Cell-edge Users Research Articles

Coverage Analysis and Proposed Cell Sizes to Enhance the Performance of the 5G Cellular System

The global demand for more digital data motivates the evolution of mobile communication systems from 2G and 3G to 4G and 5G to support high data rate applications. The International Mobile Telecommuication-2020 (IMT-2020) defined the minimum technical performance requirements and guidelines for evaluating the 5G mobile cellular system. The New Radio (NR) millimeter wave (mmWave) and massive Multiple Input Multiple Output (mMIMO) antenna systems are technologies used in the 5G cellular system. Therefore, this paper studies the performance of the mmWave system combined with the mMIMO antenna system using the IMT-2020 channel model in a dense urban microenvironment, considering different frequency bands and numbers of elements at the Base Station (BS) antenna array. The performance is evaluated through system-level simulation in terms of Signal power to Noise power Ratio (SNR), Spectral Efficiency (SE), and cell edge user SE. A cell size reduction technique is proposed to meet the target requirements set by IMT-2020. Simulation results showed that the sub. 6 GHz frequency bands achieve the target SE of IMT-2020, while in the mmWave frequency range, the target SE requirement can be achieved using a high number of antenna elements at the BS antenna for some frequency bands.

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about 60% of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of (1−1/e). Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming.

Joint beamforming and power splitting design for MISO downlink communication with SWIPT: a comparison between cell-free massive MIMO and small-cell deployments

Simultaneous wireless information and power transfer (SWIPT) has been advocated as a highly promising technology for enhancing the capabilities of 5G and 6G devices. However, the challenge of dealing with large propagation path loss poses a significant hurdle. To address this issue, massive multiple-input multiple-output (MIMO) is employed to enhance the efficiency of SWIPT in cellular-based networks with multiple small cells, and especially increase the energy for cell-edge users. In addition, by leveraging a large set of spatially distributed base stations to collaboratively serve SWIPT-enabled user equipment, the cell-free massive MIMO has the potential to provide even better performance than the conventional small-cell systems. In this work, we extend the investigation to include the application of SWIPT technology with alternating current (AC) logic in the cell-free networks and the small-cell networks and propose joint beamforming and power splitting optimization frameworks to maximize the system sum-rate, subject to the constraints on harvested energy, AC logic energy supply, and total transmit power. The optimization problem is shown to be non-convex, posing a significant challenge. To address this challenge, we resort to a two-stage decomposition approach. Specifically, we first introduce quadratic transform-based fractional programming (FP) algorithms to iteratively solve the non-convex optimization problems in the first stage, achieving near-optimal solutions with low time complexities. To further reduce the complexities, we also incorporate conventional schemes such as zero forcing, maximum ratio transmission, and signal-to-leakage-and-noise ratio for the design of beamforming vectors. Second, to determine the optimal power splitting ratio within the framework, we develop a one-dimensional (1-D) search algorithm to tackle the single variable optimization problem reduced in the second stage. These algorithms are then evaluated in the context of cell-free MIMO and small-cell networks with numerical experiments. The results show that the FP-based algorithms can consistently outperform those utilizing the conventional beamforming schemes, and the solutions of this work can achieve up to fivefold improvement in the system sum-rate than the small-cell counterpart while providing different but comparable performance trends in energy harvesting (EH).

Covert performance enhancement in NOMA enabled CoMP network with optimal RIS selection

In this paper, we consider the covert rate maximization problem for the cell edge user in a reconfigurable intelligent surface (RIS) aided downlink non-orthogonal multiple access (NOMA) network with coordinated multipoint (CoMP) transmission in the presence of channel estimation errors. We derive the analytical expression for the detection error probability (DEP) at the adversary (Willie) assuming the worst case scenario where they employ optimal detection threshold. With the derived analytical expression, we propose an optimal RIS selection scheme to boost the covert rate. To further improve the covert performance, we optimize the NOMA power allocation coefficients and phase shifts of the selected RIS subject to the target rate and covertness constraints. Simulation results demonstrate the improvement in covert rate and robustness offered by the proposed scheme in the presence of channel estimation errors over other benchmarks.

