Massive MIMO Research Articles

An Efficient Approximate EP-Based Iterative Detection and Decoding for Massive MIMO

An Efficient Approximate EP-Based Iterative Detection and Decoding for Massive MIMO

Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources

Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources

Joint Access Points-User Association and Caching Placement Strategy for Cell-Free Massive MIMO Systems Based on Soft Actor-Critic Algorithm

Joint Access Points-User Association and Caching Placement Strategy for Cell-Free Massive MIMO Systems Based on Soft Actor-Critic Algorithm

A Scalable Energy Consumption Optimization in Downlink Cell-Free Massive MIMO

A Scalable Energy Consumption Optimization in Downlink Cell-Free Massive MIMO

Performance of Spatially Diverse URLLC and eMBB Traffic in Cell Free Massive MIMO Environments

Performance of Spatially Diverse URLLC and eMBB Traffic in Cell Free Massive MIMO Environments

Deep Learning-aided Channel Estimation Combined with Advanced Pilot Assignment Algorithm to Mitigate Pilot Contamination for Cell-Free Networks

Objectives: The performance of Cell-Free Massive Multiple Input Multiple Output (CFMM) is analyzed in this paper for its two bottlenecks i.e., Pilot Contamination (PC) and Channel Estimation Error (CEE). Methods: The CFMM network is strongly affected by PC which is one of the bottlenecks due to which quality of service and accuracy of channel estimation gets impacted. Therefore, we address this problem by presenting advanced pilot assignment algorithm to mitigate PC and deep learning aided channel estimation for reducing CEE for the CFMM systems to maximize spectral efficiency (SE). We derive achievable uplink and downlink SE expressions for the proposed system, and compare with Minimum Mean Square Error and Maximum Ratio combining techniques. As well, the performance is evaluated for different antenna configurations. The advanced pilot assignment algorithm is compared with greedy pilot assignment and random pilot assignment methods. The performance of cellular massive multiple input multiple output (MIMO) is derived for comparison. The performance of CFMM system is evaluated using MATLAB software. Findings: The UL and DL performance of the proposed system in terms of SE is 3.2 times higher than the conventional CFMM with MMSE and MR combining techniques. Average sum spectral efficiency of the proposed system increases with increase in number of access points (APs). Comparison with different antenna configurations reveals that, with 400 APs equipped with single antenna, only UE with good channel condition shows performance enhancement, but when each AP is equipped with 4 antennas, the UE with unfavourable channel condition also give better performance. Advanced pilot assignment scheme proves to be better than greedy and random pilot assignment techniques. For the same cellular set up, the proposed CFMM system achieves higher SE than the cellular massive MIMO. Novelty: Due to the advanced pilot assignment algorithm used in the proposed CFMM system, at a time, only one AP is selected and the selected AP with its full received power serves the desired UE, which suppresses interference resulting in improved SE performance. The serving AP is selected considering the distance between UE and AP, rather than using large scale fading coefficient which is the unique feature of pilot assignment algorithm. The proposed deep learning-aided channel estimation method, minimizes the mean square error (MSE) between the actual channel and the channel estimates obtained from the MMSE estimation resulting in reduction in channel estimation error. Thus, the use of the proposed advanced pilot assignment algorithm and deep learning-aided channel estimation method increase the SE performance of the CFMM system. Keywords: Cell­Free Massive Multiple Input Multiple Output, Pilot Contamination, Channel Estimation Error, Minimum Mean Square Error, Maximum Ratio

Optoelectronic-aided machine learning-based stable routing protocol for MANET and beyond massive MIMO systems in 5G networks

Optoelectronic-aided machine learning-based stable routing protocol for MANET and beyond massive MIMO systems in 5G networks

Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach

Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE.

An Optimized Sequence for Sparse Channel Estimation in a 5G MIMO System

ABSTRACT Recently, the massive Multiple-Input Multiple-Output (MIMO) system has been integrated with machine learning approaches to realise automatic channel state information detection. These methods need high computational complexity due to the lack of optimal training sequences and the failure to control the sparsity assembly based on massive MIMO. Moreover, it does not consider optimised training sequences based on a compressive sensing approach in the presence of contamination. Thus, this paper proposed an enhanced deep learning model with an optimised pilot training sequence for sparse channel estimation in a 5 G massive MIMO system. Initially, the system and sparse block channel impulse response model will be constructed by considering the time-domain synchronous Generalized Frequency Division Multiplexing (GFDM) system and additive white Gaussian noise (AWGN). In the proposed work, a seagull optimisation algorithm is developed to design the training pilot sequence depending on the coherence properties of the sensing matrix, which is used to recover the channel impulse response. Then, the sparse massive MIMO channel is evaluated by proposing a new deep channel estimator with an enhanced stacked auto encoder (D-ESAE). The proposed channel MSE is 10−6, the bit error rate is 0.04, and NMSE is 10−9 at 40 dB SNR through Matlab simulation.

