Maritime communications with sea surface reflections and sea wave occlusions are susceptible to jamming attacks due to the wide geographical area and intensive wireless communication services.Unmanned Aerial Vehicles(...Maritime communications with sea surface reflections and sea wave occlusions are susceptible to jamming attacks due to the wide geographical area and intensive wireless communication services.Unmanned Aerial Vehicles(UAVs)help relay messages to improve communication performance,but the relay policy that depends on the rapidly changing maritime environments is difficult to optimize.In this paper,a reinforcement learning-based UAV relay policy for maritime communications is proposed to resist jamming attacks.Based on previous transmission performance,the relay location,the received power of the transmitted signal and the received jamming power,this scheme optimizes the UAV trajectory and relay power to save the energy consumption and decrease the Bit-Error-Rate(BER)of the maritime signals.A deep reinforcement learning-based scheme is also proposed,which designs a deep neural network with dueling architecture to further improve the communication performance and computational complexity.The performance bounds regarding the signal to interference plus noise ratio,energy consumption and the communication utility are provided based on the Nash equilibrium of the game against jamming,and the computational complexity of the proposed schemes is analyzed.Simulation results show that the proposed schemes improve the energy efficiency and decrease the BER compared with the benchmark.展开更多
The video transmission in the Internet-of-Things(IoT)system must guarantee the video quality and reduce the packet loss rate and the delay with limited resources to satisfy the requirement of multimedia services.In th...The video transmission in the Internet-of-Things(IoT)system must guarantee the video quality and reduce the packet loss rate and the delay with limited resources to satisfy the requirement of multimedia services.In this paper,we propose a reinforcement learning based energy-efficient IoT video transmission scheme that protects against interference,in which the base station controls the transmission action of the IoT device including the encoding rate,the modulation and coding scheme,and the transmit power.A reinforcement learning algorithm state-action-reward-state-action is applied to choose the transmission action based on the observed state(the queue length of the buffer,the channel gain,the previous bit error rate,and the previous packet loss rate)without knowledge of the transmission channel model at the transmitter and the receiver.We also propose a deep reinforcement learning based energy-efficient IoT video transmission scheme that uses a deep neural network to approximate Q value to further accelerate the learning process involved in choosing the optimal transmission action and improve the video transmission performance.Moreover,both the performance bounds of the proposed schemes and the computational complexity are theoretically derived.Simulation results show that the proposed schemes can increase the peak signal-to-noise ratio and decrease the packet loss rate,the delay,and the energy consumption relative to the benchmark scheme.展开更多
基金This work was supported in part by the Funds of the National Natural Science Foundation of China under Grant(U21A20444,61971366)in part by the Fundamental Research Funds for the central universities No.20720210073.
文摘Maritime communications with sea surface reflections and sea wave occlusions are susceptible to jamming attacks due to the wide geographical area and intensive wireless communication services.Unmanned Aerial Vehicles(UAVs)help relay messages to improve communication performance,but the relay policy that depends on the rapidly changing maritime environments is difficult to optimize.In this paper,a reinforcement learning-based UAV relay policy for maritime communications is proposed to resist jamming attacks.Based on previous transmission performance,the relay location,the received power of the transmitted signal and the received jamming power,this scheme optimizes the UAV trajectory and relay power to save the energy consumption and decrease the Bit-Error-Rate(BER)of the maritime signals.A deep reinforcement learning-based scheme is also proposed,which designs a deep neural network with dueling architecture to further improve the communication performance and computational complexity.The performance bounds regarding the signal to interference plus noise ratio,energy consumption and the communication utility are provided based on the Nash equilibrium of the game against jamming,and the computational complexity of the proposed schemes is analyzed.Simulation results show that the proposed schemes improve the energy efficiency and decrease the BER compared with the benchmark.
基金This work was supported by the National Natural Science Foundation of China(Nos.61971366,61671396,and 61901403)the Youth Innovation Fund of Xiamen(No.3502Z20206039)the Natural Science Foundation of Fujian Province of China(No.2020J01430).
文摘The video transmission in the Internet-of-Things(IoT)system must guarantee the video quality and reduce the packet loss rate and the delay with limited resources to satisfy the requirement of multimedia services.In this paper,we propose a reinforcement learning based energy-efficient IoT video transmission scheme that protects against interference,in which the base station controls the transmission action of the IoT device including the encoding rate,the modulation and coding scheme,and the transmit power.A reinforcement learning algorithm state-action-reward-state-action is applied to choose the transmission action based on the observed state(the queue length of the buffer,the channel gain,the previous bit error rate,and the previous packet loss rate)without knowledge of the transmission channel model at the transmitter and the receiver.We also propose a deep reinforcement learning based energy-efficient IoT video transmission scheme that uses a deep neural network to approximate Q value to further accelerate the learning process involved in choosing the optimal transmission action and improve the video transmission performance.Moreover,both the performance bounds of the proposed schemes and the computational complexity are theoretically derived.Simulation results show that the proposed schemes can increase the peak signal-to-noise ratio and decrease the packet loss rate,the delay,and the energy consumption relative to the benchmark scheme.
