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As an innovative implementation, Cell-Free Massive Multiple Input Multiple Output (MIMO) has appeared in typical Cellular Massive MIMO Networks. This protocol doesn’t recognize cells, as shown by its name, even though a significant number of APs operate on the same frequency/time resources. Connection from multiple distributed access points through joint signal processing is called Cell-Free Massive MIMO. The Cell-Free Massive MIMO System, a contrast between Cell-Free Massive MIMO Systems and Distributed Massive MIMO, the prime focus in this thesis is on Cell-free Massive MIMO and, along with this discussion, on Cell-free Massive MIMO signal processing, Channel Estimation, Uplink Signal Detection, Cumulative Distribution, Spectral Efficiency & Ubiquitous Cell-Free Massive MIMO Model. Ubiquitous Cell-free Massive MIMO contributes to a Massive MIMO system, a distributed system that implements consistent user-centre distribution to solve that constraint of mobile phone interferences as well as to introduce macro-diversity. We investigated the Cell Radius at different locations in CDF with Spectral Efficiency [bits/s/hertz]. Cell-Free Massive MIMO is an evidence-based preventive of massive MIMOs with distributed high percentage APs that serve even lower margins. The cell-free model is not segregated into cells and any individual is concurrently represented by every Access point.

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.

The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. However, future wireless networks are expected to serve a very large number of devices and the current MIMO networks are not scalable, highlighting the need for novel solutions. At this moment, Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most promising idea in this direction. Despite their appealing characteristics, CF M-MIMO systems face their own challenges, such as power allocation and channel estimation. Deep Learning (DL) has been successfully employed to a wide range of problems in many different research areas, including wireless communications. In this paper, a review of the state-of-the-art DL methods applied to CF M-MIMO communications systems is provided. In addition, the basic characteristics of Cell-free networks are introduced, along with the presentation of the most commonly used DL models. Finally, future research directions are highlighted.

The emerging fifth generation（5G） wireless communication system raises new requirements on spectral efficiency and energy efficiency. A massive multiple-input multiple-output（MIMO） system, equipped with tens or even hundreds of antennas, is capable of providing significant improvements to spectral efficiency,energy efficiency, and robustness of the system. For the design, performance evaluation, and optimization of massive MIMO wireless communication systems, realistic channel models are indispensable. This article provides an overview of the latest developments in massive MIMO channel measurements and models. Also, we compare channel characteristics of four latest massive MIMO channel models, such as receiver spatial correlation functions and channel capacities. In addition, future challenges and research directions for massive MIMO channel measurements and modeling are identified.

A cell-free massive multiple input multiple output (MIMO) system is an attractive network model that is in the spotlight in 5G and future communication systems. Despite numerous advantages, the cell-free massive MIMO system has a problem in that it is difficult to operate in reality due to its vast amount of calculation. The user-centric cell-free massive MIMO model has a more feasible and scalable benefit than the cell-free massive MIMO model. However, this model has the disadvantage that as the number of users in the area increases, there are users who do not receive the service. In this paper, the proposed scheme creates connections for unserved users under a user-centric scheme without additional access point (AP) installation and disconnection for existing users. A downlink user-centric cell-free massive MIMO system model in which the APs are connected to the central processing unit (CPU) and the APs and users are geographically distributed is considered. First, the downlink spectral efficiency formula is derived and applied to the user-centric cell-free massive MIMO system. Then, the proposed scheme and power control algorithm are applied to the derived formula. The simulation results show that the unserved users within the area disappear by using the proposed scheme, while the bit error rate (BER) performance and sum rate improve compared to the existing scheme. In addition, it is shown that the proposed scheme works well even with a very large number of users in the area, and a significant service performance improvement for the worst 10% of users and the overall improvement of per-user throughput for the bottom 70% of users are ensured.

Millimeter-wave and terahertz technologies have been attracting attention from the wireless research community since they can offer large underutilized bandwidths which can enable the support of ultra-high-speed connections in future wireless communication systems. While the high signal attenuation occurring at these frequencies requires the adoption of very large (or the so-called ultra-massive) antenna arrays, in order to accomplish low complexity and low power consumption, hybrid analog/digital designs must be adopted. In this paper we present a hybrid design algorithm suitable for both mmWave and THz multiuser multiple-input multiple-output (MIMO) systems, which comprises separate computation steps for the digital precoder, analog precoder and multiuser interference mitigation. The design can also incorporate different analog architectures such as phase shifters, switches and inverters, antenna selection and so on. Furthermore, it is also applicable for different structures, namely fully-connected structures, arrays of subarrays (AoSA) and dynamic arrays of subarrays (DAoSA), making it suitable for the support of ultra-massive MIMO (UM-MIMO) in severely hardware constrained THz systems. We will show that, by using the proposed approach, it is possible to achieve good trade-offs between spectral efficiency and simplified implementation, even as the number of users and data streams increases.

