Distributed Beamforming Algorithms For UAV Cognitive Networks

Unmanned aerial vehicles,often referred to as drones,are widely used in military operations,logistics,disaster relief,public communications,and other fields due to their excellent autonomy,flexibility,and hovering ability.However,a single-UAV system is weak in invulnerability and has a limited coverage.It is difficult to provide a stable and reliable communication link.To solve this problem,a multi-UAV cooperative communication method can be used.Distributed beamforming technology,as an important part of the collaborative communication field,has gained wide attention due to its ability to obtain full gain and high energy efficiency.The multi-UAV network,which is equipped with distributed beamforming technology can effectively combat multi-path fading and improve the system transmission capacity.In order to further improve spectrum utilization and suppress interference between users,cognitive radio technology(CR)can be used.CR can dynamically sense the spectrum environment and adapt to statistical changes.Drones equipped with CR can adjust communication schemes according to actual communication needs and complete communication tasks in unauthorized frequency bands,thereby greatly improving the reliability and safety of drone networks.Therefore,this paper mainly studies the distributed beamforming problem in the multi-UAV cognitive relay network,and explores the beamforming algorithms in different communication scenarios to provide reference and basis for the actual system design.The main work of the paper is summarized as follows:1.Given part of the channel state information,for the cognitive UAV relay network,a distributed beamforming algorithm under the transmit power minimization criterion is proposed.Under the condition that the received signal-to-noise ratio of the secondary user is higher than the threshold and the power interference received by the primary user is lower than the threshold,the algorithm obtains the distributed beamforming weight vector through the method of Schmitt orthogonalization and iteration.Following the transmission,the total power is minimized.Computer simulations not only verify the feasibility of the proposed algorithm,but also analyze the effects of channel uncertainty,the number of UAV relay and the primary user interference threshold on system performance.2.For the cognitive UAV relay network,using part channel state information,with the criterion of maximizing the received signal-to-noise ratio of the secondary users,to ensure that the power interference suffered by each primary user is below the threshold,the method of zero-forcing beamforming combined with maximum Rayleigh entropy is used to obtain the distributed beamforming algorithm under the constraints of the total relay transmit power constraints.The semi-definite relaxation method is futher used to obtain the distributed beamforming algorithm under the single relay transmit power constraints.Computer simulations have verified the feasibility of the proposed algorithms.3.Aiming at the situation of spectrum sharing amnog satellites,drones and terrestrial wireless networks,a distributed beamforming algorithm under the criterion of maximizing the received signal to interference plus noise ratio is proposed.The algorithm uses the penalty function,Charnes-Cooper transformation and convex optimization methods to solve the optimal beamforming weights under the condition that the transmission power of each relay is limited and the received signal to interference plus noise ratio of each satellite user is higher than the threshold.Computer simulations not only verifiy the feasibility of the proposed algorithm,but also analyze the effects of the number of relays,the number of satellite users,the threshold of relay transmission power,and the threshold of satellite user received signal to interference plus noise ratio on system performance.