Multiple-input Multiple-output Networks Research Articles

Full-duplex cell-free mMIMO networks with coarse ADCs/DACs over Ricean fading for beyond 5G

In this article, we explore a practical full-duplex (FD) cell-free massive multiple-input multiple-output (mMIMO) network equipped with coarse analog-to-digital converters/digital-to-analog converters (ADCs/DACs) at the access points (APs) over Ricean fading channels for beyond 5G communication. First, we suppose that the minimum mean square error scheme is applied to estimate channel state information. The accurate sum achievable rate expressions for both uplink (UL) and downlink (DL) are obtained in closed-form by performing the maximum ratio combining and conjugate beamforming at the APs, respectively. Then, the expressions of energy efficiency for UL and DL can be obtained in closed-form based on the established power dissipation model. Finally, the numerical results are conducted to demonstrate our analytical results about the spectral efficiency of considered system. It is also found that when the total number of coarse ADCs/DACs precision is fixed, employing the higher-precision ADCs at the receive end of APs can notably improve the FD sum rate of proposed networks. In addition, the simulation results also show the impacts of number of antennas at each AP and resolution of coarse ADCs/DACs on the energy efficiency (EE) performance. It is revealed that the moderate quantization bits can contribute to the optimal EE.

Double Intelligent Reflecting Surface-Assisted Multi-User MIMO Mmwave Systems With Hybrid Precoding

This work investigates the effect of double intelligent reflecting surface (IRS) in improving the spectrum efficient of multi-user multiple-input multiple-output (MIMO) network operating in the millimeter wave (mmWave) band. Specifically, we aim to solve a weighted sum rate maximization problem by jointly optimizing the digital precoding at the transmitter and the analog phase shifters at the IRS, subject to the minimum achievable rate constraint. To facilitate the design of an efficient solution, we first reformulate the original problem into a tractable one by exploiting the majorization-minimization (MM) method. Then, a block coordinate descent (BCD) method is proposed to obtain a suboptimal solution, where the precoding matrices and the phase shifters are alternately optimized. Specifically, the digital precoding matrix design problem is solved by the quadratically constrained quadratic programming (QCQP), while the analog phase shift optimization is solved by the Riemannian manifold optimization (RMO). The convergence and computational complexity are analyzed. Finally, simulation results are provided to verify the performance of the proposed design, as well as the effectiveness of double-IRS in improving the spectral efficiency.

Weighted Sum Rate Optimization for STAR-RIS-Assisted MIMO System

In this work, we study a simultaneous transmitting and reflecting reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output network. For the system under consideration, we maximize the weighted sum rate, mainly based on the energy splitting (ES) scheme. To tackle this optimization problem, a sub-optimal block coordinate descent (BCD) algorithm is proposed to design the precoding matrices and the transmitting and reflecting coefficients (TRCs) in an alternate manner. Specifically, the precoding matrices are solved using the Lagrange dual method, while the TRCs are obtained using the constrained concave-convex procedure (CCCP). The simulation results reveal that: 1) Simultaneous transmitting and reflecting RIS (STAR-RIS) can achieve better performance than conventional reflecting/transmiting-only RIS; 2) In unicast communication, time switching (TS) scheme outperforms the ES and mode selection (MS) schemes, while in broadcast communication, ES scheme outperforms the TS and MS schemes.

Flexible Beamforming of Dynamic MIMO Networks Through Fully Convolutional Model

Existing deep learning-based beamforming models’ input sizes are fixed, and they can only be used in multiple-input multiple-output (MIMO) networks containing a fixed number of users and base stations. Therefore, they cannot work and need retraining when the number of users is dynamic. In this letter, a fully convolutional beamforming model named FC-BFNet is proposed, where data of different dimensions can be addressed to adapt to the dynamic environment without retraining the model. Specifically, FC-BFNet has two computational parts which are mainly made up of convolution layers because the convolution operation is not sensitive to the size of the input data. Differently and importantly, the first part extra uses max-pooling to calculate the underlying features of the input and stores max-pooling indices, while the second part extra leverages up-pooling based on stored indices to obtain the expected results from the underlying features and ensure the size consistency of input and output. Extensive experiments demonstrate FC-BFNet has better dynamic environment adaptability and sum-rate performance than other baselines, while maintaining great computing efficiency.

