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Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments. [ABSTRACT FROM AUTHOR]

Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments.

This paper proposes a heuristic association algorithm between access points (APs) and user equipment (UE) in user-centric cell-free massive multiple-input-multiple-output (MIMO) systems, specifically targeting scenarios where UEs share the same frequency and time resources. The proposed algorithm prevents overserving APs and ensures the connectivity of all UEs, even when the number of UEs is significantly greater than the number of APs. Additionally, we assume the use of low-resolution analog-to-digital converters (ADCs) to reduce fronthaul capacity. While realistic massive access scenarios, such as those in Internet-of-Things (IoT) environments, often involve hundreds or thousands of UEs per AP using multiple access techniques to allocate different frequency and time resources, our study focuses on scenarios where UEs within each AP cluster share the same frequency and time resources to highlight the impact of pilot contamination in dense network environments. The proposed algorithm is validated through simulations, confirming that it guarantees the connection of all UEs and prevents overserving APs. Furthermore, we analyze the required fronthaul capacity based on quantization bits and confirm that the proposed algorithm outperforms existing algorithms in terms of SE and average SE performance for UEs. [ABSTRACT FROM AUTHOR]

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Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For future wireless communications, Cell-free Massive Multiple-Input Multiple-Output (CF-mMIMO) systems and Non-orthogonal Multiple Access (NOMA) schemes are considered potential candidates to meet the greater coverage and capacity demands. Nevertheless, a traditional CF-mMIMO system faces scalability issues and poses numerous challenges in handling the expanding number of user equipment and ensuring their dependable connectivity, particularly in larger geographical areas. To address this challenge, a user-centric (UC) approach is implemented in a CF-mMIMO system, wherein a designated subset of access points (APs) serves a specific number of users from the entire pool of available APs. To implement a NOMA aided CF-mMIMO system, users must be grouped using a suitable clustering scheme to achieve greater spectral efficiency (SE), sum-rate, and reduced bit error rate (BER). For efficient user clustering, unsupervised machine learning (ML) algorithms, such as k-means, k-means++, and improved k-means++ are employed. In this paper, a multiuser NOMA aided CF-mMIMO system with a UC approach is investigated and closed-form expressions for intra-cluster interference and SINR are derived and the performance of the proposed system is analyzed in terms of achievable sum-rate and BER. The proposed system with the UC approach and three ML algorithms namely k-means, k-means++, and improved k-means++ demonstrate 12%, 10%, and 17% higher achievable sum-rate as compared to the NUC approach with same ML algorithms respectively. Similarly, the proposed system with UC and ML approaches exhibits 52%, 55% and 61% improved achievable sum-rate respectively, as compared to far pairing, random pairing, and close pairing schemes. Moreover, the system model is validated through the conformity of the theoretically derived bit error rate with the simulation results for a three-user scenario.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Emerging network architectures and technical service requirements for B5G and 6G will enable MEC with CF-mMIMO, helping to handle compute-intensive and latency-sensitive tasks in distributed IoT. For CF-mMIMO-assisted MEC systems, this paper aimed to minimize the delay in completing computational tasks of different task types under energy constraint. In order to solve the above goals, this paper designed a task offloading strategy based on UEs, multiple APs and CPU (central processing unit) for cloud-edge-end collaboration. Specifically, according to the different data types of each UE and AP service, this paper firstly used the convex optimization and graph matching methods to alternately iterate to optimize the offload association and task ratio. Then, under the limitation of the backhaul link, this paper used an improved binary whale optimization algorithm to further offload the tasks of unallocated terminals and associated access points to the cloud with efficient processing. Compared with other meta-heuristics such as ant colony optimization algorithm and hybrid gray wolf optimization algorithm, the proposed algorithm has better performance on discrete offload optimization problems, which can provide a good offload optimization strategy for distributed systems and greatly reduce the average delay of the whole network. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of non-ideal user equipment (UE) hardware on a cell-free (CF) massive MIMO (mMIMO) network with centralized operation under spatially correlated channels. The minimum mean-squared error (MMSE) estimator can be derived with the help of the generic non-ideal UE hardware model. It is demonstrated that even if the effective signal-to-noise ratio approaches infinity, pilot contamination and imperfect hardware can cause a non-zero estimation error floor. After that, a lower bound is determined for the ergodic uplink capacity of the centralized CF mMIMO network under non-ideal UE hardware. Moreover, the optimal receive combining vector is obtained to maximize the uplink spectral efficiency (SE). The maximum ratio (MR) and regularized zero-forcing (RZF) combining schemes are offered as alternatives in light of the computational complexity of the MMSE receiver. Comparing the RZF to the MMSE scheme under different levels of hardware impairments, our findings indicate that the RZF receiver suffers a negligible loss in total SE. For MR combining, a novel closed-form uplink achievable SE expression is obtained based on the MMSE estimator and the use-and-then-forget bounding technique. This expression gives vital insights into the achievable uplink performance with UE hardware impairments. Besides, for various hardware impairment factors, the impact of pilot sequence length on average sum SE is disclosed for different receive combining schemes. To increase the overall SE of the max-min fairness scheme, a heuristic fractional power control scheme with UE hardware impairments is developed, which can essentially avoid sacrificing the SE of other UEs while maximizing the SE of the unluckiest UE in the whole network. Finally, our theoretical performance analysis and power control algorithm are validated by simulation results, and fundamental design guidelines are provided for selecting hardware satisfying the practical UE requirements. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For future wireless communications, Cell-free Massive Multiple-Input Multiple-Output (CF-mMIMO) systems and Non-orthogonal Multiple Access (NOMA) schemes are considered potential candidates to meet the greater coverage and capacity demands. Nevertheless, a traditional CF-mMIMO system faces scalability issues and poses numerous challenges in handling the expanding number of user equipment and ensuring their dependable connectivity, particularly in larger geographical areas. To address this challenge, a user-centric (UC) approach is implemented in a CF-mMIMO system, wherein a designated subset of access points (APs) serves a specific number of users from the entire pool of available APs. To implement a NOMA aided CF-mMIMO system, users must be grouped using a suitable clustering scheme to achieve greater spectral efficiency (SE), sum-rate, and reduced bit error rate (BER). For efficient user clustering, unsupervised machine learning (ML) algorithms, such as k-means, k-means++, and improved k-means++ are employed. In this paper, a multiuser NOMA aided CF-mMIMO system with a UC approach is investigated and closed-form expressions for intra-cluster interference and SINR are derived and the performance of the proposed system is analyzed in terms of achievable sum-rate and BER. The proposed system with the UC approach and three ML algorithms namely k-means, k-means++, and improved k-means++ demonstrate 12%, 10%, and 17% higher achievable sum-rate as compared to the NUC approach with same ML algorithms respectively. Similarly, the proposed system with UC and ML approaches exhibits 52%, 55% and 61% improved achievable sum-rate respectively, as compared to far pairing, random pairing, and close pairing schemes. Moreover, the system model is validated through the conformity of the theoretically derived bit error rate with the simulation results for a three-user scenario. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cell-free massive multiple-input multiple-output (MIMO) systems deploy a large number of access point (AP) across the coverage area which can provide uniform high-rate services to users.However, the quality of coverage would be affected by path loss, shadow fading scatters, and environmental occlusions around the randomly placed AP in conventional cell-free massive MIMO systems that do not consider their impact.Considering the impact of actual wireless propagation environments, an AP deployment scheme was proposed to acquire uniform and consistent coverage.Firstly, a hybrid probabilistic path loss model was utilized to characterize various wireless propagation environments.Then, the AP deployment optimization problem was solved with the objective of maximizing the average throughput.Finally, the problem was transformed into a Markov game process and solved by the multi-agent deep deterministic policy gradient (MADDPG) algorithm.The simulation results demonstrate that the proposed scheme can provide more uniform coverage in complex environments and serve users with reliable and consistent service compared to random AP deployment and existing AP deployment methods.

