The identification of malicious jamming pattern is significant for wireless communication system to adopt targeted anti-jamming approaches. Efficient identification of malicious jamming patterns requires sufficient sample data and computing resources for the complex electromagnetic environment. However, with the use of portable communication equipment and emergency communications, it is difficult to ensure adequate sample data and computing resources. Against this background, this letter focuses on the jamming identification based on a small sample data-driven Naive Bayes classifier. The main idea is to obtain approximate conditional feature probability via data augment and kernel density estimation (KDE) from a small sample labelled data and construct Naive Bayes classifier, so as to form a fast jamming recognition method. Simulation results show that compared with the methods using decision tree or simple neutral network, the proposed scheme achieves a better average accuracy between six jamming patterns, as well as low-complexity.

Due to the threat of various jamming attacks in wireless communications, the efficient and general anti-jamming schemes are required to guarantee communication the quality of wireless networks. To this end, this article proposes the efficient index modulation-based frequency hopping spread spectrum (IM-FHSS) scheme. Unlike the classical IM-FHSS which only considered the reactive jamming, the efficient IM-FHSS scheme extends the anti-jamming capacity to various jamming models. Specifically, we first derive the closed-form expressions of bit error rate (BER) performance of the IM-FHSS under three jamming models in an additional white Gaussian noise (AWGN) channel and a Rayleigh fading channel, which enables the design of anti-jamming strategies. Then, the unified anti-jamming framework is provided for designing the efficient anti-jamming system. Based on the framework, we further provide the strategies of the efficient IM-FHSS for reactive jamming, constant, and random jamming. Simulations show that the theoretical derivations match well with the simulated results, which validates the performance analysis. Moreover, the efficient IM-FHSS has demonstrated the superior anti-jamming performance for three typical jamming models, which is also energy efficient and reliable for achieving targeted BER performance.

Recently, symbol level jamming (SLJ) has been proposed and studied as an effective jamming technique. However, the detection performance estimation and efficient detection schemes for SLJ have not been investigated in the literature. This paper proposes a novel active detection scheme for SLJ to enhance the detection capability of the legitimate user, where the detection performance estimation and target detection performance can be actively obtained. Specifically, based on a preset tolerant threshold of the bit error rate (BER) and necessary inputs, the threatening jamming power level (TJPL) for SLJ is obtained for the legitimate user, and then the TJPL is used for active performance estimation and adaption. We first derive the theoretical detection performance for QPSK modulated SLJ and noise modulated SLJ. Moreover, the TJPL required for detection is derived based on BER expressions and analytical results. Thereafter, two modes of active detection scheme are discussed, aiming to actively estimate the detection performance and obtain expected detection performance, respectively. Simulation results show that the active detection scheme can accurately estimate the jamming detection performance. Additionally, it can achieve expected detection performance, which adapts well to different preset thresholds, SNR regions and the modulation types.

The effective and accurate recognition of communication jamming is crucial for enhancing the anti-jamming capability of wireless communication systems. At present, a significant portion of jamming data is decentralized, stored in local nodes, and cannot be uploaded directly for network training due to its sensitive nature. To address this challenge, we introduce a novel distributed jamming recognition method. This method leverages a distributed recognition framework to achieve global optimization through federated learning. Each node independently trains its local model and contributes to the comprehensive global model. We have devised an adaptive adjustment mechanism for the mixed weight parameters of both local and global models, ensuring an automatic balance between the global model and the aggregated insights from local data across devices. Simulations indicate that our personalization strategy yields a 30% boost in accuracy, and the adaptive weight parameters further enhance the recognition accuracy by 1.1%.

Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6–14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3–7% and 5–12%, respectively, under low JSR conditions compared with the existing single-node method.

Joint communication and computing power network (JCCPN) has emerged as a promising architecture of the 6G wireless networks due to low latency communication and efficient computing services. However, the existing works have not fully considered the spatiotemporal mismatch between computing power supply and practical traffic distribution, leading to failure risks or resource waste. Specifically, the mismatch arises from two aspects, i.e., 1) the competition for the data links between collaborative inference tasks and communication transmission tasks; 2) the fixed computing power allocation struggles to meet dynamic computing demand. This paper focuses on the mismatch problem in JCCPNs, and formulated a joint optimization model for inference links and computing nodes. The joint optimization model was theoretically decoupled into two submodels, efficiently addressing interdependencies between links and nodes. Then, we proposed an inference-subgraph driven multi-agent deep reinforcement learning (IsMADRL) algorithm for JCCPN, consisting of two stages. At the first stage, we formulated an inference subgraph based on ordinal potential game (OPG) to separate computing and transmission data flows, ensuring collaborative inference tasks. At the second one, multi-agent deep reinforcement learning (MADRL) framework is employed on the inference-subgraph to allocate computing power dynamically, meeting the varying traffic distribution. Simulation results show that several MADRL architectures all exhibit excellent adaptability and effectiveness in complex JCCPNs.