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This paper proposes a source encoding rate control and cross-layer data stream allocation scheme for uplink millimeter-wave (mmWave) multi-user massive MIMO (MU-mMIMO) orthogonal frequency division multiplexing (OFDM) hybrid beamforming video communication systems. Unlike most previous studies that focus on the downlink scenario, our proposed scheme optimizes the uplink transmission while also addressing the limitation of prior works that only consider single-data-stream users. A key distinction of our approach is the integration of cross-layer resource allocation, which jointly considers both the physical layer channel state information (CSI) and the application layer video rate-distortion (RD) function. While traditional methods optimize for spectral efficiency (SE), our proposed method directly maximizes the peak signal-to-noise ratio (PSNR) to enhance video quality, aligning with the growing demand for high-quality video communication. We introduce a novel iterative cross-layer dynamic data stream allocation scheme, where the initial allocation is based on conventional physical-layer data stream allocation, followed by iterative refinement. Through multiple iterations, users with lower PSNR can dynamically contend for data streams, leading to a more balanced and optimized resource allocation. Our approach is a general framework that can incorporate any existing physical-layer data stream allocation as an initialization step before iteration. Simulation results demonstrate that the proposed cross-layer scheme outperforms three conventional physical-layer schemes by 0.4 to 1.14 dB in PSNR for 4–6 users, at the cost of a 1.8 to 2.3× increase in computational complexity (requiring 3.6–5.8 iterations).

Hybrid Precoding has been adopted as a promising technology for 5th generation wireless communication systems. In this paper, we propose a hybrid precoding scheme based on minimum mean square error (MMSE) and particle swarm optimization (PSO) for multiuser massive multiple-input multiple-output systems. The closed-form solutions of baseband precoding and the combiner are solved by convex optimization method. Meanwhile, the MMSE between the transmitted signal and the received signal is considered as an objective function of PSO, and the radio frequency precoding is obtained by updating the global optimal positions of the particles. The simulation results show that the proposed hybrid MMSE-PSO precoding significantly improves achievable rate and the system reliability.

A transmit antenna selection (TxAS) aided multi-user multiple-input multiple-output (MU-MIMO) system is proposed for operating in the MIMO downlink channel environments, which shows significant improvement in terms of higher data rate when compared to the conventional MU-MIMO systems operating without adopting TxAS, while maintaining low hardware costs. We opt for employing a simple yet efficient zero-forcing beamforming (ZFBF) linear precoding scheme at the transmitter in order to reduce the decoding complexity when considering users’ side. Moreover, considering that users within the same cell may require various qualities of service (QoS), we further propose a novel user-oriented smart TxAS (UOSTxAS) scheme, of which the main idea is to carry out AS based on the QoS requirements of different users. At last, we implement the proposed UOSTxAS scheme in the software defined radio (SDR) MIMO communication hardware platform, which is the first prototype hardware system that runs the UOSTxAS MU-MIMO scheme. Our results show that, by employing TxAS, the proposed UOSTxAS scheme is capable of offering higher data rates for priority users, while reasonably ensuring the performance of the common users requiring lower rates both in simulation and in the implemented SDR MIMO communication platform.

Pilot spoofing attack (PSA) is an active eavesdropping attack in massive multiple‐input multiple‐output systems, where the eavesdroppers transmit the same pilot sequence as the legitimate user does to the base station to confuse the normal channel estimation during the uplink channel training phase. With the contaminated channel estimations, more information will be leaked to eavesdroppers in the downlink transmission phase. However, it is a challenging issue to detect the PSA attack due to the similarity of the received signals and the variations of the wireless channels. Here, a new PSA detection scheme by using the difference of two different estimators is presented, that is, the least square estimator and the minimum mean square error estimator, without requiring any priori of eavesdroppers. Following that, a new data‐aided channel estimation scheme is proposed to eliminate the PSA effect. Simulation results demonstrate that the proposed PSA detection scheme outperforms the conventional energy detector, and more accurate legitimate channel estimation and higher sum secrecy rates can be obtained with the proposed scheme. We present a pilot spoofing attack detection scheme by using the difference of two different estimators, that is, the least square estimator and the minimum mean square error estimator, without requiring any priori of eavesdroppers. In addition, we propose a data‐aided channel estimation scheme to eliminate the PSA effect.

Accurate channel acquisition remains a fundamental challenge in terahertz (THz) ultra‐massive multiple‐input multiple‐output (UM‐MIMO) communication systems, primarily due to near‐field propagation effects and the large‐scale nature of antenna arrays. To address the limitations of existing compressed sensing and deep learning‐based approaches, a novel framework referred to as transformer‐U‐Net acquisition network (TU‐AcqNet) is proposed. This hybrid model integrates multi‐head self‐attention‐based transformer encoders with a U‐Net‐inspired decoder to jointly capture global pilot signal dependencies and reconstruct high‐resolution estimates of channel parameters The novelty lies in its dual‐stage architecture that combines temporal sequence modelling with spatial feature reconstruction, enabling highly accurate channel parameter estimation even under sparse pilot constraints. TU‐AcqNet emphasizes dominant pilot features through attention mechanisms and estimates key channel parameters—including angles of arrival, path distances, and complex path gains—with high resolution. The proposed scheme achieves a normalized mean square error (NMSE) improvement of up to 6 dB and increases spectral efficiency by more than 4 bits/s/Hz in high signal‐to‐noise ratio scenarios. Overall, the framework yields up to 80% reduction in NMSE relative to state‐of‐the‐art baselines, highlighting its potential for practical deployment in next‐generation THz UM‐MIMO systems. The novelty lies in its dual‐stage architecture that combines temporal sequence modelling with spatial feature reconstruction, enabling highly accurate channel parameter estimation even under sparse pilot constraints.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

