Maximal Ratio Combining Research Articles

Sum Rate Analysis for Massive MIMO-NOMA Uplink System With Group-Level Successive Interference Cancellation

The deep integration between non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (MIMO) technology is a promising way to achieve massive connectivity and high capacity in B5G/6G wireless communication system. In this letter, a novel signal processing framework for massive MIMO-NOMA data transmission is proposed based on group-level successive interference cancellation (GLSIC), in which the users are grouped according to the distances to the base station (BS) and NOMA is applied among different groups by employing GLSIC to reduce the inter-group interference. The uplink sum rate is derived for the proposed GLSIC-based massive MIMO-NOMA system with minimum mean square error channel estimator and maximal ratio combiner at BS, and the effects of channel estimation error, inter-group pilot contamination, and imperfect GLSIC are analytically quantified in the derivation. Theoretical analysis and simulation results show that, under the same conditions, the proposed GLSIC-based system achieves higher uplink rate than the existing cluster-based MIMO-NOMA system with user-level SIC.

Performance Analysis of the STAR-RIS-Assisted Downlink NOMA Communication System With MRC

We discuss a simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS)-assisted downlink non-orthogonal multiple access (NOMA) communication system. STAR-RIS employs the energy splitting protocol for transmitting and reflecting the incident signals to users located on different sides of the surface. To study the impact of multiple antennas on the system performance, we perform maximum ratio combining (MRC) at NOMA users and beamforming towards STAR-RIS at the base station (BS). We model the distribution of cascaded Rician fading channels for the proposed system. Closed-form expressions of outage probability, asymptotic outage probability, and system throughput are derived. All theoretically derived results are verified via Monte Carlo simulation.

Enhanced Evaluation Model Based on Classification Selection Applied to Value Evaluation of Waste Household Appliances

In the process of recycling, dismantling, and reusing household appliances, implementing extended producer responsibility (EPR) has become increasingly important. Designing a reasonable pricing mechanism for waste household appliance recycling is critical for the implementation of EPR. To address the problem of labor-intensive and experience-dependent traditional manual methods for assessing the value of waste household appliances, in this paper, we propose an evaluation method based on the subtractive clustering method and an adaptive neuro fuzzy inference system (SCM–ANFIS), which outperforms traditional neural networks such as LSTM, BP neural network, random forest and Takagi–Sugeno fuzzy neural network (T–S FNN). Moreover, in this paper, we combine the five aforementioned algorithms to design a combination evaluation model based on maximum ratio combination (CEM–MRC), which can achieve a performance improvement of 0.1% in terms of mean absolute percentage error (MAPE) compared to the suboptimal BP neural network. Furthermore, an enhanced evaluation model based on classification selection (EEM–CS) is designed to automatically select the evaluation results between the optimal SCM–ANFIS and the suboptimal CEM–MRC, resulting in a 0.73% reduction in MAPE compared to the optimal SCM–ANFIS and a 1.42% reduction compared to the suboptimal CEM–MRC. In this paper, we also validate the performance of the proposed algorithms using a dataset of waste television recycling, which demonstrates the high accuracy of the proposed value assessment mechanisms achieved without human intervention and a significant improvement in evaluation accuracy as compared to conventional neural-network-based algorithms.

Performance statistics of broadcasting networks with receiver diversity and Fountain codes

ABSTRACT The performance of broadcasting networks employing Fountain codes with receiver diversity techniques is investigated in the present work. Particularly, we derive the closed-form expressions of the cumulative distribution function (CDF), the probability mass function (PMF), and the raw moments of the number of the needed time slots to deliver a common message to all users under two diversity schemes, namely, maximal ratio combining (MRC) and selection combining (SC). Numerical results are supplied to verify the accuracy of the considered networks and highlight the behaviours of these metrics as a function of some vital parameters such as the number of receivers, and the number of received antennae. Additionally, we also confirm the advantages of the MRC scheme compared with the SC scheme in the broadcasting networks.

