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\begin{document}

\title{Survey and Comparison of MIMO and Massive MIMO Evolution for 5G and 6G
\\
}

\author{
    Haider Mohammed Turki Al-Hilfi\IEEEauthorrefmark{1},
    Rasheed Hameed Kadhim\IEEEauthorrefmark{2}
    Iacovos Ioannou\IEEEauthorrefmark{3},
   }
\affil{
    \IEEEauthorrefmark{1}Ministry of Education, Iraq\\
    \IEEEauthorrefmark{2}Department of Communications Engineering, University Politehnica of Bucharest, Bucharest, Romania\\
   \IEEEauthorrefmark{3} European University Cyprus, Nicosia, Cyprus\\    Email:hi.1977@yahoo.com;rasheedasarai@gmail.com;ia.Ioannou@euc.ac.cy  
}

\maketitle

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\begin{abstract}
The emerging fifth-generation (5G) and sixth-generation (6G) technologies require highly efficient wireless communication systems. For this purpose, Multiple-Input Multiple-Output (MIMO) technology is a foundational component. However, conventional MIMO faces challenges in scalability and interference management, often limiting its use to smaller-scale applications. This work positions Massive MIMO as the solution, which utilizes a very large number of antennas at the base station. This architecture dramatically improves capacity, spectral efficiency, and energy efficiency by serving many users simultaneously through precise spatial multiplexing. A detailed comparison between conventional MIMO and Massive MIMO is presented. The comparative metrics analyzed include spectral efficiency, energy efficiency, scalability, user density, and channel state information (CSI) estimation. The analysis confirms that Massive MIMO offers superior performance compared to traditional MIMO technology. Furthermore, we explore the integration of Massive MIMO with advanced technologies such as artificial intelligence (AI), intelligent reflecting surfaces (IRS), and terahertz (THz) communication. Among these, the synergy between Massive MIMO and AI is identified as a particularly promising approach for enabling robust and efficient future wireless communication.
\end{abstract}

\begin{IEEEkeywords}
MIMO, massive MIMO, CSI, AI, IRS and THz
\end{IEEEkeywords}

\section{Introduction}
The development of sophisticated antenna systems that function effectively is required due to the quick growth of wireless communication systems, especially the introduction of fifth generation (5G) and the expected sixth-generation (6G) technologies \cite{ref1, ref2, ref3}. The capacity of the K (18–27 GHz), Ka (26.5–40 GHz), and Q (33–50 GHz) bands to handle high data rates and huge bandwidths, both necessary for satisfying the expanding needs of contemporary communication systems, has made them attractive options for these applications \cite{ref4}. These systems require antenna solutions that offer high gain, strong MC, and effective performance in small spaces, in addition to operating across broad frequency ranges \cite{ref5}. To enhance communication in a variety of ways, massive multi-input and multi-output (MIMO) \cite{ref6, ref7} makes use of numerous antennas at the base station \cite{ref8, ref9}. An essential wireless access technology that greatly handles the constantly rising demand for data worldwide is the cutting-edge M-MIMO. Despite their small size, a lot of smart antennas with Tx and Rx sides maintain energy and spectrum efficiency with minimal difficulty. Through its smooth integration with a variety of technologies, including the Internet of Flying Things (IoFTs), Internet of Medical Things (IoMTs), smart grids, smart devices, smart homes, mobile devices, industry, satellites, and Vehicle-to-Everything (V2X), Massive MIMO plays a revolutionary role in B5G/6G heterogeneous networks. In this paper, the detailed comparison between MIMO and massive MIMO in terms of beamforming, scalability, spectral efficiency, energy efficiency, and CSI estimation is described \cite{ref10, ref11}. From the analysis, it is obvious that the proposed massive MIMO is efficient for modern wireless technology. The proposed massive MIMO has superior performance compared to conventional MIMO, as it has increased spectral efficiency and highly efficient energy transfer due to the use of multiple antennas at the base station. Also, integrating with advanced AI technologies, its applications it extended to a wide area of power and reduce the power consumption and complexity associated with massive MIMO. Thus, it is clear that massive MIMO with AI technologies is suitable for efficient wireless communication.


The paper flow is as follows: Section 2 deals with key comparative metrics, Section 3 lists the comparison between MIMO and massive MIMO. Section 4 describes the massive MIMO integrating with emerging technology, and Section 5 deals with prospects for 6G networks.