Cooperative non-orthogonal multiple access (NOMA) is a technology that addresses many challenges in future wireless generation networks by delivering a large amount of connectivity and huge system capacity. The aim of this paper is to design the varied distances and power location coefficients for far users. In addition, this paper aims to evaluate the outage probability (OP) performance against a signal-to-noise ratio (SNR) for a 6G downlink (DL) NOMA power domain (PD) and DL cooperative NOMA PD networks. We combine a DL cooperative NOMA with a 16 × 16, a 32 × 23, and a 64 × 64 multiple-input multiple-output (MIMO) and a 128 × 128, a 256 × 256, and a 512 × 512 massive MIMO in an innovative method to enhance OP performance rate and mitigate the power location coefficient’s effect for remote users. The results were obtained from Rayleigh fading channels using the MATLAB simulation software program. According to the outcomes, increasing the power location coefficients for the far user from 0.6 to 0.8 reduces the OP rate because increasing the power location coefficient for the far user decreases the power location coefficient for the near user, which results in less interference between them. In terms of the OP performance rate, the DL cooperative NOMA outperforms the NOMA. According to the findings, the DL cooperative NOMA OP rate outperforms the DL NOMA by a rate of 10−0.5. Whereas the 16 × 16 MIMO enhances the OP for the far user by 78.0 × 10−4, the 32 × 32 MIMO increases the OP for the far user by 19.0 × 10−4, and the 64 × 64 MIMO decreases the OP rate for the far user by 5.0 × 10−5. At a SNR of 10 dB, the 128 × 128 massive MIMO improves the OP for the far user by 1.0 × 10−5. The 256 × 256 massive MIMO decreases the OP for the far user by 43.0 × 10−5, and the 512 × 512 massive MIMO enhances the OP for the far user by 8.0 × 10−6. The MIMO techniques improve the OP performance, while the massive MIMO technology enhances the OP performance dramatically.

Fifth-generation (5G) communication technologies, such as millimeter wave communication, massive multiple-input-multiple-output and non-orthogonal-multiple-access (NOMA) are playing a pivotal role in promoting the modern applications of the Internet-of-Things. Using non-orthogonal resource allocation, NOMA can increase spectrum efficiency and achieve wide connectivity with low transmission delay and signaling cost. Despite the high potential of NOMA in 5G communications, NOMA is susceptible to a pilot contamination attack (PCA), in which an attacker resents the same pilot signals as authorized users. Currently, using the available detection methods in NOMA gives high false positive probability since the time-division-duplex or orthogonal resource block can be allocated by many authorized user. Since the pilot contamination attack changes the signal reception at the legitimate receiver, this work introduces a novel detection scheme for identifying Pilot Contamination attack (PCA) that statistically investigates the asymmetry in received signal power levels. The main idea of the proposed detection scheme is to use various statistical measurements for normal traffic attributes (CSI) as a reference profile. Then, compute the Mahalanobis distance between the reference profile and CSI for the incoming connection and use the probability of the uniform distribution to make the final detection decision. The performance of the proposed detection technique in terms of its detection rate and false positive probabilities has been evaluated through extensive simulation. The simulation results show that the proposed scheme succeeded in detecting the pilot contamination attack with a detection rate of up to 98% and a precision reached 97.88%.

As communication systems evolve in a Cell-Free (CF) environment with the utilization of massive Multiple Input Multiple Output (mMIMO) techniques for the fifth generation and beyond, and a multitude of users access the communication networks through multiple devices and applications, it is imperative to ensure seamless connectivity with better quality of service for all the users. The rapidly increasing number of users is distributed in clusters in a CF-mMIMO communication system to efficiently manage and allocate resources. However, this increases the complexity of managing clusters, resulting in greater intra and inter-cluster interference. Therefore, an integrated solution based on wavelet transform-shaped Non-Orthogonal Multiple Access (NOMA) scheme in a CF-mMIMO system is proposed in this article for reduced intra and inter-cluster interference and to support a greater number of users. Moreover, a user-centric approach, leveraging machine learning (ML) algorithms is adopted for efficient user clustering, and closed-form expressions for intra and inter-cluster interference are derived. The system’s performance is evaluated considering the key performance indicators (KPIs), such as achievable sum-rate, bit error rate (BER), and ergodic sum-rate. Our results demonstrate significant improvement for these KPIs, compared to the conventional NOMA scheme within a CF-mMIMO framework.

This paper presents an over-the-air testing method in which a full-rank channel matrix is created for a massive multiple-input multiple-output (MIMO) antenna system utilizing a fading emulator with a small number of scatterers. In the proposed method, in order to mimic a fading emulator with a large number of scatterers, the scatterers are virtually positioned by rotating the massive MIMO antenna. The performance of a 64-element quasi-half-wavelength dipole circular array antenna was evaluated using a two-dimensional fading emulator. The experimental results reveal that a large number of available eigenvalues are obtained from the channel response matrix, confirming that the proposed method, which utilizes a full-rank channel matrix, can be used to assess a massive MIMO antenna system.