Time and relay shared full/half duplex cooperative NOMA with paired cell edge users

In this work, a novel time and relay shared (TRS) based full/half duplex (FD/HD) cooperative non-orthogonal multiple access (C-NOMA) scheme namely TRS FD/HD C-NOMA is proposed to efficiently exploit the spectrum of unpaired users in the wireless communication system. More specifically, a scenario has been investigated where cell edge users (CEUs) are more than the cell centre users (CCUs) and the base station has no direct connection with the CEUs. A CCU is employed as a shared relay to form clusters with the CEUs in a time-sharing fashion. To characterize the performance of the proposed scheme, four performance metrics ergodic capacity, outage probabilities, system throughput and system efficiency are discussed. More particularly, ergodic sum capacities are analytically derived and an exact closed-form expression of the outage probabilities is derived for three users in the system. Numerical results show that the TRS FD/HD C-NOMA scheme outperforms the conventional benchmarks. The ergodic capacities and outage performance of TRS FD C-NOMA enhanced in low SNR regime whereas due to the inclusion of loop interference in FD, TRS HD C-NOMA performs better in high SNR regime. Additionally, the system throughput and efficiency are also improved.

Best sum-throughput evaluation of cooperative downlink transmission nonorthogonal multiple access system

In cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) downlink situations, the current research investigates the total throughput of users in center and edge of cell. We focus on creating ways to solve these problems because the fair transmission rate of users located in cell edge and outage performance are significant hurdles at NOMA schemes. To enhance the functionality of cell-edge users, we examine a two-user NOMA scheme whereby the cell-center user functions as a SWIPT relay using power splitting (PS) with a multiple-input single-output. We calculated the probability of an outage for both center and edge cell users, using closed-form approximation formulas and evaluate the system efficacy. The usability of cell edge users is maximized by downlink transmission NOMA (CDT-NOMA) employing a SWIPT relay that employs PS. The suggested approach calculates the ideal value of the PS coefficient to optimize the sum throughput. Compared to the noncooperative and single-input single-output NOMA systems, the best SWIPT-NOMA system provides the cell-edge user with a significant throughput gain. Applying SWIPT-based relaying transmission has no impact on the framework’s overall throughput.

Interference Mitigation and Power Consumption Reduction for Cell Edge users in Future Generation Networks

In 5G heterogeneous networks (HetNets), a unique and promising option to address the growing demand for higher data rates is network densification of small cells (SCs) and macro cells (MCs). Unfortunately, the 5G HetNets are suffering severe issues due to the interference caused by these densely populated SCs and their high-power consumption. To lessen interference and boost network throughput, a New Soft Frequency Reuse (NSFR) technique is put forth in this work. The proposed scheme uses the Soft Frequency Reuse (SFR) for on/off switching of the SCs according to their Interference Contribution Rate (ICR) values. By splitting the cell region into edge and center zones, it resolves the interference issue caused by the densely packed SCs. Moreover, SC on/off switching addresses the issue of excessive power consumption and improves the 5G network's power efficiency. Furthermore, this work tackles the irregular shape nature problem of 5G HetNets and compares two different proposed shapes for the centre zone of the SC, existing irregular and proposed circular shapes. Additionally, the optimum radius of the centre zone, which maximizes the total system data rate, is obtained. A comparative analysis of power consumption, data rate and power efficiency was performed between the NSFR model, the SFR model and the proposed model. The results show that for 1000 number of equipment, the proposed model has a low power consumption of 1.72KW compared to 3.51KW for SFR and 3.73KW for NSFR. Data rate of 12.19kbps compared to 11.42kbps for SFR and 11.09kbps for NSFR. Also, power efficiency of 610kbps/W compared to 572kbps/W for SFR and 560kbps/W for NSFR. These results imply that the interference mitigation handled by the proposed scheme improves by approximately 22%.