This paper considers wireless power transfer (WPT) for powering low-power devices in massive Machine Type Communication (mMTC) using a distributed massive multiple-input multiple-output (MIMO) system. Each Internet of Things (IoT) device can be served by one or more access points (APs) which is equipped with a massive antenna array. During each time slot, each IoT device transmits pilot sequences to enable APs to perform channel estimation. This process is followed by the WPT using conjugate beamforming. The approach to transmission power control is formulated as a non-convex optimization problem aiming to maximize the total accumulated power achieved by all IoT devices while taking into account the power weights at the APs, pilot power control at the IoT devices, and the non-linearity of practical energy harvesting circuits. An alternating optimization approach is adopted to solve it iteratively, achieving convergence within just a few iterations. Furthermore, since the number of IoT devices might be enormous in mMTC networks, we propose a pilot sharing algorithm allowing IoT devices to reuse pilot sequences effectively. Numerical results are provided to validate the effectiveness of the proposed power control algorithms and the pilot sharing scheme. It is shown that by allowing IoT devices to share the pilot sequences instead of employing the orthogonal pilots, the per-user accumulated performance is enhanced considerably, especially when the number of IoT devices is large relative to the coherence interval. The advantage of using distributed massive MIMO compared to its collocated counterpart is demonstrated in terms of the per-user accumulated power.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging technology for beyond fifth-generation (5G) systems aimed at enhancing the energy and spectral efficiencies of future mobile networks while providing nearly uniform quality of service to all users. Moreover, multicasting has garnered increasing attention in recent years, as physical-layer multicasting proves to be an efficient approach for serving multiple users simultaneously, all with identical service demands while sharing radio resources. A multicast service is typically delivered using either unicast or a single multicast transmission. In contrast, this work introduces a subgroup-centric multicast CF-mMIMO framework that splits the users into several multicast subgroups. The subgroup creation is based on the similarities in the spatial channel characteristics of the multicast users. This framework benefits from efficiently sharing the pilot sequence used for channel estimation and the precoding filters used for data transmission. The proposed framework relies on two scalable precoding strategies, namely, the centralized improved partial MMSE (IP-MMSE) and the distributed conjugate beamforming (CB). Numerical results demonstrate that the centralized IP-MMSE precoding strategy outperforms the CB precoding scheme in terms of sum SE when multicast users are uniformly distributed across the service area. In contrast, in cases where users are spatially clustered, multicast subgrouping significantly enhances the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Interestingly, in the latter scenario, distributed CB precoding outperforms IP-MMSE, particularly in terms of per-user SE, making it the best solution for delivering multicast content. Heterogeneous scenarios that combine uniform and clustered distributions of users validate multicast subgrouping as the most effective solution for improving both the sum and per-user SE of a multicast CF-mMIMO service.

