Anti-jamming Communication Research Articles

A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

Pulse jamming is one of the common malicious jamming patterns that can significantly reduce the of wireless communication's reliability. This paper investigates the problem of anti-jamming communication in a random pulse jamming environment. In order to obtain the countermeasure in time domain, the Markov decision process (MDP) is employed to model and analyze the above problem, and a time-domain anti-pulse jamming algorithm (TDAA) based on reinforcement learning is proposed. The proposed algorithm learns from the dynamic interaction with the jamming environment to gradually approximate the optimal time-domain strategy. The optimal strategy enables the transmitter to switch between two states, i.e. “active” and “silent”, to avoid random pulse jamming. In addition, a state estimation and adjustment method for the random pulse jamming environment is introduced to improve the robustness of the proposed TDAA. Simulation results show that, compared with continuous transmission, the proposed TDAA can effectively reduce the jamming collision ratio and significantly improve the normalized throughput. And compared with transmitting terminal Q-learning algorithm (TTQA), the proposed TDAA has higher time utilization ratio and normalized throughput.

Intelligent OFDM telecommunication system. Part 1. Model of complex and quaternion systems

In this paper, we aim to investigate the superiority and practicability of many-parameter transforms (MPTs) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication systems based on complex and quaternion MPTs. The new systems use inverse MPT (IMPT) for modulation at the transmitter and MPT for demodulation at the receiver. The purpose of employing the MPT is to improve: 1) the PHY-LS of wireless transmissions against to the wide-band anti-jamming and anti-eavesdropping communication; 2) the bit error rate (BER) performance with respect to the conventional OFDM-TCS; 3) the peak to average power ratio (PAPR). Each MPT depends on finite set of independent parameters (angles). When parameters are changed, many-parametric transform is also changed taking form of a set known (and unknown) orthogonal (or unitary) transforms. For this reason, the concrete values of parameters are specific “key” for entry into OFDM-TCS. Vector of parameters belong to multi-dimension torus space. Scanning of this space for find out the “key” (the concrete values of parameters) is hard problem. MPT has the form of the product of the Jacobi rotation matrixes and it describes a fast algorithm for MPT. The main advantage of using MPT in OFDM TCS is that it is a very flexible anti-eavesdropping and anti-jamming Intelligent OFDM TCS. To the best of our knowledge, this is the first work that utilizes the MPT theory to facilitate the PHY-LS through parameterization of unitary transforms.

Intelligent OFDM telecommunication system. Part 3. Anti-eavesdropping and anti-jamming properties of system, based on many-parameter wavelet and Golay transforms

In this paper, we aim to investigate the superiority and practicability of many-parameter wavelet and Golay transforms (MPWT and MPGT) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication system (Intelligent-OFDM-TCS), based on many-parameter transforms (MPTs). New system uses inverse MPT for modulation at the transmitter and direct MPT for demodulation at the receiver. The purpose of employing the MPTs is to improve the PHY-LS of wireless transmissions against to the wide-band anti-jamming communication. Each MPT depends on finite set of independent Jacobi parameters (angles), which could be changed independently one of another. When parameters are changed, multi-parametric transform is changed too taking form of a set known (and unknown) orthogonal (or unitary) wavelet transforms. We implement the following performances as bit error rate (BER), symbol error rate (SER), peak to average power ratio (PAPR), the Shannon-Wyner secrecy capacity (SWSC) for novel Intelligent-MPWT-OFDM-TCS. Previous research has shown that the conventional OFDM TCS based on discrete Fourier transform (DFT) has unsatisfactory characteristics in BER, PARP, SWSC and in anti-eavesdropping communications. We study Intelligent-MPT-OFDM-TCS to find out optimal values of angle parameters of MPT optimized BER, PAPR, SWSC, anti-eavesdropping effects. Simulation results show that the proposed Intelligent OFDM-TCS have better performances than the conventional OFDM system based on DFT against eavesdropping.