Adaptive refined random orthogonal matching pursuit algorithm for FBMC/OQAM MIMO framework

The fifth generation of wireless communication is anticipated to provide improved quality of service and enhanced data rates to the end users. One such technology that stands out as a potential transmission scheme for 5G systems is Filter Bank Multicarrier using Offset Quadrature Amplitude Modulation (FBMC/OQAM) with an effective channel estimation technique for improved performance. However, due to the inherent imaginary interference, channel estimation methods relying on preamble structures in FBMC/OQAM systems exhibit sub-optimal performance, particularly within Multiple-Input Multiple-Output (MIMO) setups. For channel estimation schemes based on compressed sensing, the inherent sparsity of wireless channels can be exploited for accurate channel reconstruction and overall performance improvement.We propose a novel compressed sensing based algorithm namely, Adaptive Refined Random Orthogonal Matching Pursuit (ARROMP), for MIMO-FBMC system with Coordinated MultiPoint (CoMP) scheduling. This algorithm adaptively selects a support set by utilizing a double threshold for the minimization of mean squared error and for accurate channel reconstruction. The proposed algorithm's performance is compared with existing Orthogonal Matching Pursuit (OMP) schemes such as random OMP, refined random OMP, and least square-based estimation. The numerical simulations suggest that the proposed adaptive algorithm provides performance improvement in terms of reduced Mean Squared Error (MSE) of channel reconstruction and Bit Error Rate (BER). Moreover, the proposed ARROMP algorithm for MIMO-FBMC is rigorously tested with CoMP scheduling for a cellular network using frequency division duplex mode. The proposed system presents significant improvements in throughput and spectral efficiency for all types of cell users, including cell-edge users. The simulation results validate the improved performance of the proposed algorithm with CoMP scheduling over the existing single-cell system with no coordination.

Heterogeneous cyber-physical network coexistence through interference contribution rate and uplink power control algorithm (ICR-UPCA) in 6G edge cells

Optimizing power control for interference mitigation at the network cell edge is pivotal in enhancing capacity within a heterogeneous cyber-physical infrastructure, such as smart cities, manufacturing, healthcare, energy grids, transportation, and agriculture, among others. In this paper, we consider the intricate dynamics of Internet of Things (IoT) 5/6G edge users, with a particular focus on the Interference Contribution Rate (ICR), where macro and femtocells are critical network infrastructures. Existing approaches has drawbacks such as computational complexity, overhead, and co-channel interference, among others. However, to fully address interference challenges from the coexistence of diverse network hierarchies, preserving the Quality of Service for femtocell users is prioritized. The paper concurrently enhances the handoff mechanism of cell edge users in the macro cell network. A two-tier heterogeneous network (HetNet) is utilized to initially assess the contribution of edge user equipment (UE) to interference levels during its active state while quantifying it as ICR. Game theory is used to formulate a cohesive model for the coexistence of macro cell (MUE) and femtocell users (FUE). ICR-based uplink power control and reference signal received quality (RSRQ)-based handoff algorithms are deployed to regulate interference levels and enhance the Signal-to-Interference-Noise Ratio (SINR) of the MUE at the cell edge. This is achieved through coordinated transmit power adjustments by both user types. Results indicate a 6.67 % channel capacity loss (interference tolerance) by the FUE, leading to a 12.5 % improvement, translating to approximately 4 Mbps and 1 Mbps channel enhancements, respectively. The MUE and FUE can effectively coordinate power control with minimal overhead, accepting compromises in network channel quality. This approach facilitates improved MUE data access rates while ensuring the preservation of FUE. We show that interference is successfully mitigated through power control in heterogeneous networks with lower computational complexity.

On the performance of intelligent reflecting surfaces-assisted OAM with NOMA under imperfect SIC

In this paper, intelligent reflecting surface (IRS)-assisted orbital angular momentum (OAM) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI) is proposed. Importantly, the communication links of both cell-center user (CCU) and cell-edge user (CEU) are developed through IRS when they are in the non-line-of-sight (NLoS) of the transmitter. The aim of developing the proposed system is the accommodation of massive connectivity and enhancement of sum capacity as well as user capacities with the help of NOMA. The performance of the proposed IRS-OAM-NOMA system is evaluated and compared with the existing techniques such as IRS-assisted OAM with orthogonal multiple access (OMA) OMA-OAM-IRS and NOMA-IRS. The simulation results show that the proposed IRS-OAM-NOMA system outperforms the existing techniques in terms of user capacity and sum capacity under ideal conditions and with imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI).

