Radio Maps for Beam Alignment in mmWave Communications with Location Uncertainty

Next generation communication systems require accurate beam alignment to counteract the impairments that characterize propagation in high-frequency bands. The overhead of the pilot sequences required to select the best beam pair is prohibitive when codebooks contain a large number of beams, as is the case in practice. To remedy this issue, some schemes exploit information about the user location to predict the best beam pair. However, these schemes (i) involve no measurements whatsoever, which generally results in a highly suboptimal predicted beam, and (ii) are not robust to localization errors. To address these limitations, this paper builds upon the notion of radio map to develop two algorithms that attain a balance between the quality of the obtained beam pair and measurement overhead. The proposed algorithms predict the received power corresponding to each pair and measure just the Q pairs with highest prediction. While the first algorithm targets simplicity, the second one relies on a Bayesian approach to endow the prediction process with robustness to localization error. The performance of both algorithms is shown to widely outperform existing methods using ray-tracing data.

Spoofing Detection in the Physical Layer with Graph Neural Networks

In a spoofing attack, a malicious actor impersonates a legitimate user to access or manipulate data without authorization. The vulnerability of cryptographic security mechanisms to compromised user credentials motivates spoofing attack detection in the physical layer, which traditionally relied on channel features, such as the received signal strength (RSS) measured by spatially distributed receivers or access points. However, existing methods cannot effectively cope with the dynamic nature of channels, which change over time as a result of user mobility and other factors. To address this limitation, this work builds upon the intuition that the temporal pattern of changes in RSS features can be used to detect the presence of concurrent transmissions from multiple (possibly changing) locations, which in turn indicates the existence of an attack. Since a localization-based approach would require costly data collection and would suffer from low spatial resolution due to multipath, the proposed algorithm employs a deep neural network to construct a graph embedding of a sequence of RSS features that reflects changes in the propagation conditions. A graph neural network then classifies these embeddings to detect spoofing attacks. The effectiveness and robustness of the proposed scheme are corroborated by experiments with real-data.

Theoretical Analysis of the Radio Map Estimation Problem

Radio map estimation (RME) constructs representations providing radio frequency metrics, such as the received signal strength, at every location of a geographic area using a set of measurements collected at multiple positions. The resulting radio maps find a wide range of applications in wireless communications, including prediction of coverage holes, network planning, resource allocation, and path planning for mobile robots. Although a vast number of estimators have been proposed, the theoretical understanding of the RME problem has not been pursued. The present work aims at filling this gap along two directions. First, the complexity of the function space of radio maps is quantified by means of lower and upper bounds on their spatial variability, which offers valuable insight into the required spatial distribution of measurements and estimators that can be used. Second, the reconstruction error for power maps in free space is upper bounded for three simple spatial interpolators, namely zeroth-order, first-order, and sinc interpolators. In view of these bounds, the proximity coefficient, which is an increasing function of the transmitted power and a decreasing function of the distance from the transmitters to the mapped region, is proposed to quantify the complexity of the RME problem. Simple numerical experiments assess the tightness of the obtained bounds and reveal the practical trade-offs associated with the considered interpolators.

Analytical Performance Bounds for Radio Map Estimation

Radio map estimation (RME) aims at providing a radiofrequency metric, such as the received power strength, at every location of a geographical region of interest by relying on measurements acquired at multiple positions. Although a large number of estimators have been proposed so far, their performance has been analyzed mostly on simulated data. The theoretical aspects of the RME problem as well as performance bounds remain an open problem. This paper takes a step towards filling this gap by means of a theoretical analysis of the RME problem in a free-space propagation environment. First, the complexity of the estimation problem is quantified by means of upper bounds on the spatial variability of radio maps. Second, error bounds are derived for zeroth-order and first-order interpolation estimators. The proximity coefficient, which depends proportionally on the transmitted power and inversely proportionally on the cube of the distance from the transmitters to the mapped region, is proposed to quantify the complexity of the RME problem. One of the main findings is that the error of the considered estimators is roughly proportional to this proximity coefficient. Simple numerical experiments verify the tightness of the obtained bounds.

