The rapid and low-power configuration capabilities of Reconfigurable Intelligent Surfaces (RISs) have made them an attractive option for future wireless networks in terms of energy efficiency. They have the ability to greatly increase connection and facilitate low-latency communications. However, because RIS-based systems often have a large number of RIS unit elements and unique hardware constraints, accurate and low-overhead channel estimate remains a crucial challenge. In this study, we offer a channel estimation framework and concentrate on the uplink of a multi-user multiple-input multiple-output (MU-MIMO) communication system driven by RIS. Our primary goal is to enhance the achievable rate and system capacity. We derive a closed-form deterministic expression for the uplink achievable rate under practical scenarios where channel state information (CSI) is not directly known and must be estimated. In contrast to previous studies assuming perfect CSI, our approach incorporates the channel estimation process, leading to a more realistic performance assessment. Extensive simulations validate the tightness of our derived expression compared to the actual achievable rate across various system parameters (with discrepancies typically within 2-5%). The results highlight the significant impact of RIS on system performance enhancement, even with imperfect CSI. Our findings provide crucial insights into the deployment and optimization of RIS-assisted multi-user wireless networks, underscoring their potential for substantial improvements in rate and capacity.

Pattern synthesis is widely used in many radar and communication systems and received great interest. So, this paper proposes a new beamforming strategy based on a hybrid combination between grey wolf optimizer (GWO) with L2-norm called proposed GWO. This approach is applied to synthesized uniform linear arrays (ULA), Chebyshav arrays, and shaped pattern arrays. Moreover, it is utilized for side lobe level (SLL) and size reduction of antenna elements. In this strategy, the GWO is utilized to optimize the element spacing to adjust the half-power beam-width (HPBW) to save it the same as desired pattern. Furthermore, the excitations of the antenna elements are optimized via the L2-norm minimization problem. The proposed GWO has low complexity (fewer iterations and computing time) compared to other algorithms. In addition, it has a very accurate approximation of the original radiation pattern. As well, the computer simulation technology (CST) microwave package is utilized to achieve the practical validation of the proposed methodologies. As an application of the proposed GWO, it is employed to create a proposed hybrid beamforming (PHB) structure for Multi-input Multi-output (MIMO) systems. Consequently, the BS transmitting antennas are synthesized for gain maximization while utilizing the current amount of antenna elements. This results in considerable savings in antenna components and associated radio frequency (RF) chains which reduces system complexity. Furthermore, array gain maximization will increase the received signal-to-noise ratio (SNR). In addition, the SLL reduction scenario will decrease the interference from undesired users which in turn will also increase SNR. Hence, the performance of the system in terms of spectral efficiency (SE) and power utilization will be improved.

The synthesis of pencil beam and arbitrarily shaped beam patterns of linear antenna arrays (LAA) using reduced number of antenna elements attracts the attention of researchers in recent years. In this paper, a hybrid beamforming technique based on the combination of the genetic algorithm (GA) optimization technique and the $l_{1}$ minimization method denoted as (GA/ $l_{1}$ ) is introduced for LAAs synthesis. The proposed GA/ $l_{1}$ beamforming technique optimizes both the elements excitations and interelement spacing to synthesize the desired LAA pattern with a minimum number of antenna elements. The GA/ $l_{1}$ technique provides an excellent approximation to the desired radiation pattern with high accuracy and low complexity (less number of iterations and computational time) compared to the other synthesis approaches introduced in the literature. In addition, as an application of this work, the proposed GA/ $l_{1}$ technique is used to build up a proposed hybrid precoding and beamforming (HP-BF) structure for Massive Multi-input Multi-output (M-MIMO) systems. In this structure, the transmit antenna array is synthesized for maximum gain realization using the existing number of antenna elements. In the HP-BF structure, the proposed GA/ $l_{1}$ technique is used to make full use of the existing transmit array elements to synthesize the radiation pattern of much larger size and higher gain arrays without the need for additional elements. Thereby, significant savings in the number of antenna elements and their corresponding radio frequency (RF) chains are achieved, which reduces the system complexity. In addition, the array gain maximization will maximize the received signal to noise ratio (SNR) giving rise to higher system performance in terms of spectral efficiency (SE) and power utilization.

Future wireless networks could benefit from the energy-efficient, low-latency, and scalable deployments that Reconfigurable Intelligent Surfaces (RISs) offer. However, the creation of an effective low overhead channel estimate technique is a major obstacle in RIS-assisted systems, especially given the high number of RIS components and intrinsic hardware constraints. This research examines the uplink of a RIS-empowered multi-user MIMO communication system and presents a novel semi-blind channel estimate approach. Unlike current approaches, which rely on pilot-based channel estimation, our methodology uses data to estimate channels, considerably enhancing the achievable rate. We provide a closed-form deterministic expression for the uplink achievable rate in actual settings where the channel state information (CSI) must be estimated rather than assumed perfect. The results of the simulations show that the formula obtained is accurate, with a close alignment between the deterministic and actual achievable rates (generally between 2 5% deviations). The proposed approach outperforms traditional approaches, resulting in rate increases of up to 35-40%, especially in instances with more RIS elements. These findings illustrate RIS technology's tremendous potential to improve system capacity and coverage, providing useful insights for optimizing RIS adoption in future wireless networks.

This paper presents the design and analysis of a high-performance microstrip patch antenna operating at 28 GHz, along with a multiple-input multiple-output configuration optimized for beyond 5G and IoT applications. The proposed MPA incorporates a rectangular patch with two symmetrical L-shaped slots and a central rectangular slot, enhancing impedance matching and radiation characteristics. Fabricated using Rogers RT5880 substrate with a dielectric constant ( $${\varepsilon }_{r}$$ ) of 2.2 and a loss tangent of 0.0009, the antenna achieves a return loss of − 33.04 dB, ensuring minimal signal reflection and efficient power transfer. The simulated gain reaches 10.11 dBi, and the voltage standing wave ratio (VSWR) is measured at 1.04, indicating excellent performance. To further enhance system efficiency, a MIMO configuration is implemented, improving parameters such as return loss, isolation, and diversity gain. The MIMO system achieves gains of 10.2, 9.91, 10.2, and 9.84 dBi for individual elements, with a diversity gain of 10 dB, ensuring robust signal performance and increased data throughput. The design was fabricated and experimentally validated using a vector network analyzer, demonstrating strong agreement between simulations and measurements. These results confirm the antenna’s suitability for high-speed wireless communication in next-generation IoT networks.