Massive MIMO-NOMA Research Articles

Enhancing Spatially-Correlated Multi-Way Massive MIMO NOMA Relaying With Downlink Pilots

Enhancing Spatially-Correlated Multi-Way Massive MIMO NOMA Relaying With Downlink Pilots

SWIPT-Enabled Cell-Free Massive MIMO-NOMA Networks: A Machine Learning-Based Approach

SWIPT-Enabled Cell-Free Massive MIMO-NOMA Networks: A Machine Learning-Based Approach

Channel estimation for pilot contamination in massive MIMO-NOMA system

Channel estimation is crucial for massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. The nature of non-orthogonal cause pilot contamination is experienced only while estimating multi-cell MIMO scheme with the training and it is misplaced while narrowing concentration to multi-cell or one-cell setting, where information of the channel is assumed to be obtainable at no cost. Non-orthogonal multiple access (NOMA) serves numerous users concurrently utilizing channel gain differences. The advancement in massive MIMO-NOMA technology has offered diverse techniques recently for reducing pilot contamination in massive MIMO-NOMA based on pilot allocation. Here, a new approach called War Strategy Chimp Optimization+Deep Neuro-Fuzzy Network (WSChO+DNFN) is designed for the estimation of channels to reduce pilot contamination in a massive MIMO-NOMA system. It takes place in two phases, the transmitter and the receiver phase. The channel estimation is conducted by DNFN that is tuned by devised WSChO. Furthermore, WSChO is an amalgamation of War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). Additionally, the WSChO+DNFN attained minimal values of BER and normalized MSE of 0.000103 and 0.000074, respectively. The proposed method has achieved a performance gain of 44.39%, 19.26%, 9.17%, 5.22%, 9.92%, and 6.03% compared to the Orthogonal Frequency Division Multiplexing (OFDM), Group Successive Interference Cancellation assisted Semi-Blind Channel Estimation Scheme (GSIC_SBCE), Sector-Based Pilot Assignment Scheme (PAS), Convolutional Neural Network (CNN), User Segregation based Channel Estimation (USCE), Optimal Channel Estimation using Hybrid Machine Learning (OCE_HML), respectively.

UAV-Enabled Multi-Pair Massive MIMO-NOMA Relay Systems With Low-Resolution ADCs/DACs

UAV-Enabled Multi-Pair Massive MIMO-NOMA Relay Systems With Low-Resolution ADCs/DACs

Analysis and Optimization of RIS-Assisted Cell-Free Massive MIMO NOMA Systems

Analysis and Optimization of RIS-Assisted Cell-Free Massive MIMO NOMA Systems

Impact of Channel Aging and Pilot Contamination on Decoding and Power Control Strategies for Uplink Massive MIMO-NOMA Systems

Impact of Channel Aging and Pilot Contamination on Decoding and Power Control Strategies for Uplink Massive MIMO-NOMA Systems

Cell-Free Massive MIMO-NOMA Systems With Nonreciprocal Channels: Performance Analysis and Calibration Method Design

Cell-Free Massive MIMO-NOMA Systems With Nonreciprocal Channels: Performance Analysis and Calibration Method Design

Wavelet-based massive MIMO-NOMA with advanced channel estimation and detection powered by deep learning

This paper presents a massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system’s channel estimation and detection method which uses a deep learning algorithm to address the issue of erroneous signal detection caused by imperfect channel state information (CSI), user interference and channel noise. The proposed network applies deep learning (DL) model to a discrete wavelet transform (DWT)-based orthogonal frequency-division multiplexing (OFDM) approach, aiming to reduce noise and inter-channel interference (ICI) while maintaining compatibility with the existing networks. A significant advantage of this network is the reduction of transmission overhead as it can estimate and detect symbols with and without pilot signals. The study shows that the DL-based channel estimation and detection method proposed in this paper enhances the successive interference cancellation (SIC) process compared to the conventional linear scheme-based SIC in the mMIMO-NOMA network. Additionally, the wavelet-based mMIMO-NOMA is compared to the traditional fast-Fourier-transform (FFT) based mMIMO-NOMA in terms of symbol error rate (SER) for both perfect and imperfect CSI. The simulation results show that the proposed method outperforms the traditional method.

