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An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formulate this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels.


Mobile base stations on board unmanned aerial vehicles (UAVs)
promise to deliver connectivity to those areas where the
terrestrial infrastructure is overloaded, damaged, or absent. A
fundamental problem in this context involves determining a
minimal set of locations in 3D space where such aerial base
stations (ABSs) must be deployed to provide coverage to a set of
users. While nearly all existing approaches rely on average
characterizations of the propagation medium, this work


The paper studies the motion control for mobile relays imple-
menting cooperative beamforming to aid the communication
between a source-destination pair. We consider an urban com-
munication scenario, where the channels exhibit spatiotempo-
ral correlations and thus can be learned. The relays move in a
time-slotted fashion within a three-dimensional cube. During
every slot, the relays beamform optimally to maximize the
Signal-to-Interference+Noise Ratio (SINR) at the destination


Wireless channels are considered that change over time but remain constant for a certain (coherence) period. This behavior is perfectly captured by block fading channels and affects the performance of the corresponding wireless communication systems. Desired closed-form characterizations of optimal transmission schemes remain unknown in many cases. This paper approaches this issue from a fundamental, algorithmic point of view by studying whether or not it is in principle possible to construct or find such optimal transmission


Wireless communication systems are inherently vulnerable to adversarial attacks since malevolent jammers might jam and disrupt the legitimate transmission intentionally. Of particular interest are so-called denial-of-service (DoS) attacks in which the jammer is able to completely disrupt the communication. Accordingly, it is of crucial interest for the legitimate users to detect such DoS attacks. Turing machines provide the fundamental limits of today’s digital computers and therewith of the traditional signal processing. It has been


While globally optimal solutions to many convex programs can be computed efficiently in polynomial time, this is, in general, not possible for nonconvex optimization problems. Therefore, locally optimal approaches or other efficient suboptimal heuristics are usually applied for practical implementations. However, there is also a strong interest in computing globally optimal solutions of nonconvex problems in offline simulations in order to benchmark faster suboptimal algorithms. Global solutions often rely on monotonicity properties.


We consider globally optimal precoder design for rate splitting multiple access in Gaussian multiple-input single-output downlink channels with respect to weighted sum rate and energy efficiency maximization. The proposed algorithm solves an instance of the joint multicast and unicast beamforming problem and includes multicast- and unicast-only beamforming as special cases. Numerical results show that it outperforms state-of-the-art algorithms in terms of numerical stability and converges almost twice as fast.


Wireless communication systems are to use millimeter-wave (mmWave) spectra, which can enable extra radar functionalities. In this paper, we propose a multi-target velocity estimation technique using IEEE 802.11ad waveform in a vehicle-to-vehicle (V2V) scenario. We form a wide beam to consider multiple target vehicles.