Deep learning‐assisted reconfigurable intelligent surface for enhancing 6G mobile networks

AbstractThis article presents the role of Reconfigurable Intelligent Surface (RIS) using the Multi‐Input Multi‐Output (MIMO) technique as the enabling technology to boost the achievable data rate for mobile networks of the sixth generation (6G). The RIS has been adopted to mitigate the interference at the Cell Edge User (CEU) placed where two adjacent cells are separated. That is by reflecting the incident interference signal in the CEU direction out of phase with the basic interference signals coming from the interfering BS towards the CEU. This article adopts an efficient solution for designing the RIS redirecting (reflection) matrix with trivial training overhead using Deep Learning (DL) technology. Whereas a few of the reflecting elements in RIS are chosen to be active (attached to the baseband), whilst the majority are chosen to be passive, in which the active element's channels are known and used as medium indicators and indicate further the positions of the transmitter and receiver. This article illustrated the efficacy of the adopted framework with DL, by changing the training parameters regarding the data rates that can be achieved, the Spectral Energy Efficiency (SEE), and the Satisfaction Rate (SR). As a result, the proposed model improves the achievable data rate by a near average of 97% above the reference model. Furthermore, it enhances the achievable data rate above the baseline model that assumes no RIS used by an average of 115%. Therefore, The DL method demonstrated that the proposed model is promising for enhancing 6G Mobile Networks.

Enhancing QoS Performance for Cell Edge Users Through Adaptive Modulation and Coding in IEEE 802.11ac WLANs

Wireless communication networks, such as IEEE 802.11ac Wireless Local Area Networks (WLANs), often encounter challenges in providing consistent Quality of Service (QoS) to users situated at the cell edge. The inherent variations in channel conditions, particularly lower signal-to-noise ratios (SNRs) in these regions, lead to compromised data rates and reliability, resulting in significant degradation of throughput. This study presents an innovative solution in the form of an Adaptive Modulation and Coding Scheme (AMCS) algorithm tailored to enhance QoS performance for cell edge users. The primary objective of the AMCS algorithm is to optimize QoS by dynamically adjusting the transmission data rate based on the observed channel conditions, quantified using SNR as a channel state indicator. Conventional approaches might unilaterally select the lowest data rate in challenging conditions, prioritizing reliability at the expense of throughput. However, the proposed AMCS algorithm takes a distinct approach by intelligently determining the Modulation and Coding Scheme (MCS) that offers an optimal balance between throughput and reliability for the given SNR level. To achieve this, the algorithm utilizes real-time SNR measurements to select an MCS that ensures a stable connection while also maintaining an acceptable data rate. By adapting the MCS based on the current SNR, the algorithm aims to mitigate the adverse effects of poor channel conditions experienced by cell edge users. The innovation of the AMCS algorithm lies in its ability to make dynamic adjustments, allowing users to experience improved data rates without compromising connection stability. Through extensive simulations and evaluations, the proposed AMCS algorithm showcases its efficacy in enhancing QoS performance at the cell edge. The algorithm's adaptive approach successfully achieves higher data rates and improved reliability by selecting appropriate MCS configurations tailored to the observed SNR levels. This innovative technique provides a promising solution to the challenge of striking the right balance between throughput and reliability in wireless communication networks, ultimately leading to an improved user experience for those at the network's periphery.

Energy efficiency resource allocation for cell-edge users with social-aware based grouping D2D

Energy efficiency resource allocation for cell-edge users with social-aware based grouping D2D

Design of Multi-User Noncoherent Massive SIMO Systems for Scalable URLLC.

This paper develops and optimizes a non-orthogonal and noncoherent multi-user massive single-input multiple-output (SIMO) framework, with the objective of enabling scalable ultra-reliable low-latency communications (sURLLC) in Beyond-5G (B5G)/6G wireless communication systems. In this framework, the huge diversity gain associated with the large-scale antenna array in the massive SIMO system is leveraged to ensure ultra-high reliability. To reduce the overhead and latency induced by the channel estimation process, we advocate for the noncoherent communication technique, which does not need the knowledge of instantaneous channel state information (CSI) but only relies on large-scale fading coefficients for message decoding. To boost the scalability of noncoherent massive SIMO systems, we enable the non-orthogonal channel access of multiple users by devising a new differential modulation scheme to ensure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases when the antenna array size is scaled up. The key idea is to make the transmitted signals from multiple geographically separated users be superimposed properly over the air, such that when the sum signal is correctly detected, the signal sent by each individual user can be uniquely determined. To further enhance the average error performance when the array antenna number is large, we propose a max-min Kullback-Leibler (KL) divergence-based design by jointly optimizing the transmitted powers of all users and the sub-constellation assignments among them. The simulation results show that the proposed design significantly outperforms the existing max-min Euclidean distance-based counterpart in terms of error performance. Moreover, our proposed approach also has a better error performance compared to the conventional coherent zero-forcing (ZF) receiver with orthogonal channel training, particularly for cell-edge users.

