A Novel Reinforcement Learning Algorithm Based on Broad Learning System for Fast Communication Antijamming

The intelligent antijamming algorithm based on deep reinforcement learning (DRL) has become a prominent focus in communication antijamming research. However, while DRL aims to accurately fit complex behavioral strategies, communication antijamming prioritizes the speed of strategy fitting. To bridge this gap, we introduce a novel reinforcement learning (RL) antijamming algorithm called broad Q-network (BQN), which merges a specialized flattened neural network, broad learning system (BLS), with the RL framework. First, a new RL algorithm architecture is proposed based on the characteristics of BLS using ridge regression for network updates. Subsequently, an experience memory and training matrix construction mechanism is introduced for sample collection and utilization during the strategy update phase. Finally, a communication-sensing-decision parallel time slot structure is employed, addressing the issues of lagging sensing results and delayed reward computation. Simulation results demonstrate that under the environments of comb sweep jamming, nonlinear sweep jamming, and intelligent blocking jamming, our BQN's convergence speed is more than 10, 5, and 2.5 times faster than that of the most popular deep Q-network algorithm, respectively. Compared to mainstream DRL algorithms, such as dueling double deep Q-network, dropout Q-function, and proximal policy optimization, BQN also achieves faster acquisition of effective multidomain antijamming strategies, encompassing the frequency, power, and rate domains, in three dynamic jamming environments.