Thematic Talks

TT1: Thematic Talks: Advanced Topics on Signal Processing and Communications

Turing Meets Shannon: On the Algorithmic Computability of the Capacities of Secure Communication Systems

H. Vincent Poor (Princeton University, USA)

This paper presents the recent progress in studying the algorithmic computability of capacity expressions of secure communication systems. Several communication scenarios are discussed and reviewed including the classical wiretap channel, the wiretap channel with an active jammer, the problem of secret key generation, and the problem of identification over channels. Further, more complicated channel models are discussed including finite state channels and general channels. Revisiting Sparse Channel Estimation in Massive MIMO-OFDM Systems Waheed U. Bajwa (Rutgers University-New Brunswick, USA) This paper addresses the problem of sparse channel estimation in massive MIMO-OFDM systems. For this purpose, two formulations are investigated for training-based channel estimation; termed as the distinct block diagonal model and the random block diagonal model. For the first formulation, theoretical guarantees for reliable channel recovery are provided based on the total number of parameters in the training signal.Moreover, a tensor recovery technique is used to estimate the sparse channel in the second formulation and numerical experiments are performed to demonstrate that the channel estimation performance is a function of the total number of parameters as well as number of pilot tones and OFDM time symbols.

Constantinos B. Papadias (Athens Information Technology, Greece)

Coordinated multi-point (CoMP) can be used as an enabler of underlay spectrum sharing instead of legacy technologies, such as multi-user multiple-input multiple-output, in order to further increase the overall spectral efficiency (SE) and provide quality-of-service (QoS) guarantees to the end users. This concept, though, has been largely overlooked in the literature. Moreover, while the performance of CoMP transmission techniques generally improves as the number of antennas on the base stations (BS) increases, cost and power consumption constraints (and even size limitations in the case of small-cell BSs and remote radio units) prohibit the deployment of a large number of antennas on these nodes. Load-controlled parasitic antenna arrays address this issue. However, the application of arbitrary precoding on such antenna systems is far from trivial. Furthermore, previous studies on the subject did not consider multi-active multi-passive (MAMP) antenna arrays and multi-user communication, let alone coordinated transmissions and spectrum sharing setups. Our goal in this work is to fill the aforementioned gaps in the literature. To this end: (i) We derive a low-complexity coordinated QoS-aware power allocation method for sum-SE maximization in an underlay spectrum sharing setup under given transmission and interference power constraints and per-user QoS requirements; (ii) we describe simple suboptimal alternatives of this power allocation policy; and (iii) we present coordinated hybrid precoding implementations of standard linear precoding schemes for load-controlled MAMP (LC-MAMP) antenna arrays. Numerical simulations demonstrate the feasibility of the proposed resource allocation strategies, illustrate their performance gains, and highlight the impact of various parameters on their efficiency.

TT2: Thematic Talks: Distributed Robotics, Drones

Optimal UAV Relay Placement for Single User Capacity Maximization over Terrain with Obstacles

Urbashi Mitra (University of Southern California, USA)

This paper studies the optimal unmanned aerial vehicle (UAV) placement in a constrained 3-D space to build a connection between a base station (BS) and a ground user. The essential challenge is to avoid signal propagation blockage from the target user, while maintaining a good connection to the BS. Most existing work was based on stochastic terrain models, and hence the quality-of-service for a specific user was not guaranteed. In contrast, this paper seeks the optimal UAV position according to the actual terrain structure; to this end, a multi-segment propagation model is exploited. Using a novel angular coordinate transformation, a low complexity search algorithm is developed, where the search time is bounded for arbitrary terrain shapes. The paper also examines and proves the global optimality of the search algorithm. Numerical experiments are performed over a real-world urban topology and demonstrate superior performance gain of the UAV position found by the proposed algorithm. UAV Swarms as Amplify-and-Forward MIMO Relays Danijela Cabric (University of California Los Angeles, USA) Unmanned aerial vehicles provide new opportunities for performance improvements in future wireless communications systems. For example, they can act as relays that extend the range of a communication link and improve the capacity. Unlike conventional relays that are deployed at fixed locations, UAVs can change their positions to optimize the capacity or range on demand. In this paper, we consider using a swarm of UAVs as amplify-and-forward MIMO relays to provide connectivity between an obstructed multi-antenna equipped source and destination. We start by optimizing UAV placement for the single antenna case, and analyze its dependence on the noise introduced by the relay, its gain, and transmit power constraint. We extend our analysis for an arbitrary UAV swarm and show how the MIMO link capacity can be optimized by changing the distance of the swarm to the source and the destination. Then, we consider the effect of optimizing the positions of the UAVs within the swarm and derive an upper bound for the capacity at any given placement of the swarm. We also propose a simple near optimal approach to find the positions that optimize the capacity for the end-to-end link given that the source and the destination have uniform rectangular arrays.