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-Efficient Resource Optimization for Massive MIMO Networks Considering Network Load

This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output (MIMO) network in which each base station (BS) is equipped with a large number of antennas and each base station (BS) adapts the number of antennas to the daily load profile (DLP). This paper takes into consideration user location distribution (ULD) variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system. ULD variation is modeled by dividing the cell into two coverage areas with different user densities: boundary focused (BF) and center focused (CF) ULD. All cells are assumed identical in terms of BS configurations, cell loading, and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load. Together with energy efficiency (EE) we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size, available bandwidth, output power level of the BS, and maximum output power of the power amplifier (PA) at different cell loading. We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business, residential, street, and highway areas.

Distributed Coordinated Precoding for MIMO Cellular Network Virtualization

This paper presents a new virtualization method for the downlink of a multi-cell multiple-input multiple-output (MIMO) network, to achieve service isolation among multiple Service Providers (SPs) that share the base station resources of an Infrastructure Provider (InP). Each SP designs a virtual precoder for its users in each cell, as its service demand to the InP, without the need to be aware of the existence of the other SPs or to know the channel state information (CSI) outside the cell. The InP performs network virtualization to meet the SPs’ service demands while managing both the inter-SP and inter-cell interference. We consider coordinated multi-cell precoding at the InP and formulate an optimization problem to minimize a weighted sum of signal leakage and precoding deviation, with per-cell transmit power constraints. We propose a fully distributed semi-closed-form solution at each cell, without any CSI exchange across cells. We further propose a low-complexity scheme to allocate the virtual transmit power, for the InP to regulate between interference elimination and virtual demand maximization. Simulation results demonstrate that our precoding solution for network virtualization substantially outperforms the traditional spectrum isolation alternative. It can approach the performance of fully cooperative precoding when the number of antennas is large.

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.

User Rate Optimization for Fronthaul-Constrained Cell-Free Massive MIMO-OFDM Systems: A Quadratic Transform-Based Approach

As a promising candidate for the future generations of mobile communication networks, the cell-free (CF) massive multiple-input multiple-output (mMIMO) networks have been shown to provide high spectral efficiency (SE) and more uniform signal coverage than cellular mMIMO networks, enabling smart cities more diverse application services. The fronthaul link in such networks, defined as the transmission link between the central processing unit and the access point, requires a high capacity, but is often constrained. Hence, the optimization of the user rate under the limited capacity of fronthaul is the key problem to be solved in a CF networks. In this paper, we consider the downlink transmission of CF mMIMO orthogonal frequency division multiplexing (OFDM) systems with constrained transmit power and fronthaul capacity. Based on the min-max and sum-max principles, quadratic transform-min max (QT-MM) and quadratic transform-sum max (QT-SM) optimization algorithms are proposed, respectively. Simulation results show that compared with the weighted minimum mean square error (WMMSE) algorithm, both of the proposed algorithms can effectively prevent very low user rates when the fronthaul capacity is limited. When the fronthaul link capacity constraint is high, the QT-SM algorithm has better rate performance than the distributed WMMSE-d algorithm and the centralized WMMSE-c algorithm.

Utilizing Non-Orthogonal Polarization With Polarization Reuse Technique for 4 × 4 MIMO Capacity Enhancement

5G communication promises fast and large data stream which requires higher capacity of a cellular wireless network. A higher capacity could be achieved with wider bandwidth and network densification, but they are expensive approaches. Instead, upgrading the wireless network with higher-order Multiple Input Multiple Output (MIMO) antenna system with polarization diversity can inexpensively escalate the peak data rate for higher capacity. We present a 4x4 MIMO cellular network scheme utilizing polarization reuse scheme and using 4 polarization to reform the cellular network from current state of the art of dual polarization 4x4 MIMO scheme. 4 Polarizations (vertical, Horizontal, ±45° slant) is used in 2 orthogonal polarization pair form, with a beam-separation among pair, to intensify polarization diversity and maximize the MIMO network channel capacity. This beam-separated polarization reuse technique minimizes the channel correlation which maximizes the probability of four independent data streams (rank 4). The simulated result of the channel capacity with the proposed scheme achieves a 30% higher capacity compared to the baseline configuration of 3-sector 4x4 MIMO. The field trial of the presented network indicates a higher portion of rank 4, supporting four independent data streams in the rich scattering environment of a cellular network.

