Massive Massive Multiple-input Multiple-output Networks Research Articles

Secure communication of intelligent reflecting surface-aided NOMA in massive MIMO networks

A Non-Orthogonal Multiple Access (NOMA) network, in which the base station (BS) incorporates massive Multiple-Input Multiple-Output (MIMO) technology, is considered in this paper. This research study focuses on investigating physical layer security in this network when a jammer is present, leveraging intelligent Reflecting Surface (IRS) technology. The IRS is an innovative approach strategically implemented to enhance communication quality by assisting distant users in establishing a reliable connection with the BS. Two key metrics in physical layer security are evaluated: the secrecy rate (SR) for pairs of NOMA users and the secrecy outage probability (SOP). Additionally, the impact of using a jammer is assessed by comparing the network’s performance with and without a jammer. The results indicate that by increasing in the antenna numbers, the rate of secrecy is improved, and the SOP is decreased. Moreover, as the transmit signal-to-noise ratio (SNR) increases, the SR is enhanced, but the SOP is degraded. However, the increase in the IRS element numbers results in a tendency for the SOP to rise. Furthermore, it is evident that incorporating a jammer improves the network’s performance.

Learning to optimize power allocation in cell-free massive MIMO networks with hybrid green energy

In cell-free (CF) massive multiple-input multiple-output (MIMO) networks, many access points (APs) simultaneously serve user equipment (UEs) using the same time and frequency resources. The resource allocation algorithm in such networks needs to handle many calculations within a limited time frame to determine the power allocation from each AP to each UE. Further, the dispersion and large number of APs in CF massive MIMO networks pose challenges in terms of energy supply. As access to the power grid may be limited, renewable energies are often required as a supplement. However, due to the non-deterministic nature of the charging process, the resource allocation algorithm needs to consider the dynamic and uncertain amount of energy available at the APs. In this article, we propose a scalable machine learning algorithmbased on the emerging Learning to Optimize (L2O) paradigm that utilizes neural networks (NNs) for resource allocation in CF massive MIMO networks powered by electrical and renewable energies. Our objective is to minimize energy consumption from the power grid while satisfying the spectral efficiency (SE) constraints of the UEs. Additionally, our algorithm adopts a forward-looking approach and employs a green energy budgeting mechanism to prevent service interruption at green APs during energy shortages. Our algorithm design approach involves formulating the offline version of the problem as a second-order cone program (SOCP). Subsequently, we train an NN using the solutions obtained from solving the online variant of the SOCP. Our results indicated that our L2O-based algorithm can accurately mimic the solutions of the optimization problem with significantly reduced computational complexity.

ENERGY EFFICIENT OPERATION FOR NEXT GENERATION MASSIVE MIMO NETWORK

For the next generation of wireless networks, massive multiple-input, multiple-output (MIMO) is regarded as a potential technique. Base stations (BS) are provided with a relatively large number of antenna components in massive MIMO in order to increase spectral efficiency (SE) and energy efficiency (EE). Meanwhile, EE of BSs are becoming an increasingly important concern for communications operators in order to maintain profitability and reduce the overall negative impact on the environment and economic concerns for wireless network users. In this paper, an energy-aware, load-adaptive operation approach is proposed for massive MIMO for the next generation of wireless communication systems. Energy-aware, load-adaptive network operation has been identified as a promising method for improving the energy efficiency of telecommunication networks. Our research indicates that at optimal sumSE, fully load-adaptive massive MIMO may greatly increase network EE by up to 8.666%, 43.317%, and 69.367% for high, medium, and low load factors, respectively, when compared to traditional non-load-adaptive massive MIMO.

Performance Analysis of Cell-Free Massive MIMO Network With Large-Scale-Fading Decoding and Low-Resolution ADCs

Concentrating on cell-free massive multiple-input multiple-output (MIMO) networks with low-resolution analog-to-digital converters (ADCs), we derive the theoretical achievable uplink spectral efficiency (SE) for the minimum mean-squared error (MMSE)-based large-scale fading decoding (LSFD). Moreover, we investigate the bit allocation (BA) among access points (APs) to maximize the asymptotic sum SE under the constraint of total ADC quantization bits. Simulation results confirm that our theoretical analysis is accurate and validate that the proposed BA technique is preferable to the random and fixed counterparts.