This paper presents a measurement-based comparison between distributed and concentrated massive multiple-input multiple-output (MIMO) systems, which are called D-mMIMO and C-mMIMO systems, in an indoor environment considering a 400 MHz bandwidth centered at 3.5 GHz. In both cases, we have considered an array of 64 antennas in the base station and eight simultaneously active users. The work focuses on the characterization of both schemes in the up-link, considering the analysis of the sum capacity, the total spectral efficiency (SE) achievable by using the zero forcing (ZF) combining method, as well as the user fairness. The effect of the power imbalance between the different transmitters or user terminal (UT) locations, and thus, the benefits of performing an adequate power control are also investigated. The differences between the C-mMIMO and D-mMIMO channel performances are explained through the observation of the structure of their respective measured channel matrices through parameters such as the condition number or the power imbalance between the channels established by each UT. The channel measurements have been performed in the frequency domain, emulating a massive MIMO system in the framework of a time-domain duplex orthogonal frequency multiple access network (TDD-OFDM-MIMO). The characterization of the MIMO channel is based on the virtual array technique for both C-mMIMO and D-mMIMO systems. The deployment of the C-mMIMO and D-MIMO systems, as well as the distribution of users in the measurement environment, has been arranged as realistically as possible, avoiding the movement of people or machines.

In a massive multiple-input multiple-output (MIMO) system, a large number of receiving antennas at the base station can simultaneously serve multiple users. Linear detectors can achieve optimal performance but require large dimensional matrix inversion, which requires a large number of arithmetic operations. Several low complexity solutions are reported in the literature. In this work, we have presented an improved two-dimensional double successive projection (I2D-DSP) algorithm for massive MIMO detection. Simulation results show that the proposed detector performs better than the conventional 2D-DSP algorithm at a lower complexity. The performance under channel correlation also improves with the I2D-DSP scheme. We further developed a soft information generation algorithm to reduce the number of magnitude comparisons. The proposed soft symbol generation method uses real domain operation and can reduce almost 90% flops and magnitude comparisons.

This paper concentrates on the rate analysis and optimization for a downlink cell-free massive multi-input multi-output (MIMO) system with mixed digital-to-analog converters (DACs), where some of the access points (APs) use perfect-resolution DACs, while the others exploit low-resolution DACs to reduce hardware cost and power consumption. By using the additive quantization noise model (AQNM) and conjugate beamforming receiver, a tight closed-form rate expression is derived based on the standard minimum mean square error (MMSE) channel estimate technique. With the derived result, the effects of the number of APs, the downlink transmitted power, the number of DAC bits, and the proportion of the perfect DACs in the mixed-DAC architecture are conducted. We find that the achievable sum rate can be improved by increasing the proportion of the perfect DACs and deploying more APs. Besides, when the DAC resolution arrives at 5-bit, the system performance will invariably approach the case of perfect DACs, which indicates that we can use 5-bit DACs to substitute the perfect DACs. Thus, it can greatly reduce system hardware cost and power consumption. Finally, the weighted max–min power allocation scheme is proposed to guarantee that the users with high priority have a higher rate, while the others are served with the same rate. The simulation results prove the proposed scheme can be effectively solved by the bisection algorithm.

ABSTRACT The prevailing research on cell‐free massive multiple‐input multiple‐output (MIMO) systems posits that, due to channel reciprocity and channel hardening properties, user equipment (UEs) can decode downlink data solely based on uplink statistical channel state information (CSI). However, the phenomenon of channel hardening is not pronounced in cell‐free massive MIMO systems, leading to unsatisfactory spectral efficiency (SE) performance with the aforementioned decoding approach. To address this limitation, this paper examines a downlink channel estimation (DL‐CE) strategy for cell‐free massive MIMO systems. We present a closed‐form expression for the downlink achievable SE, considering a correlated Rician fading channel and imperfect CSI, enabling quantitative assessment of SE performance across diverse system configurations, such as varying numbers of access points, UEs, and antennas per AP. Subsequently, we formulate a joint optimization problem with respect to both uplink and downlink pilots to maximize the sum‐SE. Given the intricate and nonconvex nature of the problem, we propose a genetic algorithm (GA)‐based pilot assignment strategy to facilitate an effective resolution. Simulation outcomes indicate that the implementation of DL‐CE markedly enhances SE performance relative to cell‐free massive MIMO systems reliant on uplink channel estimation (UL‐CE) alone. Moreover, the proposed joint pilot assignment scheme yields significant SE improvements over benchmark pilot assignment strategies. The impact of downlink channel estimation on the achievable spectral efficiency in downlink cell‐free massive MIMO systems is investigated. A generic optimization‐based joint pilot assignment algorithm is proposed to maximize the sum*spectral efficiency.