This paper investigates the application of Cell-Free massive MIMO (CF-mMIMO) in indoor industrial environments, a key technology for ensuring reliability and spectral efficiency (SE) in 5G and beyond. We implement two schemes, centralized and distributed, to accommodate different levels of cooperation between access points (APs) and central processing units. We introduce an effective AP selection method and a pilot assignment scheme to mitigate pilot contamination (PC). We examine the factors influencing uplink SE, such as vertical distance and PC. Moreover, we employ a scalable CF-mMIMO model to reduce the complexity without compromising SE. Numerical results demonstrate the superior performance of the centralized CF-mMIMO, showcasing improved SE in uplink and downlink. The analysis shows that smaller vertical distances enhance SE, even in the presence of PC. Furthermore, the results demonstrate the effectiveness of the scalable model, revealing that connecting the user to fewer APs does not significantly degrade performance while reducing complexity based on a chosen threshold. The threshold value is the maximum allowable difference between the large-scale fading coefficients of a candidate AP and the main AP for each user to serve as an AP. Selecting a threshold value of $\Psi = -40$ dB reduces the maximum number of serving APs to 30% of the total, significantly lowering complexity while decreasing the SE by only 1 bits/s/Hz at most. The results reveal that serving UE with only one AP does not significantly compromise performance for a small number of UEs. However, it affects performance when the number of UEs is large.

Cell-free massive MIMO (CF-mMIMO) systems represent a deeply investigated evolution from the conventional multicell co-located massive MIMO (MC-mMIMO) network deployments. Anticipating a gradual integration of CF-mMIMO systems alongside pre-existing MC-mMIMO network elements, this paper considers a scenario where both deployments coexist, in order to serve a large number of users using a shared set of frequencies. The investigation explores the impact of this co-existence on the network’s downlink performance, considering various degrees of mutual cooperation, precoder selection, and power control strategies. Moreover, to take into account the effect of the proposed cooperation scenarios on the fronthaul links, this paper also provides a fronthaul-aware heuristic association algorithm between users and network elements, which allows the fulfillment of the front-haul requirement on each link. The research is finally completed by extensive simulations, shedding light on the performance outcomes associated with the various levels of cooperation and several solutions delineated in the paper.

This study investigates power control algorithms for cell-free massive MIMO (CF-M-MIMO) systems, with the aim of enhancing spectral efficiency (SE) and overall system capacity. A novel Heuristic Iterative Power Control (HIPC) algorithm is proposed and compared against the Equal Power Control (EPC) and Max-Min Fairness Power Control (MMFPC) algorithms within both small-cell and CF-M-MIMO systems under Minimum Mean Squared Error (MMSE) and Maximum Ratio (MR) conditions. The simulation results demonstrate that the HIPC algorithm significantly outperforms both EPC and MMFPC by achieving higher SE and improving user fairness, particularly in CF-M-MIMO systems. Moreover, the robustness and effectiveness of the HIPC algorithm under MR conditions underscore its practical utility. These contributions provide important guidance on the design and implementation of CF-M-MIMO systems, establishing the HIPC algorithm as a valuable tool for optimizing power control and enhancing system performance.

