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Summary: Information technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy. [ABSTRACT FROM AUTHOR]

Summary Millimeter‐wave massive multiple‐input‐multiple‐output (MIMO) is a wireless communication that enables communication at extremely high data rates. It is widely used to expand communication bandwidth to improve spectral and energy efficiency. The mm‐wave system is usually based on the radio frequency (RF) chain. Beamforming is used to minimize the interface signal and maximize each user's received signal power. The hybrid beamforming method uses channel estimation and beam generation techniques that improve overall performance and save energy. There are various limitations and disadvantages arising from different techniques of the MIMO system. Hence, the proposed model is introduced to enhance the performance and maximize the spectral and energy efficiency. Initially, the optimal RF chain selection and channel optimization are done by the differential evolution firefly‐assisted optimized channel compression‐reconstruction (DEF_OCCR) network. In this research, the RF chain selection process is done using hybrid differential evolution firefly optimization. A robust autoencoder‐driven deep learning model is proposed for channel estimation after RF chain selection. Finally, the deep learning convolutional joint adaptive hybrid beamforming network (DConvHB) is used for beamforming to maximize spectral efficiency. The overall performance of this work is analyzed using several existing models to describe its superiority. The average system throughput is 12.85 bits, and the spectral efficiency of the proposed model is 5 bits for 10 dB and 32.18 bits for 13 dB. The proposed energy efficiency model is 1.73 bits and 4.96 bits for 10 dB. [ABSTRACT FROM AUTHOR]

This article covers the potential of Filter Bank Multicarrier (FBMC) modulation as an alternative to be used in the future 5 G wireless networks in which Massive Multiple-Input Multiple-Output (MIMO) is implemented. The 'energy efficient Massive Multiple Input Multiple Output (M-MIMO) Filter bank Multi-carrier Modulation (FBMC)' is necessary and beneficial for the upcoming wireless communiqué system. This is because there is an ever-increasing demand for higher data rates with higher Spectral Efficiency (SE). This paper compares orthogonal frequency division multiplexing (OFDM) with FBMC. This helps to evaluate the Spectral Efficiency (SE) of a Massive MIMO system that performs transmission under a unicellular environment. The variation in Massive MIMO allows equilibrium of the channel because FBMC has extra benefits due to the confinement of the subcarrier in an assigned range. However, the high Peak to Average Power Ratio (PAPR) in an M-MIMO-FBMC system is recognised as a serious problem that causes nonlinear signal deformation and restricts the effectiveness of the Power Amplifier (PA). In this work, to increase the SE in the system, the antenna count is optimised, such that the SNR or SNDR is maximum. For this, the antenna counts are optimised via Self Improved SSO with a Chaotic Map (SISSO-CM). [ABSTRACT FROM AUTHOR]

Hybrid precoding is a significant procedure for decreasing the hardware complexity and power usage in massive multiple-input multiple-output (MIMO) systems. However, the effectiveness of hybrid precoding is highly dependent on precise channel state info and designing of the beamforming matrix. In recent years, deep learning-based approaches have emerged as a promising solution to address these challenges. This research focuses on improving the performance of massive MIMO systems. However, several methods have been introduced to develop the hybrid precoding model, but these models suffer from several issues such as complexity, interference and quantization error. Currently, deep learning-based methods have gained huge attention in this domain where these methods learn from the data and try to overcome the challenges. Here, a deep learning-based model is presented where our main aim is to develop a hybrid precoder along with the deep learning-based optimal power allocation model. Therefore, the proposed model overcomes the issue of hybrid precoding and power distribution resulting in improving the overall performance of massive MIMO systems on the parameters such as spectral efficiency (SE) and the sum rate. [ABSTRACT FROM AUTHOR]

To achieve the best performance in terms of accuracy and complexity of massive multiple-input multiple-output (Massive MIMO) in wireless communication systems, hybrid beamforming (HBF) is a promising technique that provides high data rate multiplexing gains and enhances the spectral efficiency (SE) of the system. In this paper, a novel received signal strength indicator (RSSI) method is proposed to design an HBF for Massive MIMO BF via multitasking deep learning (DL) that minimizes the reliance on the channel state information (CSI) feedback. The trade-off between the enhancement SE of the system and the deep neural networks (DNNs) performance is optimized, and the results reveal that the proposed novel DL techniques achieve predicted spectral efficiencies with accuracy of 99.23% and 95.64% for Deep-HBF and Deep-AFP, respectively. The processing times for Deep-HBF and Deep-AFP are 709.2914 sec and 1425.864 sec, respectively. Notably, Deep-AFP exhibits a higher range of computational complexity compared to Deep-HBF. It is worth mentioning that the proposed techniques utilize the same DNN architecture. [ABSTRACT FROM AUTHOR]