\noindent\textbf{System context and notation.}
We consider a single cell with $M$ base station antennas and $K$ single-antenna user terminals, operating over a coherence block with $T_{\mathrm{c}}$ complex symbols and $\tau_{\mathrm{p}}$ pilot symbols, where $\tau_{\mathrm{p}}\!\le\!T_{\mathrm{c}}$. The small-scale channel to user $k$ is $\mathbf{h}_k\!\in\!\mathbb{C}^{M}$ with large-scale fading $\beta_k$, the downlink precoder is $\mathbf{w}_k$, and uplink transmit power for pilots is $p_{\tau}$. The per-user spectral efficiency is
\begin{equation}
\label{eq:se_general}
\mathrm{SE}_k=\Big(1-\frac{\tau_{\mathrm{p}}}{T_{\mathrm{c}}}\Big)\log_2\!\big(1+\mathrm{SINR}_k\big),
\end{equation}
with the signal-to-interference plus noise ratio (SINR) given by
\begin{equation}
\label{eq:sinr_general}
\mathrm{SINR}_k=\frac{p_k\big|\mathbb{E}\{\mathbf{h}_k^{\mathrm{H}}\mathbf{w}_k\}\big|^2}{\sum_{i\neq k}p_i\,\mathbb{E}\{|\mathbf{h}_k^{\mathrm{H}}\mathbf{w}_i|^2\}+\sigma^2},
\end{equation}
where $p_k$ is the data transmit power for user $k$, $\sigma^2$ is the noise power, and the expectations capture the effects of channel hardening in Massive MIMO \cite{ref2}. Equations~\eqref{eq:se_general} and \eqref{eq:sinr_general} anchor the comparisons developed in Sections~\ref{sec:metrics} and \ref{sec:comparison}.

\section{Key Comparative Metrics}
\label{sec:metrics}

\subsection{Spectral Efficiency}
Spectral efficiency, or the number of bits of information that can be delivered each second per unit of bandwidth, is the term used to describe the extent to which a MIMO (Multiple-Input Multiple-Output) system uses the available frequency spectrum to send data.
The spectral efficiency equation is given by,
\begin{equation}
\label{eq:eta_mimo}
\eta_{\text{MIMO}} = \frac{1}{B} \log_2 \det(I_{N_r} + \frac{\rho}{N_t} H H^H)
\end{equation}
Where, H is the $N_r \times N_t$ channel matrix, $\rho$ is the average signal-to-noise ratio (SNR) per receive antenna, $I_{N_r}$ is the $N_r \times N_r$ identity matrix, B = system bandwidth (in hertz).

\noindent\textbf{Massive MIMO regimes.}
In Massive MIMO, as $M \to \infty$, linear precoding schemes like maximum ratio (MR) and zero forcing (ZF) become near optimal \cite{ref2}. With imperfect CSI, the achievable downlink SINR for user $k$ with MR precoding can be approximated as:
\begin{equation}
\mathrm{SINR}_k^{\mathrm{MR}}\approx
\frac{M p_k \gamma_k^2}{\sum_{i\neq k} M p_i \gamma_i^2 \cos^2(\theta_{ki}) + \sum_{i=1}^{K} p_i \mathbb{E}\{ \|\mathbf{h}_i\|^2 \} + \sigma^2},
\end{equation}
where $\gamma_k$ represents the quality of the channel estimate for user $k$ and $\cos^2(\theta_{ki})$ represents the spatial correlation between users $k$ and $i$. A key phenomenon is \emph{channel hardening}, where the channel gain $\|\mathbf{h}_k\|^2$ becomes deterministic, \ie $\|\mathbf{h}_k\|^2/M \to \beta_k$. This simplifies scheduling, as the instantaneous channel quality is close to its average.
Area spectral efficiency (ASE) is another critical metric, defined as:
\begin{equation}
\label{eq:ase}
\mathrm{ASE}=\lambda_{\mathrm{u}} \sum_{k=1}^{K} \mathrm{SE}_k\,[\mathrm{bits/s/Hz/m^2}],
\end{equation}
where $\lambda_{\mathrm{u}}$ is the density of users per unit area. Massive MIMO excels in high ASE scenarios \cite{ref19}.