SWIPT Cooperative Protocol for Improving the Communication Quality of Cell-Edge Users in NOMA Network and Its Performance Analysis

To solve the problems of complex energy supply and poor communication effect of cell-edge users, combining the advantages of simultaneous wireless information and power transfer (SWIPT) and collaborative non-orthogonal multiple access (CNOMA), two novel protocols are proposed. By employing the PS and TS strategies in SWIPT, respectively, two protocols are named: SWIPT-CNOMA-PS and SWIPT-CNOMA-TS. Based on the protocols, a new method for selecting relays is first established by considering two factors—energy state and channel condition. To further improve energy efficiency, the relay harvests the energy of the signal sent by the source and stores it. We establish a one-dimensional Markov chain with the energy state as the system state to analyze the variation of the relay energy. Exploiting the conservation equation, the probability of energy sufficiency of the node and outage probabilities of users are derived. Finally, the impact of the energy threshold and the number of relays on the outage probability of users is revealed. The protocol in this paper shows better performance than the OMA protocol when the transmit signal-to-noise ratio (SNR) is less than 14 dB and the protocol in the literature when the transmit SNR is less than 11 dB.

Improving user performance in cooperative NOMA millimeter wave networks under two-phase operation protocol

Improving user performance is the most important target of 5G and beyond 5G millimeter wave cellular network systems. Thus, cooperative communication with assistance from additional hardware has been introduced as the potential solution. The paper proposes a two-phase cooperative communication system without utilizing the additional hardware to assist the users with bad channel conditions. Particularly, each Cell-Edge User (CEU) is served by both the nearest and second nearest BSs where the former is the source and the latter works as the relay. In addition, the Non-Orthogonal Multiple Access (NOMA) technique is used to allocate the radio resource and power for the relays. To capture the performance behavior of the proposed system, the millimeter wave properties, which are modeled by Nakagami-m and the Stretch Path Loss, are used to derive the performance metrics of Cell-Center User (CCU), CEU, and the typical user. Through analytical results which are validated by Monte Carlo simulation, it is proved that the proposed system can provide a coverage probability of 0.93 (m = 2) which is 13.4% higher than the system without cooperative communication. Moreover, the proposed system can achieve an 8% higher data rate but consume an 16.7% power less than the system with full cooperative communication.

Weighted Sum-Rate Maximization in Multi-IRS-Aided Multi-Cell mmWave Communication Systems for Suppressing ICI

Intelligent reflecting surface (IRS) has been envisioned as a disruptive technology with the capability of reconfiguring the wireless transmission environment. Hence, the IRS is used to assist the communication system, however, the impacts of inter-cell interference (ICI) on cell-edge users are severe in multi-cell communications but have been ignored in most literature about IRS-assisted communication systems. In this paper, we consider the downlink transmission of cell-edge users in multi-cell millimeter wave (mmWave) communication systems with the aid of multiple IRSs deployed at the cell boundary. To suppress the ICI, we maximize the weighted sum-rate (WSR) by jointly optimizing the IRS-user association matrix, the transmit beamforming of mmWave base stations (MBSs), and the phase shifts of IRSs, subject to the power limits of MBSs, the reflection constraints of IRSs, and the association constraints. Then, a joint IRS-user association and beamforming algorithm with alternating optimization (AO) framework is developed to tackle the mixed-integer non-convex problem. Specifically, a simplified objective function with respect to the association variables is derived and the non-convex binary constraints are handled by the relaxation operation and the first-order Taylor expansion, while the arithmetic-geometric mean inequality (AGMI)-based rank-one relaxation algorithm and the ring-based penalty convex-concave procedure (CCP) method are proposed for obtaining the transmit beamforming matrix of MBSs and the phase shifts of IRSs, respectively. Simulation results demonstrate the superiority of the proposed algorithm over the benchmark schemes. The useful insights into the optimization of IRS-user association and design of the number of elements of IRSs are also presented for future networks.

