Achieving high spectral efficiency in realistic massive multiple-input multiple-output (M-MIMO) systems entails a significant increase in implementation complexity, especially with respect to data detection. Linear minimum mean squared error (LMMSE) can achieve near-optimal performance but involves computationally expensive large-scale matrix inversions. This paper proposes a novel computationally efficient data detection algorithm based on the modified Richardson method. We first propose an antenna-dependent approach for robust initialization of the Richardson method. It is shown that the proposed initializer outperforms the existing initialization schemes by a large margin. Then Chebyshev acceleration technique is proposed to overcome the sensitivity of Richardson method to relaxation parameter while simultaneously enhancing its convergence rate. We demonstrate that the proposed algorithm mitigates multiuser interference and offers significant performance gains via the iterative cancellation of the bias term by prior estimation. Hence, each step of the iteration routine gives a new and better estimate of the solution. An asymptotic expression for the average convergence rate is also derived in this work. Numerical results show that the proposed algorithm outperforms the existing methods and achieves near-LMMSE performance with a significantly reduced computational complexity.

Linear minimum mean‐square error (MMSE) detection achieves near‐optimal performance in large‐scale multiple‐input multiple‐output (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 second‐order 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.

Signal detection is a serious challenge for uplink massive multiple-input multiple-output (MIMO) systems. The traditional linear minimum-mean-squared error (MMSE) achieves good detection performance for such systems, but involves matrix inversion, which is computationally expensive due to a large number of antennas. Thus, several iterative methods such as Gauss–Seidel (GS) have been studied to avoid the direct matrix inversion required in the MMSE. In this paper, we improve the GS iteration in order to enhance the detection performance of massive MIMO systems with a large loading factor. By exploiting the property of massive MIMO systems, we introduce a novel initialization strategy to render a quick start for the proposed algorithm. While maintaining the same accuracy of the designed detector, the computing load is further reduced by initialization approximation. In addition, an effective preconditioner is proposed that efficiently transforms the original GS iteration into a new one that has the same solution, but a faster convergence rate than that of the original GS. Numerical results show that the proposed algorithm is superior in terms of complexity and performance than state-of-the-art detectors. Moreover, it exhibits identical error performance to that of the linear MMSE with one-order-less complexity.

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.

This manuscript examines the design principle and real-world validation of a new miniaturized high-performance flower-shaped radiator (FSR). The antenna prototype consists of an ultracompact square metallic patch of 0.116λ₀ × 0.116λ₀ (λ₀ is the free space wavelength at 3.67 GHz), a rectangular microstrip feed network, and a partial metal ground plane. A novel, effective, and efficient approach based on open circuit loaded stubs is employed to achieve the antenna’s optimal performance features. Rectangular, triangular, and circular disc stubs were added to the simple structure of the square radiator, and hence, the FSR configuration was formed. The proposed antenna was imprinted on a low-cost F4B laminate with low profile thickness of 0.018λ₀, relative permittivity ε_r = 2.55, and dielectric loss tangent δ = 0.0018. The designed radiator has an overall small size of 0.256λ₀ × 0.354λ₀. The parameter study of multiple variables and their influence on the performance results has been extensively studied. Moreover, the impact of different substrate materials, impedance bandwidths, resonance tuning, and impedance matching has also been analyzed. The proposed antenna model has been designed, simulated, and fabricated. The designed antenna exhibits a wide bandwidth of 5.33 GHz ranging from 3.67 to 9.0 GHz at 10 dB return loss, which resulted in an 83.6% fractional impedance bandwidth; a maximum gain of 7.3 dBi at 8.625 GHz; optimal radiation efficiency of 89% at 4.5 GHz; strong intensity current flow across the radiator; and stable monopole-like far-field radiation patterns. Finally, a comparison between the scientific results and newly published research has been provided. The antenna’s high-performance simulated and measured results are in a good agreement; hence, they make the proposed antenna an excellent choice for modern smartphones’ connectivity with the sub-6 GHz frequency spectrum of modern fifth-generation (5G) mobile communication application.