Improving the Resource Efficiency in Massive MIMO-NOMA Systems

Improving the Resource Efficiency in Massive MIMO-NOMA Systems

Performance Analysis of Uplink Index-Modulated NOMA for 6G Wireless Communications

In this letter, we mathematically analyze the bit-error rate (BER) performance of a generalized uplink index-modulated non-orthogonal multiple access (IM-NOMA) system consisting of a base station (BS) with multiple antennas and an arbitrary number of devices with different modulation schemes. We focus on the case that a single subcarrier is activated among S(≥2) subcarriers according to index bits to reduce computational complexity. The optimal joint maximum-likelihood (JML) algorithm is assumed to be used for detecting a super-imposed symbol from multiple devices. The closed-form BER is shown to be matched well with simulation results. We also investigate the effect of channel estimation error on the BER. To the best of our knowledge, the mathematical characterization of BER of the generalized uplink IM-NOMA system with an arbitrary number of uplink devices with different modulation schemes and multiple receive antennas at the BS does not exist in the literature. Finally, we show that our analytical framework can be applied to analyze a cell-free massive MIMO NOMA system, which is expected to be one of the most promising techniques for 6G wireless communication systems.

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.

Adaptive User Pairing in Multi-IRS-Aided Massive MIMO-NOMA Networks: Spectral Efficiency Maximization and Deep Learning Design

In this paper, we propose an adaptive user pairing (AUP) scheme in multi-intelligent reflecting surface (IRS)-aided massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks. In the AUP scheme, two users with different channel conditions are selected for user pairing while multiple IRSs assist to improve received signal quality at users. We consider the problem of jointly optimizing the precoding matrix, the phase shift of IRSs, and the user pairing element to maximize the overall spectral efficiency (SE) subject to the maximum power budget at the base station (BS) and user-specific quality-of-service (QoS). The SE problem formulated as the maximization of non-concave functions involves a mixed-integer program, which is very challenging to solve optimally. To tackle this problem, we first relax the user pairing elements to be continuous and then transform the formulated problem into an equivalent non-convex problem with a more tractable form. We then apply the iterative algorithm (IA) with low complexity to guarantee convergence at a relative optimum. Towards real-time optimization, we propose a deep learning (DL) framework to predict the optimal solution of the precoding matrix, the phase shift of IRSs, and user pairing elements according to the user’s locations and channel gains. Compared to the conventional optimization method, the DL-based optimization framework can achieve the optimal solution within a very short time via an efficient inference process. Numerical results verify that the proposed algorithm improves the SE over state-of-the-art approaches. Moreover, the effects of essential parameters such as the total BS transmit power, the number of UEs, IRSs, and BS’s antennas on the system are discussed and evaluated to show the effectiveness of the proposed scheme in balancing resource utilization.

Joint mmWave Beamforming and Resource Allocation in NOMA-MEC Network for Internet of Things

Mobile edge computing (MEC) technology can reduce the computational burden of Internet of Things (IoT) devices by offloading computationally intensive tasks to physically proximate edge servers. The combination of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technique has draw significant attention for its high spectral efficiency. This paper merges the computation offloading and mmWave massive MIMO-NOMA communication to seek energy efficient performance for IoT devices in MEC network. First, we propose an IoT device clustering algorithm to schedule all IoT devices into limited beams. Following this, we minimize the total energy consumption of computing and offloading in the NOMA-MEC network by jointly optimizing hybrid beamforming, offloading task assignment (OTA) ratio and power allocation. Then, the non-convex joint optimization problem is solved by proposing a suboptimal solution with two stages. In the first stage, a cyclic block-coordinate update algorithm is proposed for analog beamforming design, and then the digital beamforming vectors are obtained by zero-forcing method. In the second stage, an alternating minimization algorithm is proposed to optimize the OTA ratio and power allocation. Finally, simulation results are provided to validate the convergence of the proposed algorithms and the superiority of our proposed schemes over other benchmark schemes, which demonstrate the outstanding performance leveraging the massive MIMO-NOMA technique during computational offloading.

Massive MIMO-NOMA Based MEC in Task Offloading for Delay Minimization

Mobile edge computing (MEC) has been considered a promising technology to reduce task offloading and computing delay by enabling mobile devices to offload their computation-intensive tasks. Non-orthogonal multiple access (NOMA) is regarded as a promising method of increasing spectrum efficiency, while Massive multiple-input multiple-output (MIMO) can support a larger number of users for simultaneous offloading. These two technologies can effectively facilitate offloading and further improve the performance of MEC systems. In this work, we propose a NOMA and Massive MIMO assisted MEC system for delay-sensitive applications. Our objective is to minimize the overall computing and transmission delay under users’ transmit power and MEC computing capability. Through the pairing scheme for Massive MIMO-NOMA, the users with the higher channel gain can offload all their data, while the users with the lower channel gain can offload a portion of their data to the MEC. Performance results are provided regarding to the sum data rate and overall system delay compared with the orthogonal multiple access (OMA)-MIMO based and Massive MIMO (M-MIMO) based MEC systems.

