Cell-free Massive MIMO Networks Research Articles

$K$-Repetition for Grant-Free Random Access in Cell-Free Massive MIMO Networks

$K$-Repetition for Grant-Free Random Access in Cell-Free Massive MIMO 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.

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

Theoretical Analysis of Federated Learning Supported by Cell-Free Massive MIMO Networks With Enhanced Power Allocation

Theoretical Analysis of Federated Learning Supported by Cell-Free Massive MIMO Networks With Enhanced Power Allocation

Minimizing Energy Consumption in Cell-free Massive MIMO Networks

Minimizing Energy Consumption in Cell-free Massive MIMO Networks

A Matching-Based Pilot Assignment Algorithm for Cell-Free Massive MIMO Networks

A Matching-Based Pilot Assignment Algorithm for Cell-Free Massive MIMO Networks

Another Twist to the Scalability in Cell-Free Massive MIMO Networks

Another Twist to the Scalability in Cell-Free Massive MIMO Networks

Cooperative Beamforming for RIS-Aided Cell-Free Massive MIMO Networks

The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.

Deep Reinforcement Learning for Energy Efficiency Maximization in Cache-Enabled Cell-Free Massive MIMO Networks: Single- and Multi-Agent Approaches

Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging beyond fifth-generation (5 G) technology that improves energy efficiency (EE) and removes cell structure limitation by using multiple access points (APs). This study investigates the EE maximization problem. Forming proper cooperation clusters is crucial when optimizing EE, and it is often done by selecting AP–user pairs with good channel quality or aligning AP cache contents with user requests. However, the result can be suboptimal if we determine the clusters based solely on either aspect. This motivates our joint design of user association and content caching. Without knowing the user content preferences in advance, two deep reinforcement learning (DRL) approaches, i.e., single-agent reinforcement learning (SARL) and multi-agent reinforcement learning (MARL), are proposed for different scenarios. The SARL approach operates in a centralized manner which has lower computational requirements on edge devices. The MARL approach requires more computation resources at the edge devices but enables parallel computing to reduce the computation time and therefore scales better than the SARL approach. The numerical analysis shows that the proposed approaches outperformed benchmark algorithms in terms of network EE in a small network. In a large network, the MARL yielded the best EE performance and its computation time was reduced significantly by parallel computing.

Matched-Decision AP Selection for User-Centric Cell-Free Massive MIMO Networks

This work proposes a scalable access point (AP) selection framework based on a competitive mechanism that operates in two stages. Initially, the user equipment (UE) connects to an intermediate AP cluster, where a matched-decision among the APs and UEs establishes the best connection in terms of large-scale fading and prevents UEs from being dropped. Then, UEs can expand their AP clusters by connecting to more APs. We compare our method with baseline solutions considering different precoding techniques in centralized and distributed network implementations. We propose three strategies to fine-tune the AP clusters, reducing the number of UEs per AP without compromising the spectral efficiency (SE) or improving energy efficiency. The simulations comprise a range of UEs and APs, the number of antennas per AP varies, and each AP can serve a limited number of UEs. The results show that our solution improves up to 163% the SE of the 95% likely UEs compared with baseline solutions and that the number of UEs each AP serves is crucial for improving SE. Additional results indicate that our solution allows APs to save processing to enhance SE and evidence that the number of APs affects the SE differently in each network implementation.

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.

Power Control in Cell-Free Massive MIMO Networks for UAVs URLLC Under the Finite Blocklength Regime

In this paper, we employ a user-centric (UC) cell-free massive MIMO (CFmMIMO) network for providing ultra reliable low latency communication (URLLC) when traditional ground users (GUs) coexist with unmanned aerial vehicles (UAVs). We study power control in both the downlink and the uplink when partial zero-forcing (PZF) transmit/receive beamforming and maximum ratio transmission/combining are utilized. We consider optimization problems where the objective is to maximize either the users’ sum URLLC rate or the minimum user’s URLLC rate. The URLLC rate function is both complicated and nonconvex rendering the considered optimization problems nonconvex. Thus, we propose two approximations for the complicated URLLC rate function and employ successive convex optimization (SCO) to tackle the considered optimization problems. Specifically, we propose the SCO with iterative concave lower bound approximation (SCO-ICBA) and the SCO with iterative interference approximation (SCO-IIA). We provide extensive simulations to evaluate SCO-ICBA and SCO-IIA and compare UC CFmMIMO deployment with traditional colocated massive MIMO (COmMIMO) systems. The obtained results reveal that employing the SCO-IIA scheme to optimize the minimum user’s rate for CFmMIMO with MRT in the downlink, and PZF reception in the uplink can provide the best URLLC rate performances.