Performance Analysis and Deep Learning Assessment of Full-Duplex Overlay Cognitive Radio NOMA Networks Under Non-Ideal System Imperfections

In this paper, we investigate the effectiveness of an overlay cognitive radio (OCR) coupled with non-orthogonal multiple access (NOMA) system using a full-duplex (FD) cooperative spectrum access with a maximal ratio combining (MRC) scheme under the various non-ideal system imperfections. In view of practical realization, we ponder the impact of loop self-interference, transceiver hardware impairments, imperfect successive interference cancellation, and channel estimation errors on the system performance. We investigate the performance of the proposed system by obtaining closed-form expressions for outage probability and ergodic rate for primary as well as secondary users using Nakagami-m fading channels. As a result, we reveal some notable ceiling effects and present efficacious power allocation strategy for cooperative spectrum access. We further evaluate the system throughput and ergodic sum-rate (ESR) to assess the system’s overall performance. Our findings manifest that the FD-based OCR-NOMA can comply with the non-ideal system imperfections and outperform the competing half-duplex (HD) and orthogonal multiple access (OMA) counterparts. Due to the massive complexity of the suggested system model, direct derivation of the closed-form formula for the ESR becomes cumbersome. To address this problem, we develop a deep neural network (DNN) framework for ESR prediction in real-time situations.

Fluid Antenna Enabling Secret Communications

Recent researches have revealed that fluid antenna, a new position-switchable antenna technology, can make use of the spatial moments of deep fades of the interference signal occurred naturally due to multipath for multiple access. In this letter, this phenomenon is exploited in a physical layer security setup where a base station (BS) transmits an information-bearing signal to a legitimate user and an artificial noise (AN) signal to a potential eavesdropper while the user is equipped with a fluid antenna to overcome the AN signal. The proposed approach needs no effort from the base station (BS) to avoid the AN signal from harming the legitimate user. Trying to enhance the secrecy rate, we study the power allocation of the information-bearing and AN signals if the channel state information (CSI) is available at the BS. Both perfect and imperfect CSI scenarios are investigated. Simulation results demonstrate that the proposed system, with a single fluid antenna with one radio-frequency (RF) chain at the user, achieves the secrecy rate that is achievable by the user utilizing maximal ratio combining (MRC) with many antennas instead, and is the only approach robust to CSI uncertainties of the eavesdropper, requiring no power allocation nor beamforming at the BS.

Performance Analysis and Simulation of Millimeter Wave Cell-Free mMIMO Networks

Towards the 5Gnetworks and beyond, there are a lot of emerging technologies. These technologies include but not limited to; multiple input multiple output (MIMO), cell-Free networks, and millimeter wave bands. The cellular MIMO can provide a satisfied performance for users except the shadowed and the cell-edge ones. In order to overcome this disadvantage, the cell-Free networks are deployed. Through applications of distributed access points (APs), the cell-Free networks can provide a ubiquitous coverage for users as whole. Therefore, they can provide a high throughput for users. Moreover, the applications of millimeter wave bands can provide a high bandwidth and hence, a high throughput for users. In other words, the application of the cell-Free technology combined with the millimeter wave bands can extremely enlarge the users’ throughput. This is the motivation of our manuscript. The purpose of this manuscript is to provide mathematical model and simulation for the cell-Free mMIMO network, operating in the millimeter wave bands. The performance metrics can include; the spectral efficiency (SE), bit error rate (BER), and energy efficiency (EE). It is observed that the centralized cooperation among the APs can let users have a satisfied throughput even the system employs the maximal ratio combining (MRC). Furthermore, all cooperation levels, among APs, can perform well even for non-light of sight (NLOS) environment.

Comparison of the Combining Methods Used In Space Diversity

The basic concept of diversity; where two or more inputs at the receiver are used to get uncorrelated signals. The aim of this paper is an attempt to compare some possible combinations of diversity reception and MLSE detection techniques. Various diversity combining techniques can be distinguished: Equal Gain Combining (EGC), Maximal Ratio Combining (MRC), Selection Combining and Selection Switching Combining (SS).The simulation results shows that the MRC give better performance than the other types of combining (about 1 dB compare with EGC and 2.5~3 dB compare with selection and selection switching combining).