Distributed Learning for Uplink Cell-Free Massive MIMO Networks

Cell-free massive multiple-input multiple-output (MIMO) can resolve the inter-cell interference issue in cellular networks through cooperative beamforming of the distributed access points (APs). This paper focuses on an uplink cell-free massive MIMO network and investigates novel methods to train the central processing unit (CPU), the APs, and the users in the network. To reduce the communication burden posed on the fronthaul, each AP applies receive beamforming to compress the vector signals into scalar ones before passing them to the CPU for centralized processing. By drawing analogies between an uplink cell-free network and a quasi-neural network and borrowing the idea of backpropagation algorithm, we propose a novel scheme named the distributed learning for uplink cell-free massive MIMO beamforming (DLCB), which can achieve the multi-AP cooperation without explicit estimation of their channel state information (CSI). The DLCB has low computational complexity and is applicable to various objective functions, such as the minimum mean squared error criterion and the maximum sum rate criterion. Extensive simulations verify that the proposed scheme achieves superior performance over the state-of-the-art methods.

Learning Decentralized Power Control in Cell-Free Massive MIMO Networks

This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination. To determine the transmission policy of distributed APs, it is essential to develop a network-wide collaborative optimization mechanism. To address this challenge, we design a cooperative learning (CL) framework which manages computation and coordination strategies of the CP and APs using dedicated deep neural network (DNN) modules. To build a versatile learning structure, the proposed CL is carefully designed such that its forward pass calculations are independent of the number of APs. To this end, we adopt a parameter reuse concept which installs an identical DNN module at all APs. Consequently, the proposed CL trained at a particular configuration can be readily applied to arbitrary AP populations. Numerical results validate the advantages of the proposed CL over conventional non-cooperative approaches.

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.

Resource Allocation and Impact Analysis of the System Parameters on Performance in Multi-Cell Massive MIMO Networks

Abstract This paper investigates the energy-efficient resource allocation algorithm for a massive multiple input multiple output (MIMO) system, in which each base station adapts the number of antennas to the daily load profile. Our paper examines the effect of two user location distribution (ULD) models, on the energy-efficiency (EE) of load adaptive masive MIMO system. We propose a resource allocation strategy to adapt the number of antennas based on tracking variations of ULD and cell loading maximizing the EE. We also evaluate impact of cell size, available bandwidth and output power level of the BS on EE at different cell loading.

Optimization of Cell-Free Massive MIMO System

As an innovative implementation, Cell-Free Massive Multiple Input Multiple Output (MIMO) has appeared in typical Cellular Massive MIMO Networks. This protocol doesn’t recognize cells, as shown by its name, even though a significant number of APs operate on the same frequency/time resources. Connection from multiple distributed access points through joint signal processing is called Cell-Free Massive MIMO. The Cell-Free Massive MIMO System, a contrast between Cell-Free Massive MIMO Systems and Distributed Massive MIMO, the prime focus in this thesis is on Cell-free Massive MIMO and, along with this discussion, on Cell-free Massive MIMO signal processing, Channel Estimation, Uplink Signal Detection, Cumulative Distribution, Spectral Efficiency & Ubiquitous Cell-Free Massive MIMO Model. Ubiquitous Cell-free Massive MIMO contributes to a Massive MIMO system, a distributed system that implements consistent user-centre distribution to solve that constraint of mobile phone interferences as well as to introduce macro-diversity. We investigated the Cell Radius at different locations in CDF with Spectral Efficiency [bits/s/hertz]. Cell-Free Massive MIMO is an evidence-based preventive of massive MIMOs with distributed high percentage APs that serve even lower margins. The cell-free model is not segregated into cells and any individual is concurrently represented by every Access point.

Antenna Selection Based on Matching Theory for Uplink Cell-Free Millimetre Wave Massive Multiple Input Multiple Output Systems

In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.