In the wireless communication, the shortage of bandwidth has motivated the investigation and study of the wireless access technology called massive Multiple-Input Multiple-Output (MIMO). In multi-tier heterogeneous Fifth Generation (5G) networks, energy efficiency is a severe concern as the power utilization of macro base stations' is comparatively higher and proportional to their traffic load. In this paper, a novel African Vulture Shepherd Optimization Algorithm (AVSOA) is established that relies on macro cells and small cell system load information to determine the highly energy-efficient traffic offloading system. The proposed AVSOA model is a combination of the African Vulture Optimization Algorithm (AVOA) and the Shuffled Shepherd Optimization Algorithm (SSOA). The system load is predicted here by exploiting a Deep Quantum Neural Network (DQNN) algorithm to perform the conditional traffic offloading in that every macro-Base Station (BS) conjectures the offloading systems of other macro cells. The experimental evaluation of the adopted model is contrasted with the conventional models considering the energy efficiency, spectral efficiency, throughput, and system load. Finally, the performance analysis of the proposed model achieved better energy efficiency, spectral efficiency, and throughput of 0.250598, 0.184527, and 0.820354 Mbps and a minimum system load of 697. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output systems with spatial modulation has evolved into an energy efficient and less complex wireless transmission system due to the use of limited radio frequency chains. In this work, a novel physical layer security scheme with enhanced spatial modulation (ESM) and secret key generation using multi-level adaptive threshold binary-coded decimal quantization algorithm is introduced. The proposed method, as SPACESM, enhances spectral efficiency using ESM and achieves confidentiality by adaptively selecting and shuffling the codebook in ESM. For selection of ESM codebook, SPACESM uses physical layer signature called channel signal to noise ratio (SNR) and for shuffling of codebook and rotation of transmit vector, secret key is generated at the transmitter and receiver separately by threshold-based quantization algorithm, which uses channel state information. Adaptive modulation technique selects the modulation order for different SNR. The performance of the proposed method is evaluated in terms of confidentiality, spectral efficiency, and robustness of secret key through MATLAB simulation.The proposed SPACESM technique provides 2 bpcu improvement for both N t = 2 and N t = 4 with M = 64 than the existing SM. Also it provides 3 dB and 4 dB SNR gain than SM for N t = 2 and N t = 4 respectively. Similarly, it provides 2 dB and 4 dB SNR gain than the SMX technique for N t = 2 and N t = 4 respectively. In summary, the proposed method outperforms than the SMX and SM technique in all aspects. [ABSTRACT FROM AUTHOR]

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions. [ABSTRACT FROM AUTHOR]

Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints. [ABSTRACT FROM AUTHOR]

Summary Due to the rise in information rate prerequisite and the heterogeneity level, the modification in network traffic in the upcoming wireless communication (WC) encompasses innovative challenges in the case of energy efficiency (EE) and spectrum management. To tackle this issue, several existing techniques have been imposed but none of the frameworks provided effective solutions to compatible with recent WC applications. This framework introduces an innovative deep learning (DL)–based distributed cognitive radio network (DCRN). The proposed scheme emphasizes single base station (BS) management, where resource effectiveness is obtained by solving active resource allocation (RA) problems using a bipartite matching (BM) technique. A DL scheme is emphasized to predict the traffic load (TL) for effective EE using a residual inception‐enriched recurrent convolutional neural network (R‐InceptionRCNN). The proposed method is implemented in Python, and the performance metrics including uplink (UL) achievable capacity per secondary user (SU), UL achievable capacity per SU, cost of energy consumption, EE, and mean energy saving (MES) are scrutinized and compared with conventional techniques. The proposed scheme achieved the overall costs, EE, MES, and UL capacity of 14.33 C/J, 149.99 J/MB, 13.49%, and 22.33 Mbps, respectively, on performing RA and TL prediction in the CRN platform. [ABSTRACT FROM AUTHOR]

Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments. [ABSTRACT FROM AUTHOR]

Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments.

Abstract Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.