With the advent of the fifth-generation (5G) and beyond mobile communications, a plethora of Internet-of-Things (IoTs) applications, such as intelligent factories, smart transportation, and others are rapidly evolving. 5G and beyond networks support three typical application scenarios, i.e., ultra-reliable and low-latency communications (URLLC), enhanced mobile broadband (eMBB) and massive machine type communication (mMTC), each of which renders a distinct set of quality of service in terms of reliability, latency, transmission rate and connectivity. URLLC is seen as a crucial technology for supporting critical applications because of its emphasis on rare and extreme events, as well as its strict demands for low latency and high reliability [1]. For example, in order to effectively support applications like robot control, autonomous vehicles, and virtual reality, it is necessary to have an end-to-end delay threshold of 1 to 10 milliseconds and a block error rate (BLER) between 10−5 and 10−7 [2]. Due to the unique limitations of increased reliability and reduced latency, URLLC traffic often involves very brief transmission blocklengths, making Shannon’s capacity theorem irrelevant [3], [4]. On the other hand, existing cellular systems face difficulties in meeting the stringent quality of service (QoS) criteria needed for URLLC due to structural constraints. Therefore, it is essential to have advanced network architectures and various access technologies in order to achieve URLLC.

Cell-free massive MIMO (CF-mMIMO) networks leverage seamless cooperation among numerous access points to serve a large number of users over the same time/frequency resources. This paper addresses the challenges of pilot and data power control, as well as pilot assignment, in the uplink of a cell-free massive MIMO (CF-mMIMO) network, where the number of users significantly exceeds that of the available orthogonal pilots. We first derive the closed-form expression of the achievable uplink rate of a user. Subsequently, harnessing the universal function approximation capability of artificial neural networks, we introduce a novel multi-task deep learning-based approach for joint power control and pilot assignment, aiming to maximize the minimum user rate. Our proposed method entails the design and unsupervised training of a deep neural network (DNN), employing a custom loss function specifically tailored to perform joint power control and pilot assignment, while simultaneously limiting the total network power usage. Extensive simulations demonstrate that our method outperforms the existing power control and pilot assignment strategies in terms of achievable network throughput, minimum user rate, and per-user energy consumption. The model versatility and adaptability are assessed by simulating two different scenarios, namely a urban macro (UMa) and an industrial one.

Cell-free massive MIMO (CF-mMIMO) emerges as a pivotal technology in the landscape of beyond-5G and 6G wireless networks, addressing the ever-increasing demand for seamless connectivity and unprecedented data throughput. This paper undertakes a comprehensive exploration of scalable usercentric (UC) CF-mMIMO systems, focusing on critical aspects of downlink (DL) channel state information (CSI) acquisition and its intricate interactions with both distributed and centralized precoding strategies. The paper delves into the crucial role of DL CSI acquisition, particularly in scenarios of weak channel hardening arising from sparse subsets of access points (APs) serving specific mobile stations (MS) in UC strategies, and transmission over spatially correlated multiple keyhole Ricean fading channels. The main contributions of this research work include in-depth analyses of different detection schemes under varying precoding scenarios, offering valuable insights for practical deployment. The pivotal role of DL CSI acquisition in optimizing the performance of UC CF-mMIMO networks is fully assessed, dismissing the use of DL pilot-based detection approaches and advocating for either centralized precoding architectures with statistical CSI-based decoding strategies at the MSs or distributed precoding schemes with DL blind channel estimation-based decoders at the MSs.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) networks incorporate a cell-free architecture with distributed antennas in a geographical area, and aim to deliver high data rates and support large numbers of users. It is crucial that such networks operate in an energy-efficient manner within the available spectrum. Thus, we focus on maximizing the energy efficiency (EE) of a CF-mMIMO network, which coexists with a collocated primary network in underlay mode. The EE maximization is a non-convex problem and to compute a power allocation policy efficiently, we propose a weighted minimum-mean-square-error (WMMSE) based Dinkelbach’s algorithm. Besides this, we also provide a simplified algorithm for maximum-ratio precoding in which we approximate the non-convex EE objective function with a lower bound, transforming the non-convex EE problem into a convex problem. Subsequently, we propose a policy for downlink power allocation that maximizes the EE of the secondary CF-mMIMO network while adhering to power constraints at each access point and interference constraints at each primary user. We also compare with some heuristic power allocation policies. The results demonstrate that the proposed WMMSE based power allocation scheme outperforms the heuristic power allocation schemes to a significant degree.