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.

Team MMSE Precoding With Applications to Cell-Free Massive MIMO

This article studies a novel distributed precoding design, coined <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">team minimum mean-square error</i> (TMMSE) precoding, which rigorously generalizes classical centralized MMSE precoding to distributed operations based on transmitter-specific channel state information (CSIT). Building on the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">theory of teams</i> , we derive a set of necessary and sufficient conditions for optimal TMMSE precoding, in the form of an infinite dimensional linear system of equations. These optimality conditions are further specialized to cell-free massive MIMO networks, and explicitly solved for two important examples, i.e., the classical case of local CSIT and the case of unidirectional CSIT sharing along a serial fronthaul. The latter case is relevant, e.g., for the recently proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">radio stripe</i> concept and the related advances on sequential processing exploiting serial connections. In both cases, our optimal design outperforms the heuristic methods that are known from the previous literature. Duality arguments and numerical simulations validate the effectiveness of the proposed team theoretical approach in terms of ergodic achievable rates under a sum-power constraint.

NOMA-inspired coexistence enabling method in cell-free massive MIMO using opportunistic interference alignment

A novel scheme referred to as non-orthogonal multiple access (NOMA)-inspired coexistence enabling (NICE) is introduced for the downlink of cell-free massive MIMO (CF-mMIMO) networks. Motivated by the cognitive concept in NOMA, the NICE strategy is designed to accommodate secondary users (SUs) within the CF-mMIMO. Despite of existing works in the CF-mMIMO, this paper considers the same transmitters for both primary and secondary users. Besides, the promising opportunistic interference alignment (OIA) strategy is exploited for the first time in CF-mMIMO networks. However, CF-mMIMO deals with a large number of users and access points (APs) which may enlarge the minimum number of transmit/receive antennas. The proposed NICE scheme resolves this problem such that a large outer bound on the degrees of freedom for SUs is achieved while a small number of transmit/receive antennas is required. The effectiveness of the proposed strategy is demonstrated through simulations for both perfect and imperfect channel models.

Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks With Massive Connectivity

Cell-free massive MIMO is one of the key technologies for future wireless communications, in which users are simultaneously and jointly served by all access points (APs). In this paper, we investigate the minimum mean square error (MMSE) estimation of effective channel coefficients in cell-free massive MIMO systems with massive connectivity. To facilitate the theoretical analysis, only single measurement vector (SMV) based MMSE estimation is considered in this paper, i.e., the MMSE estimation is performed based on the received pilot signals at each AP separately. Inspired by the decoupling principle of replica symmetric postulated MMSE estimation of sparse signal vectors with independent and identically distributed (i.i.d.) non-zero components, we develop the corresponding decoupling principle for the SMV based MMSE estimation of sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components, which plays a key role in the theoretical analysis of SMV based MMSE estimation of the effective channel coefficients in cell-free massive MIMO systems with massive connectivity. Subsequently, based on the obtained decoupling principle of MMSE estimation, likelihood ratio test and the optimal fusion rule, we perform user activity detection based on the received pilot signals at only one AP, or cooperation among the entire set of APs for centralized or distributed detection. Via theoretical analysis, we show that the error probabilities of both centralized and distributed detection tend to zero when the number of APs tends to infinity while the asymptotic ratio between the number of users and pilots is kept constant. We also investigate the asymptotic behavior of oracle estimation in cell-free massive MIMO systems with massive connectivity via random matrix theory. Moreover, in order to demonstrate the potential performance loss of SMV based MMSE estimation, which does not employ the correlation between the received pilot signals at different APs, the multiple measurement vector (MMV) based MMSE estimation, i.e., joint MMSE estimation with pilot signals from all APs, is analyzed via numerical results. Numerical analysis shows that the theoretical analyze with our decoupling principle for the SMV based MMSE estimation of sparse signal vectors with i.n.i.d. non-zero components matches well with the numerical results.

Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks

Massive Multiple-Input Multiple-Output (MaMIMO) communication networks are recently being investigated for hltheir high potential for localisation services. This is enabled by the high-dimensional channel state information (CSI) captured by the many antennas in the system. Previously, it has been shown that these systems can achieve a very high localisation accuracy. However, many challenges still remain, we identified two of them. First, the recent trend towards cell-free MaMIMO with many highly distributed Access Points (AP), leads to the question of how this impacts the localisation methods. Current localisation methods process the signals in a central processing unit (CPU), resulting in a high fronthaul requirement when deploying these algorithms in a distributed network, limiting the deployment and scalability. Second, there exists a trade-off between using model-driven and data-driven localisation methods. In this work, we propose two new localisation methods which employ a distributed processing strategy and compare them against two centralised localisation methods. In addition, the four analysed methods explore the trade-off between being model- and data-driven. Moreover, the proposed ML-MUSIC method blurs the lines between the two by combining Machine Learning and traditional signal processing. Next to comparing the localisation accuracy, we evaluate the performance in a dynamic setting, the scalability and fronthaul requirement of the methods. The proposed Machine Learning-enhanced Multiple Signals Classification method, ML-MUSIC, reaches a median error of 34.2 mm on the test set while only using 500 training samples. Due to ML-MUSICs distributed design, the fronthaul throughput requirement is reduced 1200-fold in comparison to the centralised methods. Furthermore, ML-MUSIC has the lowest computational complexity of all analysed methods, making it an ideal method to localise users in upcoming distributed cell-free MaMIMO networks.

Scalable Cell-Free Massive MIMO Networks With LEO Satellite Support

This paper presents an integrated network architecture combining a cell-free massive multiple-input multiple-output (CF-M-MIMO) terrestrial layout with a low Earth orbit satellite segment where the scalability of the terrestrial segment is taken into account. The main purpose of such an integrated scheme is to transfer to the satellite segment those users that somehow limit the performance of the terrestrial network. Towards this end, a correspondingly scalable technique is proposed to govern the ground-to-satellite user diversion that can be tuned to different performance metrics. In particular, in this work the proposed technique is configured to result in an heuristic that improves the minimum per-user rate and the sum-rate of the overall network. Simulation results serve to identify under which conditions the satellite segment can become an attractive solution to enhance users’ performance. Generally speaking, although the availability of the satellite segment always leads to an improvement of users’ rates, it is in those cases where the terrestrial CF-M-MIMO network exhibits low densification traits that the satellite backup becomes crucial.

Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO

Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro-diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large.

Spectral efficiency analysis and optimal power allocation for uplink multi-user cell-free massive MIMO networks employing STBCs

Massive MIMO is a key technique in the fifth generation (5G) of wireless networks. This technique, by increasing base station (BS) antennas and exploiting spatial diversity, improves system performance in terms of common criteria such as spectral efficiency. Cell free massive MIMO is considered for 6G mobile communications to overcome the shortcomings associated with the cellular design. In this system, BS antennas are distributed across the system environment and provide uniform service to all users. Also, space time block codes (STBCs) can be used to improve the diversity gain and reliability of the system. In this paper, a cell free massive MIMO system is investigated in the uplink considering effects of channel estimation errors. Users are equipped with two antennas and employ STBC. Lower bound of spectral efficiency is derived for the zero forcing (ZF) and maximal ratio combining (MRC) decoders. Furthermore, spectral efficiency is compared for dual and single antenna users. Moreover, two power control problems are defined based on minimizing the total transmit power and maximizing the minimum rate of the users. Also, an algorithm is proposed for removing the inefficient BSs in the network. The proposed theoretical ideas are evaluated via numerical simulations.