Cellular Coverage-Aware Path Planning for UAVs

Sofie Pollin (KU Leuven, Belgium)

Up until now, path planning for unmanned aerial vehicles (UAVs) has mainly been focused on the optimisation towards energy efficiency. However, to operate UAVs safely, wireless coverage is of utmost importance. Currently, deployed cellular networks often exhibit an inadequate performance for aerial users due to high amounts of intercell interference. Furthermore, taking the never-ending trend of densification into account, the level of interference experienced by UAVs will only increase in the future. For the purpose of UAV trajectory planning, wireless coverage should be taken into account to mitigate interference and to lower the risk of dangerous connectivity outages. In this paper, several path planning strategies are proposed and evaluated to optimise wireless coverage for UAVs. A simulator using a real-life 3D map is used to evaluate the proposed algorithms for both 4G and 5G scenarios. We show that the proposed Coverage-Aware A* algorithm, which alters the UAV’s flying altitude, is able to improve the mean SINR by 3-4dB and lower the cellular outage probability by a factor of 10. Furthermore, the outages that still occur have a 60% shorter length, hence posing a lower risk to induce harmful accidents.

TT3: Thematic Talks: 5G and IoT

5G Evolution and Beyond

Erik Dahlman (Ericsson Research, Sweden)

This paper provides an overview of the evolution of the 5G NR radio-access technology, starting with 3GPP release 16 but also including potential further evolution steps. It also provides a discussion about the use of AI and machine learning as important components of the future evolution of wireless communication.

On the performance of some short block-length codes in 5G-NR

Raymond Knopp (Institut Eurecom, France)

In this paper we provide an overview of some of the short block-length codes used in 5G-NR, in particular those used for control channel signaling. We show that the 3GPP polar-code and rate-matching construction are quite close to recent information-theoretic lower bounds on error-rate performance. Higher-rate codes, however, are less efficient in this regard. We also consider the efficiency of the codes when reference signal overhead for channel estimation is included. Initial results lead us to believe that some improvement can be expected from future short block-length coding constructions which consider channel uncertainty, especially in the low spectral-efficiency regime.

TT4: Thematic Talks: Machine Learning for Communications

Learning-Based Channel Estimation for Various Antenna Array Configurations

Wolfgang Utschick (Technische Universität München, Germany)

Recently, a neural-network-based method for massive MIMO uplink channel estimation was introduced. The derivations assumed a uniform linear array (ULA) with half-wavelength antenna spacing at the base station. In this work, we show that the estimator can also be used in case of ULAs and uniform rectangular arrays (URAs) with antenna spacings given by integer multiples of half the wavelength. We then investigate how the antenna spacing and certain parameters of the channel model influence the estimation performance.

Position and LIDAR-Aided mmWave Beam Selection using Deep Learning

Robert Heath (The University of Texas at Austin, USA)

One issue in the design of modern communication systems is how to benefit from the increasing variety of sensor signals and sophisticated machine learning algorithms. We recently described how LIDAR (light detection and ranging) on a vehicle can be used for line-of-sight detection and to reduce the overhead associated with link configuration in millimeter wave communication systems. LIDAR is widely used in autonomous driving for high resolution mapping and positioning. In this paper, we present new LIDAR-based features for machine learning and compare the previously proposed distributed architecture with two centralized schemes: using a single LIDAR located at the base station (BS) and fusing LIDAR data from neighboring vehicles at the BS. We also quantify the advantages of LIDAR-based solutions over solutions based on connected vehicles informing their positions. We use deep convolutional neural networks to process images composed of LIDAR data and/or positions. Using co-simulation of communications and LIDAR in a vehicle-to-infrastructure (V2I) scenario, we find that the distributed LIDAR-based architecture provides robust performance irrespective of car penetration rate, outperforming the single LIDAR at BS and position-based solutions. We noted that, under the simulated conditions, the benefits of a centralized data fusion over distributed processing are not significant, meaning that machine learning for line-of-sight detection and beam-selection can be conveniently executed at vehicles equipped with LIDAR.

Spiking Neural Networks for Low-Power Edge Intelligence

Osvaldo Simeone (King’s College London, United Kingdom (Great Britain))

The energy and memory requirements of artificial neural networks (ANNs) limit their applicability to mobile and embedded devices. Spiking Neural Networks (SNNs), also known as third-generation neural networks, are among the most promising alternative solutions. SNNs are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse inter-neuron, or synaptic, communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by hardware implementations that have demonstrated significant energy reductions as compared to conventional ANNs. Training algorithms for SNNs have been traditionally studied in the field of theoretical neuroscience through the lens of biological plausibility. In contrast, this talk aims at providing a (very) brief introduction to models, learning rules, and applications of SNNs from the viewpoint of stochastic signal processing. (Joint work with Hyeryung Jang, King’s College London.)