System Identification I

Sphere Packings, Lattices and Groups

Distance-Regular Graphs

Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs’ communications face new challenges due to their high altitude above the ground and great flexibility of movement in the 3-D space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and induced strong aerial-terrestrial network interference, the distinct communication quality-of-service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3-D UAV mobility. In this article, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussion into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs, where UAVs are integrated into the network as new aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research.

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

The rapid growth of consumer unmanned aerial vehicles (UAVs) is creating promising new business opportunities for cellular operators On the one hand, UAVs can be connected to cellular networks as new types of user equipment, therefore generating significant revenues for the operators that can guarantee their stringent service requirements On the other hand, UAVs offer the unprecedented opportunity to realize UAV-mounted flying base stations (BSs) that can dynamically reposition themselves to boost coverage, spectral efficiency, and user quality of experience Indeed, the standardization bodies are currently exploring possibilities for serving commercial UAVs with cellular networks Industries are beginning to trial early prototypes of flying BSs or user equipments, while academia is in full swing researching mathematical and algorithmic solutions to address interesting new problems arising from flying nodes in cellular networks In this paper, we provide a comprehensive survey of all of these developments promoting smooth integration of UAVs into cellular networks Specifically, we survey: 1) the types of consumer UAVs currently available off-the-shelf; 2) the interference issues and potential solutions addressed by standardization bodies for serving aerial users with the existing terrestrial BSs; 3) the challenges and opportunities for assisting cellular communications with UAV-based flying relays and BSs; 4) the ongoing prototyping and test bed activities; 5) the new regulations being developed to manage the commercial use of UAVs; and 6) the cyber-physical security of UAV-assisted cellular communications

Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges

In this correspondence, we study the achievable rate region of the multiple-input single-output (MISO) interference channel, under the assumption that all receivers treat the interference as additive Gaussian noise. Our main result is an explicit parametrization of the Pareto boundary for an arbitrary number of users and antennas. The parametrization describes the boundary in terms of a low-dimensional manifold. For the two-user case we show that a single real-valued parameter per user is sufficient to achieve all points on the Pareto boundary and that any point on the Pareto boundary corresponds to beamforming vectors that are linear combinations of the zero-forcing (ZF) and maximum-ratio transmission (MRT) beamformers. We further specialize the results to the MISO broadcast channel (BC). A numerical example illustrates the result.

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Complete Characterization of the Pareto Boundary for the MISO Interference Channel

We illustrate the potential of Massive MIMO for communication with unmanned aerial vehicles (UAVs). We consider a scenario, where multiple single-antenna UAVs simultaneously communicate with a ground station (GS) equipped with a large number of antennas. Specifically, we discuss the achievable uplink (UAV to GS) capacity performance in the case of line-of-sight conditions. We develop a realistic geometric model, which incorporates an arbitrary orientation, of the GS and UAV antenna elements to characterize the polarization mismatch loss, which occurs due to the movement and orientation of the UAVs. A closed-form expression for a lower bound on the ergodic rate for a maximum-ratio combining receiver with estimated channel state information is derived. The optimal antenna spacing that maximizes the ergodic rate achieved by an UAV is also determined for uniform linear and rectangular arrays. It is shown that when the UAVs are spherically uniformly distributed around the GS, the ergodic rate per UAV is maximized for an antenna spacing equal to an integer multiple of one-half wavelength.

/pdf/massive-mimo-for-communications-with-drone-swarms-3xxl6q0l67.pdf

Massive MIMO for Communications With Drone Swarms

In this paper, we consider spectrum sensing of DVB-T signals in different fading environments. We compare state-of-the-art detectors including detectors based on pilot subcarriers, as well as detectors for general OFDM signals that exploit the correlation structure incurred by the cyclic prefix. Energy detection is also included for comparison. We show numerically that the choice of detector depends on the scenario, the detector requirements, and on the available prior knowledge. We also show that it is possible to obtain good detection performance by exploiting the correlation, even in a frequency selective channel.

/pdf/spectrum-sensing-methods-for-detection-of-dvb-t-signals-in-ixp8yzddoh.pdf

Spectrum sensing methods for detection of DVB-T signals in AWGN and fading channels

In this correspondence, we compute a closed-form expression for the asymptotic (large-sample) accuracy of the recently proposed squared-range least-squares (SR-LS) method for source localization [A. Beck, P. Stoica, and J. Li, ?Exact and approximate solutions of source localization problems,? IEEE Trans. Signal Process., vol. 56, no. 5, pp. 1770-1778, May 2008]. We compare its accuracy to that of the classical least-squares (LS) method and show that LS and SR-LS perform differently in general. We identify geometries where the performances of the methods are identical but also geometries when the difference in performance is unbounded.

/pdf/accuracy-comparison-of-ls-and-squared-range-ls-for-source-4y2yvwdzp0.pdf

Accuracy Comparison of LS and Squared-Range LS for Source Localization

Massive multiple-input multiple-output (MIMO) is an emerging technology for mobile communications, where a large number of antennas are employed at the base station to simultaneously serve multiple single-antenna terminals with very high capacity. In this paper, we study the potentials and challenges of utilizing massive MIMO for unmanned aerial vehicles (UAVs) communication. We consider a scenario where multiple single-antenna UAVs simultaneously communicate with a ground station (GS) equipped with a large number of antennas. Specifically, we discuss the achievable uplink (UAV to GS) capacity performance in the case of line-of-sight (LoS) conditions. We also study the type of antenna polarization that should be used in order to maintain a reliable communication link between the GS and the UAVs. The results obtained using a realistic geometric model show that massive MIMO is a potential enabler for high-capacity UAV networks.

/pdf/massive-mimo-as-enabler-for-communications-with-drone-swarms-6qs4l8uhir.pdf

Danyo Danev

Papers

Complete Characterization of the Pareto Boundary for the MISO Interference Channel

Massive MIMO for Communications With Drone Swarms

Spectrum sensing methods for detection of DVB-T signals in AWGN and fading channels

Accuracy Comparison of LS and Squared-Range LS for Source Localization

Massive MIMO as enabler for communications with drone swarms