The effect of mutual coupling between array elements on the performance of adaptive arrays is examined. The study includes both steady state and transient performance. An expression for the steady state output signal-to-interference-plus-noise ratio (SINR) of adaptive arrays, taking into account the mutual coupling between the array elements, is derived. The expression is used to assess the steady state performance of adaptive arrays. The transient response is studied by computing the eigenvalues associated with the signal covariance matrix. The steering vector required to maximize the output SINR of Applebaum-type adaptive arrays in the presence of mutual coupling is also given.

Effects of mutual coupling on the performance of adaptive arrays

Abstract Light fidelity (LiFi) has successfully achieved high data transfer rates, high security, great availability, and low interference. In this paper, we propose a LiFi system consisting of a combination of non-orthogonal multi-access (NOMA), asymmetrically-clipped optical (ACO), and filter bank multicarrier (FBMC) techniques combined with offset quadrature amplitude modulation (OQAM). The paper also applies a $$\mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> -law companding approach for a high peak to average power ratio (PAPR) reduction of the FBMC/OQAM scheme. The combination of NOMA, ACO-FBMC/OQAM, and $$\mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> -law companding allows a significant increase in throughput and a significant reduction in unserved users. Considering two scenarios, an appropriate algorithm is developed to maximize the throughput and minimize the number of blocked (unserved) users. The results show that the overall system throughput could be increased by 1.8 compared to FBMC, OFDM, and OFDM-NOMA. Furthermore, the proposed system reduces the number of unserved users below $$10\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>10</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , while the system can provide 30 or $$60\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>60</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in case only OFDM-NOMA, FBMC, or OFDM is applied. 

/pdf/hybrid-noma-based-aco-fbmc-oqam-for-next-generation-indoor-18751d4n.pdf

Hybrid NOMA-based ACO-FBMC/OQAM for next-generation indoor optical wireless communications using LiFi technology

This paper investigates the impact of mutual coupling (MC) on the achievable sum-rate of power-domain non-orthogonal multiple access (NOMA) system in both uplink (UL) and downlink (DL) transmissions. We assume a single-antenna user NOMA system with directional beamforming in the millimeter wave (mmW) channel. Due to the electromagnetic interaction between the antenna elements, called mutual coupling, at the base station (BS), the steering vector of the channel is affected. Consequently, this leads to distorted antenna pattern pointing towards different directions and reduction in channel gain, based on antenna array structure. In this paper, different antenna geometries and configurations, such as one-dimensional (1D) uniform linear array (ULA), two-dimensional (2D), and three-dimensional (3D) uniform circular array (UCA), are implemented at the BS, where induced electromotive force (EMF) and method of moment (MOM) techniques are used to generate the MC coefficients. We first examine the antenna patterns affected by MC, followed by channel gains of user terminals (UT)s. Then sum-rate of the NOMA system is modified in the presence of MC. Furthermore, mutual coupling is compensated successfully for both UL and DL systems for the given antenna structures. Simulation results show that mutual coupling degrades the sum-rate performance of the NOMA system in all three array structures, especially in the UCA structure due to the smaller spacing of the array element in a circular shape, resulting in symmetric mutual coupling from both sides of the circle. On the other hand, it is also shown that compensating the mutual coupling effect by the MOM technique in the case of unknown MC or matrix inversion in the case of known MC significantly improves the system sum-rate in all scenarios.

/pdf/impact-of-mutual-coupling-on-power-domain-non-orthogonal-3ors50c8xq.pdf

Impact of Mutual Coupling on Power-Domain Non-Orthogonal Multiple Access (NOMA)

