Nonuniform Array Geometry Research Articles

Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams That Adapt to Deployment and Hardware

Millimeter wave (mmWave) and massive MIMO systems are intrinsic components of 5G and beyond. These systems rely on using beamforming codebooks for both initial access and data transmission. Current beam codebooks, however, generally consist of a large number of narrow beams that scan all possible directions, leading to large training overhead. Further, these codebooks do not normally account for hardware impairments or possible non-uniform array geometries, and their calibration process is expensive. To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics. This is done by designing a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complex-valued neural network</i> architecture in which the neuron weights directly model the beamforming weights of the analog phase shifters, accounting for the key hardware constraints. This model learns the codebook beams through online and self-supervised training avoiding the need for explicit channel state information. This respects the practical situations where the channel is imperfect or hard to obtain. Simulation results highlight the capability of the proposed solution in learning environment and hardware aware beam codebooks, which reduce the training overhead and improve the robustness against possible hardware impairments.

Alias-Free Arrays.

Nonuniform array geometries provide freedom for increased aperture and reduced mutual coupling. A necessary and sufficient condition is given for an array of isotropic sensor elements to be unambiguous for any specified set of directions of arrival. The set of unambiguous spatial frequencies is shown to be a parallelepiped, admitting simple geometrical interpretation. Results are used in design of linear, planar, and 3D arrays.

High-Precision OMP-Based Direction of Arrival Estimation Scheme for Hybrid Non-Uniform Array

Most of compressive sensing Direction of Arrival (DoA) estimation schemes in the literature employ a uniform array for the simplicity and mathematical tractability. Non-uniform array geometries can enhance the resolution performance, substantially. However, they suffer from the problem of phase ambiguity. In this letter, we suggest a novel hybrid non-uniform and linear array and propose an efficient DoA estimation scheme. This scheme can distinguish adjacent signal sources with high accuracy. In addition, the proposed scheme tackles the problem of phase ambiguity.

Mutual coupling effect and compensation in non-uniform arrays for direction-of-arrival estimation

In this paper, we investigate the effect of mutual coupling on direction-of-arrival (DOA) estimation using non-uniform arrays. We compare and contrast the DOA estimation accuracy in the presence of mutual coupling for three different non-uniform array geometries, namely, minimum redundancy arrays (MRAs), nested arrays, and co-prime arrays, and for two antenna types, namely dipole antennas and microstrip antennas. We demonstrate through numerical simulations that the mutual coupling, if unaccounted for, can, in general, lead to performance degradation, with the MRA faring better against mutual coupling than the other two non-uniform structures for both antenna types. We also propose two methods that can compensate for the detrimental effects of mutual coupling, leading to accurate and reliable DOA estimation. Supporting numerical simulation results are provided which show the effectiveness of the proposed compensation methods.

Direction of Arrival Estimation using EM-ESPRIT with Nonuniform Arrays

This paper deals with the problem of the Direction Of Arrival (DOA) estimation with nonuniform linear arrays. The proposed method is based on the Expectation Maximization method where ESPRIT is used in the maximization step. The key idea is to iteratively interpolate the data to a virtual uniform linear array in order to apply ESPRIT to estimate the DOA. The iterative approach allows one to improve the interpolation using the previously estimated DOA. One of this method’s novelties lies in its capacity of dealing with any nonuniform array geometry. This technique manifests significant performance and computational advantages over previous algorithms such as Spectral MUSIC, EM-IQML and the method based on manifold separation technique. EM-ESPRIT is shown to be more robust to additive noise. Furthermore, EM-ESPRIT fully exploits the advantages of using a nonuniform array over a uniform array: simulations show that for the same aperture and with a smaller number of sensors, the nonuniform array presents almost identical performance as the equivalent uniform array.

