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 (2-D) antijamming 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 2-D 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 learning 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.

WiFi technology has gained a wide prevalence for not only wireless communication but also pervasive sensing. A wide variety of emerging applications leverage accurate measurements of the Channel State Information (CSI) information obtained from commodity WiFi devices. The frequency response of internal signal processing circuit is mixed with the real channel frequency response in passband, which makes deriving accurate channel frequency response from CSI measurements a challenging task. In this paper, we identify non-negligible non-linear CSI phase errors and report that IQ imbalance is the root source of non-linear CSI phase errors. We conduct intensive analysis on the characteristics of such non-linear errors and find that such errors are prevalent among various WiFi devices. Based on key observations, we propose new methods to compensate both non-linear and linear CSI phase errors. We demonstrate the efficacy of the proposed methods by applying them in CSI splicing and indoor distance ranging. Results of extensive real-world experiments indicate that accurate CSI phase measurements can significantly improve the performance of splicing and the stability of the derived power delay profiles (PDPs). Moreover, the estimated distance errors are reduced by 5.7 times on average comparing to the state-of-the-art schemes.

The TDMA-based Medium Access Control (MAC) protocol has been recently proposed as a promising solution to well support delay-sensitive safety applications in vehicular ad hoc networks (VANETs), since a time-slotted access scheme ensures the transmission within the ultra-low delays. However, due to the varying vehicle mobility, existing TDMA-based MAC protocols result in collisions of slot assignment when multiple sets of vehicles move together. To avoid slot-assignment collisions, in this paper, we propose a mobility-aware TDMA-based MAC, referred to as MoMAC, which can assign every vehicle a time slot according to the underlying road topology and lane distribution on roads with the consideration of vehicles' mobilities. In MoMAC, different lanes on the same road segment and different road segments at intersections are associated with disjoint time slot sets. In addition, each vehicle broadcasts safety messages together with the time slot occupying information of neighboring vehicles; by updating time slot occupying information of two-hop neighbors (obtained indirectly from one-hop neighbors), vehicles can detect time slot collisions and access a vacant time slot in a fully distributed way. We demonstrate the efficiency of MoMAC through theoretical analysis and extensive simulations; compared with state-of-the-art TDMA MACs, the transmission collisions can be reduced by 59.2%, and the rate of safety message transmissions/receptions can be greatly enhanced.

Connected vehicles have been considered as an effective solution to enhance driving safety as they can be well aware of nearby environments by exchanging safety beacons periodically. However, under dynamic traffic conditions, especially for dense-vehicle scenarios, the naive beaconing scheme where vehicles broadcast beacons at a fixed rate with a fixed transmission power can cause severe channel congestion and thus degrade the beaconing reliability. In this paper, by considering the kinematic status and beaconing rate together, we study the rear-end collision risk and define a danger coefficient ρ to capture the danger threat of each vehicle being in the rear-end collision. In specific, we propose a fully distributed adaptive beacon control scheme, called ABC, which makes each vehicle actively adopt a minimal but sufficient beaconing rate to avoid the rear-end collision in dense scenarios based on individually estimated ρ. With ABC, vehicles can broadcast at the maximum beaconing rate when the channel medium resource is enough and meanwhile keep identifying whether the channel is congested. Once a congestion event is detected, an NP-hard distributed beacon rate adaptation (DBRA) problem is solved with a greedy heuristic algorithm, in which a vehicle with a higher ρ is assigned with a higher beaconing rate while keeping the total required beaconing demand lower than the channel capacity. We prove the heuristic algorithm's close proximity to the optimal result and thoroughly analyze the communication overhead of ABC scheme. By using Simulation of Urban MObility (SUMO)-generated vehicular traces, we conduct extensive simulations to demonstrate the efficacy of our proposed ABC scheme. Simulation results show that vehicles can adapt beaconing rates according to the driving safety demand, and the beaconing reliability can be guaranteed even under high-dense vehicle scenarios.

Efficient and scalable media access control (MAC) protocol design is crucial to guarantee the reliable broadcast of safety messages in vehicular ad hoc networks (VANETs). To devise a MAC for safety message broadcasting with reliability and minimum delay, in this paper, we propose a novel time slot-sharing MAC, referred to as SS-MAC, which can support diverse periodical broadcasting rates. In specific, we first introduce a circular recording queue to online perceive time slot occupying status. We then design a distributed time slot sharing (DTSS) algorithm and random index first fit (RIFF) algorithm, to efficiently share the time slot and make the on-line vehicle-slot matching, respectively. We prove theoretically the efficacy of DTSS algorithm, and evaluate the efficiency of RIFF algorithm by using Matlab simulations. Finally, we conduct extensive simulations considering various driving scenarios and resource conditions to demonstrate the SS-MAC performance.