\subsection{Energy Efficiency}
Energy efficiency (EE) of a MIMO (Multiple-Input Multiple-Output) system measures the effectiveness of a system that converts consumed energy into successfully transmitted information.
\begin{equation}
\label{eq:ee_def}
\text{Energy Efficiency EE} = \frac{\text{Achievable Data Rate (bits/s)}}{\text{Total Power Consumption (W)}}
\end{equation}

\noindent\textbf{Circuit-aware model.}
A more comprehensive energy efficiency (EE) model, \cf \cite{ref35}, must account for circuit power, which becomes dominant in Massive MIMO. The total power $P_{\mathrm{total}}$ includes the radiated power $P_{\mathrm{tx}}$ and the circuit power $P_{\mathrm{circuit}}$:
\begin{equation}
\label{eq:ee}
\mathrm{EE}=\frac{B \sum_{k=1}^{K} \mathrm{SE}_k}{\frac{1}{\eta_{\mathrm{PA}}}P_{\mathrm{tx}}+ P_{\mathrm{circuit}}},
\end{equation}
where $B$ is the bandwidth and $\eta_{\mathrm{PA}}$ is the power amplifier efficiency. The circuit power can be modeled as:
\begin{equation}
P_{\mathrm{circuit}} = M P_{\mathrm{ant}} + K P_{\mathrm{UE}} + P_{\mathrm{BB}} + P_{\mathrm{BH}},
\end{equation}
where $P_{\mathrm{ant}}$ is the power per antenna (RF chains, filters), $P_{\mathrm{UE}}$ is power per user equipment (baseband processing), $P_{\mathrm{BB}}$ is the shared baseband power, and $P_{\mathrm{BH}}$ is the backhaul power. Massive MIMO achieves high EE because the array gain ($M$) allows $P_{\mathrm{tx}}$ to be scaled down proportionally ($1/M$ or $1/\sqrt{M}$), making the total power consumption less dependent on radiated power \cite{ref2}.

\subsection{Scalability and User Density}
The ability of MIMO systems to sustain or enhance performance (throughput, latency, dependability, etc.) as the number of antennas, users, or network size grows is known as scalability. The number of active users per unit area (or cell) that can be serviced concurrently with a satisfactory quality of service (QoS) is known as user density.
\begin{equation}
\text{User Density} = \frac{\text{Number of active users}}{\text{Coverage area}} [\text{users/m}^2 \text{ or users/cell}]
\end{equation}

\noindent\textbf{Loading factors.}
Define the spatial loading factor as $\alpha=K/M$. In conventional MIMO, $\alpha$ is typically greater than or equal to 1. In Massive MIMO, the system operates in the $\alpha < 1$ regime, and often $\alpha \ll 1$. For ZF precoding, a stable high SINR requires $M > K$. As $K$ increases, the inter-user interference rises, but in Massive MIMO, increasing $M$ proportionally can maintain performance.
A more practical metric for density is the number of users serviceable per physical resource block (PRB) per cell:
\begin{equation}
\lambda_{\mathrm{u,PRB}}=\frac{\text{Number of spatially multiplexed users per PRB}}{\text{Cell area}}.
\end{equation}
Massive MIMO allows $\lambda_{\mathrm{u, PRB}}$ to be significantly higher than in conventional MIMO by supporting a large $K$ simultaneously, a key enabler for massive machine type communication (mMTC) and dense urban environments.

\subsection{Channel State Information (CSI) Estimation}
In MIMO systems, the process of measuring, estimating, and learning the wireless channel characteristics between each broadcast and receive antenna is known as Channel State Information (CSI) estimation.

\noindent\textbf{Pilot design and contamination.}
In Time Division Duplex (TDD) Massive MIMO, uplink pilots are used to estimate the channel, and channel reciprocity is invoked for downlink precoding. The number of orthogonal pilots is limited by the coherence time, $\tau_p$. In a multi-cell system, users in different cells reusing the same pilot sequence cause \emph{pilot contamination} \cite{ref21}.
The MMSE estimate of the channel $\mathbf{h}_k$ based on the received pilot $\mathbf{y}_k$ is:
\begin{equation}
\hat{\mathbf{h}}_k = \mathbb{E}[\mathbf{h}_k \mathbf{y}_k^H] (\mathbb{E}[\mathbf{y}_k \mathbf{y}_k^H])^{-1} \mathbf{y}_k.
\end{equation}
When a pilot sequence is reused by a user $j$ in another cell, the estimate $\hat{\mathbf{h}}_k$ becomes polluted by $\mathbf{h}_j$. This creates a coherent interference term in the SINR (Eq. \ref{eq:sinr_general}) that does not vanish even as $M \to \infty$, setting a performance ceiling. Mitigation strategies include pilot sequence coordination \cite{ref40}, random access pilots \cite{ref20}, and covariance-aware estimation. In Frequency Division Duplex (FDD), the overhead of feeding back $M$ channel coefficients is prohibitive, motivating compressed sensing techniques based on angular sparsity \cite{ref22}.

\section{COMPARISON BETWEEN MIMO AND MASSIVE MIMO}
\label{sec:comparison}
The comparison between MIMO and massive MIMO is listed in Table~\ref{tab1}.