Spoofing Attack Detection in the Physical Layer with Robustness to User Movement

In a spoofing attack, an attacker impersonates a legitimate user to access or modify data belonging to the latter. Typical approaches for spoofing detection in the physical layer declare an attack when a change is observed in certain channel features, such as the received signal strength (RSS) measured by spatially distributed receivers. However, since channels change over time, for example due to user movement, such approaches are impractical. To sidestep this limitation, this paper proposes a scheme that combines the decisions of a position-change detector based on a deep neural network to distinguish spoofing from movement. Building upon community detection on graphs, the sequence of received frames is partitioned into subsequences to detect concurrent transmissions from distinct locations. The scheme can be easily deployed in practice since it just involves collecting a small dataset of measurements at a few tens of locations that need not even be computed or recorded. The scheme is evaluated on real data collected for this purpose.

Radio Map Estimation in the Real-World: Empirical Validation and Analysis

Radio maps quantify received signal strength or other magnitudes of the radio frequency environment at every point of a geographical region. These maps play a vital role in a large number of applications such as wireless network planning, spectrum management, and optimization of communication systems. However, empirical validation of the large number of existing radio map estimators is highly limited. To fill this gap, a large data set of measurements has been collected with an autonomous unmanned aerial vehicle (UAV) and a representative subset of these estimators were evaluated on this data. The performance-complexity trade-off and the impact of fast fading are extensively investigated. Although sophisticated estimators based on deep neural networks (DNNs) exhibit the best performance, they are seen to require large volumes of training data to offer a substantial advantage relative to more traditional schemes. A novel algorithm that blends both kinds of estimators is seen to enjoy the benefits of both, thereby suggesting the potential of exploring this research direction further.

Spoofing Attack Detection in the Physical Layer with Commutative Neural Networks

In a spoofing attack, an attacker impersonates a legitimate user to access or tamper with data intended for or produced by the legitimate user. In wireless communication systems, these attacks may be detected by relying on features of the channel and transmitter radios. In this context, a popular approach is to exploit the dependence of the received signal strength (RSS) at multiple receivers or access points with respect to the spatial location of the transmitter. Existing schemes rely on long-term estimates, which makes it difficult to distinguish spoofing from movement of a legitimate user. This limitation is here addressed by means of a deep neural network that implicitly learns the distribution of pairs of short-term RSS vector estimates. The adopted network architecture imposes the invariance to permutations of the input (commutativity) that the decision problem exhibits. The merits of the proposed algorithm are corroborated on a data set that we collected.

Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

Radio maps can be utilized to characterize a parameter of interest in a communication channel, such as the received signal strength, at every point of a certain geographical region. This article presents an introductory tutorial to radio map estimation, where radio maps are constructed using spatially distributed measurements. After describing the applications of this kind of maps, this article delves into estimation approaches. Starting by simple regression techniques, gradually more sophisticated algorithms are introduced until reaching state-of-the-art estimators. The presentation of this versatile toolkit is accompanied with toy examples to build up intuition and gain insight into the foundations of radio map estimation. As a secondary objective, this article attempts to reconcile the sometimes conflicting terminology in the literature and to connect multiple bodies of literature and sub-communities that have been working separately in this context.

Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous techniques have been proposed to construct radio maps from spatially distributed measurements, the locations of such measurements are assumed predetermined beforehand. In contrast, this paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. This is performed in two steps. First, two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. These algorithms offer complementary benefits and feature constant complexity per measurement. Second, the uncertainty metric is used to plan the trajectory of the UAV to gather measurements at the most informative locations. To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time. Numerical experiments conducted on a realistic dataset confirm that the proposed scheme constructs accurate radio maps quickly.