Multiple-input Multiple-output Networks Research Articles

Dynamic Scheduling and Power Allocation with Random Arrival Rates in Dense User-Centric Scalable Cell-Free MIMO Networks

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency.

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.

Equalization system of low differential mode delay few-mode fibers based on the neural network MIMO algorithm.

In recent years, with the development of information networks, higher requirements for transmission capacity have been recommended. Yet, at the same time, the capacity of single-mode fiber is rapidly approaching the theoretical limit. The multidimensional multiplexing technique is an effective way to solve this problem. Since the high differential mode delay (DMD) of transmission fiber increases the complexity of demultiplexing in equalization algorithms, we use an intelligent design method to optimize the trench-assisted gradient refractive index structure in this paper. The maximum DMD of the optimized optical fiber structure is 19.6 ps/km. A least mean squares-feedforward neural network constant modulus algorithm (LMS-FNNCMA) is also designed by using the theory of the least mean squares (LMS), constant modulus algorithm (CMA), and the multiple input multiple output (MIMO) neural networks. In order to verify the accuracy of the algorithm, a polarization division multiplexing-wavelength division multiplexing-mode division multiplexing (PDM-WDM-MDM) optical transmission system is constructed through simulation. The algorithm successfully realizes the de-crosstalk over a transmission distance of 1200 km at a rate of 1.2 Tbps under simulation conditions.

TDoA positioning with data-driven LoS inference in mmWave MIMO communications

Location awareness is an essential feature to support various mobile services, and cooperative positioning with channel state information (CSI) in millimeter wave multiple-input multiple-output (MIMO) networks is promising. Meanwhile, strong non-line of sight (NLoS) effects in outdoor scenarios severely reduce the model-based localization accuracy, and existing fingerprint-based methods have a critical requirement for labeled data and lack scalability. To overcome these shortcomings, we propose a hybrid data-and-model driven cooperative localization scheme using uplink wideband MIMO CSI at multiple base stations (BS) as measurements. First, we design a data-driven line of sight (LoS) inference module, realized by a transformer with an attention mechanism, to estimate the statistics of LoS arrival times. Each BS is equipped with a module, and the counterpart LoS inferences are simultaneously and distributedly performed. Second, we locate the mobile user by a model-driven approximate maximum likelihood time difference of arrival algorithm with the estimated statistics. Experiment results in urban scenarios show that the proposed localization scheme is extensible and also robust to varying number of channel paths and combination of BS measurements. Considering a small training dataset, the proposed scheme significantly outperforms the data- and model-driven baselines in NLoS scenarios, in terms of positioning accuracy.

Evaluation of User-Centric Cell-Free Massive Multiple-Input Multiple-Output Networks Considering Realistic Channels and Frontend Nonlinear Distortion

Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network’s performance when operating close to its saturation power.

Intelligent Reflecting Surface Backscatter Enabled Downlink Multi-Cell MIMO Networks

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to implement downlink communications for a multi-cell multiple-input multiple-output (MIMO) network, which represents a brand-new communication framework. In such a network, one active macro cell base station (MBS) is deployed for radiating energy signal, while each IRS acts as a small cell base station to realize its own information transmission by modulating and reflecting the signal from the MBS. Under this paradigm, we investigate two optimization problems, namely weighted sum rate maximization problem and max-min fairness problem, aiming at satisfying different communication requirements. To seek their optimal solutions, Lagrangian dual transform and alternate methods are employed to optimize the active beamforming vector at the MBS and the passive beamforming vectors at all the IRSs. Additionally, an element clustering scheme is developed to reduce computation and control complexity, in which each element cluster works like a unit and all clusters can collaborate to communicate with users. Extensive simulations are conducted to evaluate the achievable communication performance and to verify the feasibility of the proposed IRS backscatter enabled downlink multi-cell MIMO network.

Cluster Content Caching: A Deep Reinforcement Learning Approach to Improve Energy Efficiency in Cell-Free Massive Multiple-Input Multiple-Output Networks.