5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the performance of QoS parameters such as bit error rate (BER), throughput, power consumption and mean square error (MSE). The maximum energy efficiency achieved is 34% and Spectral efficiency is 36%. [ABSTRACT FROM AUTHOR]

【Objective】Aiming at the problems of low spectral efficiency and limited system throughput in Optical Quadrature Spatial Modulation (OQSM), a Signed Optical Quadrature Spatial Modulation (SOQSM) scheme is proposed in Free Space Optical Communication (FSOC) in this paper. In addition, in view of the fact that the channel used in the performance analysis of OQSM is not suitable for all turbulence, this paper also adopts the Málaga channel, which is suitable for all turbulence intensities to analyze the bit error performance of the system.【Methods】First, the scheme divides the input binary bit stream into five parts at the sending end. The first part is used for constellation symbol mapping, and the last four parts are used for laser mapping. Then, the constellation symbol mapping is used to transmit the real and imaginary parts of the constellation symbol and its inverse symbol. The laser mapping is divided into two parts, the in-phase phase and the quadrature phase, which are used to activate the laser sequence mapping. Finally, after transmitting through the channel, the signal received and processed at the receiving end.【Results】Compared with OQSM, SOQSM carries an additional2log2Nt bits of information in the space domain within a transmission period. The upper bound of the average bit error rate is then calculated in the symbol domain and the space domain respectively. Finally, the upper bound of the average bit error rate of the SOQSM scheme is obtained, and the method of calculating the upper bound of the average bit error rate has good convergence. This solution greatly improves the spectral efficiency and transmission rate of the system, and has good bit error rate performance.【Conclusion】The Monte Carlo simulation results show that compared with Optical Spatial Modulation (OSM) and OQSM, the bit error performance of the SOQSM scheme is better when the spectral efficiency is the same. The bit error rate can reach 10-6 when the signal-to-noise ratio is 16dB. When the modulation order is the same, the bit error performance of SOQSM is better, especially when the signal-to-noise ratio is high. It is also shown that the simulation and theory fit well. In the Málaga channel, the bit error rate performance of the weak turbulence is better. In addition, different turbulence intensities have little influence on the trend of the bit error rate performance of the SOQSM scheme. However, with the increase of the signal-to-noise ratio, the influence will gradually increase. Therefore, SOQSM has better bit error rate performance than OQSM and OSM.

Abstract Accurate channel state information (CSI) is crucial for optimizing wireless communication systems. In scenarios with varying user-to-base station angles, the angle-dependent coherence time impacts conventional pilot strategies. Due to small angles, the coherence time of the user decreases dramatically because of doppler shift, which causes an increase in the number of pilots. We introduces an innovative sub-block design approach for systems with different user angles. This method harmonizes coherence time of high and low-angle users, while maintaining a constant pilot count. This not only improves spectral efficiency but also ensures accurate channel estimation. Through simulations, we demonstrate the effectiveness of our approach in enhancing both spectral efficiency upt to $$10 \%$$ 10 % and CSI precision. This breakthrough contributes to the advancement of channel estimation techniques in scenarios with angle-dependent coherence time, offering practical benefits to wireless communication systems.

In this paper, we propose a novel approach to enhance spectral efficiency in communication systems by dynamically adjusting the mapping between cyclic permutation coding (CPC) and its injected form. By monitoring channel conditions such as interference levels and impulsive noise strength, the system optimises the coding scheme to maximise data transmission reliability and efficiency. The CPC method employed in this work maps information bits onto non-binary symbols in a cyclic manner, aiming to improve the Hamming distance between mapped symbols. To address challenges such as low data rates inherent in permutation coding, injection techniques are introduced by removing δ column(s) from the CPC codebook. Comparative analyses demonstrate that the proposed dynamic adaptation scheme outperforms conventional permutation coding and injection schemes. Additionally, we present a generalised mathematical expression to describe the relationship between the spectral efficiencies of both coding schemes. This dynamic approach ensures efficient and reliable communication in environments with varying levels of interference and impulsive noise, highlighting its potential applicability to systems like power line communications. [ABSTRACT FROM AUTHOR]

Summary: An evaluation of various transmit precoding schemes is proposed in this thesis on basis of spectral efficiency (SE) of massive MIMO (m‐MIMO) system in 2 GHz spectrum. The present generation of wireless networks must be rethought due to the rapidly rising user base, huge volume data transfer, wide coverage requirement, and exponential rise in data rate. 5G mobile network includes a multi‐fold increase in network capacity, energy conservation, decreased latency, higher coverage probability, and a seamless connection. m‐MIMO has provided good solution for enhancing the data rate with the coverage, overcoming the effects of multipath fading and has also increased diversity of the system. Massiveness of MIMO system along with 5G network is the key technology to address the issue of SE, throughput, improvement of QoS (quality of service), coverage probability, power delay profile, diversity simulation, energy efficiency, and improvement in bit error rate. Parameters such as SE, throughput, and coverage probability are important for any communication system, and their improvement can increase overall capacity of cellular network system and could meet the requirement of upcoming demand for mobile network. The research paper is focused on performance analysis of m‐MIMO with reference to the SE of various transmit precoding schemes. Simulation results show that SE provided by M‐MMSE (multi‐cell minimum mean‐squared error) technique of precoding ranks highest for the same number of antennas and pilot reuse factor as compared to other schemes. [ABSTRACT FROM AUTHOR]