Joint Secure Transceiver Design and Power Allocation for AN-Assisted MIMO Networks

In this paper, we focus on antieavesdropping design in a multicell multiuser interference channel coexisting with a multiantenna eavesdropper, in which multiuser interference arises as a nonneglectable factor in securing communication. Supposing the eavesdropper is equipped with an arbitrary number of antennas, we jointly exploit the role of inherent multiuser interference and artificial noise (AN) to enhance security, and propose a noniterative secure transceiver design under a multiple input multiple output (MIMO) framework. The quantity relationship of system parameters is then analyzed to ensure feasibility. And the achievable secrecy rate is then derived without any knowledge of the eavesdropper. Finally, to balance the power allocated to AN and secrecy data, a power allocation strategy aiming at maximizing the achievable secrecy rate is designed, while guaranteeing legitimate users the required quality of service. With the adopted design, both the multiuser interference and AN are leveraged to facilitate communication security such that the proposed secure transceiver design can adapt to changes in eavesdropping antennas. Extensive numerical results have verified our analysis and demonstrated that the proposed power allocation strategy outperforms the baseline algorithms in terms of the achievable secrecy rate.

MIMO Assisted Networks Relying on Intelligent Reflective Surfaces: A Stochastic Geometry Based Analysis

Intelligent reflective surfaces (IRSs) are invoked for improving both the spectral efficiency (SE) and energy efficiency (EE). Specifically, an IRS-aided multiple-input multiple-output network is considered, where the performance of randomly roaming users is analyzed by utilizing stochastic geometry tools. As such, to distinguish the superposed signals at each user, the passive beamforming weight at the IRSs and detection weight vectors at the users are jointly designed. As a benefit, by adopting a zero-forcing-based design, the intra-cell interference imposed by the IRS can be suppressed. In order to evaluate the performance of the proposed network, we first derive the approximated channel statistics in the high signal-to-noise-ratio (SNR) regime. Then, we derive the closed-form expressions both for the outage probability and for the ergodic rate of users. Both the high-SNR slopes of ergodic rate and the diversity orders of outage probability are derived for gleaning further insights. The network's SE and EE are also derived. Our numerical results are provided to confirm that: i) the high-SNR slope of the proposed network is one; ii) the SE and EE can be significantly enhanced by increasing the number of IRS elements.

Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations

This work proposes novel synchronous, asynchronous, and session-based designs for energy-efficient massive multiple-input multiple-output networks to support federated learning (FL). The synchronous design relies on strict synchronization among users when executing each FL communication round, while the asynchronous design allows more flexibility for users to save energy by using lower computing frequencies. The session-based design splits the downlink and uplink phases in each FL communication round into separate sessions. In this design, we assign users such that one of the participating users in each session finishes its transmission and does not join the next session. As such, more power and degrees of freedom will be allocated to unfinished users, resulting in higher rates, lower transmission times, and hence, higher energy efficiency. In all three designs, we use zero-forcing processing for both uplink and downlink, and develop algorithms that optimize user assignment, time allocation, power, and computing frequencies to minimize the energy consumption at the base station and users, while guaranteeing a predefined maximum execution time of each FL communication round.

Pilot Decontamination Processing in Cell-Free Massive MIMO

This letter focuses on the pilot contamination problem in the uplink and downlink of cell-free massive multiple-input multiple-output networks with different degrees of cooperation between access points. The optimum minimum mean square error processing can take advantage of large-scale fading coefficients for canceling the interference of pilot-sharing user-equipments and thus achieves asymptotically unbounded capacity. However, it is computationally demanding and can only be implemented in a fully centralized network. Here, sub-optimal schemes are derived that provide unbounded capacity with linear-growing complexity and using only local channel estimates but global channel statistics. This makes them suited for both centralized and distributed networks. In this latter case, the best performance is achieved with a generalized maximum ratio combiner that maximizes a capacity bound based on channel statistics only.

Rate-Splitting Multiple Access to Mitigate the Curse of Mobility in (Massive) MIMO Networks

Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. In this work, we study the performance of RSMA under the important setup of imperfect Channel State Information at the Transmitter (CSIT) originating from user mobility and latency/delay (between CSI acquisition and data transmission) in the network. We derive a lower bound on the ergodic sum-rate of RSMA for an arbitrary number of transmit antennas, number of users, user speed and transmit power. Then, we study the power allocation between common and private streams and obtain a closed-form solution for optimal power allocation that maximizes the obtained lower bound. The proposed power allocation greatly reduces precoder design complexity for RSMA. By Link-Level Simulations (LLS), we demonstrate that RSMA with the proposed power allocation is robust to the degrading effects of user mobility and has significantly higher performance compared to conventional multi-user (massive) Multiple-Input Multiple-Output (MIMO) strategies. The work has important practical significance as results demonstrate that, in contrast to conventional multi-user (massive) MIMO whose performance collapse under mobility, RSMA can maintain reliable multi-user connectivity in mobile deployments.