With the explosive growth of micro-video applications, the transmission burden of fronthaul and backhaul links is increasing, and meanwhile, a lot of energy consumption is also generated. For reducing energy consumption and transmission delay burden, we propose a cell-free massive multiple-input multiple-output (CF-mMIMO) system in which the cache on the access point (AP) is used to reduce the load on the link. In this paper, a total energy efficiency (EE) model of a cache-assisted CF-mMIMO system is established. When optimizing EE, forming the co-operation cluster is critical. Therefore, we propose an energy-efficient joint design of content caching, AP clustering, and low-resolution digital-to-analog converter (DAC) in a cache-assisted CF-mMIMO network based on deep reinforcement learning. This scheme can effectively cache content in APs and select the appropriate DAC resolution. Then, taking into account the channel state information and user equipment (UE)'s content request preference, a deep deterministic policy gradient algorithm is used to jointly optimize the cache strategy, AP clustering, and DAC resolution decisions. Simulation results show that the energy efficiency of the proposed scheme is 4% higher than that of other schemes without the resolution optimization and is much higher than that of the only AP clustering without the joint design of content caching and channel quality.

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.

NOMA-Based Coexistence of Near-Field and Far-Field Massive MIMO Communications

This letter considers a legacy massive multiple-input multiple-output (MIMO) network, in which spatial beams have been preconfigured for near-field users, and proposes to use the non-orthogonal multiple access (NOMA) principle to serve additional far-field users by exploiting the spatial beams preconfigured for the legacy near-field users. Our results reveal that the coexistence between near-field and far-field communications can be effectively supported via NOMA, and that the performance of NOMA-assisted massive MIMO can be efficiently improved by increasing the number of antennas at the base station.

A Bayesian Approach to Characterize Unknown Interference Power in Wireless Networks

The existence of unknown interference is a prevalent problem in wireless communication networks. Especially in multi-user multiple-input multiple-output (MIMO) networks, where a large number of user equipments are served on the same time-frequency resources, the outage performance may be dominated by the unknown interference arising from scheduling variations in neighboring cells. In this letter, we propose a Bayesian method for modeling the unknown interference power in the uplink of a cellular network. Numerical results show that our method accurately models the distribution of the unknown interference power and can be effectively used for rate adaptation with guaranteed target outage performance.

Efficient Evaluation of the Error Probability for Pilot-Assisted URLLC With Massive MIMO

We propose a numerically efficient method for evaluating the random-coding union bound with parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> on the error probability achievable in the finite-blocklength regime by a pilot-assisted transmission scheme employing Gaussian code-books and operating over a memoryless block-fading channel. Our method relies on the saddlepoint approximation, which, differently from previous results reported for similar scenarios, is performed with respect to the number of fading blocks (a.k.a. diversity branches) spanned by each codeword, instead of the number of channel uses per block. This different approach avoids a costly numerical averaging of the error probability over the realizations of the fading process and of its pilot-based estimate at the receiver and results in a significant reduction of the number of channel realizations required to estimate the error probability accurately. Our numerical experiments for both single-antenna communication links and massive multiple-input multiple-output (MIMO) networks show that, when two or more diversity branches are available, the error probability can be estimated accurately with the saddlepoint approximation with respect to the number of fading blocks using a numerical method that requires about two orders of magnitude fewer Monte-Carlo samples than with the saddlepoint approximation with respect to the number of channel uses per block.

Reducing Inter-CPU Coordination in User-Centric Distributed Massive MIMO Networks

This paper proposes a method for reducing inter-central processing unit (CPU) coordination in user-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO) networks, also called cell-free networks. A two-stage heuristic approach is proposed to reduce the number of inter-coordinated user equipment (UE) (UEs connected to multiple CPUs) in each CPU. Firstly, each UE connects to a master access point (AP) and a primary CPU; then, AP selection is executed. Secondly, each CPU performs a fine-tuning algorithm to drop the worst inter-coordinated UEs. Simulation results reveal that one can reduce inter-CPU coordination while yielding slight degradation in spectral efficiency and improving energy efficiency.

Asynchronous Cell-Free Massive MIMO With Rate-Splitting

In practical cell-free (CF) massive multiple-input multiple-output (MIMO) networks with distributed and low-cost access points, the asynchronous arrival of signals at the user equipments increases multi-user interference that degrades the system performance. Meanwhile, rate-splitting (RS), exploiting the transmission of both common and private messages, has demonstrated to offer considerable spectral efficiency (SE) improvements and its robustness against channel state information (CSI) imperfection. The signal performance of a CF massive MIMO system is first analyzed for asynchronous reception capturing the joint effects of propagation delays and oscillator phases of transceivers. Taking into account the imperfect CSI caused by asynchronous phases and pilot contamination, we derive novel and closed-form downlink SE expressions for characterizing the performance of both the RS-assisted and conventional non-RS-based systems adopting coherent and non-coherent data transmission schemes, respectively. Moreover, we formulate the design of robust precoding for the common messages as an optimization problem that maximizes the minimum individual SE of the common message. To address the non-convexity of the design problem, a bisection method is proposed to solve the problem optimally. Simulation results show that asynchronous reception indeed destroys both the orthogonality of the pilots and the coherent data transmission resulting in poor system performance. Besides, thanks to the uniform coverage properties of CF massive MIMO systems, RS with a simple low-complexity precoding for the common message obtained by the equal ratio sum of the private precoding is able to achieve substantial downlink sum SE gains, while the application of robust precoding to the common message is shown to be useful in some extreme cases, e.g., serious oscillator mismatch and unknown delay phase.