Revenue-Maximizing Resource Allocation for Multitenant Cell-Free Massive MIMO Networks

Cell-free massive multiple-input--multiple-output (MIMO) (CF-mMIMO) is a distributed massive MIMO network architecture, which leads to a better ability to resist shadow fading than centralized massive MIMO. When network slicing is applied to CF-mMIMO networks in order to serve multiple tenants, the deployment flexibility of CF-mMIMO networks could be further improved. Nevertheless, how to effectively allocate resources in network slicing based CF-mMIMO networks is still a challenging problem to be tackled. In this article, we present a novel resource allocation scheme for network slicing based multi-tenant CF-mMIMO networks to maximize the infrastructure operator’s revenue while maintaining the quality of network services. Specifically, the resource allocation scheme is first formulated as an optimization problem, which is NP-hard and nonconvex. Then, the problem is decomposed into two subproblems. For the first subproblem, we propose a joint user clustering and power control coefficient allocation algorithm, which not only maintains fairness among users but also improves the access rate of CF-mMIMO networks. For the second subproblem, the interior-point method is adopted. In our article, extensive simulations are carried out to prove the effectiveness of the proposed resource allocation scheme. Our experimental results indicate that the proposed scheme leads to a lower outage probability and a higher infrastructure provider’s revenue than existing approaches.

Beyond Supervised Power Control in Massive MIMO Network: Simple Deep Neural Network Solutions

Power control in massive multiple input multiple output (MIMO) systems is an appealing technique to improve network performance and reliability. The traditional methods to solve such problems are based on the convex optimization theory, which incurs high computational complexity. In contrast, this work leverages deep neural networks to maximize the minimum data rate of the downlink users in the massive MIMO network. Based on the different setups in the practical systems, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-distribution case</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weakly out-of-distribution case</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out-of-distribution case</i> , we propose three approaches to solve them. Specifically, first, we establish a densely connected neural network for solving the power control problem. Then, for the case where the test data and training data come from the same distributions (the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-distribution case</i> ), we use the established neural network to obtain an approximately optimal power control strategy, and propose a self-training-based algorithm to use the unlabeled samples collected in the actual system to further improve the system performance. For the case that the test data and training data come from different distributions and a small amount of labeled target domain data can be obtained (the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weakly out-of-distribution case</i> ), we propose a pre-training and fine-tuning algorithm to solve the problem. For the issue that we can not obtain the labels of the target domain data (the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out-of-distribution case</i> ), we transform the problem into a covariate shift problem and propose an algorithm by weighting the loss function. Numerical results demonstrate that the proposed schemes not only match the optimal solution well but also have low online inference complexity.

Multi-Beam Multi-Hop Routing for Intelligent Reflecting Surfaces Aided Massive MIMO

Intelligent reflecting surface (IRS) is envisioned to play a significant role in future wireless communication systems as an effective means of reconfiguring the radio signal propagation environment. In this paper, we study a new multi-IRS aided massive multiple-input multiple-output (MIMO) system, where a multi-antenna BS transmits independent messages to a set of remote single-antenna users using orthogonal beams that are subsequently reflected by different groups of IRSs via their respective multi-hop passive beamforming over pairwise line-of-sight (LoS) links. We aim to select optimal IRSs and their beam routing path for each of the users, along with the active/passive beamforming at the BS/IRSs, such that the minimum received signal power among all users is maximized. This problem is particularly difficult to solve due to a new type of path separation constraints for avoiding the IRS-reflected signal induced interference among different users. To tackle this difficulty, we first derive the optimal BS/IRS active/passive beamforming solutions based on their practical codebooks given the reflection paths. Then we show that the resultant multi-beam multi-hop routing problem can be recast as an equivalent graph-optimization problem, which is however NP-complete. To solve this challenging problem, we propose an efficient recursive algorithm to partially enumerate the feasible routing solutions, which is able to effectively balance the performance-complexity trade-off. Numerical results demonstrate that the proposed algorithm achieves near-optimal performance with low complexity and outperforms other benchmark schemes. Useful insights into the optimal multi-beam multi-hop routing design are also drawn under different setups of the multi-IRS aided massive MIMO network.