% ====== REPLACED Table I ======
% Removed \onecolumn, \begin{center}, \longtable
% Added \begin{table*}, \centering, \footnotesize, \tabularx, and \booktabs
\begin{table*}[htbp]
\centering
%\footnotesize
\tiny
\caption{Comparison between MIMO and massive MIMO\label{tab1}}
\begin{tabularx}{\textwidth}{@{} l X X @{}}
\toprule
\textbf{Parameter} & \textbf{MIMO} & \textbf{Massive MIMO} \\
\midrule
Spectral Efficiency &
Increased spectral efficiency by generating multiple data streams &
Increased capacity in an exponential manner due to spatial multiplexing and narrow beamforming. Massive MIMO base station which can serve hundreds of users without any loss in performance \\
\addlinespace
Energy Efficiency &
Requires high transmission power from fewer antennas to keep reliable communication, therefore, energy efficiency is modest &
Networks with battery bank connectivity supporting tens or hundreds of base station antennas can help 5G systems to run at even lower power, reducing energy use by a factor of 10x \\
\addlinespace
Scalability and User Density &
Performance gets degraded when exposed to dense environments, and this is due to user interference. &
Designed for ultra-dense networks. As it receives from and sends signals to IoT, a smart city in numbers of up to a thousand devices perpetually \\
\addlinespace
Channel State Information (CSI) Estimation &
Larger antenna systems are more complex and require sophisticated algorithms to accurately estimate the CSI &
The techniques, such as prediction of AI-based nature, are critical to lessen the computational load. \\
\addlinespace
Hardware and Cost &
Lower cost and simple hardware design for Small to medium-scale applications &
Low cost and simple hardware design for large-scale applications \\
\addlinespace
Deployment and Scalability &
best with small-scale deployments such as residential and enterprise networks &
The technology is designed to support macro-scale deployments in urban and industrial environments, \\
\addlinespace
Power Consumption &
Higher power consumption per antenna &
Energy-efficient per antenna \\
\bottomrule
\end{tabularx}
\end{table*}
% Removed \twocolumn
% ====== End REPLACED Table I ======


