Traditional channel estimation algorithms such as minimum mean square error (MMSE) are widely used in massive multiple-input multiple-output (MIMO) systems, but require a matrix inversion operation and an enormous amount of computations, which result in high computational complexity and make them impractical to implement. To overcome the matrix inversion problem, we propose a computationally efficient hybrid steepest descent Gauss⁻Seidel (SDGS) joint detection, which directly estimates the user’s transmitted symbol vector, and can quickly converge to obtain an ideal estimation value with a few simple iterations. Moreover, signal detection performance was further improved by utilizing the bit log-likelihood ratio (LLR) for soft channel decoding. Simulation results showed that the proposed algorithm had better channel estimation performance, which improved the signal detection by 31.68% while the complexity was reduced by 45.72%, compared with the existing algorithms.

A printed and minimal size antenna having the functionality of frequency shifting as well as pattern reconfigurability is presented in this work. The antenna proposed in this work consists of three switches. Switch 1 is a lumped switch that controls the operating bands of the antenna. Switch 2 and Switch 3 controls the beam switching of the antenna. When the Switch 1 is ON, the proposed antenna operates at 3.1 GHz and 6.8 GHz, covering the 2.5–4.2 GHz and 6.2–7.4 GHz bands, respectively. When Switch 1 is OFF, the antenna operates only at 3.1 GHz covering the 2.5–4.2 GHz band. The desired beam from the antenna can be obtained by adjusting the ON and OFF states of Switches 2 and 3. Unique beams can be obtained by different combination of ON and OFF states of the Switches 2 and 3. A gain greater than 3.7 dBi is obtained for all four cases.