The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for keeping the beams of a base station (BS) and user equipment (UE) aligned at all times. However, by introducing 3GPP Reduced Capability (RedCap) UEs utilizing the Radio Resource Management (RRM) Relaxation procedure, this may no longer be the case, as UEs are allowed to skip synchronization signal blocks (SSB) to improve energy efficiency. Thus, to characterize the performance of such UEs, methods explicitly accounting for UE mobility are needed. In this paper, we will utilize the tools of the stochastic geometry and random walk theory to derive signal-to-noise ratio (SNR), spectral efficiency, and rate as an explicit function of time by accounting for mmWave/sub-THZ specifics, including realistic directional antenna radiation patterns and micro- and macro-mobilities causing dynamic antenna misalignment. Different from other studies in the field that consider time-averaged performance measures, these metrics are obtained as an explicit function of time. Our numerical results illustrate that the macro-mobility specifies the overall trend of the time-dependent spectral efficiency, while local dynamics at 1–3 s scales are mainly governed by micro-mobility. The difference between spectral efficiency corresponding to perfectly synchronized UE and BS antennas and time-dependent spectral efficiency in a completely desynchronized system is rather negligible for realistic cell coverages and stays within approximately 5–10% for a wide range of system parameters. These conclusions are not affected by the utilized antenna array at the BS side. However, accounting for realistic radiation patterns is critical for a time-dependent performance analysis of 5G/6G mmWave/sub-THz systems. [ABSTRACT FROM AUTHOR]

Increasing the area throughput is one of the leading solutions in terms of preventing data traffic density, which is one of the current problems in cellular communication systems. It can be shown that the most effective action to increase the area throughput is to increase the spectral efficiency. However, the measures that can be made to increase the spectral efficiency negatively affect the energy efficiency, which is another critical parameter for cellular communication. In this paper, the conditions of the cells which use massive multi-input multi-output systems have been optimized with multi-objective forest optimization algorithm (MOFOA). The results obtained with MOFOA are compared with the multi-objective genetic algorithm, multi-objective particle swarm optimization, multi-objective differential evolution algorithm and multi-objective firefly algorithm. When the simulation results and performance metrics are examined, it can be seen that MOFOA is a more successful algorithm than other algorithms. [ABSTRACT FROM AUTHOR]

The advent of 5G technology aims to revolutionize wireless communication by providing significantly higher data rates, ultrareliable low latency, and enhanced connectivity. However, optimizing 5G network performance remains a challenging task, particularly in terms of Bit Error Rate (BER) and spectral efficiency. Multiple-Input Multiple-Output (MIMO) technology, coupled with beamforming algorithms, offers a promising solution to these challenges. This study investigates the application of the Fractional Least Mean Square (FLMS) algorithm for beamforming in MIMO systems to improve BER and spectral efficiency in 5G networks. The primary problem addressed is the need for effective interference mitigation and signal enhancement in densely populated environments, which traditional methods struggle to handle. The proposed method utilizes the FLMS algorithm to dynamically adjust the beamforming weights, thereby optimizing the signal reception quality. The algorithm's fractional nature allows for finer adjustments and better adaptation to varying channel conditions compared to conventional LMS algorithms. Simulation results show the efficacy of the proposed method. Specifically, the implementation of the FLMS algorithm in a 4x4 MIMO system shows a reduction in BER by 30% and an improvement in spectral efficiency by 25% compared to traditional beamforming techniques. These numerical values highlight the potential of FLMS in enhancing 5G network performance, making it a viable approach for future wireless communication systems. [ABSTRACT FROM AUTHOR]

Signal loss remains a persistent challenge in communication systems, impacting Multiple Input Multiple Output (MIMO) systems, especially in the millimeter-wave (mm-Wave) context. This paper explores the effectiveness of the proposed Hybrid Precoding/Combining Design (HPCD) algorithm within a fully connected structure of an mm-Wave downlink massive MIMO system. The primary objective is to enhance the overall system's performance, specifically focusing on improving spectral efficiency. Simulation results consistently demonstrate the superiority of the HPCD algorithm over state-of-the-art techniques, revealing substantial improvements in spectral efficiency. This thorough examination highlights the potential of the proposed approach, positioning it as a compelling solution for next-generation communication networks. The findings are anticipated to significantly contribute to spectral efficiency optimization, facilitating the seamless integration of the proposed technique into practical communication scenarios. [ABSTRACT FROM AUTHOR]

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type. [ABSTRACT FROM AUTHOR]