Intelligent OFDM telecommunication system. Part 4. Anti-eavesdropping and anti-jamming properties of the system, based on many-parameter and fractional Fourier transforms

In this paper, we aim to investigate the superiority and practicability of many-parameter Fourier transforms (MPFT) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication system (Intelligent-OFDM-TCS), based on MPFT. New system uses inverse MPFT for modulation at the transmitter and direct MPFT for demodulation at the receiver. The purpose of employing the MPFTs is to improve the PHY-LS of wireless transmissions against to the wide-band anti-jamming communication. Each MPFT depends on finite set of independent parameters (angles), which could be changed independently one from another. When parameters are changed, multi-parametric transform is also changed taking form of a set known (and unknown) orthogonal (or unitary) transforms. We implement the following performances as bit error rate (BER), symbol error rate (SER), the Shannon-Wyner secrecy capacity (SWSC) for novel Intelligent-MPWT-OFDM-TCS. Previous research has shown that the conventional OFDM TCS based on discrete Fourier transform (DFT) has unsatisfactory characteristics in BER, SWSC and in anti-eavesdropping communications. We study Intelligent-MPWT-OFDM-TCS to find out optimal values of angle parameters of MPFT optimized BER, SWSC, anti-eavesdropping effects. Simulation results show that the proposed Intelligent OFDM-TCS have better performances than the conventional OFDM system based on DFT against eavesdropping.

Cognitive Risk Control for Anti-Jamming V2V Communications in Autonomous Vehicle Networks

The future of intelligent transportation system (ITS) is expected to be composed of connected and autonomous vehicles (CAVs), the development of which will have great impact on people's everyday life. Unfortunately, this progress will be accompanied by all kinds of potential threats and attacks rising in CAV network. As a legacy from traditional wireless networks, jamming attack is still one of the major and serious threats to vehicle-to-vehicle (V2V) communications. In this paper, we investigate the anti-jamming V2V communication in CAV networks through power control in conjunction with channel selection. Bringing into play a brain-inspired research tool called cognitive dynamic system (CDS), the general structure of cognitive risk control (CRC) is well-tailored to analyze and address the jamming problem. Specifically, power control is carried out first using reinforcement learning, the result of which is then examined by a module called task-switch control. Based on the risk assessment, a multi-armed bandit (MAB) problem is formulated to perform the channel-selection process when necessary. Through continuous perception-action cycles (PACs), the feature of predictive adaptation is realized for the legitimate vehicle in its behavioral interactions with the jammer. Simulation results have shown that the proposed method has desirable performance in terms of several evaluation metrics.

An Anti-Jamming Hierarchical Optimization Approach in Relay Communication System via Stackelberg Game

In this paper, we study joint relay selection and the power control optimization problem in an anti-jamming relay communication system. Considering the hierarchical competitive relationship between a user and jammer, we formulate the anti-jamming problem as a Stackelberg game. From the perspective of game, the user selects relay and power strategy firstly which acts as the leader, while the jammer chooses power strategy then that acts as follower. Moreover, we prove the existence of Stackelberg equilibrium. Based on the Q-learning algorithm and multi-armed bandit method, a hierarchical joint optimization algorithm is proposed. Simulation results show the user’s strategy selection probability and the jammer’s regret. We compare the user’s and jammer’s utility under the proposed algorithm with a random selection algorithm to verify the algorithm’s superiority. Moreover, the influence of feedback error and eavesdropping error on utility is analyzed.

Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

A new waveform design method of Military Anti-jamming Communication

The advantages and disadvantages of wireless communication systems are the key factors that determine victory or defeat in the war. The construction of military informatization has accelerated in various countries, and the struggle for electromagnetic space has become more intense. Therefore, the communication anti-jamming techniques received extensive concern. It is of great significance to explore a waveform that can communicate effectively under interference. In this paper, the complex electromagnetic environment, typical means of military communication jamming and anti-jamming are analyzed thoroughly. A double-sequence frequency hopping waveform design method based on pseudo-random chirp is proposed, which has some practical value in engineering.

Research on Intelligent anti-jamming communication with Cognitive Radio

Research on Intelligent anti-jamming communication with Cognitive Radio

Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach

Intelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement learning. This paper considers a communication system, where the communication between a UAV swarm and the base station is jammed by multiple interferers. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, motion and antenna spatial domain to optimize the communication quality in the receiving area. This paper proposes a modified Q-Learning algorithm based on multi-parameter programming, where a cost is introduced to strike a balance between the motion and communication performance of the UAVs. The simulation results show the effectiveness of the algorithm.

Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach

This paper investigates the problem of anti-jamming communication in dynamic and intelligent jamming environment. A sequential deep reinforcement learning algorithm (SDRLA) without prior information is proposed, and raw spectrum information is used as the input of SDRLA. The proposed SDRLA algorithm mainly contains two parts: Firstly, deep learning and sliding window principle are introduced to identify jamming patterns; Secondly, reinforcement learning is carried out to make on-line channel selection based on recognized jamming patterns. In addition, channel switching cost is introduced for the purpose of formulating the trade-off relationship between throughput and overhead. Taking advantage of both deep learning and reinforcement learning, this method can realize rapid and effective anti-jamming channel selection with no need for modeling the jammer's characteristics. Simulation results show the convergence and effectiveness of the proposed SDRLA algorithm. Compared with single-mode reinforcement learning, our approach can reach better convergence performance and higher utility.

TH-NRDCSK: A Non-Coherent Time Hopping Chaotic System for Anti-Jamming Communications

Time hopping spread spectrum (THSS) system and chaotic system have been studied as candidates of the anti-jamming (AJ) systems with substantial advantages. However, THSS systems suffer from the strict synchronization prerequisite. In order to overcome the shortcomings of the THSS system and utilize the profits of the non-coherent chaotic system, we first propose a novel TH-NRDCSK system to enhance the bit error rate (BER) performance of the conventional non-coherent THSS system and relieve the strict synchronization issue of coherent THSS systems. Then, we analyze the performance of the proposed system under additive white Gaussian noise (AWGN) channel and jamming environment. Finally, we implement the simulation to demonstrate the consistency of the analysis and the simulation results and to disclose the effects of the system parameters on system's AJ performance. Moreover, the performance of the proposed system is compared to that of conventional counterparts to look at the benefits of integrating chaotic and THSS systems. The simulation results show that the proposed system significantly outperforms conventional counterparts in a practical environment.

Stackelberg Game Approaches for Anti-Jamming Defence in Wireless Networks

This article investigates the anti-jamming communications problem in wireless networks from a Stackelberg game perspective. By exploring and analyzing the inherent characteristics of the anti-jamming problem, we present and discuss some technical challenges and fundamental requirements to address them. To be specific, the adversarial characteristic, incomplete information constraints, dynamics, uncertainty, dense deployment, and heterogeneous feature bring technical challenges to anti-jamming communications in wireless networks. Then, for the purpose of improving system performance, four requirements for anti-jamming communications are presented and discussed. Following the advantages of the Stackelberg game model in anti-jamming field, we formulate an anti-jamming decision-making framework based on the Stackelberg game for anti-jamming defence in wireless networks. Moreover, two preliminary case studies are presented and discussed for better understanding of the anti-jamming Stackelberg game problem. Finally, some future research directions are also provided.

Two-Dimensional Antijamming Mobile Communication Based on Reinforcement Learning

By using smart radio devices, a jammer can dynamically change its jamming policy based on opposing security mechanisms; it can even induce the mobile device to enter a specific communication mode and then launch the jamming policy accordingly. On the other hand, mobile devices can exploit spread spectrum and user mobility to address both jamming and interference. In this paper, a two-dimensional anti-jamming mobile communication scheme is proposed in which a mobile device leaves a heavily jammed/interfered-with frequency or area. It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework. More specifically, a hotbooting deep Q-network based two-dimensional mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate the learning speed in dynamic situations. Several real-world scenarios are simulated to evaluate the proposed method. These simulation results show that our proposed scheme can improve both the signal-to-interference-plus-noise ratio of the signals and the utility of the mobile devices against cooperative jamming compared with benchmark schemes.

