In recent years, beamforming has been vital to increasing the spectral and energy efficiency of the current and next-generation wireless communication systems, such as 5G, the Internet of Things (IoT), and beyond. In such a context, this letter extends the previously proposed single user, multiple-input single-output (SU-MISO) phase-transmittance radial basis function (PT-RBF) beamforming to self-organizing wireless network receivers with multiple users, multiple-input multiple-output (MU-MIMO) beamforming. In the proposed novel approach, the PT-RBF is designed to support multiple outputs and multiple layers, an innovation compared to previous shallow single output PT-RBF. On account of this deep neural network architecture, the proposed deep PT-RBF can handle multiple users with different modulation formats and distinct orders, presenting better results when compared either with other complex-valued neural networks or with the normalized least mean square algorithm.

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

Mobile communications, not infrequently, are disrupted by multipath propagation in the wireless channel. In this context, this paper proposes a new blind concurrent equalization approach that combines a Phase Transmittance Radial Basis Function Neural Network (PTRBFNN) and the classic Constant Modulus Algorithm (CMA) in a concurrent architecture, with a Fuzzy Controller (FC) responsible for adapting the PTRBFNN and CMA step sizes. Differently from the Neural Network (NN) based equalizers present in literature, the proposed Fuzzy Controller Concurrent Neural Network Equalizer (FC-CNNE) is a completely self-taught concurrent architecture that does not need any training. The Fuzzy Controller inputs are based on the estimated mean squared error of the equalization process and on its variation in time. The proposed solution has been evaluated over standard multipath VHF/UHF channels defined by the International Telecommunication Union. Results show that the FC-CNNE is able to achieve lower residual steady-state MSE value and/or faster convergence rate and consequently lower Bit Error Rate (BER) when compared to Constant Modulus Algorithm-Phase Transmittance Radial Basis Function Neural Network (CMA-PTRBFNN) equalizer.

This letter presents an innovative design for a compact orthogonal mode transducer (OMT). The proposed OMT is a T-shaped junction with two symmetrical output ports. Two coupling elements, a ridge and a resonant cavity, have been included in the OMT design, resulting in low S₁₁ scattering parameter and high isolation between orthogonal signals. The proposed design has been simulated for 6.5 GHz and prototyped with machined aluminum components, using a computer numerical control machine. Electromagnetic (EM) CAD model and prototype have been analyzed for the frequency range of 5.925-7.125 GHz. The OMT bandwidth is slightly greater than 18.4%, with S₁₁ and isolation values below -22 and -37 dB respectively, obtained from prototype measurement. Moreover, the proposed OMT design presents a reduced size and low-wind-exposure area, which is a relevant feature when operating in outdoor dual-radio setup, resulting in a low-cost and easy manufacturing device.

This paper proposes a blind concurrent beamforming (BCB) algorithm that combines the Constant Modulus Algorithm (CMA) and Direct Decision (DD) in a cooperative structure. The proposed BCB is evaluated over a sixdipole uniform circular array in different interfering scenarios, and the results are compared with the classical supervised least mean squares (LMS). Notably, despite operating without a training sequence, BCB outperformed LSM in terms of symbol error rate (SER). The proposed BCB effectively place nulls in the angular direction of the interfering signals while steering the main lobe toward the desired signal (DS), except in scenarios where the interference is spatially too close to the DS. Results show that the proposed BCB approach offers a robust, blind beamforming solution with low computational complexity and high spectral efficiency.