Mobility may degrade the performance of next-generation vehicular networks operating at the millimeter-wave spectrum: frequent loss of alignment and blockages require repeated beam training and handover, thus incurring huge overhead. In this paper, an adaptive and joint design of beam training, data transmission and handover is proposed, that exploits the mobility process of mobile users and the dynamics of blockages to optimally trade-off throughput and power consumption. At each time slot, the serving base station decides to perform either beam training, data communication, or handover when blockage is detected. The problem is cast as a partially observable Markov decision process, and solved via an approximate dynamic programming algorithm based on PERSEUS [2]. Numerical results show that the PERSEUS-based policy performs near-optimally, and achieves a 55% gain in spectral efficiency compared to a baseline scheme with periodic beam training. Inspired by its structure, an adaptive heuristic policy is proposed with low computational complexity and small performance degradation.

Next-generation wireless networks promise to provide extremely high data rates, especially exploiting the so-called millimeter-wave frequency range. Gaining information from spectrum usage is becoming important to provide smart adaptation capabilities to future network protocol stacks. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and their identification is crucial. Despite the complexity of full analytical models, machine learning techniques are progressively being considered to improve spectrum usage at higher layers. In this paper, we design a signal processing technique that uses narrowband physical layer energy traces, obtained from one or multiple channel sniffers. The proposed technique utilizes a combination of template matching and an Explicit Duration Hidden Markov Model (EDHMM) to correctly classify frames, while coping with the non-stationarity of the traces. This leads to a protocol level monitor that does not need to decode the channel at the physical layer, but just infers the type of packets that are exchanged based on sub-sampled energy traces. The performance of this framework is evaluated using off-the-shelf mm-wave wireless devices, quantifying its detection performance in the presence of one or multiple sniffers, and assessing the impact of physical layer parameters such as noise power and signal levels.

Future vehicular communication networks call for new solutions to support their capacity demands, by leveraging the potential of the millimeter-wave (mm-wave) spectrum. Mobility, in particular, poses severe challenges in their design, and as such shall be accounted for. A key question in mm-wave vehicular networks is how to optimize the trade-off between directive Data Transmission (DT) and directional Beam Training (BT), which enables it. In this paper, learning tools are investigated to optimize this trade-off. In the proposed scenario, a Base Station (BS) uses BT to establish a mm-wave directive link towards a Mobile User (MU) moving along a road. To control the BT/DT trade-off, a Partially Observable (PO) Markov Decision Process (MDP) is formulated, where the system state corresponds to the position of the MU within the road link. The goal is to maximize the number of bits delivered by the BS to the MU over the communication session, under a power constraint. The resulting optimal policies reveal that adaptive BT/DT procedures significantly outperform common-sense heuristic schemes, and that specific mobility features, such as user position estimates, can be effectively used to enhance the overall system performance and optimize the available system resources.

Troubleshooting millimeter-wave (mm-wave) wireless networks is complex due to the directionality of the communication. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and identifying them is crucial to improve network deployments. To this end, access to lower-layer information is important. However, commercial off-the-shelf mm-wave wireless devices typically do not provide such information. Even if they would, detecting effects such as deafness based on information of a single node that forms part of the network is typically hard. In this paper, we present the design and evaluation of an external sniffing device that can infer the aforementioned performance issues only using narrowband physical layer energy traces. Our sniffer does not need to decode any data, resulting in a simple but effective approach which also preserves privacy and works on encrypted networks. Our key contribution is a machine learning framework which enables automated energy trace analysis while coping with the non-stationarity of the traces. We evaluate its performance in practice using off-the-shelf wireless devices operating in the 60 GHz band. Our results show that the above framework correctly infers physical layer events in virtually all cases, thus providing valuable information to troubleshoot issues in mm-wave networks.

We investigate a jamming problem in an underwater acoustic sensor network, where nodes try to communicate in spite of an adversary that is attempting to block their communications. We take into account that the attenuation of underwater acoustic channels is strongly dependent on the communication distance and the signal frequency. We frame the problem in a game theoretic setup, as a Bayesian zero-sum game where the sensor network acts as the maximizer of the transmission capacity, while the jammer is the minimizer. In particular, we are interested in evaluating the effect of the nodes' position on the resulting equilibrium. The Bayesian character comes into play to represent the uncertainty on the position information of the nodes. Our evaluations show that for many network configurations, the equilibrium strategy of the jammer is pure. Thus, the transmitters can act as though the jammer only causes a higher level of interference. This allows us to identify positions where the damage caused by a jammer is easier to quantify, but the jammer itself is harder to detect.