% ====== REPLACED Extended Table ======
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% ---
% --- IMPORTANT: This table is too long for one page.
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\begin{table*}[htbp]
\centering
%\footnotesize
\tiny
\caption{Extended comparison across additional approaches and dimensions (Note: This table is too long for one page and must be manually split)\label{tab:extended}}
\begin{tabularx}{\textwidth}{@{} l X X @{}}
\toprule
\textbf{Dimension} & \textbf{Conventional MIMO} & \textbf{Massive MIMO} \\
\midrule
Precoding options &
Linear precoding (e.g., ZF, MMSE) with limited multiplexing gain under moderate $M$ &
MR, ZF, and MMSE with large $M$ enable high multiplexing gain, channel hardening simplifies precoder design \cite{ref2} \\
\addlinespace
Nonlinear precoding &
Dirty Paper Coding (DPC) is capacity-achieving but computationally infeasible. Tomlinson-Harashima Precoding (THP) is a practical alternative. &
Linear precoding performance approaches that of DPC as $M$ grows, making complex nonlinear schemes unnecessary \cite{ref2} \\
\addlinespace
Duplexing and CSI &
FDD is common, but requires $M \times N_r$ CSI feedback, which is a major overhead. &
TDD is strongly preferred, exploiting channel reciprocity. Uplink pilot overhead scales with $K$, not $M$ \cite{ref21}. \\
\addlinespace
Pilot contamination &
A manageable issue in small-scale systems, as pilot reuse factors can be kept low. &
A fundamental performance bottleneck in multi-cell TDD systems. It creates coherent interference that does not average out with $M$ \cite{ref21, ref40}. \\
\addlinespace
Favorable propagation &
Limited concept. The system relies on rich scattering to provide independent paths for multiplexing. &
Achieved when user channels are (or can be made) orthogonal. This happens asymptotically as $M \to \infty$, even in line-of-sight \cite{ref2}. \\
\addlinespace
Channel hardening &
Weak. The instantaneous channel gain $\|\mathbf{h}_k\|^2$ fluctuates significantly, requiring fast scheduling. &
Strong. The gain $\|\mathbf{h}_k\|^2 / M$ converges to its average $\beta_k$. This makes the effective SINR deterministic \cite{ref2}. \\
\addlinespace
Mobility and Doppler &
OFDM performance degrades significantly under high Doppler shift due to inter-carrier interference (ICI). &
Can suffer similarly, but advanced schemes like Orthogonal Time Frequency Space (OTFS) modulation are being combined with Massive MIMO to handle high mobility \cite{ref29, ref30}. \\
\addlinespace
Frequency regime &
Primarily sub-6 GHz. mmWave is possible but challenging with a few antennas due to path loss. &
Efficient at sub-6 GHz. Essential at millimeter wave (mmWave) and Terahertz (THz) to provide beamforming gain to overcome severe path loss \cite{ref11, ref14}. \\
\addlinespace
Hardware impairments &
Highly sensitive to power amplifier (PA) nonlinearity and phase noise. Requires expensive, high-linearity components. &
Additive impairments (e.g., noise) average out. Multiplicative impairments (e.g., phase noise) persist but can be mitigated. Allows use of low-cost, low-power components \cite{ref23, ref24}. \\
\addlinespace
Reciprocity calibration &
Not applicable in FDD. TDD reciprocity is assumed to be perfect if used. &
Essential in practical TDD systems. Mismatch in transmitter and receiver RF chains breaks reciprocity, requiring over-the-air calibration \cite{ref25, ref43}. \\
\addlinespace
ADC resolution &
Requires high-resolution Analog-to-Digital Converters (ADCs) to minimize quantization noise. &
Can operate effectively with low-resolution (e.g., 1-4 bit) or mixed-resolution ADCs, thanks to the large array gain, further reducing cost and power \cite{ref26}. \\
\addlinespace
User scheduling &
Complex, fast scheduling based on instantaneous small-scale fading (e.g., Proportional Fair). &
Can use simple, slow scheduling based on long-term, large-scale fading (channel hardening), reducing signaling overhead. \\
\addlinespace
Cell edge coverage &
Poor. Users at the cell edge suffer from high inter-cell interference and low signal power. &
Significantly improved. Large array gain can be directed to cell-edge users. Also leads to the "cell-free" concept \cite{ref19}. \\
\addlinespace
Cell-free architecture &
Not applicable. Based on a cellular structure. &
Evolves into Cell-Free Massive MIMO, where distributed Access Points (APs) coherently serve all users without cell boundaries, eliminating cell-edge effects \cite{ref19, ref28, ref38}. \\
\addlinespace
Backhaul and fronthaul &
Moderate requirements for data and control signals. &
Extremely high fronthaul requirements to feed data and CSI to/from $M$ antennas, especially with centralized baseband processing. Mitigated by local processing. \\
\addlinespace
Multiple access &
OFDMA is standard. SDMA (Space Division Multiple Access) is limited by $M$. &
SDMA is the core principle. Can be combined with NOMA (Non-Orthogonal Multiple Access) or RSMA (Rate-Splitting Multiple Access) for robust interference management \cite{ref27, ref33}. \\
\addlinespace
Physical layer security &
Relies primarily on upper-layer cryptography. PHY-layer secrecy is limited. &
Inherently high physical layer security. Sharp beams make eavesdropping difficult. Artificial noise (AN) can be generated to further degrade eavesdropper channels. \\
\addlinespace
Joint sensing and communication &
Limited. Some channel sounding can be used for basic localization. &
Enables high-resolution Integrated Sensing and Communication (ISAC). The large array provides a high-resolution "radar" view of the environment \cite{ref31}. \\
\addlinespace
RIS integration &
Can be used, but the gain is multiplicative with a small array. &
Synergistic. RIS provides a programmable environment, and Massive MIMO provides the array gain to close the high-loss cascaded link (BS-RIS-User) \cite{ref13, ref32, ref37}. \\
\addlinespace
Terahertz feasibility &
Impractical. The extreme path loss and molecular absorption at THz frequencies cannot be overcome with a few antennas. &
A key enabler. Ultra-large arrays (e.g., $M > 1024$) are required to create pencil-beams with sufficient gain to compensate for THz propagation loss \cite{ref14, ref39}. \\
\addlinespace
Energy efficiency &
Moderate. Dominated by PA power, which must be high to ensure coverage. &
High. The $M$-fold array gain allows $P_{\mathrm{tx}}$ to be reduced drastically, making the system dominated by (scalable) circuit power \cite{ref2, ref35, ref44}. \\
\addlinespace
Deployment footprint &
Smaller antenna units, relatively easy to deploy on towers and rooftops. &
Large physical aperture, though the elements are small. Wind-loading and aesthetic integration are major practical challenges. \\
\addlinespace
Random access and pilots &
Standard protocols (e.g., RACH) work well for a moderate number of devices. &
Prone to "access collision" with massive IoT. Requires new random access protocols and pilot designs to handle massive connectivity \cite{ref20}. \\
\addlinespace
Standards outlook &
Mature technology forms the basis of 4G LTE-Advanced and early 5G (sub-6 GHz). &
Core technology for 5G mmWave (New Radio) and foundational for 6G, especially in cell-free, RIS, and THz contexts \cite{ref4, ref45}. \\
\bottomrule
\end{tabularx}
\end{table*}
% Removed \twocolumn
% ====== End Extended Table ======