Optimization of nonuniform array geometry for DOA estimation with the constraint on gross error probability

In this work, a novel method is proposed to optimize the array geometry for DOA estimation. The method is based on minimization of fine error variances with the constraint that the gross error probability is below a certain threshold. For this purpose, a metric function that reflects the gross and fine error characteristics of the array is proposed. Theoretical analyses show that the minimization of this metric function leads to small DOA estimation error variance and small gross error probability. Analyses have been carried out under the assumptions of planar array geometry, isotropic array elements and AWGN. The genetic algorithm is used as an optimization tool and performance simulation is performed by comparing the DOA estimation errors of optimized array to uniform circular arrays (UCAs). Computer simulations support the theoretical analyses and show that the method proposed leads to a significant improvement in array geometry in terms of DOA estimation performance. Finally, the method is applied to an aircraft model and results are presented.

Multibaseline cross-track SAR interferometry using interpolated arrays

This work deals with the problem of interferometric radar phase estimation in the presence of layover. The focus here is on multichannel interferometric synthetic aperture radar (InSAR) systems with a low number of phase centers and nonuniform array geometry. An interpolated array (IA) approach is proposed in order to apply parametric spectral estimation techniques designed for uniform linear arrays (ULAs). In particular, the interpolated MUSIC and weighted subspace fitting (WSF) algorithms are considered and compared with conventional methods. Performance analysis under different InSAR scenarios is carried out based on Monte Carlo simulations. The Cramer-Rao lower bound (CRLB) for the nonuniform interferometric array is derived and reported as a benchmark on the estimation accuracy.

Resolving manifold ambiguities in direction-of-arrival estimation for nonuniform linear antenna arrays

This paper addresses the problem of ambiguities in direction of arrival (DOA) estimation for nonuniform (sparse) linear arrays. Usually, DOA estimation ambiguities are associated with linear dependence among the points on the antenna array manifold, that is, the steering vectors degenerate so that each may be expressed as a linear combination of the others. Most nonuniform array geometries, including the so-called minimum redundancy arrays, admit such manifold ambiguities. While the standard subspace algorithms such as MUSIC fail to provide unambiguous DOA estimates under these conditions, we demonstrate that this failure does not necessarily imply that consistent and asymptotically effective DOA estimates do not exist. We demonstrate that in most cases involving uncorrelated Gaussian sources, manifold ambiguity does not necessarily imply nonidentifiability; most importantly, we introduce algorithms designed to resolve manifold ambiguity. We also show that for situations where the number of sources exceeds the number of array sensors, a new class of locally nonidentifiable scenario exists.

A simple algorithm to achieve desired patterns for arbitrary arrays

A simple iterative algorithm which can be used to find array weights that produce array patterns with a given look direction and an arbitrary sidelobe specification is presented. The method can be applied to nonuniform array geometries in which the individual elements have arbitrary (and differing) radiation patterns. The method is iterative and uses sequential updating to ensure that peak sidelobe levels in the array meet the specification. Computation of each successive pattern is based on the solution of a linearly constrained least-squares problem. The constraints ensure that the magnitude of the sidelobes at the locations of the previous peaks takes on the prespecified values. Phase values for the sidelobes do not change during this process, and problems associated with choosing a specific phase value are therefore avoided. Experimental evidence suggests that the procedure terminates in remarkably few iterations, even for arrays with significant numbers of elements.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Effect of Nonuniform Geometries on Flow Distributions and Heat Transfer Characteristics for Arrays of Impinging Jets

Two-dimensional arrays of circular jets impinging on a surface parallel to the jet orifice plate are considered. The jet flow, after impingement, is constrained to exit in a single direction along the channel formed by the jet orifice plate and the impingement surface. Experimental results for the effects of streamwise nonuniform array geometries on streamwise flow distributions and heat transfer characteristics are presented. A flow distribution model for nonuniform arrays is developed and validated by comparison with the measured flow distributions. The model is then employed to compare nonuniform array streamwise resolved heat transfer coefficient data with previously reported uniform array data and with a previously developed correlation based on the uniform array data. It was found that uniform array results can, in general, serve as a satisfactory basis from which to predict heat transfer coefficients at individual spanwise rows of nonuniform arrays. However, significant differences were observed in some cases over the first one or two rows downstream of the geometric transition line of the nonuniform array.