The goal of the study presented in this paper is to evaluate the performance of a proposed adaptive beamforming approach in cell-free massive multiple input multiple output (CF-mMIMO) orientations. To this end, mobile stations (MSs) can be served by multiple access points (APs) simultaneously. In the same context, the performance of a dynamic physical resource block (PRB) allocation approach is evaluated as well, where the set of assigned PRBs per active MS is constantly updated according to their signal strength and the amount of interference that cause to the rest of the co-channel MSs. Performance evaluation takes place in a two-tier wireless orientation, employing a system-level simulator designed for parallel Monte Carlo simulations. According to the presented results, a significant gain in energy efficiency (EE) can be achieved for medium data rate services when comparing the cell-free (CF) resource allocation approach to single AP links (non-CF). This is made feasible via cooperative beamforming, where on one hand, the radiation figures of the APs that serve a particular MS are jointly updated to ensure quality of service (QoS), and on the other hand, the effects of these updates on the other MSs are evaluated as well. Although EE for high data rate services decreases compared to the non-CF scenario, the proposed dynamic PRB allocation strategy significantly lowers the number of active radiating elements required to meet minimum QoS standards, thereby reducing both hardware and computational demands.

An intelligent reflecting surface (IRS) is a promising technology for future wireless communication. It comprises many hardware-efficient passive elements. The applications of unmanned aerial vehicles (UAVs) have expanded beyond military missions owing to their mobility, maneuverability, flexibility, ease of deployment, and cost-effectiveness. Combining IRS with UAVs, known as UAV-mounted IRSs (IRS-UAV), has gained significant attention owing to the unique advantages offered by both technologies. Wireless communication systems face critical challenges in physical layer security, particularly cell-free massive multiple-input multiple-output (MIMO) systems (CFMM). This study investigated physical layer security (PLS) in an IRS-UAV-assisted CFMM, involving multiple IRS-UAVs, access points, users, and passive eavesdroppers. To maximize the average secrecy downlink rate, this study proposes an optimization algorithm using deep reinforcement learning based on a deep deterministic policy gradient (DDPG) that achieves at least one locally optimal solution. However, this approach results in a relatively high computational complexity. A second Approach Is introduced to address this: an alternating optimization algorithm combining inner approximation (IA) methods and an advanced DDPG algorithm with a warm-up technique. The simulation results demonstrated the efficiency of both approaches in resolving complex optimization problems. Furthermore, the numerical findings confirmed that the proposed alternating optimization algorithm exhibited competitive performance and significantly reduced computational complexity compared with the DDPG-based approach.

User-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO), also known as cell-free mMIMO, is a pivotal technology for enabling future mobile communication systems. While UC D-mMIMO intrinsically follows a distributed architecture, its processing can be implemented in a distributed or centralized fashion. This paper proposes a comprehensive cost assessment methodology for UC D-mMIMO, capturing its total cost of ownership and factoring in the deployment configuration, processing implementation, computational demands, and fronthaul signaling. The methodology considers two transmission reception point (TRP) deployment strategies. The first focuses only on supporting user equipment (UE) demands, while the other fulfills these requirements and also actively strives to provide a fairer service among UEs. The proposed methodology is then used to perform a techno-economic assessment of the feasibility of centralized versus distributed processing functional splits while varying key costs and TRP capabilities, like antenna and served UE count. Results suggest that with the TRP deployment that only supports the required UE rate, distributed processing is usually the most feasible option for UE demands of up to 50 Mbps, and centralized processing is more cost-effective in other cases. Additionally, when considering the actively fairer TRP deployment, centralized processing becomes cheaper for any UE demands.

This paper introduces a novel dynamic spectrum sharing (DSS) scheme designed for cell-free massive MIMO (CF-mMIMO) networks. The motivation behind this work arises from the urgent need to enhance spectrum utilization in modern wireless communication systems. Traditional spectrum allocation methods often struggle to meet the diverse spectrum demands of different wireless operators. The proposed approach addresses these challenges by enabling the spectrum provider (SP) to flexibly allocate and sell its spectrum resources, empowering wireless virtual operators to acquire bandwidth efficiently based on their specific needs. In our framework, each wireless operator is represented by a CF-mMIMO network, characterized by an average spectral efficiency metric that quantifies the potential value of additional spectrum acquisition. Leveraging a Stackelberg game formulation, our DSS policy achieves an equilibrium point that optimally allocates bandwidths to operators and determines corresponding prices. This approach not only enhances spectrum utilization but also fosters fair competition among operators. A key innovation of our work lies in the utilization of blockchain technology, where all spectrum transactions are managed through a smart contract. This ensures transparency, integrity, and auditability throughout the spectrum trading process. The novel protocol facilitates seamless information exchange, orchestrates the Stackelberg game dynamics, and delivers conclusive outcomes to both the SP and the CF-mMIMO operators.