Identity-based attack detection using received signal strength in MIMO systems

This paper proposes RSS-based multiple-input multiple-output (MIMO) receiver diversity-combining techniques for identity-based attack detection. The diversity-combining techniques considered for identity-based attack detection using MIMO systems include selection combining (SLC), equal gain combining (EGC), and maximal ratio combining (MRC). First, we exploit the spatial correlation of the RSS hereditary from wireless nodes to detect identity-based attacks. Analytical expressions of the proposed method for the spatial correlation of the RSS hereditary using MIMO-combining diversities are provided and validated through simulation results. Second, we propose unsupervised machine learning (ML) algorithms such as k-means and k-medoids for the detection of identity-based attacks using RSS with different MIMO-combining diversities. Complete statistical derivations for the detection of identity-based attacks using k-means and k-medoids algorithms were performed. The simulation results confirm that the proposed techniques exploiting MIMO diversity-combining techniques for identity-based attack detection using unsupervised ML algorithms outperform the previously proposed techniques in terms of the false positive rate (FPR) and detection rate (DR). Furthermore, the simulation showed that the EGC-combining diversity with k-means and k-medoids outperformed the SLC- and MRC-combining techniques.

Uplink SINR and rate analysis in massive MIMO systems with two-layer linear receiver

In a previous work, a two-layer linear receiver was proposed as a low-complexity scheme that can achieve a good trade-off between performance and complexity in a massive multiple-input multiple-output (MIMO) context. For this scheme, the antenna array is split up into a number of subsets and multi-cell minimum mean-square-error (M-MMSE) combining is applied in each subset at the first layer. Then, the resulting outputs are combined using maximal-ratio combining (MRC) at the second layer. This paper presents a more complete performance analysis. First, we provide an entirely analytical derivation of the distribution of the output signal-to-noise-and-interference ratio (SINR) of first-layer processing based on multivariate statistical theory. The analysis includes both cases where the subset size is smaller or greater than the total number of signals. Furthermore, upper bounds and approximations on the average uplink SINR and achievable rate are derived. Numerical simulations confirm our analysis results.

Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems With Imperfect CSI

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest.

Deep Reinforcement Learning-Based Sum Rate Fairness Trade-Off for Cell-Free mMIMO

The uplink of a cell-free massive multiple-input multiple-output with maximum-ratio combining (MRC) and zero-forcing (ZF) schemes are investigated. A power allocation optimization problem is considered, where two conflicting metrics, namely the sum rate and fairness, are jointly optimized. As there is no closed-form expression for the achievable rate in terms of the large scale-fading (LSF) components, the sum rate fairness trade-off optimization problem cannot be solved by using known convex optimization methods. To alleviate this problem, we propose two new approaches. For the first approach, a use-and-then-forget scheme is utilized to derive a closed-form expression for the achievable rate. Then, the fairness optimization problem is iteratively solved through the proposed sequential convex approximation (SCA) scheme. For the second approach, we exploit LSF coefficients as inputs of a twin delayed deep deterministic policy gradient (TD3), which efficiently solves the non-convex sum rate fairness trade-off optimization problem. Next, the complexity and convergence properties of the proposed schemes are analyzed. Numerical results demonstrate the superiority of the proposed approaches over conventional power control algorithms in terms of the sum rate and minimum user rate for both the ZF and MRC receivers. Moreover, the proposed TD3-based power control achieves better performance than the proposed SCA-based approach as well as the fractional power scheme.