Massive MIMO NOMA: Double-Mode Model towards Green 5G Networks.

With the development of the Internet of Things (IoT), the number of devices will also increase tremendously. However, we need more wireless communication resources. It has been shown in the literature that non-orthogonal multiple access (NOMA) offers high multiplexing gains due to the simultaneous transfer of signals, and massive multiple-input-multiple-outputs (mMIMOs) offer high spectrum efficiency due to the high antenna gain and high multiplexing gains. Therefore, a downlink mMIMO NOMA cooperative system is considered in this paper. The users at the cell edge in 5G cellular system generally suffer from poor signal quality as they are far away from the BS and expend high battery power to decode the signals superimposed through NOMA. Thus, this paper uses a cooperative relay system and proposes the mMIMO NOMA double-mode model to reduce battery expenditure and increase the cell edge user's energy efficiency and sum rate. In the mMIMO NOMA double-mode model, two modes of operation are defined. Depending on the relay's battery level, these modes are chosen to utilize the system's energy efficiency. Comprehensive numerical results show the improvement in the proposed system's average sum rate and average energy efficiency compared with a conventional system. In a cooperative NOMA system, the base station (BS) transmits a signal to a relay, and the relay forwards the signal to a cluster of users. This cluster formation depends on the user positions and geographical restrictions concerning the relay equipment. Therefore, it is vital to form user clusters for efficient and simultaneous transmission. This paper also presents a novel method for efficient cluster formation.

Energy-efficient resource allocation and user grouping for multi-IRS aided MU-MIMO system

In this paper, we consider a multiple-intelligent reflecting surface (IRS)-aided multi-user multiple-input-multiple-output (MU-MIMO) down-link communication system, wherein a base station (BS), equipped with multiple antennas, serves massive single-antenna users. In order to mitigate high inter-user interference, we divide all the users into a single near-user (NU) group and multiple cell-edge user (CEU) groups based on their connection with BS. In particular, we assign a single IRS for each CEU group such that the BS communicates and serves the set of CEUs using their corresponding IRS. Subsequently, we formulate a problem of energy-efficient resource allocation design subject to optimal beamforming design at BS and multiple IRS under given power constraints. Due to the non-convexity of the formulated resource allocation problem, we proposed two different alternating optimization frameworks which solve the joint optimization problem based on 1) minimum mean square error (MMSE) and Riemannian conjugate gradient (RCG) and 2) fractional programming and successive convex approximation techniques for active beamforming vectors at the BS and passive beamforming vectors for IRSs, respectively. Simulation results confirm that the MMSE-RCG-based resource allocation design attains fast convergence when compared to sequential fractional programming (SFP) based solution. Moreover, the MMSE-RCG attains better energy efficiency whereas the SFP renders better spectral performance. Nevertheless, both the proposed solution achieve 70%–80% better performance than random phase-shift settings.

Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System.

The fifth generation (5G) marks an important advance in mobile network capabilities. When it comes to high data rates, capacity, spectrum efficiency, and availability, 5G mobile broadband goes above and beyond what was previously possible with standard mobile broadband. The construction of 5G networks is still in the planning stages. These 5G networks will create intelligent networked communication environments by connecting people, things, data, applications, and transport networks. Mobile networks have made it possible for customers' mobile devices (such as smartphones, tablets, laptops, and so on) to connect to the internet. A variety of distinct protocols will be required to take into consideration the various aspects that 5G possess. One of these is the transport protocol, which is intended to deliver extremely high data transfer rates up to 400 Gbps. The transmission control protocol (TCP) is one of the numerous protocols that are necessary for supporting 5G's many capabilities. Our work focuses on the detection and analysis, on the downlink (DL) side, of the congestion of the transport layer in single- and multicell environments. For the purpose of the analysis, the following metrics were analyzed: physical resource blocks (PRBs), user throughput, cell throughput, cell edge user throughput, and delay. The work emphasizes the activation of the TCP slow-start algorithm using file transfer protocol (FTP) model two according to 3GPP standards.