Cell-free massive MIMO (CFmMIMO) is considered as one of the enablers to meet the demand for increasing data rates of next generation (6G) wireless communications. In user-centric CFmMIMO, each user equipment (UE) is served by a user-selected set of surrounding access points (APs), requiring efficient signal processing algorithms minimizing inter-AP communications, while still providing a good quality of service to all UEs. This paper provides algorithms for channel estimation (CE) and uplink (UL) receive combining (RC), designed for CFmMIMO channels using different assumptions on the structure of the channel covariances. Three different channel models are considered: line-of-sight (LoS) channels, non-LoS (NLoS) channels (the common Rayleigh fading model) and a combination of LoS and NLoS channels (the general Rician fading model). The LoS component introduces correlation between the channels at different APs that can be exploited to improve the CE and the RC. The channel estimates and receive combiners are obtained in each AP by processing the local antenna signals of the AP, together with compressed versions of all the other antenna signals of the APs serving the UE, during UL training. To make the proposed method scalable, the distributed user-centric channel estimation and receive combining (DUCERC) algorithm is presented that significantly reduces the necessary communications between the APs. The effectiveness of the proposed method and algorithm is demonstrated via numerical simulations.

In distributed multiple-input multiple-output (D-MIMO) networks, beam selection is necessary to predict the best beam and radio units (RUs) to serve the users in an optimum way. Finding the best RU and beam requires measuring the downlink channel for all possible RU/beam pairs, which becomes a resource-heavy operation, especially at the millimeter Wave band. To overcome this problem, artificial intelligence (AI) solutions are investigated which aim to infer the best RU/beam from sounding the channel for a subset of RUs and beams. While fairly accurate AI models can be obtained, these models have some intrinsic vulnerabilities to adversarial attacks where carefully designed perturbations are applied to the input of the AI model. In this study, we consider four different adversarial attack methods that craft perturbations using gradients of the AI cost function under two different beam reporting scenarios considering sequential and one-shot reporting of reference signal received power values for all RUs and demonstrate their effectiveness over traditional methods by extensive simulations, showing the necessity of smart defense techniques. To this aim, we propose an effective mitigation solution based on scrambling of RUs against these kinds of adversarial attack threats and verify the efficacy of our solution via detailed simulations. The proposed defense method provides up to 10 dB better signal strengths at the user side by selecting more accurate RU/beam pairs under adversarial attacks.

CELL-FREE massive multiple-input multiple-output (MIMO) is a promising technology for next-generation communication systems. This work proposes a novel partially coherent (PC) transmission framework to cope with the challenge of phase misalignment among the access points (APs), which is important for unlocking the full potential of cell-free massive MIMO technology. With the PC operation, the APs are only required to be phase-aligned within clusters. Each cluster transmits the same data stream towards each user equipment (UE), while different clusters send different data streams. We first propose a novel algorithm to group APs into clusters such that the distance between two APs is always smaller than a reference distance ensuring the phase alignment of these APs. Then, we propose new algorithms that optimize the combining at UEs and precoding at APs to maximize the downlink sum data rates. We also propose a novel algorithm for data stream allocation to further improve the sum data rate of the PC operation. Numerical results show that the PC operation using the proposed framework with a sufficiently small reference distance can offer a sum rate close to the sum rate of the ideal fully coherent (FC) operation that requires network-wide phase alignment. This demonstrates the potential of PC operation in practical deployments of cell-free massive MIMO networks.

Abstract 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.

As societal needs evolve, especially with the advent of demanding applications like remote surgeries in Smart Health, current communication systems struggle to meet the required latency and reliability for such use cases. Similarly, enhancing existing applications, such as increasing transmission rates in mobile networks to provide Quality of Service (QoS) and improved user experiences, presents complex challenges. The pursuit of higher data rates has been realized by 5G wireless communication networks, which have already been commercially deployed. Nevertheless, the proliferation of smart communication devices and the emergence of IoE (Internet of Everything) applications present challenges to existing 5G networks in meeting the escalating requirements for ultra-reliable, low-latency communication. To address these limitations, researchers are focusing on the development of the sixth generation (6G) of cellular systems. Consequently, the selection of enabling technologies is crucial for designing a suitable 6G architecture that can address these evolving demands. Herein the main aim is to provide insights into the significant technologies for the future 6G, including their operation principles, potential applications, current research status, and associated technical challenges. This article proposes a methodology to analyze the relevance of enabling technologies and leverage it for designing an optimal 6G architecture. The evaluation results offer a unique perspective on 6G enablers, pinpointing issues and stimulating research for future mobile architectures. Additionally, the obtained insights provide researchers with essential information to stay updated on emerging enabling technologies and their suitability for crafting new and optimized 6G architectures that are engineered with adaptability and flexibility in mind, highlighting cell-free massive MIMO, hierarchical cell structures, and network slicing to facilitate ultra-reliable, low-latency communication and cater to various applications, all while prioritizing energy efficiency and sustainability.

