Wireless networks are a key component of the telecommunications infrastructure in our society, and wireless services become increasingly important as the applications of wireless devices have penetrated every aspect of our lives. Although wireless technologies have significantly advanced in the past decades, most wireless networks are still vulnerable to radio jamming attacks due to the openness nature of wireless channels, and the progress in the design of jamming-resistant wireless networking systems remains limited. This stagnation can be attributed to the lack of practical physical-layer wireless technologies that can efficiently decode data packets in the presence of jamming attacks. This article surveys existing jamming attacks and anti-jamming strategies in wireless local area networks (WLANs), cellular networks, cognitive radio networks (CRNs), ZigBee networks, Bluetooth networks, vehicular networks, LoRa networks, RFID networks, GPS system, millimeter-wave (mmWave) and learning-assisted wireless systems, with the objective of offering a comprehensive knowledge landscape of existing jamming and anti-jamming strategies and therefore stimulating more research efforts to secure wireless networks against jamming attacks. Different from prior survey papers, this article conducts a comprehensive, in-depth review on jamming and anti-jamming strategies, casting insights on the design of jamming-resilient wireless networking systems. An outlook on promising anti-jamming techniques is offered at the end of this article to delineate important research directions.

Recently, networks operate at frequencies over 28 GHz (mmWave) have emerged as a viable solution for 5G mobile networks to provide Gbps data rate. Due to the high directivity and attenuation of mmWave signals, mmWave communication links are highly vulnerable to the frequent mmWave channel blockages, which can trigger excessive handovers. Thanks to its ability to enrich the scattering environment and create reflective signal multipaths, Reconfigurable Intelligent Surface (RIS) has great potential to counter the blockage effect and thus greatly reduce the number of unnecessary handovers. However, this potential has not been well explored. In this paper, we propose a RIS-assisted handover scheme by leveraging deep reinforcement learning (DRL). Under various channel blockage conditions, the DRL agent manages to reduce the cumulative handover overhead by jointly adjusting beamformers and RIS phase shifts. Compared with the existing schemes without considering RIS, the RIS-assisted handover scheme significantly reduces the number of handovers and achieves higher spectrum efficiency. Besides, to alleviate the impact from the limited observations of the fast fading channels, we propose a lightweight algorithm to sense the blockage status and such sensing results can be utilized to improve the performance of model training. Numerical results show that DRL agent is able to further improve the performance when integrated with the blockage status sensing algorithm.

MU-MIMO and OFDMA are two key techniques in IEEE 802.11ax standard. Although these two techniques have been intensively studied in cellular networks, their joint optimization in Wi-Fi networks has been rarely explored as OFDMA was introduced to Wi-Fi networks for the first time in 802.11ax. The marriage of these two techniques in Wi-Fi networks creates both opportunities and challenges in the practical design of MAC-layer protocols and algorithms to optimize airtime overhead, spectral efficiency, and computational complexity. In this paper, we present DeepMux, a deep-learning-based MU-MIMO-OFDMA transmission scheme for 802.11ax networks. DeepMux mainly comprises two components: deep-learning-based channel sounding (DLCS) and deep-learning-based resource allocation (DLRA), both of which reside in access points (APs) and impose no computational/communication burden on Wi-Fi clients. DLCS reduces the airtime overhead of 802.11 protocols by leveraging the deep neural networks (DNNs). It uses uplink channels to train the DNNs for downlink channels, making the training process easy to implement. DLRA employs a DNN to solve the mixed-integer resource allocation problem, enabling an AP to obtain a near-optimal solution in polynomial time. We have built a wireless testbed to examine the performance of DeepMux in real-world environments. Our experimental results show that DeepMux reduces the sounding overhead by 62.0% ~ 90.5% and increases the network throughput by 26.3% ~ 43.6%.

ZigBee is a wireless communication technology that has been widely used to provide low-bandwidth wireless services for Internet-of-Things applications, such as building automation, medical data collection, and industrial equipment control. As ZigBee operates in the industrial, scientific and medical radio frequency bands, it may suffer from unintentional interference from coexisting radio devices (e.g., WiFi and Bluetooth) and/or radio jamming attacks from malicious devices. Although many results have been produced to enhance ZigBee security, there is no technique that can secure ZigBee against jamming attack. In this article, we propose a new ZigBee receiver by leveraging MIMO technology, which is capable of decoding its desired signal in the presence of constant jamming attack. The enabler is a learning-based jamming mitigation method, which can mitigate the unknown interference using an optimized neural network. We have built a prototype of our proposed ZigBee receiver on a wireless testbed. Experimental results show that it is capable of decoding its packets in the face of 20-dB stronger jamming. The proposed ZigBee receiver offers an average of 26.7-dB jamming mitigation capability compared to off-the-shelf ZigBee receivers.

Underwater acoustic (UWA) networks are a key form of communications for human exploration and activities in the oceanographic space of the earth. A fundamental issue of UWA communications is large propagation delays due to water medium, which has posed a grand challenge in UWA network protocol design. Conventional wisdom of addressing this issue is to live with this disadvantage by inserting a guard interval to introduce immunity to propagation delays. Recent advances in interference alignment (IA) open up a new direction to address this issue and promise a great potential to improve network throughput by exploiting large propagation delays. In this paper, we investigate propagation delay-based IA (PD-IA) in multi-hop UWA networks. We first develop a set of simple constraints to characterize PD-IA feasible region at the physical layer. Based on the set of PD-IA constraints, we develop a distributed PD-IA scheduling algorithm to greedily maximize interference overlapping possibilities in a multi-hop UWA network. Simulation results show that the proposed PD-IA algorithm yields higher throughput than an idealized benchmark algorithm without propagation delays, indicating that large propagation delays are not adversarial but beneficial for network throughput performance.