardless of the transmission of the other hop. In massive MIMO based cognitive wireless relay networks, if the relay node's energy is insufficient. It must collect energy first and then transmit information. There is an equilibrium relationship between the user's transmission sum rate and the collected energy. When the proportion of the antenna tends to infinity, the users' asymptotic rate is independent of the RF energy conversion factor. This paper obtains the optimal time allocation strategy between the energy harvesting and data transmission by convex optimization theory. The simulation results show that the performance of the system tends to be better as the number of antennas increases, and derive the optimal time allocation strategy of energy harvesting and data transmission.Finally, this paper studies the relay communication in massive MIMO based cognitive wireless network. Relay transmission is the simplest two-hop mode and cognitive wireless network adopts the underlay paradigm. The theoretical SINR and transmission rate are derived. When the numbers of base station antennas and relay antennas tend to infinity, and the transmission power is inversely proportional to the number of antennas, this paper shows that both the asymptotic SINR and the asymptotic transmission rate in the primary and secondary network are independent of fast fading. The interference between the primary and secondary network is mitigated by massive MIMO technology. The primary and secondary network can transmit independently. The user's asymptotic SINR and asymptotic transmission rate are independent of the interference temperature of primary network. The secondary network can use the peak power to transmit without causing any interference to the primary network. When the ratio between the number of primary base station antennas and relay antennas tends to infinity,the transmitting sum rate of the two-hop relay primary network is only related to the transmission parameters on the users' side, regardless of the transmission of the other hop. Then, the utility function that measures the efficiency of the primary network transmission is defined in this paper. The optimal relay transmission power is obtained by maximizing the utility function. The numerical results show that the transmission rate increases with the increase of the number of antennas.Moreover, the primary and secondary network are independent. The optimal relay transmission power is obtained to maximize the utility function.