System Performance Analysis of Sensor Networks for RF Energy Harvesting and Information Transmission

This paper investigates the problem of RF energy harvesting in wireless sensor networks, with the aim of finding a suitable communication protocol by comparing the performance of the system under different protocols. The network is made up of two parts: first, at the beginning of each timeslot, the sensor nodes harvest energy from the base station (BS) and then send packets to the BS using the harvested energy. For the energy-harvesting part of the wireless sensor network, we consider two methods: point-to-point and multi-point-to-point energy harvesting. For each method, we use two independent control protocols, namely head harvesting energy of each timeslot (HHT) and head harvesting energy of dedicated timeslot (HDT). Additionally, for complex channel states, we derive the cumulative distribution function (CDF) of packet transmission time using selective combining (SC) and maximum ratio combining (MRC) techniques. Analytical expressions for system reliability and packet timeout probability are obtained. At the same time, we also utilize the Monte Carlo simulation method to simulate our system and have analyzed both the numerical and simulation solutions. Results show that the performance of the HHT protocol is better than that of the HDT protocol, and the MRC technology outperforms the SC technology for the HHT protocol in terms of the energy-harvesting efficiency coefficient, sensor positions, transmit signal-to-noise ratio (SNR), and length of energy harvesting time.

Implications of Power Allocation Factors on the Performance of Ergodic Sum Rate in Cooperative Device-to-Device Systems with NOMA

The power allocation issue for cooperative device-to-device (D2D) systems with non-orthogonal multiple access (NOMA) is examined in this paper. In cooperative device-to-device (D2D) NOMA systems, communication occurs in two phases: the direct transmission slot, or the path from the base station to user destinations, and the relaying slot, or the path from the relay (near the user), with different power allocation factors to improve the ergodic sum rate. It is suggested that power allocation considerations affect how well the ergodic sum rate performs with different decoding techniques. The decoding techniques include Reed Solomon's (RS) encoded single signal decoding technique and Reed Solomon's (RS) encoded maximum ratio combining (MRC) decoding technique. The error probability of successive decoding will increase if any errors occur during the successive interference cancellation (SIC) procedure at any user. In order to detect and correct multiple symbol errors in a Rayleigh fading environment, RS codes are used in the system architecture for error detection and correction. Through the results of the simulation, it is demonstrated that the proposed approach performs much better than the existing work for the fixed values of the power allocation parameters.

Secrecy outage probability and limiting threshold criteria in an optimal SNR regime by maximizing desired transfer rate

In this paper, the optimum secrecy probability has been calculated using a reduction function that optimizes the transfer rate of the user's signal for a given average broadcast power while minimizing the transfer rate of the eavesdropper's signal to ensure safe transmission. In this work, the signal-to-noise ratio (SNR) of the eavesdropper signal and the received signal are accurately estimated using matrix theory. These approximated SNRs have been studied in relation to the outage probability. By making three distinct assumptions about channel correlation, it has been possible to determine the precise secrecy outage probability. In correlated multi-antenna transmit and receive channels, this research investigates the secrecy performance of three popular diversity combining strategies—maximum ratio combining (MRC), selection combining (SC), and equal gain combining (EGC). In this research, we consider a Rayleigh fading channel with overhead communication between the transmitter and recipient. In this research, we also propose a limiting bounds condition to improve the limits on the outage probability for the Rayleigh fading environment using the three diversity combining methods. Our restricting distribution is threshold-based in order to generate precise limits on the probability of secrecy outage. An application case has been presented which discusses the impact of the proposed model for device to device communication application scenario. Our findings demonstrate that channel correlation substantially influences the probability of a secrecy outage.

Receive-diversity-aided power-fading compensation for C-band IM/DD OFDM systems.

A receive-diversity-aided power-fading compensation (RDA-PFC) scheme is proposed and demonstrated to eliminate the chromatic dispersion (CD)-induced power fading for C-band double-sideband (DSB) intensity modulation and direct detection (IM/DD) orthogonal frequency division multiplexing (OFDM) systems. By combining the responses before and after a dispersive element using a maximal-ratio combining (MRC) algorithm, the CD-induced power fading dips within the signal bandwidth of around 50 GHz can be effectively compensated for, which results in an up to 17.6-dB signal-to-noise ratio (SNR) improvement for the fading subcarriers after transmission over 10 km of standard single-mode fiber (SSMF). Using the 16 quadrature amplitude modulation (QAM) format, a diversity receiver with the proposed RDA-PFC scheme can support 170.6-Gbit/s OFDM signal transmission over a 10-km SSMF and reduces the bit error rate (BER) by more than an order of magnitude compared with a conventional receiver. Moreover, 208.1-Gbit/s adaptive bit and power loading OFDM signal transmission over a 10-km SSMF is realized by the proposed RDA-PFC scheme, which improves the capacity by 15.3% compared with the case without RDA-PFC at a BER of 3.8 × 10-3. The proposed RDA-PFC scheme shows great potential in CD-induced power-fading compensation for high-speed IM/DD OFDM systems.