Spatial Modulation(SM) and Orthogonal Frequency Division Multiplexing(OFDM) Multi-Mode Index Modulation (MM-OFDM-IM) techniques have significant advantages in Energy Efficiency(EE) and Spectral Efficiency(SE), respectively. The combination of multi-dimensional index modulation technology can apply multiple physical resources to meet the high demand for data transmission and system capacity in wireless communication systems. Therefore, SM and MM-OFDM-IM systems can be flexibly combined to construct an OFDM Multiple Mode Space Frequency Composite Index Modulation (MM-OFDM-SFCIM) based on space frequency combination. This system can retain the silent subcarriers in traditional subcarrier index modulation technique, expanding new composite coefficient dimensions and improving its SE and EE. Aiming at the complex characteristics of this system, this paper proposes a joint detection algorithm EL-REML (REML based on symbolic Energy and LLR) based on symbolic energy and log-likelihood. The simulation results show that the MM-OFDM-SFCIM system achieves about 30% improvement in SE compared to traditional space-frequency combination systems, and the EL-REML algorithm proposed in this system achieves a 3-4 dB improvement in BER (Bit Error Rate) compared to the LLR algorithm. Compared with traditional ML algorithm, this algorithm has lower computational complexity. [ABSTRACT FROM AUTHOR]

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Nowadays the communication of massive multi-user multiple-input multiple-output (MU-MIMO) takes an important role in wireless systems, as they facilitate the ultra-reliable transmission of data and high performance. In order to sustain massive user equipment (UE) with tremendous reliability and spectral efficiency, more antennas are deployed per base station (BS) in the MU-MIMO system. To overcome such problems, the recurrent neural network (RNN) with crossover-gradient based optimizer (GBO) model called RNN-crossover GBO is proposed for precoding in the MU-MIMO system. However, it is essential to diminish the complexity to attain the maximum sum rate for obtaining the optimal solution. Moreover, the kernel linear discriminant analysis (KLDA) dimensionality reduction technique is employed for mapping high dimensional data into a low dimension by considering a linear combination matrix. In order to obtain the best feature the GBO is employed that predict the optimal solution and restrict falling from the local solution. Furthermore, the crossover-GBO algorithm is applied with the RNN to estimate the output precoding matrix with considerable features to obtain the best search space. The experimental results revealed that the proposed method achieves higher performance with a higher sum rate and shows significant improvement in spectral efficiency (SE) values than the existing methods. SE rises due to the selection of closely associated features. This indicates the robustness and stability of the proposed model. [ABSTRACT FROM AUTHOR]

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks.

In wireless communication networks, mobile users in overlapping areas may experience severe interference, therefore, designing effective Interference Management (IM) methods is crucial to improving network performance. However, when managing multiple disturbances from the same source, it may not be feasible to use existing IM methods such as Interference Alignment (IA) and Interference Steering (IS) exclusively. It is because with IA, the aligned interference becomes indistinguishable at its desired Receiver (Rx) under the cost constraint of Degrees-of-Freedom (DoF), while with IS, more transmit power will be consumed in the direct and repeated application of IS to each interference. To remedy these deficiencies, Interference Alignment Steering (IAS) is proposed by incorporating IA and IS and exploiting their advantages in IM. With IAS, the interfering Transmitter (Tx) first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx, and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned interference. Moreover, two improved versions of IAS, i.e., IAS with Full Adjustment at the Interfering Tx (IAS-FAIT) and Interference Steering and Alignment (ISA), are presented. The former considers the influence of IA on the interfering user-pair's performance. The orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components, thus ensuring the Spectral Efficiency (SE) of the interfering communication pairs. Under ISA, the power cost for IS at the interfered Tx is minimized, hence improving SE performance of the interfered communication-pairs. Since the proposed methods are realized at the interfering and interfered Txs cooperatively, the expenses of IM are shared by both communication-pairs. Our in-depth simulation results show that joint use of IA and IS can effectively manage multiple disturbances from the same source and improve the system's SE.

With the recent expansion of 5th generation mobile communication services, data demand is increasing. Correspondingly, the use of high-frequency millimeter waves and unmanned aerial vehicles (UAVs), which have features of high-altitude operation and mobility, and ease of deployment, is growing. However, for millimeter waves, narrow beams are used to compensate for large path losses, and appropriate beam allocation algorithms are essential. Therefore, in this paper, we propose an algorithm that utilizes a Gaussian process to predict the position of UAV-user equipment (UAV-UE). We then calculate the codebook based on the predicted position information and track the optimal beam in UAV-enabled communication systems. The results of a performance analysis confirm that the proposed algorithm can predict the UAV-UE position with high accuracy, and it has a high spectral efficiency. In addition, in the process of tracking the optimal beam, the complexity of codebook calculation is reduced by using the previously predicted UAV-UE position information.