From Table~\ref{tab1}, it is inferred that the massive MIMO has superior spectral efficiency, energy efficiency, and reduced hardware cost. It is also used across a wide range of applications.
The graphical representation of spectral efficiency versus user density and energy consumption for the number of antennas is depicted in Fig.~\ref{fig1}.

\begin{figure}[htbp]
\centerline{\includegraphics[width=\columnwidth]{fig1.jpeg}}
\caption{(a) Spectral efficiency versus user density and (b) energy consumption per user versus number of antennas. The plots illustrate Massive MIMO's superior scalability in handling user density and its improved energy efficiency as the array size $M$ increases, compared to conventional MIMO.}
\label{fig1}
\end{figure}

From Fig.~\ref{fig1}, it is inferred that the MIMO achieves moderate spectral efficiency. This is because user density increases due to interference and limited spatial multiplexing capability, and saturation occurs around 7 bps/Hz. Massive MIMO shows significant performance gains, up to 20 bps/Hz, as it effectively handles noise through advanced beamforming and supports multiple users simultaneously. MIMO systems consume more power per user as the number of antennas increases. This is approximately 4.6 watts per user for a high antenna count. Massive MIMO achieves significantly lower power consumption per user. It drops below 2 watts for a system with 50 plus antennas, showing excellent scalability and power efficiency.
The architectural differences between MIMO and Massive MIMO setups are presented in Fig.~\ref{fig2}.

\begin{figure}[htbp]
\centerline{\includegraphics[width=\columnwidth]{Fig2.png}}
\caption{Architectural comparison of (a) conventional MIMO and (b) Massive MIMO. Conventional MIMO uses wide beams with limited spatial multiplexing. Massive MIMO uses a large array to generate narrow, user-specific beams, enabling high spatial multiplexing and interference suppression.}
\label{fig2}
\end{figure}

Fig.~\ref{fig2}, it is observed that the visual difference between MIMO and massive MIMO is in beamforming and scalability. Fig.~\ref{fig2} shows that the massive MIMO, unlike the wider, less targeted beam of traditional MIMO, produces a narrow, precise beam for effective communication with multiple users simultaneously.
Further, the visual comparison of MIMO and massive MIMO is presented in three different charts as follows:
\begin{enumerate}
\item Beamforming comparison: Shows the beam pattern used in traditional MIMO and massive MIMO.
\item Signal processing in CSI estimation: Flowchart showing the steps of Channel State Information (CSI) estimation in MIMO and Massive MIMO, highlighting the role of advanced algorithms in Massive MIMO.
\item Deployment challenges: The comparison chart shows usage limitations such as hardware requirements. Power consumption and cost differences.
\end{enumerate}


The flowchart for estimating Channel State Information (CSI) values in both systems is depicted in Fig.~\ref{fig4}.

\begin{figure}[htbp]
\centerline{\includegraphics[width=\columnwidth]{Fig4.jpg}}
\caption{CSI estimation flowchart for MIMO versus Massive MIMO. Conventional MIMO relies on direct estimation and feedback. Massive MIMO (especially in TDD) leverages uplink pilot-based estimation and reciprocity, but must contend with pilot contamination, often using advanced (e.g., AI-based) mitigation and prediction.}
\label{fig4}
\end{figure}

From Fig.~\ref{fig4}, it is inferred that advanced techniques used in Massive MIMO are compared to simpler feedback and encryption. Linear advances used in traditional MIMO, such as pilot contamination mitigation and AI-powered adaptive beamforming.
The deployment challenges of MIMO and massive MIMO are presented in Fig.~\ref{fig5}.

\begin{figure}[htbp]
\centerline{\includegraphics[width=\columnwidth]{Fig5.jpeg}}
\caption{Deployment challenges for conventional MIMO versus Massive MIMO. While conventional MIMO has lower initial cost and complexity, Massive MIMO, despite higher initial costs, offers superior scalability for high user density and better long-term power efficiency per user.}
\label{fig5}
\end{figure}

From Fig.~\ref{fig5}, the deployment challenges between MIMO and Massive MIMO systems are described as follows:
\begin{itemize}
\item Initial Cost: Massive MIMO has a high initial cost due to its large antenna array and advanced hardware.
\item Hardware complexity: Massive MIMO is becoming more complex to deploy and maintain.
\item Power Consumption: MIMO consumes more power per user, while Massive MIMO is optimized for power efficiency. especially in large applications
\item Scalability: Massive MIMO excels in scalability. It supports high user density and IoT applications.
\end{itemize}

