User Throughput Research Articles

Adaptive Handover Management in High-Mobility Networks for Smart Cities

The seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity and ultra-low latency by leveraging advanced technologies like millimeter wave, massive machine-type communication, non-orthogonal multiple access, and beam forming. However, challenges persist in ensuring smooth handovers in dense deployments, especially in higher frequency bands and with increased user mobility. This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. The system selects the best target cell from the available neighbor cell list by predicting key performance indicators, such as reference signal received power and the signal–interference–noise ratio, while considering the fixed time-to-trigger and hysteresis margin values. It dynamically adjusts handover thresholds by incorporating an offset based on real-time network conditions and user mobility patterns. This adaptive approach minimizes handover failures and the ping-pong effect. Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios.

Cell-Edge User Satisfaction-Based Dynamic CoMP Clustering with Load Awareness in Ultra-Dense Networks

With the implementation of mobile communication systems in the 5G and beyond era, Ultra-Dense Networks (UDNs) deployment has been a dominant approach. A dense cell deployment offers high system capacity to serve an increasing demand for communication services. However, the system's performance highly depends on the interference coordination among neighboring cells. Under the concept of Coordinated Multipoint (CoMP) transmission, system throughput can be enhanced through base station dynamic coordination. Besides the technique for signal coordination from multiple Transmission Points (TP), another important issue is the selection approach for cell clustering. This work proposes a dynamic clustering mechanism called cell-edge user satisfaction-based dynamic CoMP clustering with load awareness, which considers cell-edge users’ performance as the main cell clustering criteria along with the load awareness for better load balance between cells. A numerical study has been performed to observe the system performance for the 5G Non-Orthogonal Multiple Access (NOMA) systems embedded with our proposed approach in comparison with the other two systems implementing the static clustering technique and the dynamic clustering with sum rate criteria. Better system performance offered by our proposed technique is illustrated via the numerical results in terms of system throughput, fairness, spectrum efficiency, and number of unsatisfied users for different test scenarios.

Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems

In this paper, we investigate the user throughputs of a Cell-Free (CF) system with multiple aerial relay stations (ARSs), where each ARS is defined as an unmanned aerial vehicle (UAV)-mounted relay station. The system operates under a decode-and-forward (DF) protocol and facilitates connectivity between a terrestrial base station (TBS) and terrestrial users. ARSs are equipped with multiple antennas and simultaneously serve users that are outfitted with single antennas and distributed in a specific area. Additionally, a small-cell (SC) system based on the CF structure, where each ARS serves one user with the best channel conditions, is also considered. We analyze system communication in two stages, including user-ARS links and ARS-TBS links, and then we derive expressions for the data rate of users and ARSs. Moreover, we propose the spatial pilot reassignment (SPR) algorithm to optimize pilot assignment, enhancing channel estimation over random pilot assignment methods. The user throughput is evaluated by altering several system parameters, including the with/without data power control, the number of users, the number of ARSs, and the time interval allocated for channel estimation. The results show that the SPR algorithm improves throughput by about 10% compared to the random pilot assignment method at a 90%-likely user throughput, which is equal to a cumulative distribution function value of 0.1.

Performance analysis of shared relay CR-NOMA network based on SWIPT

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance.

Intelligent Transition Mechanisms in 5G DSA: A Logistic Regression Approach to Reuse Factor Adaptation

This research work use Dynamic Spectrum Allocation (DSA) embedded in 5G technology and logistic regression as Machine learning tool to alter the reuse factor of assigned telecommunication frequency to a more robust and reliable frequency. The switch from a frequency reuse of 3 (FR3) to a frequency reuse of 7 (FR7) was considered. Historical data generated from a Telecommunications Company in Nigeria was utilised. This data consist of User throughput and the number of user equipment to assess traffic load and congestion from user equipment (UE).The ideal was meant to determine the probability at which a switch or transition between reuse factors will occur. With a resolution threshold of 50 users which was set to commence the shift from reuse factor of 3 when the number of users have reached its threshold of 50 to reuse factor of 7 when the number of users exceeds 50 and above. The objective of this combination is to increase spectrum utilisation efficiency and network flexibility, by optimising spectrum resources in densely populated urban areas. This study explains the mechanisms and also demonstrate how logistic regression with DSA will enhance network capacity of wireless communication systems. The logistic regression model proved effective in making intelligent decisions regarding the transition from reuse factor 3 to reuse factor 7 as it was able to accommodate up to 79 users as against the threshold value of 50 users when running under the reuse factor of 3. With a high accuracy of 91.9% and balanced precision-recall values, it showcased its ability to optimize spectral efficiency.

