The combination of non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) technologies is proposed to meet the demands of data rate, latency, and connectivity in sixth-generation (6G) networks. The two techniques can support each other to increase the performance of the 6G system. In a RIS-aided system, channel estimation is a challenging problem, especially when applying passive RIS which has no signal processing. This paper proposes a deep learning (DL)-based channel estimation method using a convolutional long-short term memory (CNN-LSTM) model for RIS-NOMA wireless communication systems that integrate RIS and NOMA techniques. CNN-LSTM leverages both the benefits of convolutional neural network (CNN) as well as long-short term memory (LSTM), in which CNN can capture special features while LSTM can capture temporal features of time-series data. The simulation results indicate that the proposed CNN-LSTM model shows its robustness toward the variation of the RIS-NOMA system parameters, i.e., transmit signal-to-noise ratio (SNR), power allocation factor, and the number of RIS elements. The impacts of the RIS-NOMA system parameters on the prediction accuracy of the proposed DL-based channel estimation methods are evaluated via different performance metrics. The results reveal that the performance accuracy in terms of normalized root mean square error (NRMSE), coefficient of determination R-squared score (R2 score), mean absolute scaled error (MASE), and mean absolute percentage error (MAPE) increases with an increased transmit SNR, power allocation factor of the first user and the number of RIS elements. Additionally, the CNN-LSTM prediction performance shows its superiority as compared to those of the four benchmark models including the CNN1D-LSTM model using one-dimensional convolution layer (conv1D), CNN1D-BiLSTM model using bidirectional long-short term memory, CNN model and LSTM model.

Applications of the unlicensed 60 GHz band include indoor wireless local area networks, outdoor short range communications and on body networks. To characterize the radio channel for such applications, a novel digital chirp sounder with programmable bandwidths up to 6 GHz with switched two transmit channels and two parallel receive channels for multiple-input multiple-output (MIMO) measurements was realized. For waveform durations of 819.2 μs, Doppler measurements can be performed up to 610 Hz for the single transmit and two receive configuration or 305 Hz for MIMO measurements. In this paper, we present the architecture of the sounder and demonstrate its performance from back to back tests and from measurements of rms delay spread, path loss and MIMO capacity in an indoor and an outdoor environment. For 20 dB threshold, the rms delay spread for 90% of the measured locations is estimated at 1.4 ns and 1 ns for the indoor and outdoor environments, respectively. MIMO capacity close to the iid channel capacity for 2 by 2 configuration is achieved in both environments.

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.

This work reports on designing and experimentally evaluating an asymmetric metasurface absorber (MA) for wide-band and polarization-insensitive operation in the C- and partial X-bands. As the core building block of the radio frequency (RF) subsystem, a unit-cell has been proposed, comprised of a dual-cut square-ring resonator (SRR) and a square patch placed above an FR4 substrate backed by a copper plate, inherently anisotropic. This arrangement efficiently converts linearly-polarized waves to cross-polarized reflected waves, achieving an 80% conversion efficiency over a wide bandwidth (BW) of 4.72–8.49 GHz. The reflected cross-polarized waves are also inhibited by strategically incorporating just three resistors on the top surface. Therefore, the polarization converter (PC) is transformed into an MA. In contrast to previously known MAs that depended on structural symmetry to maintain stable absorption performances across all polarization angles, this newly proposed asymmetric MA breaks that constraint. It achieves consistent absorption irrespective of variations in the polarization angle of the normal incident waves. The full-wave simulations have resulted in over 80% absorption, closely resembling the initial PC’s reflected BW. This MA has a compact assembly with a size of 0.22λ × 0.22λ and a thickness of 0.07λ. Absorption outputs are experimentally evaluated, resulting in more than 80% absorption, covering almost the entire C-band. This device can be used in different applications, such as radar cross-section assessments and energy-harvesting front-ends.