Achieving Energy-Efficient Massive URLLC Over Cell-Free Massive MIMO

Achieving Energy-Efficient Massive URLLC Over Cell-Free Massive MIMO

Pilot Assignment and Power Control in Secure UAV-Enabled Cell-Free Massive MIMO Networks

Pilot Assignment and Power Control in Secure UAV-Enabled Cell-Free Massive MIMO Networks

Spectral Efficiency Analysis of Uplink Cell-Free Massive MIMO Symbiotic Radio

Spectral Efficiency Analysis of Uplink Cell-Free Massive MIMO Symbiotic Radio

Variational Optimization for Sustainable Massive MIMO Base Station Switching.

Massive MIMO networks are a promising technology for achieving ultra-high capacity and meeting future wireless service demand. Massive MIMO networks, on the other hand, consume intensive energy. As a result, energy-efficient operation of massive MMO networks became a requirement rather than a luxury. Many NP-hard concavity search algorithms for optimal base station switching on-off scheme have been developed. This paper demonstrates the formulation of massive MIMO networks energy efficiency as a constrained variational problem. Our proposed method solution's uniqueness and boundedness are demonstrated and proven. The developed system is a total energy optimization problem formulation. Furthermore, the order in which the base stations are switched on and off is specified for minimal handover overhead signaling and fair user capacity sharing. Results showed that variational optimization yielded optimal base station switching on and off with considerable energy saving achieved and maintaining the user capacity demand. Moreover, the proposed base station selection criteria provided suboptimal handover overhead signaling.

Design of a novel wide-angle Rotman lens beamformer for 5G mmWave applications

The attachment of 5G with millimeter wave (mmWave) frequencies offers massive capacity and low latency to reveal the full 5G experiences. High directive gain and beamforming are considered essential for mmWave 5G systems. The main requirements of the beamforming network for 5G mmWave applications are the scanning coverage of ± 60° and SLL > 10 dB in a wide operational bandwidth over the standard 5G frequency bands. In this paper, a novel PCB-based wide-angle Rotman lens beamformer is designed, simulated, and successfully measured to meet the mentioned requirements for 5G mmWave applications. A comprehensive improved design methodology is provided for all components of the Rotman lens to reach a wide scanning angle, enhanced sidelobe level, and low scan loss. The end-fire Vivaldi antenna is selected as an array element for easy integration to the beamforming network as well as its capability to use in dual-polarization configuration. The proposed Rotman lens is operational in the 24–30 GHz frequency band covering 5G n257, n258, and n261 frequency bands. The results show a nearly constant 8 beams across the whole bandwidth steering from − 53° to 53° in 15° increments to provide ± 60° coverage with the SLL > 10 dB and scan loss < 1.9 dB. The retrieved novelties from this work contain an effective design methodology for an optimized Rotman lens with wide-scan angle and low phase and amplitude error, non-uniform distribution based array ports, and integration with end-fire antenna for possible dual polarization and 2-D beamforming capabilities. The comparison of the proposed beamformer with the most recent works shows several advantages in terms of integrated structure and performances including bandwidth, wide scanning angle, SLL, and scan loss. With such performances, this beamformer can be used for various mmWave and 5G applications such as advanced antenna systems, massive MIMO systems, and hybrid beamforming systems.

Transmission of Physical Layer Network Coding Based on Massive MIMO over Millimeter Wave Channel

Massive Multiple Input Multiple Output system and physical layer network coding (PNC) are two important technologies that are considered in new generation networks. In this paper we investigate the effect of increasing the number of antenna elements at user nodes and relay node in the presence of PNC.  Two-way wireless relay transmission is considered in conjunction with massive number of antennas at the relay node assuming millimeter wave (mmWave) band channel environment. End-to-end transmission is considered using 3-D QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) channel model. The performance measures used are the bit error rate (BER) and throughput.  Up to 100% increase in throughput of PNC coded system is obtained.  The BER performance is improved only when the number of relay node antennas is much higher than that of the user node.  Combining mMIMO with PNC gives better throughput without affecting the BER at high SNR.   This is achieved with reasonable number of antennas at the user nodes.