\section{Massive MIMO integrating with emerging technology}

\subsection{AI in Beamforming and Resource Management}
Massive MIMO with AI refers to the integration of Artificial Intelligence (AI) techniques such as machine learning and deep learning into massive Multiple Input Multiple Output (MIMO) wireless communication systems \cite{ref12}. In these systems, a base station uses a very large number of antennas to simultaneously serve multiple users, and AI is applied to improve key functions like channel estimation, beamforming, resource allocation, and interference management. This combination helps overcome the complexity and challenges of massive MIMO, enabling more efficient, adaptive, and intelligent wireless communication.

\noindent\textbf{Learning-aided transceiver design.}
AI, particularly deep learning, can optimize complex functions in Massive MIMO \cite{ref16}. For instance, beamforming can be framed as a learning problem where a neural network maps user locations or partial CSI to optimal precoding vectors $\mathbf{W} = [\mathbf{w}_1, ..., \mathbf{w}_K]$. This bypasses explicit channel estimation, which is valuable in high-mobility or FDD systems.
For resource management, reinforcement learning (RL) can solve the dynamic power allocation problem. An RL agent can learn a policy $\pi$ that maximizes the long-term weighted sum-rate:
\begin{equation}
\max_{\pi}\ \mathbb{E}_{\pi} \left[ \sum_{t=0}^{T} \sum_{k=1}^{K} \gamma^t w_k \mathrm{SE}_k(t) \right],
\end{equation}
where $\gamma$ is a discount factor and the policy $\pi$ maps the system state (e.g., queue lengths, channel quality) to power allocation actions, adapting to traffic and interference patterns in real-time \cite{ref18}.

\subsection{Intelligent Reflecting Surfaces (IRS)}
Massive MIMO uses a large number of antennas at the base station to serve multiple users simultaneously with high spectral efficiency. An IRS \cite{ref13} is a programmable surface composed of many passive reflecting elements that can intelligently control the reflection of electromagnetic waves to improve signal quality. By integrating IRS with massive MIMO, the system can dynamically shape the wireless propagation environment, enhancing coverage, boosting signal strength, reducing interference, and improving energy efficiency—all without increasing transmit power. This synergy enables smarter, more flexible, and more efficient wireless networks, especially useful for future 6G and beyond.

\noindent\textbf{Cascaded channel model.}
When an IRS with $N$ reflecting elements is deployed, the channel from the $M$-antenna BS to a user $k$ is fundamentally altered \cite{ref13}. The IRS, configured by a diagonal phase-shift matrix $\mathbf{\Theta} = \mathrm{diag}(e^{j\phi_1}, ..., e^{j\phi_N})$, creates a new, controllable propagation path. The effective channel $\mathbf{h}^{\mathrm{eff}}_k$ becomes:
\begin{equation}
\mathbf{h}^{\mathrm{eff}}_k = \mathbf{G} \mathbf{\Theta} \mathbf{f}_k + \mathbf{h}^{\mathrm{dir}}_k,
\end{equation}
where $\mathbf{G} \in \mathbb{C}^{M \times N}$ is the channel from BS to IRS, $\mathbf{f}_k \in \mathbb{C}^{N \times 1}$ is the channel from IRS to user $k$, and $\mathbf{h}^{\mathrm{dir}}_k$ is the direct path \cite{ref32}. The system must now jointly optimize the precoder $\mathbf{w}_k$ at the BS and the phase shifts $\mathbf{\Theta}$ at the IRS. This creates a "double beamforming" gain but also a challenging multiplicative control problem and a complex cascaded channel estimation task.