Performance Analysis of Channel Estimation for Massive MIMO Communication Using DL-Based Fully Connected Neural Network (DL-FCNN) Architecture

The latest research for applying deep learning in wireless communications gives several opportunities to reduce complex signal processing. The channel estimation is important to study the nature of the varying channel and to calculate channel state information (CSI) value which is utilized at the receiver to nullify the interference which occurs during multipath transmission. In the current article, considering the massive Multiple Input Multiple Output (MIMO) channel model, a DL approach is developed with a fully connected neural network (NN) architecture which is used to estimate the channel with minimum error. The proposed DL architecture uses an openly available channel dataset. Further, using generated pilot symbols of lengths 2 and 4, the performance of DL-based Fully connected NN (DL-FCNN) is analyzed to estimate the channel in uplink massive MIMO communication. The obtained results demonstrate that the channel estimation performance was calculated in terms of normalized mean square error((NMSE) for different values of SNR added at receiver base station (BS) to the signals over the range of BS antennas. Also, the channel estimation error over a large number of BS antennas for massive MIMO scenarios is observed, and it is observed that the NMSE reduces with a greater number of antennas. Hence, it can be inferred that the DL models will be the future for most physical layer signal processing techniques such as channel estimation, modulation detection, etc. within massive MIMO networks.

User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

Densification of network base stations is indispensable to achieve the stringent Quality of Service (QoS) requirements of future mobile networks. However, with a dense deployment of transmitters, interference management becomes an arduous task. To solve this issue, exploring radically new network architectures with intelligent coordination and cooperation capabilities is crucial. This survey paper investigates the emerging user-centric cell-free massive Multiple-input multiple-output (MIMO) network architecture that sets a foundation for future mobile networks. Such networks use a dense deployment of distributed units (DUs) to serve users; the crucial difference from the traditional cellular paradigm is that a specific serving cluster of DUs is defined for each user. This framework provides macro diversity, power efficiency, interference management, and robust connectivity. Most importantly, the user-centric approach eliminates cell edges, thus contributing to uniform coverage and performance for users across the network area. We present here a guide to the key challenges facing the deployment of this network scheme and contemplate the solutions being proposed for the main bottlenecks facing cell-free communications. Specifically, we survey the literature targeting the fronthaul, then we scan the details of the channel estimation required, resource allocation, delay, and scalability issues. Furthermore, we highlight some technologies that can provide a management platform for this scheme such as distributed software-defined network (SDN). Our article serves as a check point that delineates the current status and indicates future directions for this area in a comprehensive manner.

Energy-Delay-Aware Power Control for Reliable Transmission of Dynamic Cell-Free Massive MIMO

This paper presents new learning-based, energy-delay-aware power control strategies for the uplink of dynamic cell-free (CF) massive multiple-input multiple-output (MIMO) networks. We first propose a new algorithm to adaptively select an appropriate set of multi-antenna access points (APs) to serve each user at any time instant. A multi-level hidden Markov model (HMM) is constructed to predict the trajectory of a user and forecast the changing set of serving APs for the user over time. Given the selected, time-changing sets of serving APs, we also formulate a new multi-objective power control problem to minimize a weighted sum of the energy and delay costs of the users. By employing Bernoulli bandit learning (BBL) and Gaussian bandit learning (GBL), two new energy-delay-aware power control strategies are developed to adapt unpredictable channel dynamics, minimize the energy-delay costs online, and remain effective over a long time even when sharp changes occur in the channels. Numerical results demonstrate that the new predictive selection of serving APs and the new learning-based energy-delay-aware power control policies can significantly improve the reliability of uplink transmissions in dynamic CF massive MIMO networks, as compared to state-of-the-art static oracle solutions.

Energy Efficiency Optimization Using Iterative and Metaheuristic Algorithms

The communication technology industry's energy consumption and the resulting energy-related pollution are becoming serious societal and economic challenges. One of the most essential parameters for selecting potential technologies for upcoming wireless communication networks is bandwidth efficiency (BE). However, the main objective is to develop new creative network architecture and technologies that are required to meet the rapid growth in cellular data demand while maintaining same amount of power.Also, Energy Efficiency (EE) has emerged as a key parameter for assessing the performance of wireless communication networks. In this context, the recently suggested massive multiple-input multiple-output (MIMO) technology, which comes under multiuser MIMO with an excess of base station (BS) antennas to support a substantial number of user equipment's (UE) is one of the main technology enablers for 5G.In this paper, new power consumption model is addressed and also novel EE maximization algorithm is proposed which optimizes the resource allocation in massive MIMO network.