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pilot overhead as well as substantial computational complexity. With benefits from the powerful detection characteristics of MIMO radar, we aim for designing a novel sensing-assisted semi-blind detection scheme in this paper, where both the inherent low-rankness of signal matrix and the essential knowledge about geometric environments are fully exploited under a designated cooperative manner. Specifically, to efficiently recover the channel factorizations via the formulated low-rank matrix completion problem, a low-complexity iterative algorithm stemming from the alternating steepest descent (ASD) method is adopted to obtain the solutions in case of unknown noise statistics. Moreover, we take one step forward by employing the denoising convolutional neural network (DnCNN) to preprocess the received signals due to its favorable performance of handling Gaussian denoising. The overall paradigm of our proposed scheme consists of three stages, namely (1) target parameter sensing, (2) communication signal denoising and (3) semi-blind detection refinement. Simulation results show that significant estimation gains can be achieved by the proposed scheme with reduced training overhead in a variety of system settings.

Pilot contamination mitigation by pilot assignment and adaptive linear precoding for massive MIMO multi-cell systems

Pilot contamination mitigation by pilot assignment and adaptive linear precoding for massive MIMO multi-cell systems

Low-complexity hybrid precoding for sub-connected millimeter wave massive MIMO systems

Hybrid analog-digital precoding architectures facilitate practical implementation of millimeter wave (mmW) massive multiple-input multiple-output (mmW-mMIMO) systems by reducing the number of radio frequency chains required. The spectral efficiency (SE) and energy efficiency (EE) optimization problems of these systems are typically non-convex and NP-hard, as the joint optimization that involves the analog and digital precoding functions along with the constant modulus constraints required by analog phase shifters, results in highly non-linear problems. To address such problems, we propose a decoupled two-stage design. More specifically, in the first stage, a closed-form approximation of the effective channel is proposed, and thus the SE maximization problem can be transformed to an effective channel gain maximization problem. Then, the analog precoding matrices can be generated to facilitate the second stage, where the digital precoding matrix is devised for maximizing the system’s EE. In addition, the circuit power consumption statistics of the system modules are exploited and a sub-connected mmW-mMIMO architecture is adopted during EE optimization. Finally, simulation results are provided to validate the effectiveness of our proposed mmW-mMIMO scheme with hybrid precoding.

A Survey of NOMA-Aided Cell-Free Massive MIMO Systems

The Internet of Everything is leading to an increasingly connected intelligent digital world. Envisaged sixth-generation wireless networks require new solutions and technologies due to stringent network requirements. The benefits of cell-free massive MIMO (CF-mMIMO) and non-orthogonal multiple access (NOMA) have brought substantial attention to these approaches as potential technologies for future networks. In CF-mMIMO, numerous distributed access points are linked to a central processing unit, which allocates the same time-frequency resources to a smaller group of users. On the other hand, NOMA can support more users than its orthogonal counterparts by utilizing non-orthogonal resource allocation. This paper provides a comprehensive review and survey of NOMA-aided CF-mMIMO (CF-mMIMO-NOMA). Specifically, we present a comprehensive review of massive MIMO, CF-mMIMO, and NOMA. We then present a state-of-the-art research review of CF-mMIMO-NOMA. Finally, we discuss the challenges and potential of combining CF-mMIMO-NOMA with other enabling technologies to enhance performance.

Quantum mechanics denoising based channel estimation algorithm for mmWave massive MIMO systems

Channel estimation of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) is crucial for the application of wireless transmission. The signal system is susceptible to external noise, which reduces the accuracy of channel estimation. The denoising of the received signal is a research hotspot and challenge for channel estimation. Therefore, this paper proposes a quantum mechanics denoising-based channel estimation method. The proposed quantum mechanics denoising-based algorithm has the advantages of not relying on the original conditions, no grid error, and strong adaptive ability. The first part is that the received noisy signal is denoised. The conversion between the signal model of mmWave massive MIMO and the physical model of quantum mechanics needs to be solved. The received noisy signal is equivalent to the potential of the stationary Schrodinger equation. Then, the Hamiltonian matrix is constructed by the received signal and the corresponding eigenvalues and eigenvectors are calculated. The eigenvectors of the Hamiltonian matrix are related to the energy of Schrodinger equation, which are determined to the adaptive basis. In addition, the received signal is projected onto the adaptive basis to calculate the coefficients. The denoised received signal is reconstructed through the soft threshold processing of the coefficients. The second part is that channel estimation is performed on the denoised received signal using the l1/2-singular value decomposition (SVD)-based algorithm. The angle parameters are iteratively moved to the actual values by gradient descent. Besides, the initial values of the angles are obtained through the SVD preprocessing method. Simulation results show that the proposed quantum mechanics denoising-based method exhibits good estimation accuracy.