An Edge Computing Paradigm for Massive IoT Connectivity Over High-Altitude Platform Networks

With the advent of the Internet of Things (IoT) era, the ever increasing number of devices and emerging applications have triggered the need for ubiquitous connectivity and more efficient computing paradigms. These stringent demands have posed significant challenges to the current wireless networks and their computing architectures. In this article, we propose a high-altitude platform (HAP) network-enabled edge computing paradigm to tackle the key issues of massive IoT connectivity. Specifically, we first provide a comprehensive overview of the recent advances in non-terrestrial network-based edge computing architectures. Then the limitations of the existing solutions are further summarized from the perspectives of the network architecture, random access procedure, and multiple access techniques. To overcome the limitations, we propose a HAP-enabled aerial cell-free massive multiple-input multiple-output network to realize the edge computing paradigm, where multiple HAPs cooperate via the edge servers to serve IoT devices. For the case of a massive number of devices, we further adopt a grant-free massive access scheme to guarantee low-latency and high-efficiency massive IoT connectivity to the network. Furthermore, a case study is provided to demonstrate the effectiveness of the proposed solution. Finally, to shed light on the future research directions of HAP network-enabled edge computing paradigms, the key challenges and open issues are discussed.

Beamforming optimization for RIS-aided SWIPT in cell-free MIMO networks

Reconfigurable intelligent surface (RIS) is a promising technology that utilizes massive low-cost elements to reflect incident signals to desired direction. In this paper, we consider a RIS-aided cell free multiple-input multiple-output (MIMO) network that composed of multiple access points (APs), multiple RISs, multiple information receivers (IRs), and multiple energy receivers (ERs). Under the maximum transmit power constraints of the APs and the minimum total energy harvesting power constraints of the ERs, we aim to maximize the weighted sum-rate (WSR) by jointly optimizing the beamforming matrices and the passive RIS reflection matrices. However, the formulated problem is nonconvex and very challenging to solve. Therefore, we propose an alternating optimization algorithm to obtain a suboptimal solution. Explicitly, the proposed algorithm decomposes the original optimization problem into three subproblems that can be efficiently solved. To overcome the nonconvexity of the subproblems, a successive convex approximation (SCA) based algorithm is proposed to tackle the energy harvesting constraints. Moreover, an alternating direction method of multipliers (ADMM) based algorithm is proposed to optimize the constant modulus constrained RIS reflecting elements. Finally, simulations are conducted to demonstrate the performance advantages of the proposed algorithms.

A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network

In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network. The aim is to minimize the location estimation error of underlying targets under a power constraint. We formulate it as a minimum-variance estimation problem and show that the underlying variance reduction function is submodular. From the properties of submodularity, we present a balanced selection policy which minimizes the worst-case error value using a minimax strategy. A greedy algorithm with guaranteed performance with respect to optimal solutions is given to efficiently implement the scheduling policy. The effectiveness and the efficiency of the proposed algorithm are demonstrated in simulated examples.

Pilot Assignment for Cell Free Massive MIMO Systems Using a Weighted Graphic Framework

Pilot contamination resulting from pilot reuse seriously restricts the communication performance of the cell-free massive multiple-input multiple-output (MIMO) networks. To deal with this problem, an efficient pilot assignment scheme using a weighted graphic framework is proposed in this correspondence paper. Specifically, a novel metric is first introduced for capturing the severity of potential mutual pilot contamination in cell-free topology. On this basis, the weighted pilot contamination graph is constructed to depict the dynamic interference relationship corresponding to the network. Then, the pilot assignment optimization is mapped to the Max k-Cut problem, and a heuristic algorithm is exploited to realize pilot decontamination. Numerical simulation results reveal the superior performance of the proposed scheme, which is capable of achieving a significant improvement in throughput with low complexity.

Study of Handover Techniques for 4G Network MIMO Systems

For the upcoming 4G systems, network multiple-input multiple-output (MIMO) and inter-cell interference coordination (ICIC) are two of key techniques adopted in 4G systems to mitigate the serious inter-cell interference (ICI) and improve coverage and cell-edge throughput. Network MIMO is referred to as coordinated multi-point (CoMP) in LTE-A. In this paper, we propose a simulation platform to analyze the handover issue for downlink CoMP transmissions in LTE-A cellular systems. Among the variety of ICIC strategies, we apply the widely adopted soft frequency reuse (SFR) and the fractional frequency reuse (FFR) schemes. Both schemes are based on the idea of applying a frequency reuse factor of one in cell-center areas, and a higher reuse factor in cell-edge areas. Therefore, the ICI is reduced at the expense of the available frequency resources for each cell.