Spectral Efficiency of Network-Assisted Full-Duplex for Cell-Free Massive MIMO System Under Pilot Contamination

Cell-free massive multiple-input multiple-output (MIMO) systems can improve transmission performance and spectral efficiency by supporting the nearby access points (APs). This paper addresses the allocation of transmission power and the optimization of user data rate for cell-free massive MIMO systems in the ultra-high reliability and low latency communication (URLLC) scenario, where all the APs simultaneously serve users over the same time/frequency resources. Considering the cell-free environment characteristics, we establish the downlink information transmission models. To guarantee the fairness among users, we aim to maximize the weighted sum rates of users by jointly user data rate optimization and transmission power allocation, subject to the constraints of the minimum data rate requirement, maximum transmission power and signal-to-interference-plus-noise ratio (SINR). Due to the complexity of the cell-free massive MIMO model, we propose a rate approximation method to find a lower bound of the weighted sum rate for the original problem. By the problem decomposition, the closed-form expression of lower bound is derived and the optimization problem is solved iteratively by using successive convex approximation and interior point methods. Simulation results confirm the effectiveness of the proposed method and show that URLLC can significantly improve transmission performance in cell-free massive MIMO networks.

Cell-Free Massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for application to beyond-5G networks. This paper considers Cell-Free Massive MIMO systems with the assistance of an RIS for enhancing the system performance under the presence of spatial correlation among the engineered scattering elements of the RIS. Distributed maximum-ratio processing is considered at the access points (APs). We introduce an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aggregated channel</i> estimation approach that provides sufficient information for data processing with the main benefit of reducing the overhead required for channel estimation. The considered system is studied by using asymptotic analysis which lets the number of APs and/or the number of RIS elements grow large. A lower bound for the channel capacity is obtained for a finite number of APs and engineered scattering elements of the RIS, and closed-form expressions for the uplink and downlink ergodic net throughput are formulated in terms of only the channel statistics. Based on the obtained analytical frameworks, we unveil the impact of channel correlation, the number of RIS elements, and the pilot contamination on the net throughput of each user. In addition, a simple control scheme for optimizing the configuration of the engineered scattering elements of the RIS is proposed, which is shown to increase the channel estimation quality, and, hence, the system performance. Numerical results demonstrate the effectiveness of the proposed system design and performance analysis. In particular, the performance benefits of using RISs in Cell-Free Massive MIMO systems are confirmed, especially if the direct links between the APs and the users are of insufficient quality with high probability.

We consider the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-antenna users in the same time-frequency resource. A simple (distributed) conjugate beamforming scheme is applied at each AP via the use of local channel state information (CSI). This CSI is acquired through time-division duplex operation and the reception of uplink training signals transmitted by the users. We derive a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control. This closed-form result enables us to analyze the effects of backhaul power consumption, the number of APs, and the number of antennas per AP on the total energy efficiency, as well as, to design an optimal power allocation algorithm. The optimal power allocation algorithm aims at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint. Compared with the equal power control, our proposed power allocation scheme can double the total energy efficiency. Furthermore, we propose AP selections schemes, in which each user chooses a subset of APs, to reduce the power consumption caused by the backhaul links. With our proposed AP selection schemes, the total energy efficiency increases significantly, especially for large numbers of APs. Moreover, under a requirement of good quality-of-service for all users, cell-free massive MIMO outperforms the colocated counterpart in terms of energy efficiency.

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising technology for providing uniformly good services in future networks. The aim of this paper is to provide the first comparison of the uplink performance of cell-free massive MIMO systems with low-resolution analogto-digital converters under the zero-forcing (ZF) and maximum ratio combining (MRC) receivers. By utilizing an addictive quantization model, a novel closed-form uplink spectral efficiency (SE) expression is derived for the ZF receiver. Moreover, the energy efficiency (EE) of the considered system is analyzed. In contrast to previous works, it turns out that the ZF receiver outperforms the MRC receiver with a large number of quantization bits in terms of SE and EE. Furthermore, it is interesting to find that a better EE-SE trade-off can be achieved by employing a large number of antennas at the access point. Finally, pilot assignment schemes are used to minimize the pilot contamination and a maxmin power control scheme is proposed to improve both SE and EE performance.