Mitigation and control of Defeating Jammers using P-1 Factorization

Jamming-resistant broadcast communication is crucial for safety-critical applications such as emergency alert broadcasts or the dissemination of navigation signals in adversarial settings. These applications share the need for guaranteed authenticity and availability of messages which are broadcasted by base stations to a large and unknown number of (potentially untrusted) receivers. Common techniques to counter jamming attacks such as Direct-Sequence Spread Spectrum (DSSS) and Frequency Hopping are based on secrets that need to be shared between the sender and the receivers before the start of the communication. However, broadcast anti jamming communication that relies on Pollards Rho Method. In this work, we therefore propose a solution called P-Rho Method to enables spread-spectrum anti-jamming broadcast communication without the requirement of shared secrets. complete our work with an experimental evaluation on a prototype implementation.

Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach

This letter investigates the problem of anti-jamming communications in dynamic and unknown environment through on-line learning. Different from existing studies which need to know (estimate) the jamming patterns and parameters, we use the spectrum waterfall, i.e., the raw spectrum environment, directly. Firstly, to cope with the challenge of infinite state of raw spectrum information, a deep anti-jamming Q-network is constructed. Then, a deep anti-jamming reinforcement learning algorithm is proposed to obtain the optimal anti-jamming strategies. Finally, simulation results validate the the proposed approach. The proposed approach is relying only on the local observed information and does not need to estimate the jamming patterns and parameters, which implies that it can be widely used various anti-jamming scenarios.

Power control games for multi-user anti-jamming communications

This paper investigates two different anti-jamming scenarios for wireless networks in which there exist one jammer and multiple users. The jammer jams all the users simultaneously. In one scenario, the users and the jammer sense the opponent’s information, and take appropriate strategies accordingly. This scenario is modeled as a Stackelberg game in which the jammer acts as a leader, and the users act as followers. In another scenario, the users and the jammer take actions simultaneously and the anti-jamming problem is modeled as a static game. The existence and uniqueness of game equilibrium are proved. Closed-form expressions of the Stackelberg equilibrium (SE) and the Nash equilibrium (NE) are derived. Simulation results are presented to validate our theoretical analysis. It is shown that the users’ average utility of the SE is lower than that of the NE since the jammer can reduce the users’ utilities by utilizing the leader advantage to improve its own utility.

Intelligent Anti-jamming Communication Based on the Modified Q-Learning

Based on the probability of communication in different channels, the jammer launches intelligent jamming attack, which poses a serious challenge to the reliable transmission capability of wireless communication system. In this paper, the channel selection problem for anti-jamming defense is formulated as a Markov decision process (MDP). Then, based on modified Q-Learning, an intelligent anti-jamming learning algorithm (IALA), is proposed to quickly learn the jamming rule, and it can effectively predict the jamming behavior and obtain the optimal anti-jamming policy. The simulation results are conducted to show that the performance of the proposed IALA algorithm.

Need a Change? Try GeTe: A Reconfigurable Filter Using Germanium Telluride Phase Change RF Switches

There is an increasingly high demand for compact, low-loss reconfigurable filters in various advanced wireless applications: cognitive radios, modern transceivers, and antijamming communication systems, to name a few [1]. With reconfigurable filters, wireless communication systems that require multiple operating frequency bands can be implemented in a smaller size and at a reduced cost compared to conventional systems using multiple single-frequency filters. Bandpass filters provide a better RF performance compared to low-pass or high-pass filters, resulting in improved signal-to-noise ratio and out-of-band performance [2].

Fast Adaptive BSS Algorithm for Independent/Dependent Sources

Blind separation for dependent sources and multi-Gaussian sources is a challenging problem. This letter proposes a new adaptive blind source separation algorithm, which is referred to as vector auto-regressive diagonalization (VAR-JD), to deal with this problem. This algorithm estimates demixing matrix by matrix joint JD of a set of matrices containing the estimated VAR coefficients. Compared with traditional adaptive BSS method, which assumes that source signals are mutual independent and no more than one Gaussian source signal exists, the proposed VAR-JD algorithm is not only able to separate independent source signals but also dependent source signals, and there is no demand for the number of Gaussian source signals. Simulation results on the separation of synthetic and communication signals demonstrate the effectiveness of the proposed approach. This latter is important to anti-jamming communications. Moreover, VAR-JD is suitable for determined mixtures and can extend easily to overdetermined mixtures.