Secrecy Energy Efficiency Maximization for Multi-User Multi-Eavesdropper Cell-Free Massive MIMO Networks

In this paper, the secrecy energy efficiency (SEE) optimization problem is studied in multi-user multi-eavesdropper cell-free massive multiple-input multiple-output (CF-mMIMO) networks, where a large number of distributed access points (APs) equipped with multiple antennas connect to a central processing unit via backhaul links to communicate with multiple users in the presence of multiple active non-colluding eavesdroppers. In order to theoretically analyze the SEE performance of CF-mMIMO systems, we first provide a lower bound on the ergodic secrecy rate. Meanwhile, a realistic model is used to measure the power consumption of CF-mMIMO networks, which takes into account the radio transmit power of all APs, circuit power consumption, and the power consumed by the backhaul link transmissions. Then, we develop a confidential and energy-efficient design for transmit power allocation subject to individual quality-of-service requirements for the users and total transmit power constraint of all APs. For the reason that the proposed SEE maximization (SEEM) problem is non-convex, we give a two-tier iterative power allocation algorithm, capitalizing on Dinkelbach's method and successive convex approximation approach. Simulation results show that the proposed SEEM algorithm converges fast and outperforms the conventional secrecy rate maximization and energy efficiency maximization strategies in terms of SEE performance.

Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO

To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division duplex (FDD) systems, the CSI is acquired through a feedback signal from the user equipment (UE). However, this may lead to an important overhead in FDD massive MIMO systems. Focusing on these systems, in this study, we propose a novel strategy to design the CSI feedback. Our strategy allows to optimally design variable length feedback, that is promising compared to fixed feedback since users experience channel matrices differently sparse. Specifically, principal component analysis (PCA) is used to compress the channel into a latent space with adaptive dimensionality. To quantize this compressed channel, the feedback bits are smartly allocated to the latent space dimensions by minimizing the normalized mean squared error (NMSE) distortion. Finally, the quantization codebook is determined with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering. Numerical simulations show that our strategy improves the zero-forcing beamforming sum rate by 17%, compared to CsiNetPro. The number of model parameters is reduced by 23.4 times, thus causing a significantly smaller offloading overhead. At the same time, PCA is characterized by a lightweight unsupervised training, requiring eight times fewer training samples than CsiNetPro.

Hybrid Optimization Assisted Green Power Allocation Model for QoS-Driven Energy-Efficiency in 5G Networks

5G networks are progressing to offer services with high Quality of Service (QoS) features like high user mobility skills, higher data rates, high reliable communiqué, and lower latency. For this, numerous novel settings like integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) should be implemented in the network system. Here, this paper plans to design an optimization-based power allocation model to increase the Effective Power Efficiency (EPE) that offers guaranteed QoS over “Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO)-channel oriented 5 G networks”. Moreover, the statistical QoS-based green power allotment system was also analyzed to increase the EPE over other MIMO networks. Particularly, this paper makes an attempt to make the power allocation optimal, which directly ensures the 5 G network energy efficiency when guaranteeing QoS. This is considered to be the optimization issue, which is solved by MS-DA. Our suggested approach can be applied to hybrid energy supply networks with various network topologies, such as 5 G networks, which are now the most extensively used, and is capable of achieving a good resolution through iterative convergence.

Resource Allocation in Cell-Free Massive MIMO Networks With Hybrid Energy Supplies

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks were proposed as a novel architecture for radio access networks, which consists of a large number of access points (APs) over a vast area that simultaneously offer services to user equipment (UEs) in a single frequency band. However, powering a large number of APs along with impossibility or high cost of accessing the electrical grid in some scenarios remains a key challenge in unleashing the full potential of CF-mMIMO networks. One remedy is to resort to various forms of renewable energy sources and come up with AP designs equipped with energy harvesting capabilities. Regarding the unpredictable nature of renewable energy sources, energy-efficient resource allocation techniques need to be developed so that APs remain operational at all times. In this paper, we envision a CF-mMIMO system in which different APs get their power from an electrical grid, a solar energy source, or a hybrid design. We aim at designing an AP-to-UE power allocation scheme with the objective of minimizing overall energy consumption from the electrical grid while satisfying the spectral efficiency (SE) constraints. Moreover, we come up with a green energy budgeting mechanism to prevent energy deficiency when solar energy is scarce. We first propose an idealistic offline formulation of the problem, and transform it into a second-order cone programming (SOCP) problem. Then, we come up with a suite of lightweight model-free myopic heuristic algorithms, and compare their performance against offline benchmark.