Massive MIMO is considered as promising technology enabling 5G and beyond 5G cellular communication networks. High-resolution gain and angle estimation of the channel are significant challenges in the design of massive MIMO. The existing signal subspace-based estimation techniques lack the resolution for the detection of slight angles. The severity increases when both azimuth and elevation angle are jointly estimating for the 3D channel. The existing techniques rely on the combination of signal subspace-based approaches to estimate both azimuth and elevation angles. The resulting estimate’s accuracy is less due to its low resolution and angular coupling. Data-driven techniques can address this issue. This work is the first-ever attempt to apply data-driven techniques for 3D channel estimation of massive MIMO to the best of our knowledge. This paper considers two approaches for the same, one based on K nearest neighbour (KNN) and the other based on deep neural network (DNN) with restricted Boltzmann machine (RBM). We also investigated data generation and feature extraction for data-driven communication technologies in three ways. An intensive performance analysis of both architectures using these three feature vectors is carried out. The simulation reveals deep learning (DL) model’s superiority compared to the machine learning (ML)-based counterpart and other signal subspace-based estimation techniques. SNR of 10 dB lesser than other signal subspace estimation is required for ML-based approach, whereas the DL-based estimator needs 20 dB lesser SNR for the received signal to achieve the same BER value. In the comparative study on the feature vectors for data-driven estimation techniques, the data processing based on the Pearson correlation feature vector (PCFV) performs the best. Performance comparison of the DNN model with the KNN model is based on tenfold cross-validation showing an average AUC of 0.915 for DL based estimation and a coefficient of 0.904 for ML-based counterpart.

Data driven 3D channel estimation for massive MIMO

This paper proposes an extended method for direction‐of‐arrival (DOA) estimation with unknown mutual coupling for coherent signals. The proposed method employs the forward/backward spatial...

Extended reduced‐rank joint estimation of direction of arrival with mutual coupling for coherent signals

This letter proposes a partial-beam non-orthogonal multiple access (PB-NOMA) system for millimeter wave (mmWave) channels. In the proposed system, the user terminals (UT)s partially belong to different user groups and power-domain partial NOMA (P-NOMA) technique is exploited in their allocated spectrum. While an individual beam serves the UTs in one group, the UTs in the same group, including the overlapped beam UTs, employ the P-NOMA scheme. Overlapped beam UTs are present when two near UTs are closely correlated and when they are equally belonging to two user groups. Fuzzy C-means (FCM) clustering method, which considers the channel correlation coefficient as a similarity measure, is proposed to group the users. Then, user pairing is performed based on the channel gain differences and fuzzy membership values. Simulation results reveal that the sum-rate performance of the proposed PB-NOMA scheme with fuzzy clustering is shown to be better than that of the conventional P-NOMA systems by adjusting overlapped beam ratio (OBR) and lower near UT overlap ratio (NOR) in a preferable group.

Partial-Beam Non-Orthogonal Multiple Access (PB-NOMA) With Fuzzy Clustering

In this paper, an extended reduced-rank direction-of-arrival (DOA) estimation with unknown mutual coupling in uniform linear array (ULA) structure is presented. The proposed method is based on the joint iterative subspace optimization (JIO) and the auto-calibration of mutual coupling. Given an initial coupling vector, reduced-rank correlation matrix is estimated with the JIO approach, followed by iterative joint estimation of DOA and coupling with an auto-calibration algorithm. Simulation results show that both the proposed method and the existing auto-calibration method have similar performance in terms of DOA root mean square error, while the complexity of the proposed method is much lower than that of the auto-calibration approach due to the reduced rank.

Reduced-rank joint estimation of DOA with mutual coupling

This paper analyzes the effect of fast fading Doppler channel on user scheduling algorithms. Considering a millimeter wave (mmWave) downlink massive multiple-input multiple-output (M-MIMO) system, we adopt a two-stage precoding method to cancel the interference from users. Improved K-mean clustering method with weighted likelihood (WLD) similarity measure is employed to group the active users within a given region. After grouping the users, different scheduling methods, semi-orthogonal user selection (SUS) algorithm and greedy algorithm, are implemented. Initially, grouping and scheduling are performed based on the static channels. Then, the scheduled users are simultaneously served for the next time frames without rescheduling while the users are moving. With this approach, this paper reveals how static and time-varying user grouping and scheduling methods perform in terms of system sum-rate when the fast moving users experience the Doppler effect. Different scenarios of number of active users and time frames are simulated. It is observed that rescheduling is required when greedy algorithm is used in order to maintain high sum-rate, while SUS algorithm does not achieve high sum-rate compared to greedy algorithm.

Nann Win Moe Thet

Papers

Partial-Beam Non-Orthogonal Multiple Access (PB-NOMA) With Fuzzy Clustering

Impact of Mutual Coupling on Power-Domain Non-Orthogonal Multiple Access (NOMA)

Extended reduced‐rank joint estimation of direction of arrival with mutual coupling for coherent signals

Reduced-rank joint estimation of DOA with mutual coupling

Performance Analysis of User Scheduling in Massive MIMO with Fast Moving Users