In this paper, we propose clustering schemes appropriate for a cell-free massive multi-input multi-output (CF mMIMO) system with centralized hybrid beamforming (BF) in the millimeter wave (mmWave) band, with the aim of reducing computational complexity and achieving scalability at the central processing unit (CPU) while ensuring system performance. Conventional access point (AP)-wise clustering schemes have been proposed for a decentralized architecture that mitigates inter-user interference at each AP through local digital BF and analog BF of each radio frequency (RF) chain to efficiently improve performance. However, in a centralized architecture, in which the digital BF weights is designed at the CPU while considering all RF chains of all APs together. In cases where each AP employs multiple RF chains forming different analog beams, the coupling loss (the sum of the path loss and BF gain of the analog beam) of each RF chain may differ even between the same AP and UE due to differences in the analog BF gain. AP-wise clustering is not well suited because RF chains with a high coupling loss (the sum of the path loss and BF gain of the analog beam) may be included among the APs of the cluster assigned to each UE. In the method proposed in this paper, clusters are formed on a per-RF-chain basis (RF chain-wise clustering). In this way, RF chains with lower coupling loss can be selected for each UE regardless of to which AP an RF chain belongs when forming a cluster for each UE. Moreover, the existing clustering schemes do not account for the amount of inter-cluster interference. Accordingly, we propose a cluster recombination scheme to effectively mitigate inter-cluster interference. Then, by combining RF chain-wise clustering and cluster recombination, a higher received signal power can be achieved while mitigating inter-cluster interference. Through simulation-based evaluations, we show that the proposed RF chain-wise clustering and cluster recombination schemes can achieve superior spectral efficiency while effectively reducing the complexity of centralized digital BF.

This paper addresses the joint design of a beamformer and user-centric clustering for scalable cell-free massive multiple-input multiple-output (CF-mMIMO) under severe quantization noise generated in backhaul links for central processing units (CPUs) cooperation. The system model comes up with a plan in which multiple CPUs exchange physical layer data under limited bandwidth to enhance performance while introducing clustering across multiple CPUs. We derive the joint optimization of the minimum mean-square error (MMSE) beamformer and user-centric clustering under quantization noise, and propose its low-complexity design. The superiority of our proposed design method is clarified by comparing its performance with cellular distributed MIMO systems. Throughout the paper, we first answer the problem of how much gain CF-mMIMO systems with multiple CPUs cooperation can obtain.

This paper derives approximate outage probability (OP) expressions for uplink cell-free massive multiple-input-multiple-output (CF-mMIMO) systems with and without pilot contamination. The system’s access points (APs) are considered to have imperfect channel state information (CSI). The signal-to-interference-plus-noise ratio (SINR) of the CF-mMIMO system is approximated via a Log-normal distribution using a two-step moment matching method. OP and ergodic rate expressions are derived with the help of the approximated Log-normal distribution. For the no-pilot contamination scenario, an exact expression is first derived using conditional expectations in terms of a multi-fold integral. Then, a novel dimension reduction method is used to approximate it by the sum of single-variable integrations. Both the approximations derived for the CF-mMIMO systems are also useful for single-cell collocated massive MIMO (mMIMO) systems and lead to closed-form expression. The derived expressions closely match the simulated numerical values for OP and ergodic rate.

Cell-free massive multi-input-multi-output (MIMO) systems comprise a large number of distributed access points (APs) to serve a small number of user equipments (UEs). In this paper, compute-and-forward (CF) is investigated for uplink in cell-free massive MIMO. We propose a novel reverse CF precoding scheme for downlink transmission for cell-free massive MIMO. The APs send the linear equations of messages over a multiple access channel, and the UEs recover messages without interferences. The max–min power allocation is investigated to provide uniformly good service to all UEs. Numeral results demonstrate the performance improvement provided by the proposed reverse CF precoder against zero-forcing, conjugate beamforming, and linear minimum mean-square error precoder.

This paper introduces the novel IRS-based Optimal Relay Selection (ORS-IRS) method, aimed at analyzing the performance of wireless communication systems with an emphasis on symmetry. The ORS-IRS approach presents an innovative communication algorithm that seamlessly integrates Intelligent Reflecting Surfaces (IRS) with relay selection techniques. Through adaptive adjustments of reflection coefficients, IRS elements efficiently manipulate incoming signals, fostering symmetry in signal strength enhancement and latency reduction for improved signal delivery to the intended destination. This symmetrical optimization in channel capacity and transmission power ensures reliable data transmission with low latency, achieved through the seamless integration of IRS and relay selection techniques. In contrast, the Cell-Free Massive MIMO (CF-M-MIMO), with its decentralized architecture, excels in serving a larger user base and attaining remarkable capacity gains, showcasing a different dimension of symmetry. The Decode-and-Forward (DF) relaying approach demonstrates its potential in enhancing signal reliability across extended distances, contributing to the overall symmetry of the comparative analysis. This comprehensive evaluation provides valuable insights into selecting appropriate transmission strategies, particularly for applications that demand high capacity and reliability in the design of modern wireless communication systems with a symmetrical focus. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