Optimizing the Deployment of an Aerial Base Station and the Phase-Shift of a Ground Reconfigurable Intelligent Surface for Wireless Communication Systems Using Deep Reinforcement Learning

In wireless networks, drone base stations (DBSs) offer significant benefits in terms of Quality of Service (QoS) improvement due to their line-of-sight (LoS) transmission capabilities and adaptability. However, LoS links can suffer degradation in complex propagation environments, especially in urban areas with dense structures like buildings. As a promising technology to enhance the wireless communication networks, reconfigurable intelligent surfaces (RIS) have emerged in various Internet of Things (IoT) applications by adjusting the amplitude and phase of reflected signals, thereby improving signal strength and network efficiency. This study aims to propose a novel approach to enhance communication coverage and throughput for mobile ground users by intelligently leveraging signal reflection from DBSs using ground-based RIS. We employ Deep Reinforcement Learning (DRL) to optimize both the DBS location and RIS phase-shifts. Numerical results demonstrate significant improvements in system performance, including communication quality and network throughput, validating the effectiveness of the proposed approach.

An IOV Spectrum Sharing Approach based on Multi-Agent Deep Reinforcement Learning

Highly dynamic Internet of Vehicles spectrum sharing can share spectrum owned by vehicle-to-infrastructure links through multiple workshop links to achieve efficient resource allocation. Aiming at the problem that the rapid variations in channel states in highly dynamic vehicular environments can make it challenging for base stations to gather and manage information about instantaneous channel states, we present a multi-agent deep reinforcement learning-based V2X spectrum access algorithm. The algorithm is designed to optimize the throughput of V2I user under V2V user delay and reliability constraints, and uses the experience gained from interacting with the communication environment to update the Q network to improve spectrum and power allocation strategies. Implicit collaborative agents are trained through an improved DQN model combined with dueling network architecture and long short-term memory network layers and public rewards. With lagged Q-learning and concurrent experience replay trajectories, the training process was stabilized and the non-stationarity problem caused by concurrent learning of multiple agents was resolved. Simulation results demonstrate that our presented algorithm achieves a mean successful payload delivery rate of 95.89%, which is 16.48% greater than that of the randomized baseline algorithm. Our algorithm obtains approximately the optimal value and shows performance close to the centralized brute force algorithm, which provides a better strategy for further minimizing the signaling overhead of the Internet of Vehicles communication system.

Динамическое использование спектра в LTE и NR для развертывания 5G

Both Static allocation of the frequency spectrum as deployed in 4G-LTE as well as dynamically configured 5G NR physical layer channels does not satisfy the demand of current exponential growth of wireless users. As 5G NR will perform better in Line of Sight (LOS) scenarios while 4G-LTE provides remarkable performance in non-Line of sight (NLOS) scenarios. Dynamic Spectrum Sharing (DSS) allows operators to run LTE and 5G NR on the same band simultaneously. This paper illustrates the relationship and working theory of Dynamic Spectrum Sharing to comprehend how the latest 5G standard will result in a substantial improvement in average user throughput and improved network efficiencies. This is an effort to propose a generic framework to initial Deployment of 5G network.

Pilot and data power optimization problems in multiple Aerial Relay Stations cell‐free communication systems

SummaryA cell‐free (CF) technology and unmanned aerial vehicles (UAVs) acting as aerial relay stations (ARSs) are gradually viewed as viable technologies for usage in sixth‐generation (6G) wireless networks owing to their outstanding advantages, such as large coverage, uniform service quality, huge connections, flexible deployment in all geographical areas, and high line‐of‐sight (LoS) probability. As a result, the combination of the CF model and UAVs is suitable for densely populated urban areas, shopping malls, festivals, and locations with complex geography. Furthermore, the integration of the CF model and UAV communication can improve system quality and is considered an entirely new research direction as there is no comprehensive investigation of this combined model. In this study, we investigate the quality of the multi‐ARS CF communication system, where ARSs are equipped with multiple antennas and stochastic distribution within a specific region to simultaneously serve numerous ground users. We establish a closed‐form formulation for the uplink and downlink throughput of individual users by using the matched filtering method and conjugate beamforming technology, respectively. Moreover, we propose a novel method to optimize the pilot and data transmission power coefficients for improving channel estimation and increasing throughput per user, thereby ensuring a high quality of service. The power optimization method is executed via the successive convex approximation (SCA) method, second‐order cone program (SOCP), and linear program. We evaluate system performance according to the cumulative distribution function (CDF) of user throughput based on various parameters, such as the number of users, the number of ARSs, and the length of pilot sequences. The analytical findings also reveal that the system with the proposed power optimization method is always superior to the system without the proposed method in both uplink and downlink.

