Thinning of antenna array via adaptive memetic particle swarm optimization

Abstract

Massive multiple input multiple output antenna array is crucial for the fifth generation wireless communication.Proper antenna array design can reduce interference among different signals and generate desirable beamforming. Sparse antenna array is able to form narrower beam with lower sidelobe than equally spaced antenna array given the same number of array elements. However, determining the position of elements is non-deterministic polynomial-time hard. To effectively solve such problem, this paper proposes adaptive memetic particle swarm optimization (AMPSO) algorithm. The algorithm adaptively tunes algorithmic parameters of particle swarm optimization (PSO). Moreover, crossover operator is added to enhance local exploiting search information of PSO. Sparse antenna array design is modeled as a minimization by thinning method. It is then tackled by the proposed algorithm. In terms of peak sidelobe level, the AMPSO algorithm shows good performance compared with PSO and genetic algorithm.

A Multi-objective Array Pattern Optimization via Thinning Approach

“…The constraint shown in ( 5) is pairs of linear inequalities in |w new |; therefore, |w n | is convex, and it is easy to introduce other constraints to achieve multi-objectives of the desired pattern. The constraint shown in (5) provides direct control of the main beam region (MBR) in (7a) and the modification of the side lobe region (SLR) (7b), as well as the null regions (NRs) (7c) as follows: min |w n |δ (7)…”

“…A thinning array can be categorized into two main types: one that the array elements can be placed anywhere in the array aperture, and the other that the distances between elements in the array aperture are evenly spaced [5]. In the first state, some of the elements can be placed arbitrarily, and therefore there will be continuous changes in the locations of these elements, while in the second state, the elements can be placed in the antenna array aperture, and therefore, its search space is separate and specific [5]. However, when the number of array elements is to be increased up to hundreds, the search space for the combinatorial optimization problem is greatly increased, which causes a big problem for traditional and numerical optimization algorithms [6].…”

Spatial Channel Degrees of Freedom for Optimum Antenna Arrays

“…In this paper, we adopt the PSO algorithm to solve the previously described capacity maximization problem for the following reasons. The design of optimum antenna arrays is well known to be a highly nonlinear and nonconvex programming problem [27]. PSO can solve complex and multidimensional problems without restricting the solution domain and does not need to consider convexity.…”

“…For the restricted uniform AOA, the SDoF for spherical arrays are derived in Appendix D as |Ω| = 2 Hu(θ,ϕ) = 4∆ϕ cos θ 0 sin ∆θ (27) which coincides with (24). Thus, just considering the AOA range as in [15] is equivalent to assigning a restricted uniform distribution within that AOA range.…”

“…Thus, just considering the AOA range as in [15] is equivalent to assigning a restricted uniform distribution within that AOA range. If the propagation channel contains more than one scattering region, i.e., multi-clustered wireless propagation (see [19]), then |Ω| will arise by summing contributions similar to (27) for each scattering region. The SDoF for linear and circular arrays are given by (see Appendix D)…”