Reliability problems and Pareto-optimality in cognitive radar (Invited paper)

Cognitive radar refers to an adaptive sensing system exhibiting high degree of waveform adaptivity and diversity enabled by intelligent processing and exploitation of information from the environment. The next generation of radar systems are characterized by their application to scenarios exhibiting non-stationary scenes as well as interference caused by use of shared spectrum. Cognitive radar systems, by their inherent adaptivity, seem to be the natural choice for such applications. However, adaptivity opens up reliability issues due to uncertainties induced in the information gathering and processing. This paper lists some of the reliability aspects foreseen for cognitive radar systems and motivates the need for waveform designs satisfying different metrics simultaneously towards enhancing the reliability. An iterative framework based on multi-objective optimization is proposed to provide Pareto-optimal waveform designs

Efficient sum-rate maximization for medium-scale MIMO AF-relay networks

We consider the problem of sum-rate maximization in multiple-input multiple-output (MIMO) amplify-and-forward relay networks with multi-operator. The aim is to design the MIMO relay amplification matrix (i.e., the relay beamformer) to maximize the achievable communication sum rate through the relay. The design problem for the case of single-antenna users can be cast as a non-convex optimization problem, which, in general, belongs to a class of NP-hard problems. We devise a method based on the minorization-maximization technique to obtain quality solutions to the problem. Each iteration of the proposed method consists of solving a strictly convex unconstrained quadratic program. This task can be done quite efficiently, such that the suggested algorithm can handle the beamformer design for relays with up to ~70 antennas within a few minutes on an ordinary personal computer. Such a performance lays the ground for the proposed method to be employed in medium-scale (or lower regime massive) MIMO scenarios

Rate optimization for massive MIMO relay networks: A minorization-maximization approach

We consider the problem of sum-rate maximization in massive MIMO two-way relay networks with multiple (communication) operators employing the amplify-and-forward (AF) protocol. The aim is to design the relay amplification matrix (i.e., the relay beamformer) to maximize the achievable communication sum-rate through the relay. The design problem for the case of single-antenna users can be cast as a non-convex optimization problem, which in general, belongs to a class of NP-hard problems. We devise a method based on the minorization-maximization technique to obtain quality solutions to the problem. Each iteration of the proposed method consists of solving a strictly convex unconstrained quadratic program; this task can be done quite efficiently such that the suggested algorithm can handle the beamformer design for relays with up to ~ 70 antennas within a few minutes on an ordinary PC. Such a performance lays the ground for the proposed method to be employed in massive MIMO scenarios

Optimized Transmission for Parameter Estimation in Wireless Sensor Networks

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large

Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry

Compressive sensing is a sensing protocol that facilitates the reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, typically manifested as signal sparsity. Compressive sensing’s vast repertoire of applications in areas such as communications and image reconstruction stems from the traditional approach of utilizing non-linear optimization to exploit the sparsity assumption by selecting the lowest-weight (i.e., maximum sparsity) signal consistent with all acquired measurements. Recent efforts in the literature consider instead a data-driven approach, training tensor networks to learn the structure of signals of interest. The trained tensor network is updated to “project” its state onto one consistent with the measurements taken, and is then sampled site by site to “guess” the original signal. In this paper, we take advantage of this computing protocol by formulating an alternative “quantum” protocol, in which the state of the tensor network is a quantum state over a set of entangled qubits. Accordingly, we present the associated algorithms and quantum circuits required to implement the training, projection, and sampling steps on a quantum computer. We supplement our theoretical results by simulating the proposed circuits with a small, qualitative model of LIDAR imaging of earth forests. Our results indicate that a quantum, data-driven approach to compressive sensing may have significant promise as quantum technology continues to make new leaps