Uplink resource shared interference mitigation scheme for in-band D2D underlay 5G networks

The benefits of Device-to-Device (D2D) communication often include increased overall system throughput, reduced latency, and increased spectrum efficiency. These advantages usually come from improved spatial reuse of time and frequency resources and the last but not least, the proximity of users engaged into D2D communication. Radio Resource allocation is a critical issue in D2D model and if not dealt with carefully, it can lead to significant interference to both pure cellular and D2D users, thereby proving detrimental to the desired outcomes. This paper presents novel Uplink Resource Shared Interference Mitigation Scheme called URSIMS. URSIMS adopts guard zones, further it implements radio resource allocation algorithm (RRAA) and distance dependent dynamic power control algorithm (DDDPA) with the goal to mitigate interference and achieve increased overall system throughput and spectral efficiency. URSIMS has been compared with other similar state of the art both for static and user mobility based scenarios; it is found that URSIMS responded adequately well and outperformed Random and Greedy schemes in terms of salient performance indicators including overall system throughput, pure cellular user throughput, spectral efficiency and D2D pair throughout. URSIMS did provide sustained higher system throughput, which significantly improved further in case of increased user mobility while compared to Random and Greedy schemes respectively.

Handover management procedures for future generations mobile heterogeneous networks

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection.

Throughput and coverage based Base Station–Relay Station deployment for 5G cellular network

SummaryFifth generation cellular networks have high data rates and connectivity demands. The relaying approaches are widely used in cellular networks to improve coverage, user throughput, and capacity at low cost. The increasing data requirements of the mobile network increase energy consumption. Energy efficiency‐based approaches also impact network coverage and throughput. Therefore, developing an energy‐efficient network with optimal coverage and throughput is considered a challenging issue. It can be resolved with optimal deployment of Base Station (BS), Relay Station (RS), and minimizing power consumption. In this research, a joint clustering‐based deployment technique is proposed for BS–RS deployment by considering the network throughput and coverage ratio. After enabling optimal BS–RS deployment, the network traffic is estimated with an On‐demand real‐time traffic estimation framework (ODTE). It estimates user association values using Beacon frames. In addition, the Relay Assisted Base station Power Scheduling (RABPS) approach to minimize power consumption is included. Based on Erlang's probability, the RABPS algorithm is enhanced with sleep mode activation during off‐peak hours. The proposed 5G network ensures uninterrupted connectivity and energy efficiency with varying time slots of power scheduling. The throughput performance of the clustering‐based approach is increased with a coverage ratio of 88%. The simulation results show the superiority of the proposed 5G BS–RS deployment and power scheduling in terms of throughput, coverage ratio, and power consumption.

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Mobility aware blockage management of a multi-user multi-cell hybrid Li-Fi Wi-Fi system with freeform diversity receiver

In order to accommodate high data traffic, the co-deployment of heterogeneous networks like light fidelity (Li-Fi) and wireless fidelity (Wi-Fi) can offer supplementary small-cell layers of support and bring about a paradigm shift in achievable system throughput and quality of service. However, dense deployment of Li-Fi access points(APs) in an indoor arena often leads to co-channel-interference (CCI) that can be fruitfully diminished by adopting a customized optical front-end called freeform diversity receiver (FDR). With regard to multi-user association, this work judiciously imparts the motivation behind the adoption of FDR in a hybrid Li-Fi Wi-Fi network (HLWNet). Based on different mobility scenarios and blockage conditions the performance of the proposed HLWNet has been evaluated. A Li-Fi channel model with FDR and a rule-based resource allocation algorithm (RBRA) has been proposed for the purpose. Nevertheless, the network data quality of the multi-user system has been estimated in terms of packet loss, latency, and fairness index. Unlike the existing optimal resource allocation (ORA), the RBRA demonstrates superior network performance. Additionally, the execution time in the RBRA is reduced by a factor of 89 compared to the optimal resource allocation algorithm. Simulation results show a satisfactory fairness index of more than 0.85 and latency within 2.1 ms for a 10-user association inside an indoor environment of 25m2 floor area. In the absence of LOS blockage, the system throughput exhibits minimal variation, staying consistent for both methods with less than a 2% difference. However, significant improvement in average user throughput and effective system throughput has been observed compared to the existing studies. Despite line-of-sight (LOS) blockage, the proposed system with RBRA consistently maintains throughput within the range of 2.01 Gbps to 2.55 Gbps. The average user throughput, varying from 182 Mbps to 480 Mbps, is contingent upon the number of associated users, which ranges from 4 to 14.

Power control in LTE based on heuristic game theory for interference management

In the conventional LTE homogeneous network, sufficient transmit power of user equipment (UE) is determined by open-loop power control (OL-PC) and closed-loop power control (CL-PC) schemes. However, in a Het-Net environment, setting the UE’s transmit power requires delicate responsiveness to handle the severe and complicated uplink interference. In this paper, an interference-aware uplink power control mechanism based on Heuristic game theory approach is proposed for devices coexisting in a heterogeneous wireless network. Various wireless constraints like channel response, path loss, fading, shadowing, interference and metrics like SNR, SINR, throughput and bit rates are considered. Uplink power is controlled by suitably selecting the penalization factor (β) based on a simple Heuristic game theory approach considering the possible wireless constraints of each user depending on its location in the cell under consideration. The algorithm is framed in such a way to reduce inter-cell interference, limit transmit power, enhance bit rates and throughput of users. A significant improvement of 5.2% in the user coverage/distribution is achieved as a result of interference management compared to conventional power control scheme and power control with convex pricing.