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising technology for satisfying higher rate requirement of users in beyond-5G networks. This paper investigates the impact of phase noise on the performance of cell-free MIMO systems which employ a large number of multi-antenna access points (APs). For both downlink and uplink transmissions, we derive the tractable closed-form achievable rate expressions when signal detection with channel state information (CSI) and channel distribution information (CDI), respectively, by utilizing the extended deterministic equivalence method in random matrix theory. The closed-form achievable rate expressions provide efficient evaluation for the impact of phase noise on system performance. The analytical results indicate that the phase noise influences the power of the desired signal, the beamforming uncertainty gain, and the inter-user interference increment caused by the pilot contamination. In addition, the special cases that only APs have phase noise and only users have phase noise are discussed. It is found that the impact of phase noise at users is more severe than that at APs. Besides, we discuss the system performance at low and high SNR regimes. It demonstrates that the phase noise is the major limitation of the system performance in high SNR regime while it can be neglected in low SNR regime. Furthermore, we formulate the max-min power control problem for both downlink and uplink transmissions to guarantee the users' fairness and prove that the downlink max-min problem is a quasi-concave problem while the uplink max-min problem is a geometric programming (GP) problem. Then, the phase noise-aware max-min power control schemes for both downlink and uplink transmissions are proposed to ensure the users' fairness. Finally, numerical results are provided to validate the analytical results and the proposed phase noise-aware power control scheme.

In an era where ubiquitous connectivity and escalating data demands are altering the landscape of wireless communications, our paper proposes a pioneering enhancement for the OMNeT++ simulator to support the advanced features of IEEE 802.11ax high efficiency (HE) alongside cell-free massive multiple-input multiple-output (MIMO) systems. Traditional wireless networks face daunting challenges in sustaining elevated quality of service (QoS), primarily due to fluctuating user densities and signal quality. Cell-free massive MIMO serves as a compelling answer to this predicament by decentralizing the cellular architecture. It eradicates conventional cell boundaries, furnishing uniform QoS regardless of user locations. However, these advancements come at the expense of complex backhaul networks and articulated joint signal processing. The 802.11ax standard, touted for its robustness and efficiency, remains underexplored in this new paradigm. Our research not only dissects the architectural elements and constraints of both 802.11ax and cell-free massive MIMO but also elaborates on the adaptations required to extend OMNeT++ functionalities for these technologies. By doing so, we bridge a crucial gap, enabling the simulator to provide a more precise, detailed, and scalable evaluation of emerging 6G scenarios and directional communications also taking into account the impact of the most known routing protocols such as dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV), optimized link state routing (OLSR), and dynamic mobile ad hoc network on-demand (DYMO) that were selected for this comparative study. The proposed extensions promise to revolutionize network simulations and lay the foundation for in-depth analyses of wireless systems in complex and dynamic environments. Through extensive simulations, our study demonstrates that cell-free massive MIMO configurations significantly improve network throughput in high-density mobile ad hoc network (MANET) environments, with results indicating an average throughput gain of up to 30% compared with non-cell-free configurations. This improvement highlights the efficacy of cell-free massive MIMO to take advantage of the spatial and frequency multiplexing capabilities inherent in the 802.11ax standard, making it a promising solution for future wireless systems in densely populated areas.

We investigate the feasibility of employing non-orthogonal multiple-access (NOMA) in cell-free massive multiple-input multiple-output (MIMO) operating with underlay spectrum-sharing. In our proposed system model, multiple clusters of NOMA-enabled secondary users (SUs) are concurrently served by geographically distributed secondary access-points (S-APs) via conjugate beamforming. The uplink channels are estimated locally at each S-AP via pilots sent by SUs. A set of orthogonal pilots is shared among the secondary and primary clusters to strike a balance between the throughput and training overhead, while enabling massive connectivity in primary and secondary systems. We derive the achievable rates of the secondary system by capturing the adverse effects of inter/intra-cluster interference, primary/secondary pilot contamination, imperfect successive interference cancellation (SIC) and partial channel state information (CSI). We propose a transmit power allocation policy for the secondary system to mitigate the detrimental impact of near-far effects by virtue of max-min fairness criterion. Through an achievable rate analysis, we reveal that although the number of concurrently served users can be substantially boosted by employing the proposed system model, the achievable rates are adversely affected due to detection uncertainties with imperfect SIC and statistical CSI at NOMA-enabled SUs.