\begin{table*}[htbp]
\centering
\caption{Massive MIMO with AI, IRS, and THZ communication}
\begin{tabularx}{\textwidth}{@{} l X X X @{}}
\toprule
\textbf{Parameters} & \textbf{Massive MIMO with AI} & \textbf{Massive MIMO with IRS} & \textbf{Massive MIMO with THz Communication} \\
\midrule
Channel Estimation & Improved accuracy by learning channel characteristics \cite{ref16} & IRS requires combined, cascaded channel estimation \cite{ref32} & Challenging due to fast channel variations and sparsity \cite{ref14} \\
\addlinespace
Beamforming & Adaptive beamforming and precoding, learning optimal beams dynamically in real-time \cite{ref18} & Reflects and redirects signals intelligently to enhance direct beams (joint active/passive beamforming) \cite{ref37} & Highly directional beamforming required to overcome path loss \cite{ref39} \\
\addlinespace
Resource Allocation & Reinforcement learning and optimization algorithms dynamically allocate resources under varying channel and traffic conditions \cite{ref18} & IRS can optimize passive reflection patterns to improve resource usage indirectly & Resource allocation is challenging due to fast channel dynamics and hardware constraints \\
\addlinespace
Interference Management & AI helps predict and mitigate interference patterns, enabling more efficient interference cancellation & IRS can suppress or redirect interference by controlling reflection phases, improving SINR \cite{ref32} & Narrow THz beams reduce interference naturally \\
\bottomrule
\end{tabularx}
\label{tab2}
\end{table*}
\subsection{Terahertz (THz) Communication}
Massive MIMO uses a large number of antennas at the transmitter and/or receiver to serve multiple users simultaneously with high spatial multiplexing gains. THz communication \cite{ref14} operates in the frequency range of roughly 0.1 to 10 THz, offering ultra-wide bandwidths that enable extremely high data rates.
The difference in massive MIMO with AI, IRS, and THz communication is listed in Table~\ref{tab2}.

\noindent\textbf{Propagation and capacity indicators.}
THz communication (0.1-10 THz) offers enormous bandwidth ($B$) but suffers from high path loss and molecular absorption \cite{ref39}. The free-space path loss (FSPL) is already severe, and it is compounded by molecular absorption loss $L_{\mathrm{abs}}(f,d)$:
\begin{equation}
L(f,d) = \underbrace{\left(\frac{4\pi f d}{c}\right)^2}_{\text{FSPL}} \cdot \underbrace{ \frac{1}{\tau(f,d)} }_{\text{Absorption}},
\end{equation}
where $\tau(f,d) = e^{-k_{\mathrm{abs}}(f)d}$ is the transmissivity of the medium, with $k_{\mathrm{abs}}(f)$ being the frequency-dependent absorption coefficient. Massive MIMO (or "Ultra-Massive" MIMO) is not just helpful but \emph{essential} at THz frequencies. It is the only known technology that can provide the massive array gain needed to create pencil-beams that compensate for $L(f,d)$ and establish a reliable communication link, albeit over short distances \cite{ref14}.


From Table~\ref{tab2}, it is clear that massive MIMO with AI is preferable as it has improved accuracy, adaptive beamforming, dynamically allocates resources under varying channel and mitigates the interference patterns.

\section{Future Prospects for 6G Networks}
Massive MIMO will play a foundational role in 6G networks, offering:
\begin{itemize}
\item Ultra-Low Latency: Essential for applications like driverless vehicles and factory automation.
\item Massive Connectivity: Provided for IoT ecosystems with billions of connected devices.
\end{itemize}
Rapid advances in quantum computing for combinatorial optimization problems have started gaining interest and traction \cite{ref15}.

\noindent\textbf{Architectural directions for 6G.}
Future 6G networks will likely evolve Massive MIMO into new architectures \cite{ref4}. \emph{Cell-Free Massive MIMO} will dissolve cell boundaries, providing uniform high-quality service by coordinating a large number of distributed access points \cite{ref19, ref28}. \emph{Extremely Large Aperture Arrays} (ELAAs) or Large Intelligent Surfaces (LIS) will integrate communication and high-resolution sensing (ISAC) \cite{ref31}. Furthermore, \emph{Rate-Splitting Multiple Access} (RSMA) is emerging as a robust interference management strategy for Massive MIMO, generalizing both SDMA and NOMA \cite{ref27}. Finally, AI will be natively integrated into the air interface, not just as an add-on, to manage the unprecedented complexity of these dynamic, large-scale systems \cite{ref16, ref18}.

\section{Conclusion}
In this paper, massive MIMO technology is proposed for efficient wireless communication. The massive MIMO uses a large number of antennas at both the transmitter and receiver sides and, in turn, improves capacity, spectral efficiency, and energy efficiency. The proposed massive MIMO is compared with conventional MIMO. The comparative metrics taken for analysis are spectral efficiency, energy efficiency, scalability and user density, and channel state information (CSI) estimation. The graphical representation of spectral efficiency versus user density and energy consumption for the number of antennas is depicted. In addition to that, the visual representation of architectural differences between MIMO and Massive MIMO setups is presented. From this, it is obvious that the massive MIMO has superior performance compared with conventional MIMO. Further, massive MIMO is integrated with AI, IRS, and THz communication. In that massive MIMO with AI is preferable as it has improved accuracy, adaptive beamforming, dynamically allocates resources under varying.


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\end{document}