The non-orthogonal multiple access (NOMA) characteristics of 5G radio access allow for more efficient resource distribution. NOMA improves spectral efficiency by allowing Power domain users to transmit power concurrently with other users of time and spectrum resources. The selection of the subcarrier and the nomination of users to the subcarrier is of the utmost importance to maximize spectral efficiency fairness among the users and improve the data rate of weak users in the downlink NOMA system. The least gain subcarrier First-threshold-based Adaptive user grouping algorithm (LGSF-TBAUGA) has been devised for this purpose. This algorithm maintains a significant channel gain differential based on a threshold, thus avoiding successive interference cancellation (SIC) process imperfections. The computational complexity of the proposed technique is calculated and compared to other approaches. The power coefficients of the selected users are used to allocate power to them. The proposed method improves the weak user data and overall system sum rates. As a result, the spectrum efficiency and user fairness index have been greatly enhanced compared to some existing algorithms.

Abstract Communication services that are dependable are crucial, particularly during emergencies when the regular infrastructure for communication may be disrupted or nonexistent. In such situations, device-to-device (D2D) communication can be a helpful choice since it allows user equipment (UE) that is close to one another to connect directly, bypassing the cellular network infrastructure. The primary focus of this thesis is the application of D2D communication in a decentralized emergency scenario with a damaged eNodeB. The main objective is to find an appropriate strategy for finding and selecting D2D couples by simulating several methods in MATLAB. This study compares three D2D pair selection algorithms: distance-based, Signal-to-Interference and Noise Ratio (SINR)-based, and data rate-based distance-based. The simulation results show that the data rate-based strategy is the most effective method for selecting D2D couples in emergency scenarios. In contrast to algorithms that rely on distance and SINR, this one reduces the chance of an outage by 20%. Bit error rate (BER), capacity, spectral efficiency, and energy efficiency are the three types of links that are assessed: direct links, relay links, and UE relay links. The results show that, with the lowest BER and maximum data throughput, the direct link is the most reliable and efficient communication option. However, the relay connection and the UE relay link show better overall spectral efficiency in comparison to the direct link, indicating their ability to transport more data per unit of bandwidth. The option that consumes the least energy among the three is the direct link. The study demonstrates the great potential of D2D communication in emergency scenarios where conventional communication infrastructure may not be available. The direct link is the most dependable and effective alternative for communication, according to the data, although the UE link can still function effectively in the event that the direct link is compromised. The data rate-based method is a useful strategy for finding and choosing D2D partners. The results of this study can direct the development of D2D emergency communication solutions in 5G networks.

Abstract The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications.

Summary: The advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter‐wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave‐based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub‐connected with phase shifter selection (SPSS), and fully connected and sub‐connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency. [ABSTRACT FROM AUTHOR]

With the advent of 5G communications, the demand for high data rates and improved spectral efficiency has necessitated the exploration of advanced modulation techniques. Orthogonal Frequency Division Multiplexing (OFDM) and Universal Filtered Multi-Carrier (UFMC) are two prominent modulation schemes for 5G systems. However, both OFDM and UFMC suffer from a high Peak-to-Average Power Ratio (PAPR), leading to signal distortion and inefficient power amplification. The Partial Transmit Sequence (PTS) approach is a promising strategy to minimize PAPR in OFDM and is applied to UFMC to minimize PAPR in UFMC and is known as scrambled UFMC (S-UFMC). PTS, however, involves an extensive search to identify the best phase factors that increase computational complexity. The artificial bee colony algorithm is proposed to reduce the computational complexity S-UFMC and OFDM. A novel modulation scheme, amplitude phase shift keying (APSK), is used instead of quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) in S-UFMC and OFDM, which has better performance than conventional modulation schemes. In this research, OFDM, PTS OFDM, UFMC and S-UFMC are compared and it is found that the proposed scrambled UFMC with APSK modulation using ABC algorithm performs better than OFDM and UFMC in terms of PAPR, spectral efficiency and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we present a multi-cluster multiple-input multiple-output non-orthogonal multiple access technique to improve spectral efficiency by simultaneously transmitting multiple data streams to multiple clusters in multi-cell systems. This technique employs receive beamforming matrices for cell-edge users that minimize the multi-cell interference power, irrespective of the number of interfering cells. Based on the receive beamforming matrices, the technique designs transmit beamforming matrices for a serving base station (BS) that eliminate inter-cluster interference (ICI) between cell-edge users and maximize the lower bounds of the received SNRs at the cell-edge users. Then, the technique uses the transmit beamforming matrices to find receive beamforming matrices for cell-center users that eliminate ICI between the cell-center users and maximize the lower bounds of the received SNRs at the cell-center users. This technique does not require channel feedback from the users to the serving BS in order to find all the receive beamforming matrices, thereby reducing system overhead. In addition, based on the transmit and receive beamforming matrices, the proposed technique allocates the transmit power to clusters and users of each cluster in a manner that maximizes the sum rate performance under power constraints supporting fairness between clusters and between users in each cluster. We demonstrate through simulations that the proposed technique achieves better sum rate and outage performances than the existing techniques and can provide a good balance between the performances and the number of clusters simultaneously supported by the serving BS. [ABSTRACT FROM AUTHOR]