Cell-free massive multiple-input multiple-output (MIMO) system is a promising architecture for next generation wireless systems by deploying a very large number of distributed access points (APs), which simultaneously serve a smaller number of user equipments (UEs) over the same time-frequency resources. It guarantees uniformly good service at high spectral efficiency with simple linear precoding techniques and max-min power control. In this article, we propose a new joint maximum-ratio and zero-forcing (JMRZF) precoding scheme, where part of APs are combined to perform centralized zero-forcing (ZF), while other APs apply simple maximum-ratio transmission (MRT). Our proposed precoder offers an adaptable trade-off between the spectral efficiency and front-haul signalling overhead. A corresponding AP subset selection scheme is also proposed which is based on large-scale fading coefficients. A closed-form expression for the achievable spectral efficiency of our proposed scheme is derived, which represents a generalized result including both fully distributed MRT and fully centralized ZF cases. Based on this closed-form expression, max-min power control is formulated and solved via the second order cone and first order methods. The former can obtain the global optimal solution, but its computational complexity is very high. On the other hand, the latter technique is sub-optimal, yet, it has very low computational complexity. Hence, it is suitable for large-scale cell-free massive MIMO systems with hundreds or thousands of APs and users. Numerical results show that our proposed JMRZF scheme can substantially outperform the local precoding schemes, even when a small part of APs are combined to deploy ZF and is implementable even when each AP has very few antennas. In addition, it is shown that our max-min power controls improves the spectral efficiency significantly, compared to the uniform power control scheme.

Cell-free massive multiple-input multiple-output (MIMO) is a form of distributed massive MIMO aiming to provide massive access and improve spectral efficiency by inheriting favorable properties of traditional massive MIMO, while mitigating detrimental effects such as shadowing and spatially correlated fading. This paper investigates how the throughput of cell-free massive MIMO is affected by non-orthogonal multiple access (NOMA) for the next-generation cellular networks under stochastic access point (AP) and user locations. Thus, we consider homogeneous Poisson point processes (PPPs) to model node locations, while considering a Rayleigh channel with log-distance path loss. The time division duplexing (TDD) mode is employed and uplink channels are estimated autonomously&#x002F;locally at each AP via uplink pilots sent by users. Moreover, while unique pilots are used between NOMA clusters, pilots are reused within each cluster in order to strike a balance between the training overhead and number of clusters. Matched filter based precoding is performed within the downlink based on the estimated channels. The aggregate signal received from all access points is characterized based on the moment generating function and approximated via moment matching. Thereby, the achievable rates for the users are derived, under the consideration of error propagation due to imperfect successive interference cancellation (SIC). It is shown that NOMA increases the overall rate under environments with low path loss exponents and networks with high access point densities, while careful power allocation can significantly improve user fairness.

Cell-free massive multiple-input multiple-output (MIMO) is a novel beyond 5G (B5G) and 6G paradigm that, through the use of a common central processing unit (CPU), coordinates a large number of distributed access points (APs) to coherently serve mobile stations (MSs) on the same time/frequency resource. By exploiting the characteristics of new less-congested millimeter wave (mmWave) frequency bands, these networks can improve the overall system spectral and energy efficiencies by using low-complexity hybrid precoders/decoders. For this purpose, the system must be correctly dimensioned to provide the required quality of service (QoS) to MSs under different traffic load conditions. However, only heavy traffic load conditions are usually taken into account when analysing these networks and, thus, many APs might be underutilized during low traffic load periods, leading to an inefficient use of resources and waste of energy. Aiming at the implementation of energy-efficient AP switch on/off strategies, several approaches have been proposed in the literature that only consider rather unrealistic uniform spatial traffic distribution in the whole coverage area. Unlike prior works, this paper proposes energy efficient AP sleep-mode techniques for cell-free mmWave massive MIMO networks that are able to capture the inhomogeneous nature of spatial traffic distribution in realistic wireless networks. The proposed framework considers, analyzes and compares different AP switch ON-OFF (ASO) strategies that, based on the use of goodness-of-fit (GoF) tests, are specifically designed to dynamically turn on/off APs to adapt to both the number and the statistical distribution of MSs in the network. Numerical results show that the use of properly designed GoF-based ASO strategies under a non-uniform spatial traffic distribution can serve to considerably improve the achievable energy efficiency.