Channel Estimation in RIS-Assisted MIMO Systems Operating Under Imperfections

The promising gains of reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems, in terms of extended coverage and enhanced capacity, are critically dependent on the accuracy of the channel state information. However, traditional channel estimation (CE) schemes are not applicable in RIS-assisted MIMO networks, since passive RISs typically lack the signal processing capabilities that are assumed by CE algorithms. This becomes problematic when physical imperfections or electronic impairments affect the RIS due to its exposition to different environmental effects or caused by hardware limitations from the circuitry. While these real-world effects are typically ignored in the literature, in this paper we propose efficient CE schemes for RIS-assisted MIMO systems taking different imperfections into account. Specifically, we propose two sets of tensor-based algorithms, based on the parallel factor analysis decomposition schemes. First, assuming a long-term model-where the RIS imperfections, modeled as unknown phase shifts, are static within the channel coherence time-we formulate an iterative alternating least squares (ALS)- based algorithm for the joint estimation of the unknown phase deviations and the communication channels. Then, we develop the short-term imperfection model, which allows both amplitude and phase RIS imperfections to be non-static with respect to the channel coherence time. We propose two iterative ALS-based and closed-form higher-order singular value decomposition-based algorithms for jointly estimating the channels and the unknown impairments. We also investigate the computational complexity and the identifiability of the proposed algorithms and study the effect of various imperfections on the CE quality. Simulation results show the effectiveness of our proposed tensor-based algorithms in terms of estimation accuracy and computational complexity.

A QoS-Adaptive Interference Alignment Technique for In-Band Full-Duplex Multi-Antenna Cellular Networks

In this paper, we propose a novel interference alignment (IA) technique for an in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) cellular network where a base station (BS) and user equipment (UE) are equipped with multiple antennas, and the local channel state information (CSI) is available at all nodes. Considering a practical IBFD MIMO cellular network, it is assumed that only the BS operates with full-duplex (FD) communication while UE operate in half-duplex (HD) mode. These IBFD networks introduce a new type of interference called cross-link interference (CLI), in which uplink UE affects downlink UE. The proposed IA technique consists of two symmetric IA schemes according to the number of antennas in the uplink and downlink UE, and both schemes effectively mitigate CLI in the IBFD MIMO network. It is worth noting that both IA schemes are adaptively applicable according to the network's quality-of-service (QoS) requirements, such as uplink and downlink traffic demands. Furthermore, we theoretically characterize and prove the achievable sum-degrees-of-freedom (DoF) of the proposed IA technique. Simulation results show that the proposed IA technique significantly improves the sum rate performance compared to conventional HD communications (multi-user MIMO) while achieving the same achievable DoF as the interference-free IBFD MIMO network.

Online Multicell Coordinated MIMO Wireless Network Virtualization With Imperfect CSI

We consider online coordinated precoding design for downlink wireless network virtualization (WNV) in a multi-cell multiple-input multiple-output (MIMO) network with imperfect channel state information (CSI). In our WNV framework, an infrastructure provider (InP) owns each base station that is shared by several service providers (SPs) oblivious of each other. The SPs design their precoders as virtualization demands for user services, while the InP designs the actual precoding solution to meet the service demands from the SPs. Our aim is to minimize the long-term time-averaged expected precoding deviation over MIMO fading channels, subject to both per-cell long-term and short-term transmit power limits. We propose an online coordinated precoding algorithm for virtualization, which provides a fully distributed semi-closed-form precoding solution at each cell, based only on the current imperfect CSI without any CSI exchange across cells. Taking into account the two-fold impact of imperfect CSI on both the InP and the SPs, we show that our proposed algorithm is within an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\delta)$ </tex-math></inline-formula> gap from the optimum over any time horizon, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> is a CSI inaccuracy indicator. Simulation results validate the performance of our proposed algorithm under two commonly used precoding techniques in a typical urban micro-cell network environment.