Improving Cell-Free Massive MIMO Networks Performance: A User Scheduling Approach

Cell-Free (CF) massive multiple-input multiple-output (MIMO) system is a distributed antenna system, wherein a large number of access points wish to simultaneously communicate with a relatively small number of users. Similar to co-located massive MIMO system, pilot contamination and multi-user interference are two major impediments to CF massive MIMO network performance improvement. One can mitigate the detrimental effect of pilot contamination and multi-user interference through judicious resource allocation. In this work, we formulate and investigate the problem of frequency assignment for a CF massive MIMO. The formulated optimization problem is proven to be generally NP-hard. Local optima can be found by approaches such as the Lagrangian method that however, has considerably slow rates of convergence. To circumvent this issue, we propose an alternative solution being of two-folds. Firstly, we reformulate the problem as a grouping strategy which enables to attenuate the effect of intra-group multi-user interference. In the second fold, frequencies are assigned in a non-overlapping fashion to each scheduled group to palliate the effect of inter-group interference and pilot contamination. To further improve the performance of the proposed approach, power coefficients are allocated to the users via a sequential convex approximation (SCA)-based framework. The effectiveness of the proposed algorithms is then verified through extensive numerical simulations which demonstrate a non-negligible improvement in the performance of the studied scenario.

Performance comparisons of FDD MIMO and 2.6 GHz TDD massive MIMO: An experimental analysis

Massive multiple-input multiple-output (MIMO) is considered as a breakthrough technology in 5G and beyond 5G systems. Some of its main advantages are providing high spectral efficiency to many users simultaneously in the same time–frequency blocks, strong directive signals towards short-range areas and little interference leaks. However, while massive MIMO exhibits interesting benefits, it is important to investigate its main gains through real deployment scenarios in an operator’s infrastructure. In this paper, we focus on performance comparisons of traditional frequency-division duplex (FDD)-based MIMO and 2.6 GHz Time-division duplex (TDD)-based massive MIMO deployments through experimental analysis under different spectrum and bandwidth in a total of three separate sites and one co-site in an operational infrastructure of an operator in Turkey. We also provide design guidelines and requirements for massive MIMO network deployment and proper acceptance of Key Performance Indicators (KPIs) collection and comparisons criteria. Our experimental results reveal up to 66%, 56% and 23% performance benefits in terms of downlink (DL) cell throughput of 2.6 Ghz TDD-based massive MIMO compared to FDD-based MIMO sites in 800 Mhz (site with approximately same number of User Equipment (UEs) compared with TDD massive MIMO), 1800 Mhz (site with higher number of UEs compared with TDD massive MIMO) and 2600 Mhz (site with lower number of UEs compared with TDD massive MIMO) respectively each having 10 Mhz bandwidth. On the other hand, LTE 1800 Mhz FDD MIMO at 20 Mhz can yield higher user throughput values in comparison to 2.6 GHz TDD-based massive MIMO at 10 Mhz. We also observed that the maximum paired layer reached 14 layers in DL of TDD-based massive MIMO. At the end of the paper, we address the main observations and takeaways of TDD-based massive MIMO deployments.

Direction‐based jamming detection and suppression in mmWave massive MIMO networks

In this paper, we study the problem of physical layer security in the uplink of millimeter-wave massive multiple-input multiple-output (MIMO) networks and propose a jamming detection and suppression method. The proposed method is based on directional information of the received signals at the base station antenna array. The proposed jamming detection method can accurately detect both the existence and direction of the jammer using the received pilot signals in the training phase. The obtained information is then exploited to develop a channel estimator that excludes the jammer's angular subspace from received training signals. The estimated channel information is then used for designing a combiner at the base station that is able to effectively cancel out the deliberate interference of the jammer. By numerical simulations, we evaluate the performance of the proposed jamming detection method in terms of correct detection probability and false alarm probability and show its effectiveness when the jammer's power is substantially lower than the user's power. Also, our results show that the proposed jamming suppression method can achieve a very close spectral efficiency as the case of no jamming in the network