We present a general framework for the characterization of the packet error probability achievable in cell-free Massive multiple-input multiple output (MIMO) architectures deployed to support ultra-reliable low-latency (URLLC) traffic. The framework is general and encompasses both centralized and distributed cell-free architectures, arbitrary fading channels and channel estimation algorithms at both network and user-equipment (UE) sides, as well as arbitrary combing and precoding schemes. The framework is used to perform numerical experiments on specific scenarios, which illustrate the superiority of cell-free architectures compared to cellular architectures in supporting URLLC traffic in uplink and downlink. Also, these numerical experiments provide the following insights into the design of cell-free architectures for URLLC: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) minimum mean square error (MMSE) spatial processing must be used to achieve the URLLC targets; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) for a given total number of antennas per coverage area, centralized cell-free solutions involving single-antenna access points (APs) offer the best performance in the uplink, thereby highlighting the importance of reducing the average distance between APs and UEs in the URLLC regime; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i> ) this observation applies also to the downlink, provided that the APs transmit precoded pilots to allow the UEs to estimate accurately the precoded channel.

Cell-free (CF) massive Multiple-Input Multiple-Output (MIMO) with large number of distributed access points (APs) has emerged as a new paradigm allowing higher macro diversity for randomly distributed users. However, the fronthaul traffic bandwidth between central processing unit and the APs can explode in particular in the uplink, requiring expensive star-topology with point-to-point fronthaul links. To achieve a scalable CF massive MIMO architecture and a cost-effective fronthauling solution, we consider, in this paper, a point-to-multipoint fronthaul topology where (a subset of) the APs share a serial fronthaul link offering a per-user limited fronthaul bandwidth. We develop a novel unified optimization framework for iterative power control and AP scheduling that provides a systematic user-centric solution towards scalable uplink CF massive MIMO. Experimental results show that power control is not sufficient to guarantee the best objective and, therefore, the appropriate association of the users to the APs is required to improve the overall system signal-to-noise ratio. Under the stringent fronthaul bandwidth, the proposed joint optimization framework results in i) significant 5% outage data rate increase ii) near uniform distribution of the served users per APs and, hence, an increased diversity and iii) fast convergence of the algorithm within a few iterations.

Energy-efficient power allocation in cell-free massive MIMO with zero-forcing: First order methods

The increasing densification of cell-free massive multiple-input multiple-output (MIMO) networks makes access point switch on/off (ASO) a key mechanism for improving energy efficiency in future wireless systems. While reinforcement learning (RL) has been explored for ASO, differences in modeling assumptions and evaluation scope leave open questions regarding robustness and scalability. In this work, ASO is investigated from an explicit energy-efficiency perspective using a RL framework based on Proximal Policy Optimization (PPO). The policy learns state-dependent AP activation under partial observability using compact per-access point (AP) large-scale fading statistics and power parameters, without requiring instantaneous small-scale channel state information or combinatorial search, enabling practical online implementation. A comprehensive evaluation is conducted under a unified and reproducible simulation framework across three cell-free deployment scenarios of increasing size that preserve AP density while incorporating realistic channel and power consumption models. Performance is assessed through both average and distribution-based metrics. Numerical results show that the PPO-based policy consistently outperforms random activation and the all-on baseline, achieving energy-efficiency improvements of up to 66% and nearly 50%, respectively, while activating a comparable number of APs. Moreover, the learned policy maintains robust performance as the network scales, reducing the likelihood of highly energy-inefficient operating regimes.

we study the uplink performance of a cell-free massive MIMO (Multiple Input Multiple Output) system. The channel is modeled as a rician fading channel that includes line-of-sight and scattering components, and the phase of the line-of-sight is uniformly distributed. In order to relieve the fronthaul burden and the computational complexity of the system, an access point selection scheme is researched. In addition, we derive the uplink spectral efficiency expression of the system with rician fading propagation taking into account the proposed access point selection. Numerical results show that the access point selection scheme reduces the fronthaul pressure in a cell-free massive MIMO system, and the spectral efficiency is only slightly reduced.