Resource Allocation and Pairing Techniques in Multiuser Massive MIMO-NOMA

Resource Allocation and Pairing Techniques in Multiuser Massive MIMO-NOMA

Corrections to “Massive MIMO-NOMA Based MEC in Task Offloading for Delay Minimization”

Corrections to “Massive MIMO-NOMA Based MEC in Task Offloading for Delay Minimization”

Energy efficiency optimization for MM-NOMA heterogeneous network with wireless backhauling and energy harvesting

A novel architecture of massive MIMO-NOMA (MM-NOMA) based two-tier heterogeneous network is proposed to accommodate huge traffic while considering wireless backhaul transmission is presented in this paper. In addition, full-duplex transmission and energy harvesting are also explored to enhance system performance. Simultaneous wireless information and power transfer (SWIPT) technique has been applied at small cell base station for harvesting energy. First, signal to interference plus noise ratio (SINR) and rate equations are modeled for the proposed network where harvested energy and energy efficiency (EE) are also defined. EE is then optimized by determining the time splitting factor assignment, bandwidth allocation, and power allocation subject to constraints of backhauling, minimum harvested power and quality of service. The formulated optimization problem is non-linear and non-convex. It is difficult to obtain the optimal solution, hence a sub-optimal algorithm is proposed. Furthermore, the complexity of the proposed algorithm is examined and simulation results demonstrate the effectiveness of the proposed algorithm by comparing with existing benchmark schemes.

The dynamic power allocation to maximize the achievable sum rate for massive MIMO‐NOMA systems

The demand of mobile networks and quality of service have recently increased to higher Spectral Efficiency (SE) or Energy Efficiency (EE) and massive connectivity for 5G wireless communications. The concept of beamforming Multiple-Input Multiple-Output (MIMO) is capable of significantly reducing the amount of required Radio Frequency Chains (RFCs) used by massive MIMO systems without remarkable performance loss. However, in existing beamformed MIMO, the amount of supported devices cannot be higher than the amount of RFCs using the same time-frequency resources, and it is the basic limit for these systems. To address this issue, non-orthogonal multiple access (NOMA) has been recently recommended, which can accommodate higher covered using via non-orthogonal resource allocation. Nevertheless, power allocation is a core factor of the NOMA scheme. However, maximizing the sum rate problem based on power allocation in Massive MIMO-NOMA scenarios is non-convex and non-linear, which creates a very challenging situation to acquire the closed-form approach. Thus, in this paper, an approach to this difficulty is outlined for Massive MIMO-NOMA systems to maximize the sum rate as a convex and linear problem. Simulation results of the suggested Beamformed MIMO-NOMA (BMN) algorithm show that a higher achievable sum rate is achieved compared with the usual beamformed MIMO.

Power minimization for GSIC-based uplink cell-free massive MIMO-NOMA systems

Non-orthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) are considered promising techniques to satisfy the demands for high spectrum efficiency and massive connectivity in future wireless communication. In this paper, a novel framework to realize transmission in cell-free massive MIMO-NOMA system with deep integration between MIMO and NOMA is proposed. A new method is developed to divide users into different groups according to their equivalent path loss, and then “group-level successive interference cancellation” (GSIC) is used to cancel the inter-group interference when demodulating the users. Based on the new framework, a rigorous closed-form expression of the achievable sum rate in uplink cell-free massive MIMO-NOMA system is derived. In addition, a parallel iterative method is used to obtain the best power control scheme. Simulations show that the proposed scheme can effectively reduce the total power consumption and outperforms the orthogonal multiple access (OMA) transmission and traditional SIC-NOMA schemes based on user clustering.

A Gradual Resource Allocation Technique for Massive MIMO-NOMA

In this letter, a new perspective for channel allocation and user pairing is proposed for a crowded downlink system using massive multiple-input–multiple-output, nonorthogonal multiple access (NOMA), and zero-forcing beamforming. The proposed user clustering technique allocates users to subbands and beams by a gradual relaxation of the correlation constraints in such a way to enhance the conditioning of the channel matrix. Channel measurements are performed in this context to validate the proposed technique in an experimental setup. Results show an important gain in performance in terms of both throughput and fairness with respect to orthogonal multiple access and to two state-of-the-art NOMA clustering methods.