On the Performance of LDPC-Coded Large Intelligent Antenna System

This article studies Large Intelligent Systems (LIS) along with Single Carrier with Frequency Domain Equalization (SC-FDE), utilizing Low-Density Parity-Check (LDPC). Four different receivers are studied in the scenarios described above, namely Equal Gain Combining (EGC), Maximum Ratio Combining (MRC), Zero Forcing (ZF), and Minimum Mean Squared Error (MMSE). The results of this article show that the use of LDPC codes leads to an improvement of performance by about 2 dB for a 4X25 LIS system and by 3 dB for a 4X225 LIS system, as compared to similar systems without LDPC codes. Moreover, for all simulations, the MMSE receiver achieves the best overall performance, while EGC performs the worst.

Spectral Efficiency of Time-Variant Massive MIMO Using Wiener Prediction

Massive multiple-input multiple-output (MIMO) serves as technological pillar in current 5G deployments. The spectral efficiency (SE) reduction due to time-variance of the wireless channel, and respective mitigation strategies, are still not sufficiently understood. In this paper, we propose a multi-step Wiener predictor with a general temporal covariance function to quantify the impact of time-variance on the SE for different numbers of consecutive pilot symbols and prediction horizons. We provide asymptotic expressions of the SE for maximum ratio combining (MRC) beam-forming that show excellent agreement to numerical simulations. We investigate the channel hardening capability of massive MIMO systems in time-variant propagation channels and show that it is largely independent of the prediction horizon. The numerical evaluations show that channel prediction allows to double SE utilizing four instead of one uplink pilot symbol for a prediction horizon of 0.3 λ for both MRC and regularized zero-forcing (RZF) beam-forming.

The Curious Case of Effective Isotropic Sensitivity: Polarization mismatch in over-the-air testing of wireless user equipment

This article provides the theory behind the two-step over-the-air (OTA) test method widely used in 3rd Generation Partnership Project (3GPP) test specifications and, in particular, the testing and characterization of 5G millimeter-wave user equipment (UE) in compact antenna test ranges (CATRs). Special attention is paid to receiver (Rx) characteristics and the polarization mismatch problem, which is encountered in spherical coverage tests. The received power of a UE with a dual-polarized antenna system is derived, and the output signal-to-noise ratio (SNR) is calculated using the maximal ratio combining (MRC) principle. And finally, flaws in the dominant interpretation of this test method in the development of 5G millimeter-wave standards and their consequences are addressed.

Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

AbstractMassive multiple‐input multiple‐output (MIMO) is a critical component of 5G cellular networks, which utilizes large numbers of antennas at both the transmitter and receiver to enhance throughput and radiated energy efficiency. Various linear detection techniques are employed with massive MIMO to counteract path loss and interference, and maximize throughput. The first aim of this paper is to analyse the performance of uplink massive MIMO system for different linear detection techniques including: Maximum ratio combining (MRC), zero‐forcing (ZF), regularized ZF (RZF) and minimum mean squared error (MMSE) over Rayleigh channel model. The second aim is to jointly investigate the optimal values of signal‐to‐noise ratio (SNR), the number of antennas M and the number of users K for maximizing the spectral efficiency (SE) and energy efficiency (EE) through simulation using MATLAB and 3D plotting patterns. The obtained results show that the best SE and EE are achieved by uplink massive MIMO setup while using optimal values of SNR, M and K. It is observed that MMSE achieved the best performance. However, it requires estimation of average SNR at BS. Therefore, the best choice is ZF or RZF without any need for SNR estimation.