Recently, sparse arrays have received considerable attention as they provide larger array aperture and increased degrees‐of‐freedom (DOFs) compared to uniform linear arrays. These features are essential to enhance the direction‐of‐arrival estimation performance. However, most of the existing sparse arrays are mainly designed for circular sources and realize limited increment in DOFs for non‐circular sources. In this letter, a new sparse array configuration for non‐circular sources is presented, which significantly increases the achievable DOFs and improves the direction‐of‐arrival estimation performance. The proposed geometry comprises two effectively configured uniform linear arrays that exploit the characteristics of non‐circular sources and extend the array aperture. For a given number of sensors, its virtual array is advantageously a hole‐free uniform linear array. Moreover, the precise sensor locations, achievable DOFs, and optimal distribution of physical sensors are determined analytically by closed‐form expressions. Owing to these benefits, the proposed array efficiently resolve multiple sources in under‐determined conditions and achieves better direction‐of‐arrival estimation performance than its counterpart structures. Simulation results validate the superiority of the proposed configuration.
Linear minimum mean-square error (MMSE) detection achieves near-optimal performance in large-scale multiple-input multipleoutput (LS-MIMO) systems but entails high computational complexity due to large matrix inversion operations. In this Letter, a novel computationally efficient algorithm based on second-order Richardson method is proposed to solve the LS-MIMO detection problem. While no a priori information for the first iteration of the secondorder Richardson method is available, the conjugate gradient scheme is exploited that greatly reduces the number of iterations to achieve the desired performance. Moreover, the eigenvalue-based acceleration parameters are proposed to further accelerate the convergence rate. Numerical results demonstrate that the proposed detector outperforms the existing methods and approaches the performance of MMSE with a small number of iterations.
Large-scale multiple-input multiple-output (LS-MIMO) is one of the promising technologies beyond the 5G cellular system in which large antenna arrays at the base station (BS) improve the system capacity and energy-efficiency. However, the large number of antennas at the BS makes it challenging to design low-complexity high-performance data detectors. Thus, a number of iterative detection methods, such as Gauss-Seidel and conjugate gradient, are introduced to achieve complexity-performance tradeoff. However, their performance deteriorates for the systems with small BS-to-user antenna ratio or for the channels that exhibit correlation. This paper proposes a new efficient iterative detection algorithm based on the improved Gauss-Seidel iteration to address this problem. The proposed method performs one conjugate gradient iteration that enables better performance with less number of iterations. A new hybrid iteration is introduced and a low-complexity initial estimation is utilised to enhance detection accuracy while reducing the complexity further. In addition, a novel preconditioning technique is proposed to maintain the benefits of the proposed detector in correlated MIMO channels. It is mathematically demonstrate that the proposed detector achieves low approximated error. Theoretical analysis and numerical results show that the proposed algorithm provides a faster convergence rate compared to conventional methods.
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