Wireless Interference Classification with Low Complexity Multi-Branch Networks

In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is low.To this end,we propose three effective approaches.Firstly,we introduce multibranch convolutional neural networks(CNNs)for interference recognition.The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for WIC.Our design avoids the carefully crafted selection of each transformation.Unfortunately,multi-branch CNNs are computationally expensive and memory-inefficient.To this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference.Thirdly,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of LCMN.Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.

Multi-Agent Few-Shot Meta Reinforcement Learning for Trajectory Design and Channel Selection in UAV-Assisted Networks

Unmanned aerial vehicle(UAV)-assisted communications have been considered as a solution of aerial networking in future wireless networks due to its low-cost, high-mobility, and swift features. This paper considers a UAV-assisted downlink transmission,where UAVs are deployed as aerial base stations to serve ground users. To maximize the average transmission rate among the ground users, this paper formulates a joint optimization problem of UAV trajectory design and channel selection, which is NP-hard and non-convex. To solve the problem, we propose a multi-agent deep Q-network(MADQN) scheme.Specifically, the agents that the UAVs act as perform actions from their observations distributively and share the same reward. To tackle the tasks where the experience is insufficient, we propose a multi-agent meta reinforcement learning algorithm to fast adapt to the new tasks. By pretraining the tasks with similar distribution, the learning model can acquire general knowledge. Simulation results have indicated the MADQN scheme can achieve higher throughput than fixed allocation. Furthermore, our proposed multiagent meta reinforcement learning algorithm learns the new tasks much faster compared with the MADQN scheme.

Performance analysis of FFH / BPSK system with partial band noise jamming and channel estimation error in high-mobility wireless communication scenarios

In this paper,we analyze the bit error ratio(BER) of fast frequency hopping(FFH) system with binary phase shift keying(BPSK) in high-mobility wireless channels.By taking into account partial band noise jamming and channel estimation errors as Gaussian noise,both of maximal-ratio combining(MRC) and equal-gain combining(EGC) schemes are considered,and exact analytical BER expressions for MRC and EGC schemes are obtained.Especially,a closed-form BER expression for the MRC scheme is also presented in high signal-to-noise ratio region.Analytical and simulation results demonstrate that,the FFH/BPSK schemes suffer from performance degradation in the presence of channel estimation errors.The worst case jamming factor is shown to be inversely proportional to the signal to jamming ratio.With properly designed frequency hopping schemes and channel estimation schemes,the performance improvement is also observed as compared with the traditional noncoherent FFH/BFSK system.