This work addresses the evolving landscape of internet of things (IoT) applications and large antenna array systems, where optimizing spectral efficiency and simplifying design complexities are crucial. Focusing on two key challenges, the study introduces a novel hybrid analog-digital transceiver strategy tailored for frequency-selective channels. By integrating Shannon and Hartley theorems, the approach enhances data transfer rates, thereby optimizing radio frequency (RF) chain utilization in large-scale antennas. To achieve a balance between transceiver performance and hardware complexity, the study employs a decentralized alternating direction method of multipliers (ADMM) framework. The proposed hybrid consensus ADMM algorithm (HC-ADMM) ensures efficient convergence in decentralized optimization scenarios. Comparative analyses with ADMM and existing system transceiver optimization (ESTO) models highlight HCADMM's superior performance across key metrics such as spectral efficiency, efficiency per cell, total efficiency, and optimal scheduling of user equipment (UEs). Particularly notable is HC-ADMM's advanced optimization capabilities as the number of transmit antennas increases, positioning it as a promising approach for enhancing overall communication network performance. [ABSTRACT FROM AUTHOR]

This paper introduces a new index modulation method called Grouping Frequency-Time Index Modulation Non-Active for Differential Chaos Shift Keying (GFTIM-DCSK-II). The proposed system exploits the advantages of index modulation (IM) so that both the energy and spectrum efficiency of the system are enhanced. The suggested system effectively employs both frequency and time resources, allowing for efficient transmission at large data rates. For each symbol period, the new approach divides all data bits into equal G groups. For each group, the system includes frequency index bits, denoted as m1, which are utilized to choose a non-active subcarrier from a total of 2m1 subcarriers. Additionally, time index bits, denoted as m2, are employed to select a single time slot from 2m2 total time slots. After that, the modulated bits m0 are subjected to DCSK modulation and thereafter sent over the active subcarriers. Therefore, G(m0+m1+m2), where m0 = (2m1 2m2 - 1), is the total amount of bits transmitted with the GFTIM-DCSK-II system. To better demonstrate the distinctive characteristics of the proposed scheme the energy efficiency, complexity, and spectral efficiency of the GFTIM-DCSK-II system in comparison to the most advanced alternatives available are thoroughly analyzed. The system with q=8, m1=1, and m2=1 saves around 40% more energy than the CTIM-DCSK system also the proposed system enhanced the spectral efficiency around five times more than CTIM-DCSK and GSIMDCSK-II with q=8, m1 =1, and m2 =4. Bit error rate (BER) formulas across multipath Rayleigh fading channels and additive white Gaussian noise (AWGN) are calculated analytically. The proposed system showed an enhancement in BER performance around 5 dB than CI-DCSK2 and GSTIM-DCSKII across the AWGN channel at a BER of 10-4 at the same data rate. [ABSTRACT FROM AUTHOR]

The non-orthogonal multiple access (NOMA) characteristics of 5G radio access allow for more efficient resource distribution. NOMA improves spectral efficiency by allowing Power domain users to transmit power concurrently with other users of time and spectrum resources. The selection of the subcarrier and the nomination of users to the subcarrier is of the utmost importance to maximize spectral efficiency fairness among the users and improve the data rate of weak users in the downlink NOMA system. The least gain subcarrier First-threshold-based Adaptive user grouping algorithm (LGSF-TBAUGA) has been devised for this purpose. This algorithm maintains a significant channel gain differential based on a threshold, thus avoiding successive interference cancellation (SIC) process imperfections. The computational complexity of the proposed technique is calculated and compared to other approaches. The power coefficients of the selected users are used to allocate power to them. The proposed method improves the weak user data and overall system sum rates. As a result, the spectrum efficiency and user fairness index have been greatly enhanced compared to some existing algorithms. [ABSTRACT FROM AUTHOR]

In intelligent transport system (ITS) networks, it is very useful to reduce the transmission energy consumption. Device-to-device (D2D) communication is a key technology that can improve the performance of the ITS networks. In this paper, we investigate a D2D-enabled vehicular network and study a power allocation strategy to maximize the spectral efficiency of the network. Also, for safety-critical vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) applications, it is presented a reliable connection during the V2V and V2I links. The performance analysis of the investigated network shows that the studied method can maximize the total spectral efficiency of the network subject to satisfy some constraints when the channel state information (CSI) is perfectly known or not. [ABSTRACT FROM AUTHOR]