Genetic Algorithm for Mode Selection in Device-to-Device (D2D) Communication for 5G Cellular Networks

The widespread use of smart devices and mobile applications is leading to a massive growth of wireless data traffic. With the rapidly growing of the customers’ data traffic demand, improving the system capacity and increasing the user throughput have become essential concerns for the future fifth-generation (5G) wireless communication network. In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular spectrum and all communications take place through the base stations (BS) and core network. Device-to-Device (D2D) communication refers to a technology that enables devices to communicate directly with each other, without sending data to the base station and the core network. This technology has the potential to improve system performance, enhance the user experience, increase spectral efficiency, reduce the terminal transmitting power, reduce the burden of the cellular network, and reduce end to end latency. In D2D communication user equipment’s (UEs) are enabled to select among different modes of communication which are defined based on the frequency resource sharing. Dedicated mode where D2D devices directly transmit by using dedicated resources. Reuse mode where D2D devices reuse some resources of the cellular network. Outband mode where D2D communication uses unlicensed spectrum (e.g. the free 2.4 GHz Industrial Scientific and Medical (ISM) band or the 38 GHz millimetre wave band) where cellular communication does not take place. Cellular mode where the D2D communication is relayed via gNode B (gNB) and it is treated as cellular users. In this work, the target was to reach the optimal mode selection policy and genetic algorithm method was used with the objective of maximizing the total fitness function. Optimal mode selection policy was presented and analysed amongst cellular, dedicated, reused and outband mode. In the present study of mode selection issues in D2D enabled networks, genetic algorithm was proposed for the case when the cellular user equipment (UE) moves in the network. Quality of service (QoS) parameters, mobility parameters and Analytic Hierarchy Process (AHP) method were used to define the mode selection algorithm. To evaluate the performance of the proposed genetic algorithm, a study of the convergence of the algorithm and the signal-to-interference plus noise ratio (SINR) was done.

Evaluation of User-Centric Cell-Free Massive Multiple-Input Multiple-Output Networks Considering Realistic Channels and Frontend Nonlinear Distortion

Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network’s performance when operating close to its saturation power.

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.

Data Power Optimization Algorithm for Downlink Multi-ARS Small Cell Communication Systems

In this paper, we investigate a novel downlink Small Cell (SC) system based on the Cell Free (CF) model,with the deployment of Unmanned Aerial Vehicles (UAVs) as Aerial Relay Stations (ARSs) simultaneously serving users in a specific area. The new SC model concept is expressed in the user-centric idea that the user will choose only one ARS with the best conditions for communication, and the concept of “cell" or “cell boundary" will no longer exist. The Time Division Duplex (TDD) mechanism is used for system-wide communication protocol, and the Minimum Mean Squared Error (MMSE) technique is applied for downlink channel estimation. We establish the downlink user throughput expression and then propose a Bisection optimization algorithm to control power for downlink data transmission to improve system quality and save energy. We evaluate the downlink multi-ARS SC communication system with and without power optimization in different aspects, such as altering the number of users, the number of ARSs, the number of antennas, and the transmission channel estimation interval. The results also reveal that the system quality is significantly improved when applying the downlink power factor optimization problem

Developing Uplink Power Optimization and ARS Selection Algorithm for Multi-ARS Small Cell Communication System

Small Cell (SC) models and unmanned aerial vehicles (UAVs) acting as aerial relay stations (ARSs) are both promising advancements in the development of upcoming wireless networks that contribute significantly to improving the overall service quality. In this work, we rely on the Multi-ARS Cell-Free (CF) model, where a large number of ARS coordinated by the ground base station (GBS) and cooperate to serve a large number of users within the same frequency and time resources, to develop the uplink of a multi-ARS SC system, in which each user is served by only one ARS. The time division duplex (TDD) mechanism is used for communication protocol, and the Minimum Mean Square Error (MMSE) method is implemented to estimate the uplink channel. We derive an closed-form expression for uplink user throughput. In addition, we introduce the ARS selection method based on channel conditions and propose the Bisection algorithm to optimize uplink power. The system performance is evaluated by the cumulative distribution function (CDF) of user throughput according to different parameters, such as changing the number of ARS, the number of users, the number of antennas, and the length of pilot sequences with/without power optimization. The results reveal that the ARS selection method is effectively resolved to reduce complexity and improve the practicality of the proposed system, and the power optimization problem for better throughput is non-optimization.