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

Cell-free massive multiple-input multiple-output (CF-MIMO) system has been considered a promising technology for 5G and 6G networks for its ability to handle the rise in demand effectively. With CF-MIMO, improved energy efficiency can be obtained from straightforward signal processing. One of the potential problems in CF-MIMO systems is high power consumption due to the large numbers of distributed Access Points (APs), which decrease energy efficiency. This research proposes a modified algorithm to improve overall energy efficiency by reducing total power consumption via using the APs selection technique while maintaining the system's sum of rate. The technique used for APs selection is the largest large-scale-based selection, where each user is served by a subset of APs that offer the best channel condition rather than by all of APs. Total energy efficiency has been calculated for three cases: without APs selection, fixed APs selection, and dynamic APs selection (proposed approach). The simulation result shows that the proposed approach significantly improves energy efficiency by 45% at the signal-to-noise ratio (SNR) equal to 6 dB than the case where the selection of APs is fixed due to the optimal APs selection for each user.

To solve the problem that how users choose access points in cell-free massive multiple-input multiple-output (CF-mMIMO) network, a prioritized access strategy for poorer users based on channel coefficient ranking was proposed.First, users were evaluated and ranked for their channel quality and stability after channel sensing, and suitable access points were selected in sequence according to the order of the channel state information.Second, considering issues such as users' energy consumption and data security, a federal learning framework was used to enhance user's data privacy and security.Meanwhile, an alternating optimization variables algorithm based on energy consumption optimization was proposed to optimize the multi-dimensional variables, for the purpose of minimizing the total energy consumption of the system.Simulation results show that compared with the traditional user-centric in massive MIMO, the proposed access strategy can improve the average uplink reachable rate of users by 20%, and the uplink rate of users with poor channels can be double improved; in terms of energy consumption optimization, the total energy consumption can be reduced by much more than 50% after optimization.

Cell-free Massive multiple-input multiple-output can offer many degrees of freedom for the high data rate and coverage probability. This network topology is one of the cutting-edge technologies potentially applied for next-generation wireless communications. In the canonical structure, however, each access point needs to accurately estimate the channel state information for the signal detection in the uplink and the precoding design in the downlink. Due to the finite lengths of coherence intervals, pilot contamination is one of the bottlenecks, reducing the data throughput of each user. We propose an efficient pilot assignment scheme called location-based pilot assignment (LPA) to overcome this limitation. It takes full advantage of the number of orthogonal pilot signals into account. The proposed algorithm divides the coverage area into smaller areas based on the number of users, where each area uses a subset of the pilot signals. Numerical results show that the proposed pilot assignment, LPA, offers good spectral efficiency to every users in the network with a tolerable computational complexity.

This paper proposes a heuristic association algorithm between access points (APs) and user equipment (UE) in user-centric cell-free massive multiple-input-multiple-output (MIMO) systems, specifically targeting scenarios where UEs share the same frequency and time resources. The proposed algorithm prevents overserving APs and ensures the connectivity of all UEs, even when the number of UEs is significantly greater than the number of APs. Additionally, we assume the use of low-resolution analog-to-digital converters (ADCs) to reduce fronthaul capacity. While realistic massive access scenarios, such as those in Internet-of-Things (IoT) environments, often involve hundreds or thousands of UEs per AP using multiple access techniques to allocate different frequency and time resources, our study focuses on scenarios where UEs within each AP cluster share the same frequency and time resources to highlight the impact of pilot contamination in dense network environments. The proposed algorithm is validated through simulations, confirming that it guarantees the connection of all UEs and prevents overserving APs. Furthermore, we analyze the required fronthaul capacity based on quantization bits and confirm that the proposed algorithm outperforms existing algorithms in terms of SE and average SE performance for UEs.

Absrtact: In order to reduce the pilot contamination in the cell-free massive multi-input multi-output (MIMO) system, a low complexity pilot assignment algorithm based on user polar coordinates was proposed.Firstly, a Gaussian weighted density algorithm was proposed to determine a centroid as the polar coordinates center point in the system coverage area, then pre-assigned the pilot in order according to the angular coordinates, so that users who reused the same pilot had a greater probability of having a longer distance, and henced reduce the pilot contamination.A low complexity distance detection algorithm was then proposed to ensure that the user spacing between any two users multiplexing the same pilot was greater than the threshold.The simulation results show that the proposed pilot assignment algorithm effectively reduce pilot contamination, improve the uplink throughput of 95% users of the system, and achieve a good compromise between performance and complexity.