In the context of fifth generation (5G) technology, Device-to-Device (D2D) communication plays a pivotal role, requiring swift and intelligent decision-making in mode selection and device discovery. This study addresses the challenge of rapid mode selection and device discovery within 5G communication networks, focusing specifically on enhancing spectral and energy efficiency for Internet of Things (IoT) applications. A novel self-centered game theory-based algorithm is introduced to optimize spectral efficiency and support intelligent mode selection. Additionally, the utilization of the support vector machine (SVM) expedites mode selection decisions. For D2D discovery, the Frank-Wolfe method is adopted, significantly improving the differentiation between D2D and Cellular users based on signal strength and interference, thereby enhancing spectral efficiency. The proposed approach maximizes spectral efficiency while adhering to strict power and interference constraints, intelligently partitioning bandwidth into two subparts using game theoretic principles to amplify spectral efficiency. Furthermore, the emphasis on energy efficiency is underscored through iterative calculations to achieve maximum energy-efficient spectral allocation. Numerical analyses validate the efficacy of the proposed technique, revealing substantial improvements in accurately predicted labels. As the number of devices increases, precision and recall rates experience noteworthy enhancements, ultimately leading to superior bandwidth utilization. This research presents a significant contribution to the field of 5G communication, particularly concerning energy efficiency, which is paramount for IoT applications. By accelerating D2D connectivity and optimizing energy and spectrum resources, it advances the goals of energy-efficient D2D communication within 5G-IoT networks. [ABSTRACT FROM AUTHOR]

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Communication services that are dependable are crucial, particularly during emergencies when the regular infrastructure for communication may be disrupted or nonexistent. In such situations, device-to-device (D2D) communication can be a helpful choice since it allows user equipment (UE) that is close to one another to connect directly, bypassing the cellular network infrastructure. The primary focus of this thesis is the application of D2D communication in a decentralized emergency scenario with a damaged eNodeB. The main objective is to find an appropriate strategy for finding and selecting D2D couples by simulating several methods in MATLAB. This study compares three D2D pair selection algorithms: distance-based, Signal-to-Interference and Noise Ratio (SINR)-based, and data rate-based distance-based. The simulation results show that the data rate-based strategy is the most effective method for selecting D2D couples in emergency scenarios. In contrast to algorithms that rely on distance and SINR, this one reduces the chance of an outage by 20%. Bit error rate (BER), capacity, spectral efficiency, and energy efficiency are the three types of links that are assessed: direct links, relay links, and UE relay links. The results show that, with the lowest BER and maximum data throughput, the direct link is the most reliable and efficient communication option. However, the relay connection and the UE relay link show better overall spectral efficiency in comparison to the direct link, indicating their ability to transport more data per unit of bandwidth. The option that consumes the least energy among the three is the direct link. The study demonstrates the great potential of D2D communication in emergency scenarios where conventional communication infrastructure may not be available. The direct link is the most dependable and effective alternative for communication, according to the data, although the UE link can still function effectively in the event that the direct link is compromised. The data rate-based method is a useful strategy for finding and choosing D2D partners. The results of this study can direct the development of D2D emergency communication solutions in 5G networks. [ABSTRACT FROM AUTHOR]

The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications. [ABSTRACT FROM AUTHOR]

With the exponential growth of data demand and the advent of 5G networks, the need for efficient resource allocation algorithms has become paramount. This study presents a dynamic resource allocation algorithmic framework aimed at optimizing efficiency and performance in 5G networks. The framework focuses on frequency reuse at the edges while employing fractional pilots for enhanced spectrum utilization. 5G networks promise unprecedented speeds and low latency, enabling a wide array of applications from IoT to augmented reality. However, the efficient allocation of resources remains a challenge, especially at the network edges where interference is high. Traditional static resource allocation schemes fail to adapt to dynamic network conditions, leading to suboptimal performance. The main challenge lies in effectively managing resources to meet the diverse demands of various applications while mitigating interference and maximizing spectral efficiency. The proposed framework employs a dynamic resource allocation algorithm that adapts to changing network conditions in real-time. Leveraging fractional pilots, the algorithm optimizes frequency reuse at the network edges, thereby enhancing spectral efficiency. The framework integrates stochastic learning for predictive analytics to anticipate resource demands and interference patterns. Simulation results demonstrate significant improvements in spectral efficiency and network performance compared to traditional static allocation methods. The utilization of fractional pilots effectively reduces interference, enabling higher throughput and lower latency, especially at the network edges. The dynamic nature of the algorithm ensures adaptability to varying traffic loads, leading to enhanced overall network efficiency. [ABSTRACT FROM AUTHOR]